cognitive and perceptual mechanisms in clinical and non ... · cognitive and perceptual mechanisms...
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Cognitive and perceptual mechanisms in
clinical and non-clinical auditory
hallucinations
Saruchi Chhabra, BSc (Hons)
School of Psychology
The University of Western Australia
This thesis is presented for the degree of Doctor of Philosophy of
The University of Western Australia
Year of submission: 2012
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Abstract
Auditory hallucinations (AH) are one of the most persistent, distressing, and
functionally disabling symptoms of schizophrenia. Despite significant research into
aetiology and treatment, the full picture of the mechanisms involved in these
experiences remains unclear. AH also occur relatively frequently in healthy individuals
in the general population, supporting a continuum model of psychotic symptoms.
However, there have been recent challenges to this view, including evidence of
important differences in the phenomenology and cognitive mechanisms in patient and
non-patient voice hearers. The overarching goal of this thesis is to advance our
understanding of the commonalities and differences in cognitive and perceptual
mechanisms underlying clinical and non-clinical AH.
One of the core features of AH involves them being experienced as separate
from one’s own mental processes. These experiences have predominantly been
explained by failures of self-recognition, or reality monitoring difficulties; however
evidence points to a broader array of context memory impairments in AH. The first part
of this thesis sought to explore the exact nature of context memory deficits in clinical
and non-clinical AH. By assessing memory binding of voice and location information,
the first two experiments revealed that healthy, hallucination-predisposed individuals
are not impaired in either automatic or intentional binding of two external, contextual
features of information in memory. In order to make firm conclusions about whether
context memory impairments are/are not present in non-clinical compared to clinical
AH, the third experiment applied an identical word-voice memory binding task in two
separate studies of: (1) hallucination-prone individuals, and (2) schizophrenia patients
(with and without AH). Analyses revealed no evidence of impaired binding in high
hallucination-prone individuals relative to controls. In contrast, compared to controls,
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individuals with schizophrenia (both with and without AH) had difficulties binding the
two stimulus features (remembering ‘who said what’), alongside difficulties
remembering individual words and voices. These results suggest that the extent of
context memory deficits in schizophrenia is more wide-ranging than simply a deficit in
identifying the self as a source of mental events. Poorer memory for these real, external
voices and impaired binding of words to voices were also associated with higher ratings
of the loudness of hallucinated voices reported by individuals with AH.
The findings in the first part of this thesis underscore the importance of voice
recognition difficulties in patients with schizophrenia, including a functional link to AH.
The second part of this thesis explored the particular contribution of voice identity
processing to clinical and non-clinical AH. Two separate experiments were designed
using identical methodology, and age appropriate controls, to assess voice identity
discrimination in: (1) individuals with schizophrenia (with and without AH), and (2)
healthy undergraduates with a tendency to hallucinate. Results revealed atypical
processing of resonance, though not pitch-based cues to vocal identity in patients with
and without AH, but intact voice identity discrimination in hallucination-predisposed
individuals. Resonance-based cues have been linked to perceptions of vocal dominance
and masculinity in healthy individuals; consequently, they may be relevant to
heightened perceptions of dominance and masculinity of hallucinated voices in
schizophrenia.
Difficulties processing perceptual cues to voice identity, and binding these
contextual cues in memory, are discussed in terms of their potential contribution to the
external attribution of AHs. The non-specificity of these findings, however, suggests
that these perceptual and cognitive processes also play a functional role in other
symptoms of schizophrenia. The findings also add to a growing list of differences in
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cognitive function between clinical and non-clinical hallucinations, and demand a re-
evaluation of the continuum model of psychosis. Importantly, such differences offer
valuable insights into those mechanisms that may promote, or alternatively prevent, the
emergence of clinically significant hallucinatory experiences.
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Table of Contents
Abstract ........................................................................................................................................ III
Manuscripts and Publications Arising from this thesis .............................................................. XV
Author Contributions .............................................................................................................. XVII
Acknowledgments ..................................................................................................................... XIX
Section One: General Introduction
An overview of schizophrenia, and clinical and non-clinical auditory hallucinations .................. 3
Synopsis ......................................................................................................................................... 3
Schizophrenia ................................................................................................................................. 4
Auditory hallucinations .................................................................................................................. 6
Definition .......................................................................................................................... 6
Auditory hallucinations in schizophrenia .......................................................................... 6
Auditory hallucinations in the general population ............................................................ 9
The continuum model of psychotic symptoms ............................................................... 10
Thesis overview – Aims and outlines .......................................................................................... 12
References .................................................................................................................................... 14
Foreword to All Experimental Chapters ...................................................................................... 27
Section Two: Context memory binding in relation to clinical and non-clinical
auditory hallucinations
Chapter One: An overview of cognitive impairments in clinical and non-clinical auditory
hallucinations ............................................................................................................................... 31
Synopsis ....................................................................................................................................... 31
Cognitive impairments associated with auditory hallucinations in schizophrenia ...................... 32
Failures of self-recognition .......................................................................................................... 32
Source memory framework ............................................................................................. 34
Memory for contextual features ...................................................................................... 36
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Memory binding of contextual features .......................................................................... 37
Cognitive impairments in auditory hallucinations in the general population (hallucination
predisposition) ............................................................................................................................. 39
Specific aims ............................................................................................................................... 42
References ................................................................................................................................... 44
Chapter Two: Context binding and hallucination predisposition .............................................. 53
Abstract ....................................................................................................................................... 53
Introduction ................................................................................................................................. 54
Method ......................................................................................................................................... 56
Participants .................................................................................................................................. 56
Memory-binding task (Maybery et al., 2007) ................................................................. 57
Stimuli ............................................................................................................................ 59
Procedure ........................................................................................................................ 60
Additional Measures ....................................................................................................... 61
Results ......................................................................................................................................... 62
Descriptive statistics .................................................................................................................... 62
Frequency of hallucinations ............................................................................................ 63
Memory-binding task...................................................................................................... 64
Accuracy ............................................................................................................ 65
Reaction Time (RT) ........................................................................................... 66
Correlations between the frequency of hallucinations and binding ability ..................... 66
Discussion.................................................................................................................................... 67
Acknowledgements ..................................................................................................................... 69
References ................................................................................................................................... 71
Chapter Three: Context binding and hallucination predisposition: Evidence of intact
intentional and automatic integration of external features .......................................................... 75
Abstract ....................................................................................................................................... 75
Introduction ................................................................................................................................. 76
Method ......................................................................................................................................... 79
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Participants ................................................................................................................................... 79
Memory-binding tasks .................................................................................................... 80
Apparatus and stimuli......................................................................................... 80
Automatic binding task (Maybery et al., 2007) .................................................. 81
Intentional binding task ...................................................................................... 83
Additional measures ..................................................................................................................... 84
General procedure ........................................................................................................... 84
Results .......................................................................................................................................... 84
Descriptive statistics .................................................................................................................... 85
Automatic binding task ................................................................................................... 85
Accuracy ............................................................................................................ 86
Reaction Time (RT) ........................................................................................... 88
Intentional binding task ................................................................................................... 88
Accuracy ............................................................................................................ 89
Reaction Time (RT) ........................................................................................... 89
Discussion .................................................................................................................................... 90
Acknowledgement ....................................................................................................................... 92
References .................................................................................................................................... 93
Foreword to Chapter 4 ................................................................................................................. 97
Chapter Four: Memory binding in clinical and non-clinical psychotic experiences: How does
the continuum model fare? ........................................................................................................... 99
Abstract ........................................................................................................................................ 99
Introduction ................................................................................................................................ 100
Study 1 ....................................................................................................................................... 103
Method ....................................................................................................................................... 103
Participants ................................................................................................................................. 103
Measures .................................................................................................................................... 104
Questionnaires............................................................................................................................ 104
Memory-binding task (Chhabra et al., 2010) ................................................................ 104
Stimuli ........................................................................................................................................ 106
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Procedure ................................................................................................................................... 106
Statistical Analyses .................................................................................................................... 106
Results ....................................................................................................................................... 107
Descriptive Statistics ................................................................................................................. 108
Memory binding task .................................................................................................... 108
Binding ability ................................................................................................. 109
Recognition of individual stimulus features .................................................... 110
Study 2 ....................................................................................................................................... 111
Method ....................................................................................................................................... 111
Participants ................................................................................................................................ 111
Measures .................................................................................................................................... 112
Memory-binding task.................................................................................................... 113
Statistical Analyses .................................................................................................................... 113
Diagnostic-level analyses ................................................................................ 113
Symptom-level analyses .................................................................................. 114
Results ....................................................................................................................................... 114
Descriptive statistics .................................................................................................................. 114
Memory binding task .................................................................................................... 116
Diagnostic-level analysis .............................................................................................. 116
Binding ability ................................................................................................. 116
Recognition of individual stimulus features .................................................... 117
Symptom-level analysis ................................................................................................ 119
A more direct test of the continuum model .................................................................. 120
Discussion.................................................................................................................................. 121
Diagnostic-level effects ............................................................................................................. 122
Symptom-level effects ............................................................................................................... 124
Limitations ................................................................................................................................. 125
Ethical statement ....................................................................................................................... 126
Acknowledgments ..................................................................................................................... 127
References ................................................................................................................................. 128
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Section Three: Voice identity processing in relation to clinical and non-clinical
auditory hallucinations
Chapter Five: An overview of voice processing in healthy individuals, in individuals with
schizophrenia, and in relation to clinical and non-clinical auditory hallucinations ................... 137
Synopsis ..................................................................................................................................... 137
Human voice processing ............................................................................................................ 138
A model of human voice processing .......................................................................................... 139
Voice affect perception .................................................................................... 141
Voice identity perception ................................................................................. 141
Voice processing in schizophrenia and its link to auditory hallucinations ................................ 143
Voice identity perception ................................................................................. 144
Voice processing and auditory hallucinations in the general population ................................... 146
Specific aims and hypotheses .................................................................................................... 147
References .................................................................................................................................. 149
Chapter Six: Voice identity discrimination in schizophrenia ................................................... 157
Abstract ...................................................................................................................................... 157
Introduction ................................................................................................................................ 158
Method ....................................................................................................................................... 161
Participants ................................................................................................................................. 161
Similarity rating task ..................................................................................................... 162
Stimuli .............................................................................................................. 162
Procedure .......................................................................................................... 163
Acoustic analyses of voices .............................................................................. 164
Data analyses ................................................................................................................. 165
Multidimensional scaling (MDS) of similarity judgments ............................... 165
Results ........................................................................................................................................ 166
MDS of group dissimilarity matrices within the same model .................................................... 166
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MDS using dissimilarity matrices for individual participants ...................................... 168
Discussion.................................................................................................................................. 170
Symptom-level analysis ................................................................................................ 173
Limitations .................................................................................................................... 174
Ethical statement ....................................................................................................................... 174
Acknowledgements ................................................................................................................... 175
References ................................................................................................................................. 176
Chapter Seven: Voice identity discrimination and hallucination-proneness in healthy young
adults: A further challenge to the continuum model of psychosis ............................................. 181
Abstract ..................................................................................................................................... 181
Introduction ............................................................................................................................... 182
Method ....................................................................................................................................... 184
Participants ................................................................................................................................ 184
Similarity rating task (Chhabra, Badcock, Maybery, & Leung, 2012) ......................... 185
Analysis of acoustic characteristics .............................................................................. 186
Data analyses ................................................................................................................ 186
Multidimensional scaling (MDS) of similarity judgments .............................. 186
Results ....................................................................................................................................... 188
Descriptive statistics ..................................................................................................... 188
MDS of group dissimilarity matrices ........................................................................................ 188
MDS using dissimilarity matrices for individual participants ...................................... 190
Discussion.................................................................................................................................. 192
Limitations .................................................................................................................... 195
Acknowledgement ..................................................................................................................... 196
References ................................................................................................................................. 197
Section Four: General Discussion
Synopsis ..................................................................................................................................... 207
Findings ..................................................................................................................................... 209
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What is the nature of context memory deficits in clinical and non-clinical AH? ...................... 209
Summary of findings and interpretation........................................................................ 209
General comments regarding context memory and AH ............................................................. 215
What is the particular contribution of voice identity processing to clinical and non-clinical
AH? ............................................................................................................................................ 217
Summary of findings and interpretation........................................................................ 217
General comments regarding voice processing and AH ............................................................ 222
Methodological considerations and implications for future research ........................................ 224
Clinical implications .................................................................................................................. 227
Final comments .......................................................................................................................... 228
References .................................................................................................................................. 231
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Manuscripts and Publications Arising from this Thesis
This thesis consists of a collection of papers prepared in journal format, supplemented
by three introductions, a foreword connecting experimental papers, and a general
discussion. The papers and publications presented in this thesis are as follows:
Chapter 2
Badcock, J. C., Chhabra, S., Maybery, M. T., & Paulik, G. (2008). Context binding
and hallucination predisposition. Personality and Individual Differences, 45,
822-827.
Chapter 3
Chhabra, S., Badcock, J. C., Maybery, M. T., & Leung, D. (2011). Context binding
and hallucination predisposition: Evidence of intact intentional and automatic
integration of external features. Personality and Individual Differences, 50,
834-839.
Chapter 4
Chhabra, S., Badcock, J. C., & Maybery, M. T. (2012). Memory binding in clinical and
non-clinical psychotic experiences: How does the continuum model fare?
Cognitive Neuropsychiatry. DOI:10.1080/13546805.2012.709183
Chapter 6
Chhabra, S., Badcock, J. C., Maybery, M. T., & Leung, D. (2012). Voice identity
discrimination in schizophrenia. Neuropsychologia, 50, 2730-2735
Chapter 7
Chhabra, S., Badcock, J. C., Maybery, M. T., & Leung, D. Voice identity
discrimination in schizophrenia. Manuscript ready for submission to
Personality and Individual Differences.
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Author Contributions
In all of the studies included in this thesis, the candidate took a major role in study
design, task development, participant recruitment and testing, data entry, analysis,
interpretation, preparation of manuscripts and revisions. Programming of the
experimental protocols reported in Chapters 2, 3, 4, 6 and 7 was developed with the
assistance of Doris Leung. For the manuscript presented as Chapter 2 (Badcock,
Chhabra, Maybery, & Paulik, 2008), the introduction and discussion sections of the
manuscript were prepared by one of the candidate’s supervisors, Prof. Johanna
Badcock, however the candidate conducted all the participant recruitment and testing,
data analysis and interpretation, and preparation of the methods and results sections.
Two external authors also played a role in this thesis. Georgie Paulik contributed some
questionnaire data to one chapter (Chapter 2) and commented on a final written draft of
the manuscript presented for this chapter. Doris Leung brought her statistical expertise
to the analyses of the data in Chapters 6 and 7. The additional authors on all the
included manuscripts provided approval for the publications produced during the
primary author’s candidature to be included in this thesis.
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Acknowledgements
The fruition of this thesis would have never been possible without the guidance and
support of the following people. First, I would like to thank my supervisors, Johanna
Badcock and Murray Maybery. I consider myself extremely lucky to have worked with
such brilliant, dedicated scientists. My deepest gratitude goes to Jo for her wisdom,
guidance, and enthusiasm. Her phenomenal passion for good quality research has been
inspiring. Likewise, I am very grateful to Murray. His incredible knowledge, patience,
calmness, and constant encouragement, have been instrumental to the development of
this thesis. A special thank you also, to Doris Leung, for her programming assistance
and invaluable statistical advice.
I am indebted to the team at the Centre for Clinical Research in Neuropsychiatry at
Graylands Hospital for their recruitment expertise and diagnostic interview training. In
particular, I would like to thank David Vile, Danielle Lowe, Lisa Dawson, Melanie
Clark, and Tammy Hall, for their efforts in ensuring my testing ran smoothly.
My most heartfelt thanks go to all of the projects’ participants, especially to the
patients who shared their fascinating experiences with me. I truly hope this research
contributes to a better understanding of auditory hallucinations, and provides a basis for
further research to ultimately help individuals with schizophrenia find hope and relief.
I was privileged to work closely with some exceptionally bright and entertaining
PhD students. Not only did you keep me sane through the tough bits, you have become
dear and special friends. Thank you, Lynsey, Shannon, Steph, Danny, and Clare.
I am deeply grateful to my wonderful friends. A special thank you to Alysia, Fiona,
Kellie, and Nat, for the endless support and countless laughs. To Sean and Stewart,
thank you for the much-needed lunch distractions and shopping trips over the years.
Finally, but most importantly, I would like to thank my family. To my sister Anika,
one of my closest friends, for the chats over coffee and for everything else you’ve
taught me over the years. To my mum, Naaz, and my dad, Vijay, for their unwavering
support, constant encouragement, strength, and celebration of every achievement. You
two have been my inspiration throughout life. Thank you for everything.
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1
Section One
General Introduction
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An overview of schizophrenia, and clinical & non-clinical auditory
hallucinations
Synopsis
The aim of this chapter is to provide a brief overview of the relevant theoretical and
empirical background pertaining to the phenomena under investigation in this thesis:
namely auditory hallucinations in schizophrenia and in the general population. In this
section, the main epidemiological and clinical features of schizophrenia, auditory
hallucinations in schizophrenia, and auditory hallucinations in the general population
are reviewed, with particular reference to the continuum model of psychotic symptoms.
Similarities and differences in the characteristics of auditory hallucinations in clinical
and non-clinical (healthy) groups are briefly highlighted, providing a context for
methodologies adopted in experimental chapters in this thesis. Finally, the aims of the
proceeding chapters will be presented.
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Schizophrenia
Schizophrenia is a chronic, severe, and often disabling mental disorder with
considerable variation in incidence rates between locations (reports range between 7.7
and 43.0 per 100,000; McGrath, Saha, Chant, & Welham, 2008; McGrath & Susser,
2009; Tandon, Keshavan, & Nasrallah, 2008). The disorder is defined by the existence
of several key symptom clusters, including positive (e.g. hallucinations and delusions)
and negative (e.g. flat affect, poverty of speech) symptoms and disorganized
thinking/behaviour. These symptoms occur in the context of significant impairments in
social and occupational functioning, though considerable heterogeneity in presentation
also exists (American Psychiatric Association, 2000; Elvevag & Goldberg, 2000;
Jablensky et al., 2000; Tandon, Nasrallah, & Keshavan, 2009).
Attenuated psychotic symptoms (affective, cognitive and social) – the pre-
warning signs to full-blown psychosis – appear in the form of a prodromal phase of
illness (Addington et al., 2007; McGorry, 2009; Yung & McGorry, 1996; Yung et al.,
2003), whilst formal diagnosis of schizophrenia typically occurs in late adolescence or
early adulthood. For example, the second Australian National Survey of People Living
with a Psychotic Illness (Morgan et al., 2011) showed that onset was typically before
the age of 25 years, with 40% of all cases first showing psychotic symptoms in their
teenage years. Thereafter, for the majority of individuals, the illness is experienced as a
recurring, lifelong disorder and is associated with substantial costs both to the individual
and to society (Morgan et al., 2011).
Research has identified several important contributors to the underlying causes
of schizophrenia, including genetics, early environmental factors, cognitive,
neurobiological, and socio-psychological processes (Barnett & Fletcher, 2008; Tandon
et al., 2008). Of particular relevance to this thesis, persistent, generalised but highly
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variable cognitive deficits (such as poor memory, executive dysfunction, slow
processing speed and inability to maintain attention) punctuate this disorder (Fioravanti,
Carlone, Vitale, Cinti, & Clare, 2005; Gonzalez-Blanch et al., 2007; Gur et al., 2007;
Hallmayer et al., 2005; Heinrichs & Zakzanis, 1998; Lee & Park, 2005; Nuechterlein et
al., 2004), and have been shown to have considerable detrimental effects on social and
occupational outcomes (Chung, Mathews, & Barch, 2011; Green, 1996; Green, 2006;
Green, Kern, Braff, & Mintz, 2000), as well as for treatment rehabilitation and success
(Silverstein, 2000; Silverstein, Schenkel, Valone, & Nuernberger, 1998; Smith, Hull,
Huppert, & Silverstein, 2002). Furthermore, the disappointing outcomes associated
with newer forms of pharmacological treatments (Abbott, 2010; Tandon, Nasrallah, &
Keshavan, 2010) have led to a rethinking of the focus of schizophrenia research (Insel,
2010; Morris & Insel, 2011) and, in particular, much greater recognition of the central
role played by cognitive deficits, including the proposal to include cognition as a key
dimension of psychosis in the fifth revision of the Diagnostic and Statistical Manual of
Mental Disorders (DSM-V; American Psychiatric Association, 2012).
Given the considerable heterogeneity of clinical presentation, course, prognosis,
and cognitive profile of schizophrenia, it has been proposed that investigation of
individual symptoms provides an additional, possibly better approach to understanding
the mechanisms of this disorder (Bentall, 2003; Bentall, Jackson, & Pilgrim, 1988;
David & Halligan, 2000; Shapleske et al., 2002). Support for this method of research
has been highlighted in the cognitive literature (Carter, Robertson, Chaderjian, O'Shora-
Celaya, & Nordahl, 1994; Seal, Aleman, & McGuire, 2004). As such, the present thesis
endeavours to further identify and dissociate the cognitive and perceptual processes
underlying the development of auditory hallucinations, given their importance in the
diagnosis of schizophrenia.
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Auditory hallucinations
Definition
Auditory hallucinations (AH) have been defined as a “sensory experience which occurs
in the absence of corresponding external stimulation of the relevant sense organ, has
sufficient sense of reality to resemble a veridical perception over which the subject does
not feel s/he has a direct and voluntary control, and which occurs in the waking state”
(David, 2004, p. 110). These unwanted mental events are intrusive and interrupt the
sense of ongoing reality (Morrison, 2005; Slade & Bentall, 1998).
Auditory hallucinations in Schizophrenia
AH are one of the most prevalent symptoms of schizophrenia, with estimates of
prevalence ranging from 60 to 74% (Blashki, Rudd, & Piterman, 2007; Okulate &
Jones, 2003; Sartorius et al., 1986; Sartorius, Shapiro, & Jablensky, 1974; Silbersweig
et al., 1995), and accordingly have been a major target of symptom-based investigations
of schizophrenia. AH more commonly occur in the form of voice or speech, rather than
music or other auditory percepts and are assumed to arise, at least in part, from
abnormal activation of language-related neural networks (Aleman & Vercammen, 2012;
Beck & Rector, 2003; Johns, Hemsley, & Kuipers, 2002; Wible, Preus, & Hashimoto,
2009). A striking feature of AH is their phenomenological complexity. Several studies
have examined the internal structure of AH, with a variety of factors found to be
involved in these experiences. For example, Haddock, McCarron, Tarrier and Faragher
(1999) reported three factors that defined AH (emotional, physical, and cognitive
interpretation); Stephane, Thuras, Nasrallah, and Georgopoulos (2003) also found three
factors (linguistic complexity, self-other attribution, and inner-outer space location); and
Hayashi, Igarashi, Suda, and Nakagawa (2004) reported four factors (intractability,
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delusion, influence, and externality) involved in the structure of AH. Although there is
limited consistency in these findings, they emphasize the likelihood that AH do not
stem from a single unitary cognitive dysfunction. Several cognitive explanations have
been proposed to explain the different phenomenological features of these experiences
(see Waters et al., 2012 for a review). As such, different phenomenological “subtypes”
of AH may also require different approaches to treatment.
Detailed re-examination of AH in schizophrenia shows they are typically
frequent (varying between once a week and continuously), negative in content (e.g.,
commanding, critical, and controlling), and with a preponderance of male voices -
irrespective of the gender of the patient (Daalman et al., 2011; Haddock et al., 1999;
Honig et al., 1998; Johns et al., 2002; Nayani & David, 1996). Not surprisingly, this
combination of features is often associated with considerable distress, including a
possible increase in the risk of suicide, and increased rates of social isolation (Evensen
et al., 2011; Lui, 2009; Nayani & David, 1996). The distress experienced by voice-
hearers has been proposed to be associated with the perceived relationship between
voice and hearer; in particular, greater distress has been linked to appraisals of the voice
identity as dominant and intrusive, malevolent, high in supremacy, and of personal
acquaintance to the individual, as well as to attitudes of disapproval and rejection
towards voices (Hayward, 2003; Mawson, Cohen, & Berry, 2010; Sorrell, Hayward, &
Meddings, 2010). Interestingly, despite the important role it appears to play in
producing distress, studies investigating identity of voice have been largely overlooked
in research into AH.
Treatment of AH typically involves pharmacological modification of the
salience of the experience itself (i.e., to reduce the frequency and intensity of AH), or
adjustment of psychological interpretations of the experience (e.g., cognitive appraisals,
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coping responses, emotional responses) via cognitive-behavioural therapy (Laroi, de
Haan, Jones, & Raballo, 2010), but also includes modification of cortical activity via
repetitive transcranial magnetic stimulation methods (Montagne-Larmurier, Etard,
Maiza, & Dollfus, 2011). However, these experiences often persist regardless of
intensive and/or prolonged intervention (Rector & Beck, 2002). Therefore, despite the
accelerating body of evidence into the aetiology and treatment of AH, it is clear that the
full picture of the cognitive mechanisms involved in these persistent, distressing, and
functionally disabling experiences remains unclear. A greater understanding of the
cognitive mechanisms underlying AH may lead to earlier and more effective treatments
to alleviate this symptom, or potentially even to prevention of the symptom developing
in the first place (Kuhn & Gallinat, 2011).
The trans-diagnostic nature of AH provides further impetus to investigate this
symptom, with reports of AH occurring in other psychiatric and neurological
populations including mood disorders (Carlson & Goowdin, 1973; Ohayon &
Schatzberg, 2002), anxiety disorders such as post traumatic stress disorder (Kastelan et
al., 2007), Alzheimer’s disease (Bassiony & Lyketsos, 2003), epilepsy (Korsnes,
Hugdahl, Nygard, & Bjornaes, 2010), as well as in healthy individuals in the general
population (Sommer et al., 2010). A strict symptom-based approach would argue that
the experience and its cognitive basis would be the same irrespective of the diagnosis,
hence, gaining a better understanding of the cognitive mechanisms underlying AH in
schizophrenia may potentially inform how they occur in other populations. In fact,
Laroi and colleagues (2010) suggest that an exploration of non-clinical hallucinatory
states and experiences is fundamental in order to produce better intervention strategies
for people suffering from clinical AH via a better understanding of their
psychopathological origin.
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Auditory hallucinations in the general population
Although most commonly associated with schizophrenia, hallucinatory experiences
have been found to be relatively frequent in healthy individuals in the general
population, including among children and adolescents (Bartels-Velthuis, Jenner, van de
Willige, van Os, & Wiersma, 2010; Johns, 2005; McGee, Williams, & Poulton, 2000;
Sommer et al., 2010; Stip & Letourneau, 2009; Tien, 1991; van Os, Linscott, Myin-
Germeys, Delespaul, & Krabbendam, 2009), as well as in individuals who may be at
elevated genetic risk for schizophrenia, such as family members of patients with
schizophrenia (Kendler & Walsh, 1995). Prevalence rates of AH reported in the general
population vary (ranging from 1.5-71%), with these marked differences likely related, in
part, to differences in study design and cohort demographics (Beavan, Read, &
Cartwright, 2011). While many of these healthy individuals will experience occasional
hallucinations with no other consequences, for others, hallucinations will progress to
full psychosis (De Loore et al., 2011; Dominguez, Wichers, Lieb, Wittchen, & van Os,
2011; Johns & Van Os, 2001). Given the frequency and potential significance of
hallucinatory experiences in the general population, an increasing number of studies
have been conducted on non-clinical (healthy) voice hearers with the aim of uncovering
the aetiological mechanisms underpinning all experiences of AH (see Badcock &
Hugdahl, 2012b; Esterberg & Compton, 2009; Johns & Van Os, 2001 for reviews). This
approach has the advantage of ostensibly minimising the potential effects of
stigmatisation, medication, and general deterioration in functioning that accompanies
schizophrenia (Fonseca-Pedrero et al., 2010). Such studies commonly employ the
Launay-Slade Hallucination Scale-Revised (LSHS-R; Bentall & Slade, 1985), which
was developed to identify and examine the predisposition to hallucinate in the general
population. The LSHS-R has been found to produce a similarly complex architecture of
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hallucinatory experiences in the general population as that found in schizophrenia
(Paulik, Badcock, & Maybery, 2006; Serper, Dill, Chang, Kot, & Elliot, 2005). Of note,
this scale is often used to compare cognitive processes in high and low-scoring groups,
with differences in performance between groups purported to reveal mechanisms
specifically predisposing individuals to experiencing AH.
The continuum model of psychotic symptoms
Since AH occur both in schizophrenia and in healthy individuals, some authors have
argued that clinical and non-clinical AH represent points on a continuum (Choong,
Hunter, & Woodruff, 2007; Eysenck & Eysenck, 1976; Linscott & Van Os, 2010;
Meehl, 1989; Shevlin, Murphy, Dorahy, & Adamson, 2007; Strauss, 1969; Van Os,
Hanssen, Bijil, & Ravelli, 2000; van Os et al., 2009). These researchers assume that AH
in both groups involve the same phenomenology (i.e. are qualitatively equivalent
experiences) and arise from the same underlying cognitive and neural mechanisms (e.g.,
Esterberg & Compton, 2009). However, from the available evidence so far, we cannot
conclude if these experiences are identical (David, 2010; Lawrie, Hall, McIntosh,
Owens, & Johnstone, 2010).
Healthy individuals predisposed to hallucinations commonly exhibit similar (i.e.,
overlapping) characteristics as clinical individuals with AH. At the phenomenological
level, a recent review has indicated that the perceived location of voices (inside/outside
the head), the number of voices, the loudness of voices, and personification of voices
are similar in psychotic and healthy individuals (Daalman et al., 2011). Furthermore,
similar biological, cognitive, and emotional characteristics have been revealed in
clinical and non-clinical AH (e.g., Diederen et al., 2011; Paulik, Badcock, & Maybery,
2007; van't Wout, Aleman, Kessels, Larøi, & Kahn, 2004). However, recent authors
11
have challenged the traditional continuum model, with studies noting fundamental
differences in the nature or characteristics of these hallucinatory experiences (Badcock
& Hugdahl, 2012a; Escher, Romme, Buiks, Delespaul, & Van Os, 2002). In contrast to
the more frequent, intrusive, and distressing phenomenological experiences in the
psychiatric population, hallucinatory experiences in the non-clinical population are
often positive and nonthreatening, less frequent, more controllable, and are not as
distressing or functionally impairing (Choong et al., 2007; Daalman et al., 2011; Honig
et al., 1998; Tien, 1991). In addition, there is evidence of at least some different
cognitive and neural mechanisms associated with the predisposition to hallucinate in
healthy individuals compared to those underpinning active clinical hallucinations
(Badcock & Hugdahl, 2012a; Kaymaz & van Os, 2010). As a result, there has been a
growing call for more debate and research on the continuum model of psychotic
symptoms (Daalman et al., 2011; David, 2010; Kaymaz & van Os, 2010; Linscott &
Van Os, 2010). In keeping with this perspective, Lawrie, Hall, McIntosh, Owens, and
Johnstone (2010, p. 424) state that “just because psychotic symptoms are continuously
distributed in the general population does not mean that schizophrenia and other
psychoses are qualitatively indistinct from normal experience, or each other; nor does it
exclude the possibilities of distinct underlying latent categories,” which raises the
question of whether there is one or more continua underpinning psychotic symptoms
(van Os et al., 2009).
In summary, it is especially important, at this time, to advance our understanding
of both clinical and non-clinical (healthy) AH. Understanding the similarities and
differences between hallucinatory experiences in the general population and those in
psychosis may reveal those cognitive processes that protect some individuals with AH
from developing psychosis, or may provide an early point to intervene to stop the
12
development of schizophrenia (Johns & Van Os, 2001; Paulik, Badcock, & Maybery,
2008; van Os et al., 2009).
Thesis overview – Aims and outlines
There were two general aims of this thesis. The first aim was to disentangle the exact
nature of context memory deficits in AH (Section Two). The second aim was to explore
the particular contribution of voice identity processing to AH (Section Three). Given the
recent debate over the continuum model of psychotic symptoms, the overarching goal
was to gain a better understanding of the commonalities and differences in the
mechanisms underlying clinical and non-clinical (healthy) AH.
In brief, Section Two consists of four chapters. Chapter 1 presents a brief review
of the literature on cognitive mechanisms underlying clinical and non-clinical AH and
highlights the need to investigate different forms of context memory binding in these
groups. This section also includes three experimental papers (Chapters 2, 3 & 4), which
present data on various forms of external context binding in relation to clinical and non-
clinical AH. The specific aims are to determine whether:
1. Healthy individuals predisposed to hallucinations are impaired in automatic
and/or intentional binding of voice and location information in memory
(Chapters 2 & 3).
2. AH in schizophrenia and in the general population are both associated with
difficulties binding word and voice information in memory when an
identical task is used to assess performance in both samples of hallucinators
(Chapter 4).
13
These experimental chapters aim to expand upon the dominant form of cognitive
impairment that is typically investigated in relation to AH, namely, failures of self-
recognition.
Section Three comprises three chapters. Chapter 5 consists of a brief review of
the literature on voice processing in healthy individuals, individuals with schizophrenia,
and in relation to clinical and non-clinical AH, and highlights the challenges remaining
in the area of voice processing research in AH. This section also presents two
experimental papers which describe novel investigations into the discrimination of
voice identity in AH in schizophrenia (Chapter 6) and AH in the general population
(Chapter 7), respectively – again using an identical task in both chapters. Specifically,
these chapters aim to investigate, via multidimensional scaling of similarity judgments,
whether:
1. AH in schizophrenia are associated with atypical discrimination of
unfamiliar voices (Chapter 6).
2. AH in non-clinical individuals are linked to deficits discriminating
unfamiliar voices (Chapter 7).
Finally, Section Four consists of the General Discussion, which presents a
summary and critical analysis of the findings reported in the thesis. Methodological
strengths and limitations of the studies are noted and clinical implications and new
directions for future research are highlighted. This thesis therefore contributes to an
exciting and rapidly growing field of examination of the continuum model of psychotic
symptoms by advancing our understanding of the commonalities and differences in
mechanisms involved in AH in schizophrenia versus in the general population.
14
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27
Foreword to All Experimental Chapters
In all experimental chapters involving non-clinical samples who were high and
low in the predisposition to hallucinate (Chapters 2, 3, 4, and 7), all participants were
first year undergraduate Psychology students. Participants were tested individually and
offered either course credit points or $10 per hour reimbursement for time and expenses.
Participants who completed the feature binding study in Chapter 4 also
completed the voice identity discrimination studies in Chapters 6 and 7. That is, the
same samples of schizophrenia patients and healthy controls used in Chapter 4 were
used in Chapter 6. Similarly, the same samples of high and low scorers on the Launay-
Slade Hallucination Scale-Revised were used in the studies reported in Chapters 4 and
7. Any differences in participant numbers between these studies reflect the removal of
outliers for individual analyses. Different participant samples were involved in the
studies reported in Chapters 2 and 3.
28
29
Section Two
Context memory binding in relation to clinical
and non-clinical auditory hallucinations
30
31
Chapter One
An overview of cognitive impairments in clinical and non-clinical auditory
hallucinations
Synopsis
This chapter provides a brief summary of the nature of cognitive impairments associated
with hallucinatory experiences in both schizophrenia and in the general population. The
dominant cognitive model in the literature regarding the development of auditory
hallucinations is critically reviewed, highlighting the need to investigate binding of
externally generated information in context memory in both clinical and non-clinical
(healthy) auditory hallucinations. A source memory framework is then introduced, and
two main themes in the literature on context memory and auditory hallucinations are
briefly summarised. Finally, an outline of the aims and hypotheses of the proceeding
experimental chapters will be presented.
32
Cognitive impairments associated with auditory hallucinations in schizophrenia
Most contemporary models of auditory hallucinations (AH) have abandoned a single
cognitive mechanism account, and assume that a combination of different cognitive
processes contributes to the development and phenomenological complexity of AH.
There are several recent, extensive reviews of cognitive dysfunctions in AH, with some
of the cognitive impairments covered including abnormalities of language lateralisation,
dysfunctional intentional inhibition, dual deficits in intentional inhibition and context
memory, and more (e.g., Badcock & Hugdahl, 2012; Jones, 2010; Sommer & Diederen,
2009; Waters et al., 2012). The model receiving the most attention, which has dictated
the design of the majority of tasks in the literature, focuses on failures of self-
recognition (Frith & Done, 1988).
Failures of self-recognition
One of the core features of AH involves an identity being ascribed to voices heard,
which is typically reported to be separate to the voice-hearer (or ‘self’) (e.g., Nayani &
David, 1996). Predominantly, these experiences have been explained with reference to a
two-step process involving: (1) alienation – that is, an inability to identify inner, self-
generated information (i.e., a failure of self-recognition), and (2) a misattribution – in
which inner, self-generated material (which can include speech, memory, knowledge, or
belief) is misattributed to someone else (i.e., an “externalisation bias”). A large variety
of related terms have been used to explain this process, including reality monitoring,
reality discrimination, source monitoring, and self monitoring. Some authors use these
inter-related terms with a specific meaning, whereas others use them in a more general
sense (see Ditman & Kuperberg, 2005, for a review). Although most of the current
33
literature now accepts that there are two steps to this process, for ease of explanation,
this review will refer to both steps as “failures of self-recognition”, due to the
commonality of involvement of the ‘self’ in both steps. An extensive amount of
research has been conducted in this area, with reviews showing that failures of self-
recognition are consistently reported across a range of paradigms, inter-stimulus
intervals, and modalities in AH (Aleman & Laroi, 2008; Waters, Woodward, Allen,
Aleman, & Sommer, 2010), although this is not always the case (e.g., Diaz-Asper,
Malley, Genderson, Apud, & Elvevag, 2008).
There are several significant limitations of this conceptualization of the
cognitive basis of AH. Importantly, at the theoretical level, failures of self-recognition
are not sufficient to explain the rich phenomenological variety of AH, including reports
of voices being assigned to a specific other identity, voices heard in the third person
(e.g., two people commenting to each other about a voice hearer’s activities), voice-
hearers hearing more than one voice at a time (such as the voices of crowds), or the
presence of non-verbal AH (such as environmental noise and music) (Gallagher, 2004;
Jones, 2010; Laroi & Woodward, 2007). Laroi and Woodward (2007) also criticized
this literature because: (1) an "externalisation bias" is simply a redescription of
hallucinations – which must involve internal events being experienced as external – but
does not help to unravel how this occurs, and (2) the research designs commonly
employed to examine self-recognition (which typically involve making a decision about
the origin of self-generated words versus experimenter-generated words; e.g., Seal,
Aleman, & McGuire, 2004) confound the contextual cues involved, since self-generated
words involve a mixture of both internal and external sources of information.
Additionally, failures in self-recognition seem to be a feature of positive
symptoms of schizophrenia in general (having also been associated with delusions; e.g.,
34
Johns, Gregg, Allen, & McGuire, 2006), and so may not be specific to AH (Waters et
al., 2012). This finding raises the possibility that deficits in self-recognition may be
necessary for AH, but alone are not sufficient to explain these complex experiences.
Hence, it is evident that more information is needed to understand, for example, which
contextual cues are used to determine who is speaking (vocal identity), where the
speakers are (spatial location) (Laroi & Woodward, 2007), how this information is
combined, and whether these processes contribute to AH. As such, this thesis uses a
source memory framework to provide a broader perspective on the cognitive
mechanisms underpinning AH than the dominant focus on failures of self-recognition.
Source memory framework
Research in episodic memory usually distinguishes between content and context
(Chalfonte & Johnson, 1996): content typically refers to the event/item being retrieved
(e.g., a word or a visual object), whilst context refers to important features surrounding
the event concerning ‘who’ was involved (identity), and ‘when’ (temporal), or ‘where’
(spatial location) an event took place. Associative processes (e.g. binding) connect these
features together, helping to differentiate one event or episode in memory from another
(Johnson, Hashtroudi, & Lindsay, 1993). Of note, these binding processes may arise
automatically (i.e., incidentally) as part of a processing sequence, or may be initiated
intentionally to consciously and explicitly integrate features of information. Moreover
“when brought to mind (revived) moments, weeks, months, or even years later, it is
these types of details (or some subset of such details) that provide evidence about the
source of a mental experience” (Mitchell & Johnson, 2009, p. 639). Although the terms
‘content’ and ‘context’ are essentially arbitrary (Mitchell & Johnson, 2009), for ease of
explanation, this review will refer to ‘content’ as the words in speech (what), with
35
‘context’ referring to features surrounding this content (e.g., who spoke, when, and
where).
A wealth of empirical findings has indicated that impairments in processing
contextual information in memory are central to schizophrenia (Bazin, Perruchet,
Hardy-Bayle, & Feline, 2000; Boyer, Phillips, Rousseau, & Ilivitsky, 2007; Cohen,
Barch, Carter, & Servan-Schreiber, 1999; Hemsley, 2005; Waters, Maybery, &
Badcock, 2004). Woodward, Menon, and Whitman (2007) drew attention to evidence in
the literature suggesting that AH may be associated with impaired context memory for
two or more internal sources of information (i.e., did I say that or did I imagine that)
(Franck et al., 2000), or two or more external sources (Laroi & Woodward, 2007),
pointing to a more encompassing deficit in context memory (Johnson et al., 1993;
Waters, Badcock, Michie, & Maybery, 2006a) than that captured by self-recognition
tasks. Similarly, a meta-analysis by Achim and Weiss (2008) confirmed that there is no
evidence for a deficit specific to self-recognition in schizophrenia. It seems probable,
therefore, that there are aspects of context memory other than self-recognition which are
just as important to investigate as causal mechanisms contributing to the experience of
AH.
There are two main themes in the literature examining context memory and AH
that are particularly relevant to this thesis: (1) hallucinators may be failing to remember
critical contextual features separately (e.g., Nayani & David, 1996), and (2)
hallucinations involve deficits in binding contextual features in memory (e.g., Badcock,
Waters, & Maybery, 2007; Waters, Badcock, & Maybery, 2006b).
36
Memory for contextual features
One perspective to research on context memory suggests that AH may be associated
with problems remembering contextual features separately. For example, as an
explanation for why schizophrenia patients with AH (mis)attribute information to an
external source, Nayani and David (1996) proposed that these individuals may recollect
some information surrounding the event correctly (e.g., speech), and some incorrectly
(e.g., time and place).
Memory for external contextual information has largely been studied in
schizophrenia in general (i.e., at the diagnostic level). For example, when compared to
healthy controls, individuals with schizophrenia are impaired in the ability to encode
and remember spatial information (Badcock, Badcock, Read, & Jablensky, 2008;
Mazhari et al., 2010). In fact, there is an extensive body of work examining spatial
processing impairments (e.g., spatial working memory deficits; Cameron et al., 2003;
Lee & Park, 2005; Park & Holzman, 1992) in schizophrenia. Interestingly however,
most of these studies have examined visual spatial working memory (e.g., Brebion,
David, Ohlsen, Jones, & Pilowsky, 2007b), which has mostly been linked to negative
symptoms of schizophrenia, and thus may be less relevant to the experience of AH. So
far, the only research conducted into auditory spatial location in AH has been a recent
magnetic resonance imaging study which examined the phenomenological spatial
location of AH (i.e., voices heard either inside or outside the head). The results of this
study indicate that the spatial location of AH is associated with differences in activation
in the right temporoparietal junction (Plaze et al., 2011). Thus, the association between
auditory spatial context and AH clearly warrants further examination.
It is necessary to note that context is typically referred to in relation to
something else (e.g., when or where something happened), and hence, these contextual
37
features may not always be tested in isolation in these studies. As such, it is important to
clarify the principal contextual deficit involved in AH: a deficit in remembering
individual contextual features themselves (e.g., voice identity or spatial location) or a
deficit in binding/integrating contextual features in memory (i.e., binding content-
context or context-context).
Memory binding of contextual features
A number of authors (but not all, e.g., Diaz-Asper et al., 2008; Luck, Buchy, Lepage, &
Danion, 2009; Luck, Foucher, Offerlin-Meyer, Lepage, & Danion, 2008) have argued
that AH in schizophrenia are associated with deficits in binding the contextual features
of stimuli properly with target information (Bazin et al., 2000; Bentall, 1990; Brebion,
David, Jones, Ohlsen, & Pilowsky, 2007a; Brebion, Gorman, Amador, Malaspina, &
Sharif, 2002; Guillem et al., 2003; Waters et al., 2006b; Woodward, Menon, Hu, &
Keefe, 2006) , resulting in an inability to form a complete representation of the origins
of mental events. At the phenomenological level, this binding deficit may be reflected,
for example: in the form of a person with AH identifying the words spoken by his/her
hallucinated voice as resembling his/her fathers’, but being spoken in a different voice
with no gender features (Davies, Thomas, & Leudar, 1999); or in the form of a real and
familiar voice saying things they would be unlikely to say, for example, the voice of a
friend swearing abusively at the voice hearer. The purported deficits in binding
contextual information to target item information in memory permit considerable intra-
and inter-personal variation, involving contextual features from different modalities
(e.g., visual and auditory) and of various forms (e.g., spatial, temporal, perceptual,
emotional) (see Laroi & Woodward, 2007, for a review) (see Figure 1).
38
At the neural level, difficulties binding external contextual information in
schizophrenia have been strongly linked to functional abnormalities both within the
medial temporal lobes (MTL), and between this region and the prefrontal cortex (Boyer
et al., 2007). Furthermore, significant deactivation of the hippocampus and
parahippocampal gyrus occurs immediately prior to the onset of hallucinated voices in
patients with schizophrenia (Diederen et al., 2010; Hoffmann, Anderson, Varanko,
Gore, & Hampson, 2008; Silbersweig et al., 1995). Thus, abnormal memory functions,
especially those related to binding of contextual information, might play a triggering
role in producing AH.
Figure 1. Schematic representation of reported deficits in binding numerous contextual
features of events into a complete representation in memory in AH.
However, there are several limitations of the research into context memory
binding and AH to date. First, all the research has been conducted in a fairly non-
systematic way, with the literature adopting a rather ad-hoc progression of tasks and
theorising. A large variety of tasks have been used, with findings reported to depend on
how groups are selected and how tasks are set up (Achim & Weiss, 2008). Researchers
39
have largely chosen to utilise tasks involving binding of visual objects to locations.
However, this form of binding has been examined in schizophrenia in general (e.g.,
Burglen et al., 2004; Salame, Burglen, & Danion, 2006; van't Wout, Aleman, Kessels,
& Kahn, 2006), but not in relation to specific symptoms, such as AH. Therefore, despite
substantial research in this area, important questions still remain regarding the form of
contextual memory integration deficits (i.e., what kind of features are involved), and
whether deficits apply to schizophrenia in general, or to specific symptoms, such as AH.
For example, Brebion, David, Jones, Ohlsen, and Pilowsky (2007a) identified a
specific link between a deficit in temporal context memory and AH whilst Waters et al
(2006b; 2004) showed that impaired memory for when an event occurred may be
present in individuals with schizophrenia with and without current hallucinations. The
latter finding suggests deficits in temporal context memory binding may not be specific
to AH, or might be a vulnerability marker (i.e., increase the risk for hallucinations). It is
important to note, however, that the tasks employed in these studies differed – the one
employed by Brebion et al (2007a) involved remembering the temporal order of words
(i.e., verbal/linguistic information) within lists, whereas that used in Waters et al (2004)
involved remembering the temporal order of visually-presented object pairs between
sessions. Thus, the differing tasks involved in the two studies may potentially be
tapping into somewhat different underlying mechanisms.
In addition, given the importance of voice identity to AH, it also seems
surprising that few tasks have specifically manipulated binding of voice information.
Furthermore, there is extensive evidence that schizophrenia is more strongly related to
memory deficits on tasks that load heavily on intentional forms of processing
(Racsmany et al., 2008). Similarly, recent evidence suggests that AH may be associated
with more severe impairments on tasks involving an intentional (conscious) – as
40
opposed to automatic (incidental) – form of binding (Luck et al., 2008). Further studies
are therefore required to examine whether context memory binding deficits occur at a
conscious/intentional level or at a more automatic/incidental level in AH.
In sum, there are still many gaps in the literature, with the current research
unable to disentangle the exact nature of deficits in voice hearers. Thus a systematic re-
examination of context memory deficits associated with the experience of AH is
required.
Cognitive impairments in auditory hallucinations in the general population
(hallucination predisposition)
Further research into the cognitive mechanisms underlying both clinical and non-
clinical hallucinations is vital given the recent shift toward early detection and clinical
intervention at the time of the prodrome in schizophrenia (McGorry, 2009). In
comparison to psychotic AH, fewer studies have been conducted investigating cognitive
impairments in non-clinical AH/psychosis-proneness. Healthy individuals predisposed
to hallucinations (e.g., those with high scores on the Launay-Slade Hallucination Scale-
Revised; Bentall & Slade, 1985) have been found to experience some similar cognitive
deficits as individuals with clinical AH, including dysfunctions in intentional inhibition,
and associations with intrusive thoughts, ruminations and thought suppression attempts
(Jones & Fernyhough, 2009; Paulik, Badcock, & Maybery, 2008). However, the self-
recognition literature has revealed mixed findings in relation to hallucination
predisposition, with some studies showing difficulties in non-clinical AH (Johns et al.,
2010; Laroi, Van der Linden, & Marczewski, 2004), and others finding no relation
between hallucination-proneness and self-recognition (Allen, Freeman, Johns, &
McGuire, 2006). For example, Versmissen et al (2007b) found poor action self-
41
recognition in participants with sub-clinical psychotic symptoms. However, using a
shortened version of the same paradigm, Versmissen et al (2007a) found no evidence of
self-recognition deficits in the same high-risk group. These inconsistencies may, at least
in part, be explained by differences in task length between studies, and thus highlight
the need to use the same methodology in clinical and non-clinical hallucinators in order
to tease out both similarities and differences in the cognitive processes involved (Laroi,
2012).
Aside from investigation into self-recognition deficits, there has been limited
research on other forms of contextual memory impairment in healthy individuals
predisposed to hallucinations. Steel, Fowler, and Holmes (2005) proposed that healthy
individuals prone to positive symptoms of schizophrenia have poorer temporal context
integration (binding) in memory. In contrast, studies have demonstrated intact ability to
remember the spatial location of spoken words (McKague, McAnally, Puccio, Bendall,
& Jackson, 2012) and to integrate pictorial and verbal sources of information with each
other in memory (Ruiz-Vargas, Cuevas, & Lopez-Frutos, 1999) in healthy individuals
prone to hallucinations. Such findings also raise the possibility that at least some forms
of context memory deficits may emerge only in psychosis (Badcock & Hugdahl, 2012),
potentially challenging the assumption of identical cognitive mechanisms underlying
clinical and nonclinical psychotic symptoms. While similarities in cognitive processes
have been used to support the continuum model of psychotic symptoms, recent
commentators have argued that evidence contrary to the continuum viewpoint would be
more informative (Linscott & Van Os, 2010). Hence, it is important to gain a better
understanding of the processes that are/are not involved in the predisposition to AH in
nonclinical groups.
42
Specific aims
The research reported in the following chapters rigorously examined the nature of
context memory deficits in clinical and non-clinical AH. The studies systematically
focussed on memory for external contextual information in the auditory modality only.
Furthermore, given the importance of vocal identity to hallucinatory experiences, all the
studies incorporated ‘voice’ as one of the context features examined. A preliminary
study, reported in Chapter 2, set the seed for the rest of the studies in the thesis. This
study investigated the frequency of AH experienced in a healthy sample of
undergraduate students, and examined whether healthy voice-hearers experienced
difficulties binding two external, contextual features of information (voice and location)
in memory. If binding of these two features is impaired in non-clinical AH, this would
be consistent with the continuity model of psychotic symptoms. Conversely, intact
binding of the two features in non-clinical AH might cast some doubt on the continuum
model. Findings from this chapter revealed no evidence of a binding deficit in
hallucination-proneness; however this study investigated only automatic binding,
leaving open the possibility that non-clinical AH might still be impaired when
intentional binding is required. The research reported in Chapter 3 thus investigated the
issue of intentionality in relation to binding in non-clinical AH – that is, it sought to
determine whether healthy individuals predisposed to hallucinations are impaired in
intentional, as opposed to automatic binding of voice and location information in
memory.
Finally, the study reported in Chapter 4 directly investigated the continuum
notion of psychotic symptoms in relation to binding word and voice information in
memory, with an identical task used in individuals with schizophrenia (with and without
AH), as well as in healthy individuals predisposed to hallucinations. If deficits in
43
binding voice and word information are found for both clinical and non-clinical
hallucinatory experiences, this would provide support for the continuum model of
psychotic symptoms. If deficits are found only in the schizophrenia sample, however,
this might point to a discontinuity in the cognitive processes underlying clinical and
non-clinical hallucinations.
44
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van't Wout, M., Aleman, A., Kessels, R. P. C., & Kahn, R. S. (2006). Object-location
memory in schizophrenia: Interference of symbolic threatening content.
Cognitive Neuropsychiatry, 11, 272-284.
Versmissen, D., Janssen, I., Johns, L. C., McGuire, P., Drukker, M., Campo, J., et al.
(2007a). Verbal self-monitoring in psychosis: A non-replication. Psychological
Medicine, 37, 569-576.
Versmissen, D., Myin-Germeys, I., Janssen, I., Franck, N., Georgieff, N., Campo, J., et
al. (2007b). Impairment of self-monitoring: Part of the endophenotypic risk for
psychosis. The British Journal of Psychiatry, 191, s58-s62.
51
Waters, F., Allen, P., Aleman, A., Fernyhough, C., Woodward, T. S., Badcock, J. C., et
al. (2012). Auditory hallucinations in schizophrenia and nonschizophrenia
populations: A review and integrated model of cognitive mechanisms.
Schizophrenia Bulletin, 10.1093/schbul/sbs045.
Waters, F., Badcock, J., Michie, P., & Maybery, M. (2006a). Auditory hallucinations in
schizophrenia: Intrusive thoughts and forgotten memories. Cognitive
Neuropsychiatry, 11, 65-83.
Waters, F. A., Badcock, J. C., & Maybery, M. T. (2006b). The who and when of context
memory: Different patterns of association with auditory hallucinations.
Schizophrenia Research, 82, 271-273.
Waters, F. A., Maybery, M. T., & Badcock, J. C. (2004). Context memory and binding
in schizophrenia. Schizophrenia Research, 68, 119-113.
Waters, F. A., Woodward, T. S., Allen, R., Aleman, A., & Sommer, I. E. (2010). Self-
recognition deficits in schizophrenia patients with auditory hallucinations: A
meta-analysis of the literature. Schizophrenia Bulletin, 10.1093/schbul/sbq144
Woodward, T. S., Menon, M., Hu, X., & Keefe, R. S. E. (2006). Optimization of a
multinomial model for investigating hallucinations and delusions with source
monitoring. Schizophrenia Research, 85, 106-112.
Woodward, T. S., Menon, M., & Whitman, J. C. (2007). Source monitoring biases and
auditory hallucinations. Cognitive Neuropsychiatry, 12, 477-494.
52
53
Chapter Two
Context binding and hallucination predisposition1
Abstract
Patients with schizophrenia and current auditory hallucinations exhibit a combination of
deficits in context binding and intentional inhibition. Hallucinations also occur in the
general population suggesting an underlying continuity of causal mechanisms, however,
these experiences may also differ (e.g. in frequency), indicating some differences in
aetiology. The aim of this study was to examine the frequency of hallucinatory
experiences in healthy young adults and to assess whether difficulties in context binding
characterize individuals highly predisposed to hallucinations. A modified version of the
Launay-Slade Hallucination Scale-Revised, including an assessment of the frequency of
hallucination experiences, was completed by 615 undergraduates from which sub-
samples of high (n = 25) and low (n = 27) scorers were drawn. Context memory ability
was assessed using a voice-location binding task. The results showed that the frequency
of hallucinations in high LSHS-R scorers was much less than that previously reported
for individuals with schizophrenia. Furthermore, no group differences in context
memory binding were observed, nor any association between hallucination frequency
and context binding difficulties. The continuity model of hallucinations may overlook
some important differences in hallucinatory experiences in the general population
versus psychosis.
Keywords: Hallucination predisposition; Hallucinations; Schizophrenia; context binding
1 This chapter is a reproduction of the following article: Badcock, J. C., Chhabra, S., Maybery, M. T., &
Paulik, G. (2008). Context binding and hallucination predisposition. Personality and Individual
Differences, 435, 822-827.
54
1. Introduction
We have previously proposed a model of auditory hallucinations (AH) in schizophrenia
comprised of deficits in both context memory binding and intentional inhibition
(Waters, Badcock, Michie, & Maybery, 2006a). As a result of these combined deficits,
mental events are experienced as involuntary and intrusive and are not correctly
recognized because the necessary contextual cues (e.g. who was speaking, where and
when) are missing or incomplete. The relative risk of exhibiting these deficits has been
shown to be significantly elevated in patients with active (i.e. frequent) AH compared to
patients who are not currently hallucinating (Waters et al., 2006a). It is possible that this
combination of deficits underpins all forms of hallucinations, however, this proposal has
not been directly tested (Badcock & Maybery, 2005).
AH (‘voices’) also occur in the general population, including children and
adolescents, and do not necessarily presage mental illness (McGee, Williams, &
Poulton, 2000; Tien, 1991). Some research has emphasized that AH in patients and non-
patients are broadly similar in nature (Honig et al., 1998; Waters, Badcock, & Maybery,
2003a) suggesting that similar cognitive mechanisms may be involved in their
development. For example, healthy young adults (undergraduates) with high scores on
the Launey-Slade Hallucination Scale-revised (LSHS-R; Bentall & Slade, 1985) – a
common measure of predisposition to hallucinations – show a specific difficulty with
intentional inhibition (Paulik, Badcock & Maybery, 2007) similar to that observed in
patients with schizophrenia (Waters, Badcock, Maybery & Michie, 2003b). It has been
suggested that what may differ between these groups of individuals with AH is how
they cope with, or interpret the experience (Escher, Romme, Buiks, Delespaul & van
Os. 2002; Morrison, 2005). Others, however, have noted significant differences in the
characteristic features of AH in schizophrenia and non-schizophrenia populations,
55
especially in terms of the frequency, valence and complexity of the experience (Choong,
Hunter, & Woodruff, 2007). These findings suggest that there may also be some
important differences in the underlying cognitive mechanisms of AH, hence our model,
based on deficits in context memory binding and intentional inhibition may not apply to
non-patient voice hearers.
The aim of the current study was to investigate whether healthy individuals
predisposed to AH have difficulties with context binding similar to that described in
patients with AH (Bentall, 1990; Brebion, Gorman, Amador, Malaspina, & Sharif,
2002; Seal, Aleman, & McGuire, 2004; Waters, Maybery, Badcock, & Michie, 2004;
Waters, Badcock, & Maybery, 2006b; Woodward, Menon, & Whitman, 2007). Many
of these studies report a difficulty recalling the source (i.e. ‘who’- self vs. other) of
spoken words and suggest that AH are associated with a bias in attributing self-
generated words to an external source. However, the design of these studies has recently
been criticised, since self-generated words involve both internal and external qualities
(Laroi & Woodward, 2007), thus confounding the context memory cues involved (i.e.
voice and location). In addition, recent evidence suggests that AHs may be associated
with impaired context memory for multiple external sources (Woodward et al. 2007),
which points to a more encompassing deficit in context binding (Waters et al., 2006a).
In the current study we used a variant of a voice-location binding task designed
to assess binding in context memory. This task examines binding of contextual
information from two external sources (voices and locations) and therefore avoids the
criticisms previously outlined by Laroi & Woodward (2007). Importantly, the design of
this task also allows context binding to be assessed whilst minimizing the need to
inhibit a response (see Method) which might lead to differences between high and low
hallucination predisposed groups. We reasoned that if high LSHS-R scorers exhibited
56
impaired binding of voice and location information for auditory stimuli (words)
compared to individuals with low LSHS-R scores, this would support the continuum
model. Alternatively, should no difference in binding be observed, it would point to
qualitative differences between patient and non-patient hallucinators. We also
examined the frequency of AH in healthy individuals predisposed to hallucinations.
Since the LSHS-R assesses a wide range of hallucinatory experiences (including visual
as well as auditory events) this additional assessment of the frequency of AH allowed us
to explore whether impaired context binding is associated with either a higher general
predisposition to hallucinate or, more specifically, with more frequent AH experiences.
Finally we also examined individual differences in intelligence, emotional response
(depression, anxiety and stress) and negative schizotypal experiences in order to check
the specificity of any significant results.
2. Method
2.1 Participants
Six hundred and fifteen undergraduates completed a modified Launay-Slade
Hallucination Scale-Revised (LSHS-R) questionnaire (Bentall & Slade, 1985)
comprising the standard 12-item scale plus 3 additional questions examining the
frequency of AH-like experiences (see Section 2.2.3). Individuals with high and low
scores on the standard LSHS-R (from the upper and lower quintiles of the distribution)
were invited to take part in the memory binding study. Twenty-five high scorers (16
females) and 27 low scorers (22 females) responded to this invitation and completed the
study (see Table 1).
57
Table 1
LSHS-R group means, standard errors (SEs), and t-tests for the age, WASI, O-LIFE-
introvertive anhedonia, and DASS-21 data.
Low LSHS-R
(n = 27)
High LSHS-R
(n = 25)
Mean SE Mean SE t
LSHS-R 5.33 .50 29.08 .79 25.85*
AGE (years) 18.74 0.59 17.80 .17 1.49
WASI 111.30 1.79 115.24 1.81 1.55
Introvertive anhedonia 1.88 .42 2.92 .36 1.88
DASS Anxiety 3.93 .94 11.60 1.83 3.73*
Depression 2.08 .28 13.64 1.28 8.81*
Stress 6.83 .78 20.09 1.27 8.89*
* p < .05
2.2 Memory-Binding Task (Maybery et al., 2007)
This task assesses individual differences in the ability to bind together two auditory
contextual features (speaker voice and location) of spoken words. On each trial
participants heard a single word spoken by two different voices in sequence, emanating
from two different loudspeaker locations, followed by a visual recognition cue -
“VOICE” or “LOCATION” – in concert with an auditory mask (see Figure 1). A single
spoken word from a single location (a recognition probe) was then presented. The
participants’ task was to judge if the probe was the same as one of the two study items
58
(yes/no response) with respect to the auditory feature - voice identity or loudspeaker
location - indicated by the visual cue.
Figure 1. Configuration of the memory-binding task illustrating the sequence of events
from study (S1 & S2) to the presentation of the recognition probe (P): different fill
patterns represent different voices and separate loudspeakers represent the different
locations.
The two study items will be represented as V1L1 and V2L2 (where V and L
denote the voice and location features, and the subscripts denote the features for the 1st
and 2nd
study items). Five probe types were employed for each of the two recognition
cues. The two critical probe types were designated “intact” and “recombined” probes.
Intact probes were identical to a study stimulus, consisting of a word spoken in the same
voice and presented from the same location as in the study phase (i.e., V1L1 or V2L2),
whereas recombined probes consisted of a word spoken in the voice of one study item
but emanating from the location of the other study item (i.e., V1L2 or V2L1). Binding of
59
voice and location features to form an integrated representation in memory, results in
faster and more accurate responses to intact probes relative to recombined probes
(Maybery et al., 2007). Consequently, binding ability was examined in the current study
by comparing responses to these two probes; impaired binding will result in a reduced
advantage for intact compared to recombined probes. Importantly, both critical probe
types use ‘old’ voice and location features (i.e. features present in the study items) and
require a positive or “yes” response; thus neither probe type requires inhibiting a
response to a new source or inhibiting a response to an old but currently irrelevant
contextual feature within a stimulus pair. Consequently individual differences in
inhibitory ability – which may be expected to vary between high and low LSHS-R
groups - are experimentally controlled within the current design.
Three additional recognition probes introduced either a new voice (e.g., V3L1 or
V3L2), new location (e.g., V1L3 or V2L3), or both (e.g., V3L3 or V3L3). These probe
types were included to keep participants honest in their judgments by forcing them to
refer to the cued feature in making recognition judgments. For each recognition
judgment (voice or location), two of the additional probes required a negative (i.e. “no”)
response.
2.2.1 Stimuli
The stimuli included 64 digitally-recorded spoken words, derived from Taylor (2005).
They comprised eight five-syllable words (consideration, discolouration, elaboration,
elimination, humiliation, impersonation, justification, representative), spoken in eight
different Australian native English voices (half male). Stimuli were 1000 ms in duration
and presented at 58dB. A white-noise stimulus, presented at the same sound pressure
level, was used as the auditory mask.
60
2.2.2 Procedure
Testing was carried out in a sound-proof, darkened room and began with detailed
instructions emphasizing fast but accurate responding. Stimulus presentation was
controlled using a 400Hz Edsys PC fitted with a Sound Blaster 16 card. Auditory
stimuli were presented via eight Yamaha 10-watt YST M20DSP loudspeakers which
were arranged in azimuth in front of the participant with even spacing (36o separation of
adjacent loudspeakers), and at a radius of 1.2m around the seated participant. There
were 10 practice and 100 test trials, with the stimuli for these trials selected anew for
each participant of one LSHS-R group, and the same stimulus set used for a randomly
selected participant of the other LSHS-R group. The voice and location features for the
study items were selected randomly, as were any new features required for recognition
probes. A single word was used on each trial, which was also selected at random, with
different words used on consecutive trials. Each of the five probe types for each
recognition cue (voice/location) occurred once every 10 trials, with the order of these 10
trials randomized, yielding a total of 20 responses for each of the 5 probe types. Each
trial began with a 1000ms visual warning signal (“READY”), followed by the two study
items in sequence, then the visual recognition cue and auditory mask, and finally the
auditory recognition probe. A stimulus onset asynchrony of 1500ms was used to
separate all consecutive stimulus events. Recognition responses were collected using a
button box; reaction time (RT) was calculated from the onset of the recognition probe.
The next trial began 2000ms after the participant’s response, or 9000ms after onset of
the recognition probe if no response had been made in that period. Overall task duration
was approximately 30 minutes.
61
2.2.3 Additional measures
The 12 item LSHS-R (Bentall & Slade, 1985) assesses a range of visual and auditory
experiences, rated on a 5-point scale (0 = certainly does not apply to me, 4 = certainly
does apply to me). The wording of LSHS-R items varies (e.g. ‘always’, ‘sometimes’,
‘on occasions’) thus high scores may represent participants endorsing a range of
hallucinatory experiences that have been present but, nonetheless, occurred relatively
infrequently. Consequently, three additional questions were also provided to directly
assess the frequency with which hallucinatory-type experiences occur. These questions
were generated based on previous factor analysis of the LSHS-R (Waters et al., 2003a).
Specifically, the item with the highest factor loading for each of the three factors
extracted was used to assess the frequency of hallucinatory experiences2; these three
items specifically assess auditory hallucination-like experiences. The three frequency
questions were rated on a 7 point scale (0 = I have never had this experience, 6 = daily;
see Table 2). IQ was estimated using the vocabulary and matrix reasoning subtests
from the Weschler Abbreviated Scale of Intelligence (WASI; Weschler, 1999). The short
(21-item) version of the Depression Anxiety Stress Scales (DASS-21; Lovibond &
Lovibond, 1995) was used to assess the presence of enduring symptoms of depression,
anxiety, and stress in a typical week. The Oxford-Liverpool Inventory of Feelings and
Experiences (O-LIFE; Mason, Linney, & Claridge, 2005) assessed schizotypal
personality traits that closely correspond to negative schizophrenic symptomatology
(Introverted Anhedonia score range 0-10).
The study was approved by the University of Western Australia Human
Research Ethics Committee and written informed consent was obtained from each
2 The three items used to assess frequency were: (1) The sounds I hear in my daydreams are usually clear
and distinct; (2) In the past, I have had the experience of hearing a person’s voice and then found that no
one was there; and (3) In the past I have heard the voice of God speaking to me.
62
participant prior to testing. Participants were tested individually and offered course
credit points or $15 reimbursement for time and expenses.
3. Results
Preliminary analyses indicated that all scores were normally distributed. Extreme scores
(> 3 SDs away from respective group means) were excluded (5 data points), however,
analyses showed that inclusion of outliers had no effect on the outcomes reported
below. Where tests for homogeneity of variance were significant, outcomes are reported
for analysis of variance (ANOVA) conducted without the assumption of equal
variances. No multivariate outliers were detected. An alpha level of .05 was used
throughout.
3.1 Descriptive Statistics
A summary of demographic, cognitive, schizotypy and emotion measures for the high
and low LSHS-R group is presented in Table 1. Substantial group separation was
obtained on the LSHS-R as expected. The high and low LSHS-R groups did not
significantly differ in age or in scores from the WASI or the O-LIFE (introvertive
anhedonia) subscale. However, the high LSHS-R group obtained significantly higher
scores than the low LSHS-R group on all three DASS subscales - Anxiety, Depression,
and Stress. Thus, to account for these differences, a DASS-Anxiety factor was formed
by dividing the entire sample into those scoring above the median (six) and those
scoring at or below the median. This factor was then included along with LSHS-R
group in all analyses. These analyses were repeated using a median split on either
DASS-Depression or DASS-Stress instead of DASS-Anxiety3. For brevity these
3 As a further check, the main analyses were repeated using DASS-Anxiety, DASS- Depression, and
DASS -Stress as covariates, revealing no change in outcome.
63
analyses are not reported since the outcomes were consistent with those of the analysis
based on the DASS-Anxiety factor.
3.2 Frequency of Hallucinations
The data were screened for internal consistency on an item-by-item basis to ensure that
participants who reported a low LSHS-R score (i.e. “certainly does not apply to me”)
reported a consistent response on the frequency items (i.e. “I have never had this
experience”, rather than “I experience this daily”), and that similar consistency applied
for participants reporting a high LSHS-R score (they should not report “I have never
had this experience”). No inconsistency between responses to the LSHS-R and
frequency questions was evident. The mean of responses to the three frequency
questions was calculated to provide a summary index of the frequency of hallucinations
(mean scores were rounded down to the nearest whole number in order to preserve the
scale). Table 2 summarizes the percentage of individuals reporting the various
frequencies of hallucinatory experiences, based on this summary index, for the entire
sample of undergraduate students initially tested, and separately for the high and low
LSHS-R subgroups. The specific AH-like experiences assessed occurred infrequently,
even in individuals in the high LSHS-R group.
64
Table 2
The percentages of healthy young adults reporting the various frequencies of
hallucinatory experiences.
Frequency of
hallucinatory
experiences
Entire
sample*
(n = 614)
Low LSHS-R
(n = 27)
High LSHS-R
(n = 25)
Never 34.20 88.90 0
Only once before 28.66 11.11 20
Once a year 24.43 0 48
Once every 3-6 months 10.42 0 20
Monthly 2.12 0 8
Weekly 0.16 0 4
Daily 0 0 0
*Frequency data for one participant from the entire sample was missing
3.3 Memory-Binding Task
The percentage of correct responses (accuracy) and median RTs for correct responses,
for the different trial types was calculated for each participant (see Table 3). The central
analyses focused on comparing the intact and recombined probes since these address
binding ability. A 2 (LSHS-R group: high, low) x 2 (DASS-Anxiety group: high, low) x
2 (recognition probe: intact, recombined) x 2 (recognition cue: voice, location) mixed-
design ANOVA was conducted for each dependent variable, where the last two factors
were repeated-measures.
65
Table 3
Descriptive statistics for accuracy (percent correct) and RT (ms) as a function of probe
type. The old/new status of the two features of the probe is shown for the cued feature
followed by the uncued feature (in parentheses); for example, old(new) refers to using
an old value for the cued feature and a new value for the uncued feature.
Low LSHS-R High LSHS-R
Mean SE Mean SE
Accuracy
Intact 98.33 .53 98.60 .61
Recombined 92.59 1.08 93.60 1.10
Old (new) 92.96 1.29 91.20 1.39
New (old) 92.04 1.37 90.40 1.29
New (new) 93.33 1.31 91.40 1.65
RT
Intact 1040.71 38.77 1023.53 44.59
Recombined 1149.36 40.06 1133.34 41.83
Old (new) 1180.92 43.73 1198.20 48.19
New (old) 1134.31 41.71 1076.67 38.13
New (new) 1127.81 43.24 1097.86 49.14
Accuracy. Accuracy for the two critical recognition probes for both high and low
LSHS-R groups is displayed in Figure 2. Analysis revealed a significant main effect of
probe type, with higher accuracy for intact (M = 98.76%, SE = .42%), compared to
recombined probes (M = 93.29%, SE = .96%), F(1,46) = 28.95, p < .05, partial-η2 =
0.39. However, none of the other main effects or interactions was significant. The
66
absence of significant interactions with probe type indicates comparable memory
binding for the two LSHS-R groups and for the two DASS-Anxiety groups.
Low LSHS-R High LSHS-R
70
80
90
100
Intact Recombined Intact Recombined
Probe Type
Accu
racy (
%)
Low LSHS-R High LSHS-R
600
800
1000
1200
Intact Intact Recombined Recombined
Probe type
Reacti
on
tim
e (
ms)
Figure 2. Mean accuracy and RT (and 95% confidence intervals) for both high and low
LSHS-R groups for the critical recognition probes.
Reaction Time (RT). RT for the two critical recognition probes for both LSHS-
R groups is also displayed in Figure 2. Consistent with the outcomes for accuracy,
participants were faster at responding to intact (M = 1045.14ms, SE = 35.89ms),
compared to recombined (M = 1146.89ms, SE = 35.62ms) probes, F(1,46) = 28.92, p <
.05, partial-η2 = 0.39. However, all the other main effects and interactions were non-
significant. Again the absence of significant effects involving either LSHS-R group or
DASS-Anxiety group indicates comparable memory binding for the two LSHS-R
groups and for the two DASS-Anxiety groups.
3.4 Correlations Between the Frequency of Hallucinations and Binding Ability
In order to examine if impaired binding was associated with more frequent AH in the
high LSHS-R subgroup, the summary index of AH frequency was correlated with two
67
indices of binding ability derived from the context memory task. These indices were
calculated by subtracting mean RT (or mean accuracy) for intact probes from the mean
RT (or mean accuracy) for recombined probes. The results showed that frequency of
AH experiences was not significantly correlated with either measure of binding ability:
accuracy, r(25) = .20, p > .05; RT, r(25) = -.10, p > .05. Similarly, there was no
significant correlation between these measures of binding ability and scores on the
standard LSHS-R reflecting increased predisposition to hallucinations in general:
accuracy, r(25) = .12, p > .05; RT, r(25) = .01, p > .05.
4. Discussion
This study utilized a voice-location binding task, entailing two external sources, to
examine context binding in individuals predisposed to hallucinations. The main findings
of the study show that the integration of voice and location features in context memory
is intact in healthy young adults predisposed to hallucinations in general and, in
particular, that context binding deficits are not associated with more frequent AH
experiences.
The current findings emphasize that the phenomenology of hallucinatory
experiences – at least in terms of frequency - is markedly different in healthy
individuals predisposed to hallucinations compared to that reported in patients with
schizophrenia. Approximately 75% of individuals with psychosis have been reported to
experience AH at least once a day (Steel et al., 2007). Predisposition to hallucinatory
experiences in healthy individuals is commonly assessed using the LSHS-R (Bentall
and Slade, 1985). High scores on this scale may be achieved by endorsing a wide range
of experiences – including visual and auditory hallucinations which may occur ‘often’,
‘sometimes’ or ‘on occasion’. However, when asked specifically to report the frequency
68
of three LSHS-R items which focus on AH-like experiences only 32% of the high
LSHS-R subgroup reported experiencing these as occurring at least once every 3-6
months and the modal frequency was only ‘once a year’. Analogue samples (e.g. of
undergraduate students assessed with schizotyy measures) are frequently used with the
intention of examining cognitive and/or biological mechanisms relevant to symptoms of
schizophrenia whilst avoiding potential confounds related to the effects of medication or
hospitalization. The current data suggest that more caution may be needed when
assuming continuity of experiences - and underlying mechanisms - between patient and
non-patient hallucinators.
The present data also indicate that there is no evidence of impaired context
binding ability in healthy young adults who are highly predisposed to hallucinations.
Events in episodic memory are usually encoded as an integrated representation together
with relevant contextual details. Consequently intact recognition probes typically yield
responses which are faster/more accurate than recombined probes. Indeed, such an
advantage was found in the present study, consistent with previous findings (Maybery et
al., 2007). Importantly, the high level of accuracy for intact probes did not limit the
sensitivity of the task; the difference in performance for intact and recombined probes
was of medium size (partial-η2 = 0.39 for each dependent variable), consisting of a
difference in accuracy of 5.47% and a difference in RT of 101.75 ms. Impaired memory
binding should have resulted in a reduced advantage for intact compared to recombined
probes in the high LSHS-R group; however, there was no evidence of impaired binding
of auditory context (voice or location) in the current data. Furthermore, we tested
whether there was a correlation between context binding ability and the frequency of
AHs in the high LSHS-R subgroup: this association was also non-significant. Overall
these results diverge from recent studies revealing context binding impairments in
69
patients with schizophrenia (Seal et al. 2004; Waters et al., 2004; Woodward et al.
2007).
These findings raise some interesting possibilities. First, hallucinations in patient
and non-patient (healthy) groups may be subserved by some common (e.g. intentional
inhibition) and some partially distinct (e.g. memory binding) mechanisms. For example,
Paulik et al. (2007) have shown a pattern of impaired intentional inhibition for high
LSHS-R scorers (relative to low LSHS-R scorers) which matches the pattern exhibited
by schizophrenia patients with AH (Waters, et al. 2003b). That outcome is important
since it shows that the current design (comparing high and low LSHS-R groups) is
potentially sensitive to cognitive differences, yet differences in context binding ability
could not be detected. Clearly a related possibility is that context memory binding
deficits may only emerge as psychosis fully develops (Doré, Caza, Gingras, & Rouleau,
2007).
Secondly, the current memory binding task entails automatic encoding of context.
In contrast, many studies in psychotic patients with hallucinations, which have shown
memory binding deficits, have employed tasks favouring intentional encoding of
context (Waters et al., 2006a, b; Dore et al., 2007). There are now several studies
suggesting that intentional cognitive processing is consistently impaired in
schizophrenia whilst automatic processing is often spared (e.g., Racsmany et al., 2008).
Thus, it is possible that memory binding difficulties in individuals predisposed to
hallucinations may be revealed when intentional binding is assessed. We are currently
investigating this issue in our laboratory.
Finally, we have previously shown that schizophrenia patients with current AH
exhibit a combination of deficits in context binding and intentional inhibition. The
current findings raise the possibility that this model may not be appropriate for healthy
70
individuals predisposed to hallucinations or may require modification to clarify that
memory binding deficits are specifically intentional in nature. Furthermore, the
increased need for care in schizophrenia patients with hallucinations may be due to the
particular cognitive consequences arising from context memory difficulties. However, it
must be noted that in order to determine whether context binding deficits do not occur
on a continuum with patients with schizophrenia it would be necessary for future studies
to employ precisely the same voice-location binding task used in the current study to
assess schizophrenia patients with AH. More generally, the current findings strongly
suggest that a more systematic investigation of different forms of context binding linked
to AH both in patient and non-patient groups is warranted.
Acknowledgements
This work was partially supported by the Schizophrenia Research Institute, utilizing
funding from the Ron and Peggy Bell Foundation, and by an Australian Research
Council Discovery Grant DP0773836 (to MTM). We would especially like to thank
Doris Leung for providing programming assistance.
71
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Chapter Three
Context binding and hallucination predisposition: Evidence of intact
intentional and automatic integration of external features1
Abstract
Difficulties binding together information in memory have often been reported in
individuals with schizophrenia, and have been linked with auditory hallucinations and
the predisposition to hallucinate in particular. However, some inconsistencies remain.
For example, we previously examined binding of two external sources (voice and
location) in hallucination-prone individuals and found no evidence of a binding deficit
using a working memory task that involved automatic binding. Consequently, the
current study examined both automatic and intentional binding. The Launay-Slade
Hallucination Scale-Revised (LSHS-R) was administered to 559 undergraduates from
which high (25) and low (25) scorers were drawn. The binding tasks assessed either
automatic or intentional binding of voice and location features. The results showed no
significant differences between high and low hallucination-prone individuals in binding
these two external sources of information, regardless of the type of binding (automatic
or intentional) assessed. Furthermore, hallucination-prone individuals demonstrated no
difficulties recognising individual features of voice identity or location. These findings
suggest that some memory deficits may emerge only as psychosis fully develops.
Keywords: Hallucination predisposition; Hallucinations; Schizophrenia; cognition;
context binding
1 This chapter is a reproduction of the following article: Chhabra, S., Badcock, J. C., Maybery, M. T., &
Leung, C. (2011). Context binding and hallucination predisposition: Evidence of intact intentional and
automatic integration of external features. Personality and Individual Differences, 50, 834-839.
76
1. Introduction
A wealth of empirical evidence indicates that individuals with schizophrenia have
difficulties binding multiple features of events (content, context) into a complete
representation (for reviews, see Achim & Weiss, 2008; Mitchell & Johnson, 2009). This
deficit is not specific to self/other distinctions (reality monitoring - i.e., did I say that or
did you?), and includes difficulties distinguishing between two or more internal sources
(internal monitoring – i.e., did I say that or did I imagine that?) (e.g., Franck et al.,
2000), or two or more external sources (external monitoring) (e.g., Laroi & Woodward,
2007), as well as difficulties distinguishing between two temporal events (Waters,
Maybery, Badcock, & Michie, 2004). This deficit in memory appears to be related to
abnormal hippocampal or fronto-hippocampal functioning (Mitchell & Johnson, 2009).
Conscious recollection is considered to be essential for making these memory
judgements (Yonelinas, 2002) and there is extensive evidence that, in schizophrenia,
memory is most impaired on tasks that load heavily on intentional/control processes
(e.g., Racsmany et al., 2008).
Difficulties binding or integrating the various features of events in memory have
particularly been linked to auditory hallucinations (AH) and delusions. For example,
Bentall’s (1990; Bentall, Baker & Havers, 1991) influential work on reality monitoring
explains hallucinations as arising from a tendency to misremember internally generated
events as originating from an external source. Healthy, hallucination-prone individuals
(those with high scores on the Launay-Slade Hallucination Scale-revised [LSHS-R;
Bentall & Slade, 1985], a common measure of predisposition to hallucinations) have
also been found to have difficulties with reality monitoring (e.g., Laroi, Van der Linden,
& Marczewski, 2004) suggesting a continuum of cognitive and neural mechanisms
underlying patient and non-patient hallucinations. Hallucinations may also involve
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difficulties binding stimulus features to their temporal context or source (e.g., Waters,
Badcock, Michie, & Maybery, 2006a; Laroi, Collignon, & Van der Linden, 2005),
resulting in incomplete or inaccurate representations in memory. This suggests that
more general difficulties in contextual binding may contribute to the diverse
phenomenology of hallucinations (Waters, Badock, & Maybery, 2006b).
However, a number of inconsistencies remain, the resolution of which will be
informative. For example, Badcock, Chhabra, Maybery, and Paulik (2008) examined
binding in hallucination-prone individuals for two external sources (voice, location),
finding no evidence of a deficit. There appear to be at least two explanations for this
result: (a) binding two external sources may be impaired in patients with schizophrenia,
but intact in individuals predisposed to hallucinate, suggesting some important
differences in the characteristics and mechanisms of clinical and non-clinical AH
(David, 2010), or (b) processes associated with the task may be a key element to
consider. In particular, binding processes may be initiated intentionally (either at
encoding or recall) to consciously and explicitly integrate sources of information, or
may arise incidentally (i.e., automatically) as part of a processing sequence. Recent
evidence suggests that AH may be associated with more severe impairments of
intentional binding in which recognition of conjunctions of features is explicitly tested
(Luck, Foucher, Offerlin-Meyer, Lepage, & Danion, 2008). However, Badcock and
colleagues (2008) utilized a task assessing only automatic binding. Consequently,
similar binding deficits could be observed in hallucination-prone individuals to those
previously described in schizophrenia when intentional binding is examined. No study
to date has directly examined this intentionality aspect of memory binding in relation to
AH predisposition.
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Consequently, the current study utilised two voice-location binding tasks, each a
variant of that used by Badcock et al (2008), to assess both automatic and intentional
binding in memory for two external sources of information in hallucination-prone
individuals. These tasks involved the presentation of four memory/study items followed
by a recognition probe. In the automatic binding task, participants were instructed to
focus exclusively on one feature at a time (i.e., voice or location), ignoring the other,
and therefore not on integrating the combination of features. Importantly, binding is
assumed to be intact when recognition of a feature is enhanced by incidentally retaining
its association with another feature. Hence, this task provides a purer assessment of
automaticity of binding in hallucination-prone individuals than was the case in Badcock
et al (2008), where both features were attended to, but there was no requirement to
encode their combination. In contrast, in the intentional binding task, participants were
required to explicitly discriminate whether the combination of features (voice and
location) provided in the recognition probe was present or not in the memory items.
Poor performance on the intentional task alone by high LSHS-R scorers relative
to low LSHS-R scorers may suggest that hallucination predisposition is associated with
deficits in only intentional memory binding. Conversely, no deficits in performance on
either type of binding for the high LSHS-R group relative to the low LSHS-R group
may suggest that hallucination predisposition is not associated with difficulties binding
external sources. It is possible that any differences in binding ability observed between
high and low hallucination-prone groups could be due to difficulties remembering
individual contextual features (voices or locations), since there is evidence of
difficulties processing voices and spatial location in AH (e.g., Park & Holzman, 1992).
Our design allows us to examine this possibility. If high LSHS-R scorers demonstrate
poorer performance than low LSHS-R scorers on voice (location) compared to location
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(voice) trials of the automatic binding task, this would indicate that hallucination-prone
individuals have difficulties processing voice (location) information.
Finally, intelligence, emotional response (anxiety, depression, & stress), and
delusional and negative schizotypal experiences were also recorded to assess the
specificity of any significant differences in memory performance that may be obtained
in comparing the high and low LSHS-R groups.
2. Method
2.1. Participants
Five hundred and fifty nine undergraduate psychology students completed the LSHS-R
questionnaire. Individuals scoring in the upper and lower quintiles were invited to take
part in the study. Twenty-five high scorers (14 female) and 25 low scorers (15 female)
responded to this invitation and completed the study (see Table 1 for descriptive
statistics).
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Table 1
LSHS-R group means, standard errors (SEs), and t tests for the age, WASI, O-LIFE-
introvertive anhedonia, PDI, and DASS-21 data.
Low LSHS-R
(n = 25)
High LSHS-R
(n = 25)
Mean SE Mean SE T
LSHS-R 3.00 .33 31.72 .60 41.96*
AGE (years) 19.72 .48 18.52 .37 1.97
WASI 114.08 1.73 112.00 2.70 .76
Introvertive anhedonia 1.00 .21 1.44 .35 1.09
PDI 2.76 .46 9.92 .46 10.95*
DASS Anxiety 3.60 .93 14.00 1.77 5.20*
Depression 4.8 .87 12.48 1.93 3.62*
Stress 7.60 1.10 16.08 1.66 4.27*
* p < .05
2.2. Memory-Binding Tasks
2.2.1 Apparatus and Stimuli
Auditory stimuli were presented via eight Yamaha YST M20DSP loudspeakers
arranged in azimuth in front of the seated participant, on a 1.2m radius, spaced 36o
apart. Stimuli included 64 digitally-recorded spoken words comprising eight
phonologically dissimilar five-syllable words, spoken in eight different Australian
native English voices (half male). A white-noise stimulus was used as the auditory
mask. All stimuli were 1000ms in duration.
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2.2.2 Automatic binding task (Maybery et al., 2007).
On each trial participants heard a single word spoken by four different voices in
sequence, emanating from four different loudspeaker locations, followed by a visual
recognition cue - “VOICE” or “LOCATION” – in concert with an auditory mask (see
Figure 1). A single spoken word from a single location (a recognition probe) was then
presented. The participants’ task was to judge if the probe was the same as one of the
four study items (yes/no response) with respect to the auditory feature - voice identity or
loudspeaker location - indicated by the visual cue. Critically, this task was divided into
two blocks of trials, one focused on voice recognition and the other on location
recognition. Each block contained 5 practice and 60 test trials. At the start of each
block, participants were informed which feature, location or voice, would be tested, and
were told to ignore the other feature. The order of testing for the two cue types was
counterbalanced within each LSHS-R group.
The four study items can be represented as V1L1, V2L2, V3L3, and V4L4. The two
critical probe types are designated “intact” and “recombined” probes. Intact probes were
identical to a study stimulus, consisting of a word spoken in the same voice and
presented from the same location as in the study phase (e.g., V1L1, or V3L3), whereas
recombined probes consist of a word spoken in the voice of one study item but
emanating from the location of another study item (e.g., V1L2, or V4L3). Binding of
voice and location features to form an integrated representation in memory results in
faster and more accurate responses to intact probes relative to recombined probes
(Maybery et al., 2007, 2009). Consequently, impaired automatic binding is expected to
result in a reduced advantage for intact compared to recombined probes. Three
additional recognition probes introduced either a new voice (e.g., V5L1), new location
(e.g., V1L5), or both (e.g., V5L5). These probe types were included to force participants
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to refer to the cued feature in making recognition judgments. For each recognition
judgment (voice or location), two of the additional probes required a negative (“no”)
response. The features of the four study items were used equally often in constructing
each type of probe2. Each of the five probe types occurred once every five trials, with
the order of these five trials randomized.
Figure 1. Configuration of the memory-binding task illustrating the sequence of events
from study (S1, S2, S3, & S4) to the presentation of the recognition probe (P).
Testing was carried out in a sound-proof, darkened room. Each trial began with
a 1000ms visual warning signal (“READY”) on the computer screen, followed by the
four study items in sequence, then the visual recognition cue concurrent with the
auditory mask, and finally the auditory recognition probe. A stimulus onset asynchrony
of 1500ms was used to separate all consecutive stimulus events. Recognition responses
(“yes” or “no”) were collected using a keyboard, and recording of the reaction time
2 The stimuli for this study were the same as those used in Chapter 2
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(RT) started from onset of the recognition probe. The next trial began 2000ms after the
participant’s response.
2.2.3 Intentional binding task.
Three changes were made to the automatic binding task. First, the task was to judge
whether the probe word was exactly the same in terms of voice identity, loudspeaker
location, and their combination, as one of the first four study words (i.e., assessing an
intentional form of binding). Second, the visual cue participants observed following
presentation of the four study items was “VOICE + LOCATION”. Third, this task used
only intact and recombined recognition probes since the requirement to distinguish the
two provided an assessment of an intentional form of binding (see Burglen et al., 2004;
Wheeler & Treisman, 2002). Performance on this task involves fundamentally different
cognitive processes compared to the automatic version as participants have to explicitly
consider the link between stimulus features to consciously discriminate intact from
recombined probes. Consequently, for this intentional task, intact and recombined
probes will be labelled according to the responses participants made (i.e., positive
[“yes”] and negative [“no”] probes respectively). Impaired binding on this intentional
task will be evidenced by poorer performance (reduced accuracy or slower RTs) for
either or both of the two probe types since the critical requirement of the task is to
discriminate the two (i.e., to decide whether associations between voice and location
features have been preserved or broken from study to test). Thus relative performance
on the positive and negative probes is of lesser importance. Half the trials comprised
positive probes, and the other half, negative probes. There were four practice and 48 test
trials.
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2.3 Additional measures
The 12-item LSHS-R (Bentall & Slade, 1985) assesses a range of visual and auditory
experiences, however, due to the wording, high scores may still represent relatively
infrequently occurring hallucinatory experiences. IQ was estimated using the
vocabulary and matrix reasoning subtests from the Weschler Abbreviated Scale of
Intelligence (WASI; Weschler, 1999). The 21-item version of the Depression Anxiety
Stress Scales (DASS-21; Lovibond & Lovibond, 1995) was used to assess enduring
symptoms of depression, anxiety, and stress. Schizotypal personality traits that closely
correspond to negative schizophrenic symptomatology were assessed using the
Introvertive Anhedonia subscale of the Oxford-Liverpool Inventory of Feelings and
Experiences (O-LIFE; Mason, Linney, & Claridge, 2005). The yes/no version of the
Peter’s Delusion Inventory (PDI; Peters, Joseph, Day, & Garety, 2004) assessed
delusional thinking.
2.4 General procedure
Ethics approval was obtained from the University of Western Australia Human
Research Ethics Committee and written informed consent was obtained from each
participant. Order of testing for the automatic and intentional bindings tasks was
counterbalanced within each LSHS-R group.
3. Results
All variables were normally distributed. Participants’ scores on single measures were
excluded if they were three or more standard deviations (SDs) away from their
respective group means (10 extreme data points were identified, with no more than three
excluded for a single analysis). When tests for homogeneity of variance were not met, F
85
tests were adjusted accordingly. Because the automatic and intentional binding tasks
used different sets of probes and required fundamentally different judgments, the two
were analysed separately.
3.1 Descriptive Statistics
Table 1 provides a summary of cognitive, schizotypy and emotion measures for the high
and low LSHS-R groups. Substantial group separation was obtained on the LSHS-R as
expected. No significant group differences were observed on the WASI scores or the O-
LIFE introvertive anhedonia subscale. However, the high LSHS-R group obtained
significantly higher scores than the low LSHS-R group on the PDI as well as all three
DASS subscales - Anxiety, Depression, and Stress.
3.2 Automatic Binding Task
The mean percentage of correct responses (accuracy) and median RTs for correct
responses, for the different trial types were calculated for each participant (see Table 2).
The central analyses focused on comparing intact and recombined probes since these
address binding ability. A 2 (LSHS-R group: high, low) x 2 (recognition probe: intact,
recombined) x 2 (recognition cue: voice, location) mixed-design ANOVA, with
repeated measures on the last two factors, was conducted for each dependent variable.
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Table 2
Descriptive statistics for accuracy (% correct) and RT (ms) as a function of probe type
for the automatic binding task. The old/new status of the two features of the probe is
shown for the cued feature followed by the uncued feature (in parentheses); for
example, old (new) refers to using an old value for the cued feature and a new value for
the uncued feature.
Automatic Binding Task
Low LSHS-R High LSHS-R
Mean SE Mean SE
Accuracy
Intact 94.97 1.20 92.94 1.22
Recombined 87.33 1.56 84.42 1.59
Old (new) 87.15 3.05 88.00 2.64
New (old) 84.33 2.78 82.67 3.15
New (new) 91.33 2.23 88.00 2.10
RT
Intact 1288.26 50.65 1270.33 49.63
Recombined 1418.91 57.81 1402.19 56.64
Old (new) 1369.88 59.48 1406.76 64.69
New (old) 1329.04 61.97 1318.46 65.68
New (new) 1320.78 59.45 1330.68 69.41
3.2.1. Accuracy
Figure 2 shows accuracy for the two critical recognition probes for the high and low
LSHS-R groups. Accuracy was higher for intact (M = 93.95%, SE = .85%) compared to
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recombined probes (M = 85.87%, SE = 1.12%), F(1,45) = 50.18, p < .05, generalized-η2
= 0.17, as predicted. There was also a significant main effect of cue type, with higher
accuracy for location (M = 91.83%, SE = 1.02%), compared to voice (M = 87.99%, SE
= 1.05%) recognition, F(1,45) = 8.92, p < .05, generalized-η2 = 0.04. Furthermore, there
was a significant cue × probe interaction, F(1,45) = 9.25, p < .05, generalized-η2 = 0.06,
indicating that the advantage in accuracy attributable to (automatic) binding (indexed by
the difference in accuracy for intact and recombined probes) is more pronounced for
voice recognition (Mintact = 93.95%, SEintact = 1.16%; Mrecombined= 82.04%, SErecombined =
1.61%) than it is for location recognition (Mintact = 93.95%, SEintact = 1.03%; Mrecombined=
89.71%, SErecombined = 1.48%). Nevertheless, simple effects showed that the effect of
probe type was significant for both voice, F(1,46) = 43.33, p < .05, generalized-η2 =
0.29, and location, F(1,47) = 6.08, p < .05, generalized-η2 = 0.04, recognition.
Furthermore, the main effect of LSHS-R group, and the interaction between LSHS-R
group and cue were not significant, suggesting that the high LSHS-R group did not have
any difficulty in retaining either the voice or location information. Critically, the
interactions involving LSHS-R group and probe were not significant, indicating
comparable memory binding for the two LSHS-R groups. ANOVA comparisons of
corrected recognition (hits minus false alarms) scores were also conducted, with no
significant group differences revealed. Furthermore, hits for identical probes and hits for
recombined probes were corrected with lures comprising a new target feature to get a
purer measure of recognition memory discrimination due to binding (as in Buchler,
Light, & Reder, 2008), with no differences between the high and low LSHS-R groups
obtained.
88
Low LSHS-R High LSHS-R
70
80
90
100
Intact Recombined Intact Recombined
Probe Type
Accu
racy (
%)
Low LSHS-R High LSHS-R
800
1000
1200
1400
1600
Intact Intact Recombined Recombined
Probe typeR
eacti
on
tim
e (
ms)
Figure 2. Mean accuracy and RT (and 95% confidence intervals) for the high and low
LSHS-R groups for the critical recognition probes on the automatic binding task.
3.2.2. RT
RT for the two critical recognition probes for both LSHS-R groups is also displayed in
Figure 2. In accordance with the outcomes for accuracy, participants were faster at
responding to intact (M = 1279.30ms, SE = 35.46ms), compared to recombined (M =
1410.55ms, SE = 40.47ms) probes, F(1,47) = 47.00, p < .05, generalized-η2 = 0.13,
consistent with automatic binding of the voice and location features. There were no
significant interactions involving LSHS-R group and probe, once again suggesting
comparable memory binding for both high and low LSHS-R groups.
3.3 Intentional Binding Task
A 2 (LSHS-R group) x 2 (recognition probe: positive, negative) mixed-design ANOVA,
with repeated measures on the last factor, was conducted for the accuracy and median
RT variables.
89
3.3.1. Accuracy
Accuracy for the two recognition probes for the two LSHS-R groups is displayed in
Figure 3. Accuracy was higher for positive (M = 88.35%, SE = 1.46%), compared to
negative (M = 74.55%, SE = 1.95%) probes, F(1,47) = 36.23, p < .05, generalized-η2 =
0.25. However, the central main effect of group was not significant, F(1,47) = .01, p >
.05, and this factor did not interact with probe type. Groups were also compared on
corrected recognition scores, with no group difference in these values being shown.
Low LSHS-R High LSHS-R
50
60
70
80
90
100
Positive Negative Positive Negative
Probe Type
Accu
racy (
%)
Low LSHS-R High LSHS-R
800
1000
1200
1400
1600
Positive Positive Negative Negative
Probe type
Reactio
n t
ime (
ms)
Figure 3. Mean accuracy and RT (and 95% confidence intervals) for the high and low
LSHS-R groups for the two probe types on the intentional binding task.
3.3.2. RT
RT for the two recognition probes for both LSHS-R groups is also displayed in Figure
3. In accordance with the outcomes for accuracy, participants were faster at responding
to positive (M = 1279.70ms, SE = 36.16ms), compared to negative (M = 1410.30ms, SE
= 43.51ms) probes, F(1,47) = 15.67, p < .05, generalized-η2 = 0.05. Neither the main
90
effect of group, F(1,47) = .50, p > .05, nor the interaction was significant, once again
suggesting comparable memory binding for the high and low LSHS-R groups.
4. Discussion
The main findings show that healthy young adults predisposed to hallucinations
demonstrate intact integration of voice and location in memory, irrespective of the type
of binding tested (i.e., intentional or automatic). These results suggest some important
differences in the mechanisms underlying hallucinations in clinical and non-clinical
populations and add to the growing debate on the continuum model of psychotic
symptoms (David, 2010; Kaymaz & van Os, 2010).
Binding involves integration of events together with contextual details in
memory (Wheeler & Treisman, 2002). Hence, for the automatic binding task, intact
probes should result in faster, more accurate responses compared to recombined
recognition probes (Maybery et al., 2007); participants in both groups demonstrated
such an advantage in the current study, indicating that automatic binding did occur. This
provides further evidence of intact automatic processes in hallucination-prone
individuals (Luck et al., 2008).
In requiring an explicit judgment as to whether binding of features was retained
from study to test, our intentional binding task is comparable to test conditions utilised
in other studies that have reported impairments in binding in clinical samples (e.g.,
Burglen et al., 2004). Furthermore, performance on this task was not at ceiling in terms
of accuracy. Therefore the task provided a reasonable test of whether intentional
binding is impaired in hallucination-prone individuals. Impaired intentional binding
should have resulted in poorer performance in general in the high LSHS-R group
compared to the low LSHS-R group, however the two groups exhibited non-significant
91
differences for both accuracy and RT data. These results differ from recent findings
revealing significant binding impairments in individuals with schizophrenia (e.g.,
Burglen et al., 2004; Waters et al., 2004).
The present data also indicate that hallucination-prone participants have no
difficulties processing the individual stimulus features (i.e., voices or locations)
employed in the current tasks. For the automatic binding task, there was a significant
effect of cue, with poorer accuracy for voice compared to location recognition. The
interaction between cue and probe was also significant, indicating a more substantial
influence of binding on voice recognition than on location recognition. However, these
effects were evident across both LSHS-R groups. In the previous binding task,
involving only two voice and location features, Badcock et al (2008) reported
comparable accuracy rates for voice and location recognition. With the current task
involving four voice and location features, this increased memory load appears to have
had an adverse effect on voice recognition, arguable because of the difficulty in
remembering four unfamiliar features. The more pronounced binding observed for voice
recognition could therefore reflect the “bootstrapping” of the voice features to the
corresponding easier location features in order to assist the retention of the former (see
Maybery et al., 2009).
The current findings suggest that binding of two external sources is intact in
hallucination-prone individuals, consistent with recent suggestions that there may be
partially different mechanisms underlying hallucinations in patient and non-patient
(healthy) groups (Badcock et al., 2008)3, and that (at least some) context binding
deficits may only emerge as psychosis fully develops (Dore, Caza, Gingras, & Rouleau,
3 The frequency of hallucinations endorsed by the high LSHS-R group was also examined (as in Badcock
et al., 2008), with no significant correlations between hallucination frequency and binding ability
observed. Data available on request.
92
2007). Therefore, one may need to be in the active phase of hallucinations before a
deficit in (at least some forms of) memory binding is seen (Waters et al., 2006b). It is
also possible that there are important differences in the phenomenology of
hallucinations in the general population versus in psychosis (Daalman et al., 2011;
David, 2010) or that some types of binding are impaired in hallucination-prone
individuals (Laroi et al., 2005) while others are not.
The current research is limited by a relatively small sample size. Additionally, it
is possible that variables not measured in this study (e.g., working memory capacity;
Oberauer, 2005) contributed to variance in binding ability within each group, and this
may have reduced the sensitivity for detecting differences in binding between high and
low hallucination-predisposed groups. It should be noted, however, that across all of the
experiments conducted in our research group thus far, we have found consistent
evidence of substantial binding in both high and low hallucination-prone groups. Future
research should test a larger, more phenomenologically varied sample of hallucination-
predisposed individuals. In addition, future studies should determine whether binding
difficulties linked to hallucinations or hallucination-proneness reflect difficulties at
encoding or recall.
Acknowledgement
This research was supported by an Australian Research Council discovery grant
DPO773836.
93
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97
Foreword to Chapter 4
So far, the literature has relied largely on indirect links between performance in
clinical and non-clinical AH groups. Similarly, Chapters 2 and 3 relied on an indirect
comparison of performance in non-clinical AH to previous results in clinical AH in the
literature. This research design does not provide a robust test of the continuum model of
psychotic symptoms. It is possible that the dissimilarities identified may be associated,
at least in part, with differences in stimuli and design employed between studies in
clinical and non-clinical AH populations. Few, if any, studies to date have assessed the
role of cognitive mechanisms, using the same paradigm, in both patient and non-patient
AH within the same study. These designs would help tease out both similarities and
differences between these two groups and could provide valuable information about
what protects some individuals from developing schizophrenia.
Consequently, in order to make stronger conclusions about whether external
context binding deficits are/are not present in non-clinical compared to clinical AH,
Chapter 4 aimed to directly investigate the continuity model of AH. An identical task
was employed to assess memory binding of voice and word information in two separate
studies of: (1) healthy, hallucination-prone individuals and controls, and (2)
schizophrenia patient samples (with and without AH) and healthy age-matched controls.
If voice-location binding had been examined in these samples, then no impairment
would be expected for the healthy hallucination-prone sample (given results from
Chapters 2 & 3). In addition, if no voice-location binding deficits were to be identified
for schizophrenia patients, the study would not inform the debate on the continuum
model. However, since word-voice binding has been found to be impaired in
schizophrenia patients, were this result to be replicated, then either outcome
(impairment, no impairment) for the AH predisposed group would inform the debate.
98
99
Chapter Four
Memory binding in clinical and non-clinical psychotic experiences: How
does the continuum model fare?1
Abstract
Both clinical and non-clinical auditory hallucinations (AH) have been associated with
source memory deficits, supporting a continuum of underlying cognitive mechanisms,
though few studies have employed the same task in patient and non-patient samples.
Recent commentators have called for more debate on the continuum model of
psychosis. Consequently, the current study investigated the continuity model of AH
with reference to memory-binding. We used an identical voice and word recognition
memory task to assess binding in two separate studies of: (1) healthy hallucination-
prone individuals and controls (30 high and 30 low scorers on the Launay-Slade
Hallucination Scale-Revised) and (2) schizophrenia patient samples (32 with AH, 32
without AH) and 32 healthy controls. There was no evidence of impaired binding in
high hallucination-prone, compared to low hallucination-prone individuals. In contrast,
individuals with schizophrenia (both with and without AH) had difficulties binding
(remembering ‘who said what’), alongside difficulties remembering individual words
and voices. Binding ability and memory for voices were also negatively linked to the
loudness of hallucinated voices reported by patients with AH. These findings suggest
that different mechanisms may exist in clinical and non-clinical hallucinators, adding to
the growing debate on the continuum model of psychotic symptoms.
Keywords: Hallucination predisposition; auditory hallucinations; continuum;
schizophrenia; memory binding
1 A revised version of this chapter has been accepted subsequent to thesis submission: Chhabra, S.,
Badcock, J. C., & Maybery, M. T. (2012). Memory binding in clinical and non-clinical psychotic
experiences: How does the continuum model fare? Cognitive Neuropsychiatry.
DOI:10.1080/13546805.2012.709183
100
1. Introduction
Schizophrenia has been linked to deficits binding multiple features of events into a
complete representation in memory (see Achim & Weiss, 2008; Mitchell & Johnson,
2009, for reviews). These deficits have been associated with abnormal
hippocampal/parahippocampal functioning in the medial temporal lobes (Mitchell &
Johnson, 2009; Ranganath, 2010). Deficient binding disrupts the ability to accurately
determine the source of mental experiences and has commonly been associated with
positive symptoms of schizophrenia, including delusions (Corlett, Krystal, Taylor, &
Fletcher, 2009), but has received the most extensive theoretical and empirical analysis
in relation to auditory hallucinations (AH) (Bentall, 1990; Bentall, Baker, & Havers,
1991; Waters, Badcock, Michie, & Maybery, 2006; Woodward, Menon, & Whitman,
2007), though findings are not uniformly positive (Diaz-Asper, Malley, Genderson,
Apud, & Elvevag, 2008). Much of the existing research has focused on reality
monitoring tasks (discriminating internal/self from external/other sources) or the
tendency for hallucinators to misattribute self-generated words to external sources
(Bentall et al. 1991; Brebion et al. 2000; Woodward et al. 2007). However, these
individuals have also been found to have difficulties distinguishing between numerous
other sources and events (e.g., internal monitoring - Franck et al. 2000; external
monitoring - Laroi & Woodward, 2007; distinguishing between temporal events –
Waters, Maybery, Badcock, & Michie, 2004). A recent meta-analysis conducted by
Achim and Weiss (2008) confirmed that binding deficits in schizophrenia are global,
rather than specific to self/other distinctions. Hence, it is important to explore various
types of source memory in schizophrenia and their relevance to specific symptoms, such
as hallucinations.
101
AH also commonly occur in healthy individuals in the general population (see
Stip & Letourneau, 2009; van Os, Linscott, Myin-Germeys, Delespaul, & Krabbendam,
2009, for reviews). Healthy hallucination-prone (predisposed) individuals have also
been found to have difficulties with reality monitoring and misattributing self-generated
items to an external source (e.g., Johns et al. 2010; Laroi, Van der Linden, &
Marczewski, 2004), as well as with binding stimulus features to their temporal context
(e.g., Laroi, Collignon, & Van der Linden, 2005), supporting a continuum model of
cognitive and neural mechanisms underlying patient and non-patient hallucinations.
Studying hallucinations in these “predisposed” samples has become a popular strategy
in the literature due to perceived advantages such as minimising the influence of
confounds (e.g., medication, length of hospitalisation, etc.). However, several
differences between clinical and non-clinical AH have recently been highlighted in the
literature, suggesting some important differences in the phenomenology (Choong,
Hunter, & Woodruff, 2007; Daalman et al. 2011), and mechanisms (Badcock &
Hugdahl, 2012) of clinical and non-clinical AH. Of particular interest to the current
studies, empirical research has shown intact, rather than impaired, binding of two
contextual features of information (voice, location) in healthy hallucination-prone
individuals (Badcock, Chhabra, Maybery, & Paulik, 2008; Chhabra, Badcock, Maybery
& Leung, 2011). Recent commentators have noted that evidence of discontinuities may
point to important differences in aetiology between psychotic and healthy voice hearers,
resulting in a growing call for more debate and research on the continuum model (e.g.,
David, 2010; Kaymaz & van Os, 2010; Sommers, 2010).
The current study aimed to investigate the continuum model of AH with
reference to memory binding. Studies assessing both patient and non-patient AH groups
are rare, and those that exist have typically used different tasks for the two groups.
102
Another recently adopted strategy has involved directly comparing performance
between different groups (e.g., see Goghari, MacDonald, & Sponheim, 2011, who
compared schizophrenia patients, their family members, and a separate sample of
healthy controls). We adopted a variation on this approach because of possible
neurodevelopmental confounds that may have been introduced had we directly
compared a typically-younger hallucination-prone group to an older schizophrenia-
patient group (Bentall, Fernyhough, Morrison, Lewis, & Corcoran, 2007). The
variation in approach we adopted was to include appropriate comparison groups for the
hallucination-predisposed and patient groups to control for age-related effects. As such,
we used an identical voice and word recognition task to assess memory-binding in two
separate studies of (1) healthy, hallucination-prone individuals and age-matched
controls who reported experiencing hallucinations infrequently, and (2) individuals with
schizophrenia and healthy aged-matched controls free of symptoms of the disorder.
Poor performance relative to controls on the binding task by both healthy
individuals predisposed to hallucinations as well as individuals with schizophrenia
would provide support for the continuum hypothesis of psychotic symptoms.
Conversely, if deficits in binding are found in only the schizophrenia sample, and not
the hallucination-prone sample, this would challenge the continuum model of psychosis.
If memory binding deficits are found in both healthy hallucination-prone individuals
and schizophrenia patients, then it will be of interest to establish whether the difficulty
in binding in the patient sample is limited to those experiencing AH. Furthermore, given
contrasting claims from previous literature regarding the specificity of binding deficits
to AH (e.g., Brebion et al., 2002) versus applying to schizophrenia in general (e.g.,
Diaz-Asper et al., 2008), it will be relevant to compare the performance of AH and no-
AH patient subgroups in order to clarify this issue.
103
Our task design also allowed us to examine whether any differences observed in
binding ability between groups could be due to difficulties remembering individual
stimulus features, in particular, words (Dore, Caza, Gingras, & Rouleau, 2007, Diaz-
Asper et al. 2008) or voices (McKay, Headlam, & Copolov, 2000, Zhang et al. 2008,
Hirano et al. 2010). This issue is important since the ability to process external voices is
particularly impaired when hallucinations are more prominent, suggesting that the
perception of both real and hallucinated voices draw on similar neural resources
(Hugdahl et al. 2008; Vercammen, Knegtering, Bruggeman, & Aleman, 2011). Finally,
we also examined individual differences in intelligence, emotional response
(depression, anxiety, and stress) and delusional experiences in order to check the
specificity of any significant results that may be obtained in comparing groups.
Study 1
2. Method
2.1 Participants
Each participant provided written, informed consent using forms and procedures
approved by the Human Research Ethics Committee of the University of Western
Australia. Five hundred and 22 undergraduate psychology students from the University
of Western Australia completed the Launay-Slade Hallucination Scale-Revised (LSHS-
R; Bentall & Slade, 1985) questionnaire (M = 14.08; range = 0 - 40). The top 30 scorers
(upper quartile - scores of 28 and above; 23 female) and bottom 30 scorers (lower
quartile - scores of 6 and below; 22 female) who responded to an invitation to
participate, completed the study. Substantial group separation was obtained on the
LSHS-R as expected (Low LSHS-R, M = 3.53; High LSHS-R, M = 31.37; t (58) =
34.56, p <.001, Cohen’s d = 8.92). Exclusion criteria for participants included poor
104
fluency in English, self-reported hearing impairments, a current diagnosis or treatment
for a mental illness, diagnosis of schizophrenia in a first-degree relative, or past or
current treatment for substance-use disorder. Hearing thresholds were assessed, with
hearing levels poorer than 30 dB at the frequencies tested being the cut-off for exclusion
(as in Waters, Price, Dragovic, & Jablensky, 2009). Based on these criteria, no
individuals were excluded.
2.2 Measures
2.3 Questionnaires
The 12-item LSHS-R (Bentall & Slade, 1985) – a common measure of hallucination
predisposition – assessed a range of visual and auditory experiences. Participants were
screened for psychopathology using the Mini International Neuropsychiatric Interview
for Schizophrenia and Psychotic Disorders Studies (MINI; Sheehan et al., 1998). IQ
was estimated using the vocabulary and matrix reasoning subtests from the Weschler
Abbreviated Scale of Intelligence (WASI; Weschler, 1999). The 21-item version of the
Depression Anxiety Stress Scales (DASS-21; Lovibond & Lovibond, 1995) was used to
assess enduring symptoms of depression, anxiety, and stress. The yes/no version of the
Peter’s Delusion Inventory (PDI; Peters, Joseph, Day, & Garety, 2004) assessed
delusional thinking.
2.4 Memory-binding task (Chhabra et al., 2010)
This task assessed individual differences in the conscious/intentional ability to bind
together the content and context (i.e., who said what) of spoken words. On each trial,
participants heard two words spoken in two different voices in sequence, followed by a
visual recognition cue (“VOICE + WORD”) together with an auditory mask. A single
105
spoken word (the probe) was then presented. The participants’ task was to judge if the
probe was a “match” to one of the two study items (same/different response): that is,
they were instructed to decide whether the combination of word and voice identity in
the third spoken word was exactly the same as the word and voice identity of one of the
first two spoken words. The two study items can be represented as V1W1 and V2W2
(where V and W denote the voice and word features, and the subscripts denote the
features for the 1st and 2
nd study items). Four probe types were employed. The two
critical probe types were designated “intact” and “recombined” probes. Intact probes
were identical to a study stimulus, consisting of a word spoken in the same voice as in
the study phase (i.e., V1W1 or V2W2 was re-presented as the probe, with the two used
equally often), whereas recombined probes consisted of a word from one study item but
spoken in the voice of the other study item (i.e., V2W1 or V1W2, used equally often).
The critical requirement of the task was to discriminate these two probes types (i.e., to
decide whether associations between individual voice and word features had been
preserved or broken from study to test). Hence, impaired binding should result in poorer
performance (reduced accuracy or slower RTs) for either or both of these two critical
probe types (Chhabra et al., 2010). Two additional probes introduced either a new voice
(i.e., V3W1 or V3W2) or new word (i.e., V1W3 or V2W3). These probe types were
incorporated to examine whether participants demonstrated any difficulties
remembering individual stimulus features. Participants were instructed to respond
“same” to positive recognition probes (i.e., intact probes) and “different” to negative
recognition probes (i.e., recombined, new-voice, and new-word probes). Positive probes
were used on 40% of trials, while each of the three negative probes was used on 20% of
trials.
106
2.4.1 Stimuli
Stimuli included 64 digitally-recorded spoken words. They comprised eight three-
syllable words (adherence, blasphemy, commencement, dismissal, gratitude, interim,
ownership, rotation) spoken in eight different native Australian-English voices (half
male). Stimuli were 1000 ms in duration and presented at 69.22 dB. A white noise
stimulus, presented at the same sound pressure level, was used as an auditory mask.
2.4.2 Procedure
Stimulus presentation was controlled via a laptop computer. Auditory stimuli were
presented via Sennheiser HD 205 headphones. There were six practice and 50 test trials,
with the stimuli for these trials selected anew for each participant of one LSHS-R group,
and the same stimulus set used for a randomly selected participant of the other LSHS-R
group. The voice and word features for the two study items were selected randomly, as
were any new features required for recognition probes. Each trial began with a 1000 ms
visual warning signal (“READY”), followed by the two study items in sequence, then
the visual recognition cue and auditory mask, and finally the auditory recognition probe.
A stimulus onset asynchrony of 1500 ms was used to separate all consecutive stimulus
events. Recognition responses were collected using the left and right arrow keys on a
keyboard; RT was recorded from the onset of the recognition probe. The next trial
began 2000ms after the participant’s response. Overall task duration was approximately
10 minutes.
2.5 Statistical Analysis
Statistical analyses were conducted using IBM SPSS statistics (version 19).
Participants’ scores on single measures (d’ scores and RTs for each condition) were
107
excluded if they were 3.29 standard deviations (SDs) or more away from their
respective group means (corresponding to p < .001; see Tabachnick & Fidell, 2001).
Signal detection analysis was conducted in order to compare binding performance
between groups, as well as to investigate recognition of individual stimulus features for
the two groups. Hit rates were based on correct responses to intact recognition probes
and false alarm rates were based on incorrect responses to either recombined, new-
word, or new-voice recognition probes. These rates were then used to obtain d’ scores
(Swets, 1961), which served as an index of the capacity to bind (binding d’), and as
indices of recognition for individual word (new-word d’) and voice (new-voice d’)
stimulus features, respectively. Independent-samples t tests were then conducted to
compare the two groups as to binding performance and the recognition of individual
stimulus features. As a secondary form of analysis, mixed-design ANOVAs were
conducted to compare the two groups as to their RTs in responding to the two probes
(intact, recombined) that tested for binding, as well as to the two probes (new voice,
new word) that tested for recognition of individual stimulus features. When the
assumption of homogeneity of variance was not met, F tests were adjusted accordingly.
To check for the possible influence of confounds, memory binding performance
(binding d’ score) was correlated with any of the control variables on which the high
and low LSHS-R groups differed. No further action was taken if these correlations were
not significant.
108
3. Results
All variables were normally distributed apart from new-word d’, which was
substantially skewed. Six extreme data points were identified2, with no more than two
excluded for a single analysis.
3.1 Descriptive Statistics
A summary of cognitive, schizotypy and emotion measures for the high and low LSHS-
R groups is provided in Table 1. No significant group differences were observed on the
WASI scores. However, the high LSHS-R group obtained significantly higher scores
than the low LSHS-R group on the PDI as well as on each of the three DASS subscales
- Anxiety, Depression, and Stress.
Table 1
LSHS-R group means, standard errors (SE), and t-tests for the age, WASI, PDI, and
DASS-21 data.
Low LSHS-R
(n = 30)
High LSHS-R
(n = 30)
Mean SE Mean SE t
AGE (years) 17.93 .18 17.80 .16 0.55
WASI 109.23 1.37 109.87 1.39 .33
PDI 3.97 .44 8.43 .51 6.64**
DASS Anxiety 5.73 .83 14.00 1.63 4.53**
Depression 5.80 1.03 14.13 1.70 4.19**
Stress 10.33 1.34 21.80 1.74 5.22**
** p < .001
2 Outliers were from both high LSHS-R (1 data point) and low LSHS-R (5 data points) groups
109
3.2 Memory Binding Task
Accuracy rates and median RTs for correct responses for the different trial types were
calculated for each participant. Hit rates and false alarm rates taken from the accuracy
data were then used in calculating d’ scores. Table 2 displays summary statistics for the
d’, accuracy, and RT measures for the two LSHS-R groups.
3.2.1 Binding ability
The central analyses focused on performance on the intact and recombined probes,
which addresses binding ability. The high and low LSHS-R groups did not differ
significantly in binding d’ scores, t (57) = .98, p = .33 (see Table 2 and Figure 1). A 2
(group: high LSHS-R, low LSHS-R) x 2 (probe type: intact, recombined) mixed-design
ANOVA, with repeated measures on the last factor, was then conducted for RT.
Participants were faster at responding to intact (M = 1302.95ms, SE = 26.85ms),
compared to recombined (M = 1557.39ms, SE = 46.84ms) probes, F (1, 57) = 50.46, p <
.05, partial-η2 = 0.47. Notably, there was no significant main effect of group, F (1, 57) =
.27, p = .60, nor was the interaction between group and probe significant (see Table 2).
It is unlikely that delusional tendency or emotional response influenced the
performance of participants, since no significant correlations were found for binding
ability (d’) paired with any of the background variables that significantly differentiated
the groups (PDI scores, r (60) = .05, p = .72; Depression, r (60) = .11, p = .41; Anxiety,
r (60) = .11, p = .39; Stress, r (60) = -.00, p = .98). Given these non-significant
correlations, no further action was taken to account for the PDI and DASS variables.
110
Table 2
Descriptive statistics for d’ scores, accuracy and RT as a function of probe type in low
and high LSHS-R groups.
Low LSHS-R High LSHS-R
Mean SE Mean SE
d’
Binding 2.94 .11 3.08 .09
New Voice 2.99 .11 3.08 .10
New Word 3.48 .05 3.49 .04
Accuracy (%)
Intact 98.93 .46 98.50 .44
Recombined 84.29 2.74 89.00 2.65
New Voice 87.93 2.09 89.31 2.09
New Word 100.00 .00 100.00 .00
RT (ms)
Intact 1305.24 38.29 1300.65 37.64
Recombined 1590.22 66.80 1524.55 65.68
New Voice 1407.55 51.94 1391.08 50.18
New Word 1300.11 52.58 1313.35 50.80
3.2.2 Recognition of individual stimulus features
Analysis of performance for the negative probe types was then conducted to identify
whether high hallucination-prone individuals had any difficulties remembering
individual stimulus features. Accuracy rates for new-word and new-voice probes are
presented in Table 2. Accuracy was perfect for new-word probes for both the high and
111
low LSHS-R groups. The LSHS-R groups did not differ in their new-voice d’ scores, t
(58) = .64, p = .52. For the RT data, a 2 (group: high LSHS-R, low LSHS-R) x 2
(recognition probe: new voice, new word) mixed-design ANOVA, with repeated
measures on the last factor, was conducted. Participants were faster at responding to
new word (M = 1306.73ms, SE = 36.56ms) relative to new voice (M = 1399.32ms, SE =
36.11ms) probes, F (1, 56) = 7.34, p < .05, partial-η2 = 0.12. No other effects in the
analysis were significant, with the main effect of group yielding F (1, 56) < 1, p = .98
(see Table 2 for descriptive statistics).
Study 2
4. Method
4.1 Participants
Seventy patients with schizophrenia (34 with current AH, 36 without current AH) and
34 healthy comparison controls participated in this study. Each participant provided
written, informed consent using forms and procedures approved by the Human
Research Ethics Committees of the University of Western Australia, and the North
Metropolitan Area Mental Health Service (Perth). The patient sample was drawn from
the Western Australian Family Study of Schizophrenia (WAFSS), met DSM-IV and/or
ICD-10 criteria for a lifetime diagnosis of schizophrenia or schizophrenia spectrum
disorder (F20 = 52, F22 = 2, F25.0 = 3, F25.1 = 4, F25.2 = 4, F28 = 5), and was
recruited from community mental health centres and inpatient services of Graylands
Hospital (Perth). Patients were receiving their usual medication (mean chlorpromazine
(CPZ) equivalent = 614) at the time of testing (n = 51 atypical antipsychotics, n = 8
typical antipsychotics, n = 10 anxiolytics, n = 27 antidepressants, n = 17 mood
stabilisers). Exclusion criteria for the patients with schizophrenia included the presence
112
of neurological disorders, loss of consciousness > 15 minutes, poor fluency in English,
and self-reported hearing impairments. Also, as for Study 1, hearing thresholds were
assessed, and anyone with a hearing level poorer than 30 dB at the frequencies tested
was excluded. The healthy comparison control group was recruited from the WAFSS or
through email advertisements in health department and university networks. Exclusion
criteria for controls were the same as for patients, except that individuals with a current
diagnosis or treatment for a mental illness (including any endorsement of recent
hallucination-like experiences), diagnosis of schizophrenia in a first-degree relative, or
past or current treatment for substance-use disorder were also excluded. Following
exclusion criteria, 64 individuals with schizophrenia (32 with current AH, and 32
without AH) and 32 healthy controls remained in the study.
4.2 Measures
Diagnostic and symptom assessment was made using the semi-structured Diagnostic
Interview for Psychosis (DIP; Castle et al, 2006), from which patients reporting any
auditory hallucinatory experiences within the past 4 weeks were assigned to the 'with'
AH subgroup, and those reporting no current (not within the past 4 weeks) auditory
hallucinatory experiences were assigned to the 'without' AH subgroup. The Psychotic
Symptom Rating Scales (PSYRATS; Haddock, McCarron, Tarrier, & Faragher, 1999)
were used to assess details of AH for those currently experiencing them. Healthy
controls were also screened for psychopathology using the MINI, and were assessed for
hallucinatory experiences using the LSHS-R (M = 6.18 out of a possible 40, range = 0-
19, i.e. no score approached the lowest score [28] for the high LSHS-R group of Study
1). As in Study 1, IQ (WASI; Weschler, 1999), delusional thinking (PDI; Peters, Joseph,
Day, & Garety, 2004), and enduring symptoms of depression, anxiety, and stress
113
(DASS-21; Lovibond & Lovibond, 1995) were estimated in both patient and control
groups.
4.3 Memory-binding task
The memory binding task was identical to the task used in Study 1.
4.4 Statistical Analysis
Statistical analyses were conducted using IBM SPSS statistics (version 19). Following
the first study, memory performance was assessed using d’ scores and RTs. Outliers
were excluded as for Study 1.
4.4.1 Diagnostic-level analyses
Comparisons of patients with schizophrenia and healthy controls as to their memory
binding performance were conducted initially. As in Study 1, d’ scores were calculated
as indices of the capacity to bind and to recognize new-word and new-voice stimulus
features. The patient and control groups were compared on their d’ and RT scores using
the same parametric and nonparametric tests as used in Study 1. To control for possible
confounds, memory binding performance (d’) was correlated with any background
variables on which the patient and control groups differed. For any significant
correlation, the relevant background variable was used to create a factor (via dividing
the entire sample into those scoring above and below the median). Then, for any
significant group effects, analyses were repeated, including this additional factor, in
order to test the specificity of the group effects (see Suckling, 2010, for rationale on
appropriate means to account for variables that differ between schizophrenia patient and
control groups).
114
4.4.2 Symptom-level analyses
Patients with AH were compared to patients without AH in order to examine whether
there was a specific influence of hallucinations on binding ability. Independent-samples
t tests (of d’ scores) and mixed-design ANOVAs (of RTs) were conducted for this
purpose. When tests for homogeneity of variance were not met, F tests were adjusted
accordingly.
5. Results
As in Study 1, all variables were normally distributed, apart from new-word d’, which
was substantially skewed. Using the screening procedure described for Study 1, no
more than two data points were excluded for a single analysis3.
5.1 Descriptive statistics
At the demographic level, patients and controls did not differ in terms of age and level
of education. Patients obtained lower WASI IQ scores than controls. Additionally,
patients had significantly higher scores than controls on the PDI as well as on each of
the DASS subscales - Anxiety, Depression, and Stress (see Table 3). Table 3 also details
comparisons between the two patient subgroups. Patients with current AH obtained
significantly higher scores on the PDI than patients with no AH. No other differences
were significant.
3 Outliers were from both schizophrenia patient (8 data points) and healthy control (6 data points) groups
115
Table 3
Means, standard errors (SE), and t-tests of demographic and clinical information for
the schizophrenia patient and healthy control groups, as well as current AH and no AH
patient groups.
Controls
(n = 32)
Patients
(n = 64)
Mean SE Mean SE t
AGE (years) 40.34 1.61 41.36 1.13 .52
EDUCATION (years) 12.02 .32 11.88 .26 .33
WASI 116.22 1.72 103.59 1.98 4.13**
PDI 4.28 .65 9.17 .66 4.70**
DASS Anxiety 3.00 .95 11.25 1.15 4.66**
Depression 3.19 .66 13.44 1.27 5.50**
Stress 6.44 .81 15.53 1.34 4.65**
No AH
(n = 32)
Current AH
(n = 32)
Mean SE Mean SE t
AGE (years) 42.41 1.56 40.31 1.64 .93
EDUCATION (years) 12.00 .33 11.75 .41 .48
Chlorpromazine equiv. 653.08 95.94 537.35 74.99 .95
WASI 103.72 2.68 103.47 2.96 .06
PDI 7.38 .92 10.97 .85 2.88*
DASS Anxiety 9.06 1.43 13.44 1.75 1.94
Depression 12.13 1.79 14.75 1.81 1.03
Stress 14.56 1.71 16.50 2.02 .73
Levels of significance: * p < .05, ** p < .001
116
5.2 Memory Binding Task
5.3 Diagnostic-level analysis
Table 4 displays d’ scores along with accuracy rates and median RTs for the various
probe types for the schizophrenia-patient and healthy-control groups.
5.3.1 Binding ability
A comparison of binding d’ scores demonstrated that patients were worse at holding
bound features in memory compared to controls, t (93) = 3.45, p < .05 (see Table 4 and
Figure 1). A 2 (group: patients, controls) x 2 (probe type: intact, recombined) mixed-
design ANOVA was conducted for median RTs. Participants were faster at responding
to intact (M = 1443.97ms, SE = 28.09ms), compared to recombined (M = 1887.45ms,
SE = 71.22ms) probes, F (1, 92) = 42.42, p < .05, partial-η2 = 0.32. Notably, the main
effect of group was significant, with patients (M = 1780.34ms, SE = 48.34ms) being
slower than controls (M = 1551.08ms, SE = 68.91ms), F (1, 92) = 7.42, p < .05, partial-
η2 = 0.08. There was no significant interaction between probe type and group (see Table
4).
In order to control for possible confounds, binding ability (d’) was correlated
with all variables that significantly differed between groups (i.e., DASS-21 scores, PDI,
and WASI IQ). Only WASI IQ scores significantly correlated with binding d’ scores, r
(94) = .45, p < .05. To account for this potential confound, an IQ factor was formed by
dividing the entire sample into those scoring below the median (113) and those scoring
at or above the median. For any significant group effects reported thus far, analyses
were repeated, including this IQ factor. All group effects remained significant even after
accounting for this IQ factor. Additionally, to determine whether antipsychotic
medication was influencing the performance of schizophrenia patients, the relationship
117
between medication dosage (CPZ) and binding ability (d’) in patients was examined.
CPZ equivalents did not correlate with d’, r (60) = .16, p = .26.
bin
din
gd'
0
1
2
3
Lo
w L
SH
S-R
Hig
h L
SH
S-R
Pa
tie
nts
no
A
H
Co
ntr
ols
AH
Figure 1. Binding d’ scores for High and Low LSHS-R groups (Study 1), schizophrenia
patients and control groups (Study 2), and AH and no AH subgroups of the
schizophrenia patients (Study 2).
5.3.2 Recognition of individual stimulus features
Analysis of performance for the negative probe types was then conducted to identify
whether patients had particular difficulties remembering individual stimulus features,
which might account for their binding difficulty. Based on new-voice d’ scores, patients
were found to be worse at remembering new voices relative to controls, t (93) = 4.34, p
< .05 (see Table 4 for means). Similarly, a Mann-Whitney U test of new-word d’ scores
indicated that patients (M rank = 43.37) were poorer at remembering new words relative
to controls (M rank = 55.90), z = 2.35, p < .05. Patients appeared to find identifying the
new voices especially difficult (see Table 4). To check for this more directly, a new
variable was created by subtracting new voice d’ scores from new word d’ scores. This
variable was approximately normally distributed. A t test revealed that patients (M =
118
.87, SE = .10) were significantly worse at remembering new voices relative to new
words compared to controls (M = .49, SE = .13), t (94) = 2.17, p < .05.
A 2 (group: patients, controls) x 2 (recognition probe: new voice, new word)
mixed-design ANOVA was then conducted for RT. Participants were faster at
responding to new-word (M = 1571.01ms, SE = 38.82ms) relative to new-voice (M =
1751.80ms, SE = 52.59ms) probes, F (1, 92) = 19.04, p < .05, partial-η2 = 0.17.
Importantly, the main effect of group was also significant, with patients being slower in
their responses (M = 1812.63ms, SE = 48.21ms) compared to controls (M = 1510.19ms,
SE = 67.11ms), F (1, 92) = 13.40, p < .05, partial-η2 = 0.13. The interaction between
probe and group was not significant.
A secondary analysis was performed to investigate any links between binding
ability (d’) and memory for individual stimulus features. Patient (n =40) and control (n
=25) subgroups matched on d’ for recognizing individual features4 (new-voice d’ or
new-word d’) no longer differed significantly in binding ability, F (1, 63) = .45, p = .50.
4 The matching procedure was done at the group level, with lower scoring patients and higher scoring
controls on both new-voice d’ and new-word d’ eliminated.
119
Table 4
Descriptive statistics for d’ scores, accuracy and RT as a function of probe type in
patients with schizophrenia and healthy controls.
Controls Patients
Mean SE Mean SE
d’
Binding 2.67 .13 1.99 .14
New Voice 2.90 .10 2.14 .15
New Word 3.50 .03 3.05 .12
Accuracy (%)
Intact 98.44 2.51 91.41 1.78
Recombined 76.25 4.55 63.00 3.22
New Voice 85.67 4.03 69.36 2.81
New Word 99.67 1.29 96.45 .90
RT (ms)
Intact 1363.77 45.99 1524.16 32.26
Recombined 1738.39 116.60 2036.52 81.79
New Voice 1606.98 85.42 1896.61 61.37
New Word 1413.39 63.05 1728.64 45.30
5.4 Symptom-level analysis
There was no significant difference in identifying feature conjunctions (binding d’)
between groups with (M = 2.07, SE = .20) and without (M = 1.91, SE = .21) active
hallucinations, t (61) = .56, p = .58 (see Figure 1). A 2 (patient subgroup: no AH, AH) x
2 (probe type: intact, recombined) ANOVA was conducted for median RT. Both patient
120
subgroups were significantly faster at responding to intact (M = 1524.05ms, SE =
34.95ms) compared to recombined probes (M = 2036.20ms, SE = 90.95ms), F (1, 61) =
33.46, p < .05, partial-η2 = .35. There was no significant effect of patient subgroup, with
patients with AH and patients without AH exhibiting similar RTs, F (1, 61) = .06, p =
.80. The interaction between patient subgroup and probe type was also non-significant.
We also examined whether the lack of differences between AH and no AH groups could
be due to the presence of co-occurring symptoms of schizophrenia (i.e., delusions). PDI
scores were not significantly correlated with d’ scores for binding ability in both patient
subgroups (AH & no AH).
Finally, associations between binding ability (d’), memory for individual
features (i.e. new-voice d’ and new-word d’), and phenomenological features of
hallucinatory experiences - as measured on the PSYRATS - were examined. Binding d’
was significantly negatively correlated with the perceived loudness of AH, r (32) = -.42,
p < .05 but not with items related to distress. Similarly, new-voice d’ correlated
negatively with loudness of hallucinated voices, r (31) = -.36, p < .05. No other
correlations were significant.
5.5 A more direct test of the continuum model
As a way of evaluating whether the deficit in context memory binding is specific to AH
in schizophrenia patients, and not associated with AH in a healthy, predisposed group,
a more direct test of the continuum model of hallucinatory symptoms was applied. To
do this, high LSHS-R scorers and patients with AH were categorized together under one
level of a factor (symptomatic), and low LSHS-R scorers and healthy controls were
categorized under the other level (non-symptomatic). A 2 x 2 ANOVA was then
121
conducted on binding d’ scores, with study (Study 1 = high and low LSHS-R groups,
Study 2 = AH patient and healthy control groups) and group (non-symptomatic,
symptomatic) as between-subjects factors. A significant interaction between study and
group is expected if the binding deficit is located in the patient group only, an outcome
that would reinforce the results reported in the two separate studies. On the other hand,
a significant effect of group, with no significant interaction between study and group,
would suggest the binding deficit applies to both patient and non-patient AH (i.e., these
groups fall on a continuum of AH).Turning to outcomes of the analysis, there was a
significant effect of study, with participants in Study 2 (M = 2.37, SE = .10)
demonstrating poorer binding ability than those in Study 1 (M = 2.98, SE = .11), F (1,
120) = 17.23, p < .05, partial-η2 = 0.13. However the effect of group was not significant,
F (1, 120) = 1.70, p = .20. The interaction between study and group was significant, F
(1, 120) = 7.72, p < .05, partial-η2 = 0.06, reinforcing the results reported in the two
studies separately, and reflecting no significant difference in binding for high and low
LSHS-R scorers (see Study 1), but a significant difference in binding d’ between the
current AH subgroup of schizophrenia patients (M = 2.07, SE = 1.12) and healthy
controls (M = 2.67, SE = .76), t (62) = 2.49, p < .055.
6. Discussion
An identical task was used to examine binding of voices and words in memory in
separate studies of healthy hallucination-prone individuals and schizophrenia patient
samples. The main findings revealed no evidence of impaired memory binding in
hallucination-prone individuals relative to controls matched for age, whilst patients with
schizophrenia exhibited difficulties in binding compared to age-matched community
5 This analysis was repeated with the full patient dataset – that is, including those with and without AH, as
opposed to only those with AH – producing the same pattern of outcomes.
122
controls, indicating a discontinuity in cognitive function across these groups (David,
2010). Of note, there was no difference in binding ability between schizophrenia
patients with or without AH, suggesting that this deficit was not specific to AH.
6.1 Diagnostic-level effects
The results from Study 1 did not reveal any evidence of impaired binding of words and
voices in healthy (non-psychotic) individuals predisposed to hallucinations. Impaired
memory binding on this task should have resulted in reduced binding d’ scores and
slower RTs on either or both of the critical probe types (intact and recombined) in high
compared to low LSHS-R groups; however this was not evident. This result contrasts
with findings of significant binding difficulties in clinical hallucinators (e.g., Bentall et
al. 1991; Brebion, Gorman, Amador, Malaspina, & Sharif, 2002), and suggests that the
ability to integrate information about voices and words is intact in healthy individuals
predisposed to hallucinations. Recent studies (Badcock et al. 2008; Chhabra et al. 2011)
have also found intact binding of contextual features of information in healthy
hallucination-prone individuals. The current study adds to these findings, demonstrating
intact binding in memory of the content (word) and context (speaker identity) of speech
sounds, whereas our previous studies demonstrated intact binding of two contextual
features of speech sounds (location and speaker identity). Additionally, Bendall,
Jackson, and Hulbert (2011) failed to find an external misattribution bias in individuals
with first-episode psychosis. Together with the results of this study, this suggests that at
least some forms of binding deficit may only emerge as psychosis fully develops (Dore
et al. 2007; Badcock & Hugdahl, 2012; McKague, McAnally, Puccio, Bendall, &
Jackson, 2011), in keeping with a progressive deterioration in memory function
(Frommann et al. 2011).
123
Using exactly the same task, Study 2 revealed significant differences in memory
binding performance between individuals with schizophrenia and healthy controls.
Patients demonstrated poorer binding d’ scores and slower RTs compared to healthy
controls, suggesting that individuals with schizophrenia have difficulties remembering
‘who said what’. This finding is consistent with previous evidence of binding deficits in
schizophrenia (e.g., Woodward et al. 2007; Talamini, de Haan, Nieman, Linszen, &
Meeter, 2010), which have been linked to impaired activation in the hippocampal and
parahippocampal areas of the medial temporal lobe (Mitchell & Johnson, 2009;
Ranganath, 2010; Shimamura, 2010). It is unlikely that the deficits in binding are
reflective of generalised cognitive impairment in schizophrenia as patients still
demonstrated poorer binding ability compared to controls after accounting for
differences in IQ scores. The ability to bind information is essential not only for
forming episodic memories, but is also involved in most other aspects of cognition (e.g.,
visual perception, auditory perception, motor planning, language comprehension;
Treisman, 1999). Hence the binding difficulties found in this study are likely to
contribute to the many social and cognitive deficits, and symptom-level effects
observed in schizophrenia.
In addition to the binding deficit, individuals with schizophrenia also
demonstrated difficulties remembering specific words and voices (i.e., poorer d’ scores
and slower RTs when a new word or new voice was introduced in the recognition
probe). In particular, patients were markedly less accurate at remembering individual
voices than words compared to healthy controls. This finding is consistent with a
growing literature evidencing deficits in processing voices in individuals with
schizophrenia (Hirano et al. 2010; Zhang et al. 2008). Importantly, secondary analyses
conducted using subsets of patients and controls matched on memory for individual
124
features (voices and words) resulted in the elimination of binding deficits in patients.
The association between the patients’ difficulty in binding and their difficulty in
remembering individual features could reflect either: (1) the impact of a deficit in
processing the individual features, especially the voices, feeding through to affect the
capacity to remember combinations of features (Rizzo, Danion, Van der Linden,
Grange, & Rohmer, 1996); or (2) the impact of a deficit in binding on the capacity to
recognize individual features. For instance, recognition that a new-voice probe (paired
with an old word) is indeed a new voice could be facilitated by being able to retrieve
information on the other voice that had been paired with the old word at study. In other
words, memory for voice-word bindings could assist recognition of the individual
features. More detailed investigation in schizophrenia patients of the perception and
memory of voice and word features, including their binding, is clearly warranted.
6.2 Symptom-level effects
No differences in binding performance were identified between hallucinating and non-
hallucinating patient subgroups. It seems likely that this form of memory binding is a
general vulnerability factor for psychosis, that is, it is relevant to a broad range of
psychotic symptoms. In the AH group in particular, binding ability and memory for new
voices were both significantly negatively correlated with the loudness of AH (as rated
on the PSYRATS). Processing hallucinated voices and real (external) speech sounds
have been proposed to draw on similar neural substrates in the temporal lobe (Hugdahl
et al. 2008; Vercammen et al. 2011). The current findings are consistent with this view:
as the perceptual salience (loudness) of hallucinated voices increased, the ability to
recognize and integrate real (external) voices in memory decreased (see also
Vercammen et al. 2011), arguably because fewer resources were available for this
125
purpose. More fine-grained analysis of hallucinated and real (external) speech
processing should be undertaken in future studies of AH.
In summary, the contrasting results from Studies 1 and 2 (represented most
directly in the interaction of study and group on the binding d’ scores) suggest that
different cognitive mechanisms may exist in clinical and non-clinical hallucinators
(Chhabra et al. 2011). These findings highlight a caution to researchers utilising
hallucination-predisposed groups, and add to the recent challenges to the continuum
model of schizophrenia (David, 2010; Daalman et al. 2011). Future research should
undertake more fine-grained analysis of commonalities and differences in
phenomenology and cognition between AH in psychotic and non-psychotic groups.
6.3 Limitations
The current research was subject to several limitations. First, we have assumed that
memory binding is assessed in asking participants to distinguish between intact and
recombined probes. However, additional cognitive processes may be involved in the
current task, that is, it may not be a pure measure of context binding. For example, since
voice and word exemplars are repeated in different combinations across trials,
performance may depend in part on the ability to inhibit information from previous
trials. Consequently, the contribution of a broader difficulty in cognitive control in
schizophrenia cannot be ruled out. However, if poor binding solely reflected difficulties
with this aspect of cognitive control, then we also would have expected to find a
significant group difference in Study 1, given previous evidence of deficits in inhibition
in high LSHS-R scorers on tasks which did not involve binding (Paulik, Badcock, &
Maybery, 2007, 2008).
126
Participants in both studies found it more difficult to remember voices than
words, arguably because the unfamiliar voices carried more complex information
(Belin, Fecteau, & Bedard, 2004) than the familiar words. Ceiling levels of performance
for word recognition may have limited the identification of group differences in this
capacity. However, it is unlikely that the lack of any difference in binding for the high
and low LSHS-R groups was due to the relative ease of remembering one of the features
(words) relative to the other (voices), since in our earlier work we also found no
evidence of a memory binding deficit for high LSHS-R samples using tasks for which
memory for the two features (voices and locations) was well-matched (Badcock et al.,
2008; Chhabra et al., 2011).
Additionally, it is important to note that high scores on the LSHS-R in Study 1
may have been achieved by participants endorsing a wide range of visual and auditory
experiences that have been present but, nonetheless, occurred relatively infrequently.
An informative study would be to test binding in an hallucination-prone group for
whom the hallucinatory experiences are more similar to the AH experienced by patients
(i.e., typically auditory, and more frequent and distressing), and potentially more
predictive of long-term risk for psychosis (see Laroi, 2012). The potential role of
context memory binding in other modalities of hallucinations, such as visual
hallucinations, should also be explored. Patients in Study 2 were taking psychotropic
medication, which could have affected their performance, although no significant
correlation was obtained between Chlorpromazine equivalents and binding
performance. Finally, the patient subgroups in Study 2 differed on symptoms other than
hallucinations, which could possibly have affected the lack of finding of particular
deficits in the AH sample. Future studies should explore the role of memory binding in
other symptoms of psychosis (e.g., delusions, disorganised thought).
127
Ethical Statement
All research described within this manuscript conformed to the ethical guidelines
recommended by the Declaration of Helsinki and was approved by the Human Research
Ethics Committees of the University of Western Australia, and the North Metropolitan
Area Mental Health Service (Perth). Written informed consent was obtained from each
participant prior to testing.
Acknowledgements
This research was partially supported by the Australian Schizophrenia Research Bank
(ASRB), which is supported by the National Health and Medical Research Council of
Australia (NH&MRC Enabling grant 386500), the Pratt Foundation, Ramsay Health
Centre, the Viertel Charitable Foundation and the Schizophrenia Research Institute. The
research was also partially supported by an Australian Research Council Discovery
Grant (DPO773836). The authors would like to thank Doris Leung for her assistance
with programming the binding task.
128
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135
Section Three
Voice identity processing in relation to clinical
and non-clinical auditory hallucinations
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Chapter Five
An overview of voice processing in healthy individuals, in individuals with
schizophrenia, and in relation to clinical and non-clinical auditory
hallucinations
Synopsis
The previous experimental chapters have revealed the importance of voice recognition
difficulties in individuals with schizophrenia, and their particular relevance to auditory
hallucinations – clearly indicating a need to target voice processing and in particular
voice identity processing in this group. This chapter briefly summarises the dominant
model of human voice perception which provides the general framework for the current
research. Current empirical literature on voice processing in schizophrenia and links to
auditory hallucinations in schizophrenia and in the general population are then critically
reviewed, providing a context for the methodology adopted in the following
experimental chapters. Finally, an outline of the aims of the proceeding experimental
chapters will be presented.
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Human voice processing
The human voice is often considered to be an “auditory face”, which carries a wealth of
socially relevant information (Belin, Bestelmeyer, Latinus, & Watson, 2011). This
analogy arises because, much like faces, voices contain not only speech, but also a large
amount of non-linguistic information about the identity (e.g., physical characteristics
such as gender, age, and size) and affective state of the speaker. In fact, this vocal
information can be determined even in the absence of speech. For example, when we
hear a baby cry, we are still readily able to extract important information about the
identity (approximate age) and affective state (distress/pleasure) of the infant (Belin,
Fecteau, & Bedard, 2004). Similarly, listeners have been shown to be good at
determining the gender (Mullennix, Johnson, Topcu-Durgun, & Farnsworth, 1995) and
age of speakers (Hartman & Danahuer, 1976; Zäske & Schweinberger, 2011), as well as
other physical (e.g. height, weight, racial group), biological (e.g., sexual behaviour), and
psychological characteristics (e.g., trustworthiness and competence) from the voice
alone (Hughes, Dispenza, & Gallup, 2004; Ko, Judd, & Stapel, 2009; Kreiman, 1997).
The perception of human voices lies in how voices are produced. As
summarised by several authors (see Belin et al., 2004; Ghazanfar & Rendall, 2008;
Latinus & Belin, 2011b, for detailed information), human vocal sounds are the result of
the interplay of a source (the vocal folds in the larynx) and a filter (the vocal tract above
the larynx). The periodic oscillation of the vocal folds in the larynx determines the
average fundamental frequency (F0) of phonation, which is largely a function of the
size of the vocal folds; men have much larger vocal folds than women or children,
resulting in generally lower F0 values. The vocal tract above the larynx acts as a filter
reinforcing certain frequencies of the source called ‘formants’. Formant frequencies
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depend on the size of an individual’s vocal tract (which is correlated with body size), as
well as its shape (determined by the particular configuration of the articulators during
speech), thus men typically have lower formant frequencies than women or children.
Both clinical and neuroimaging evidence suggests the existence of voice selective
cortical regions. These ‘temporal voice areas’ (TVA) are located bilaterally along the
mid and anterior parts of the superior temporal sulcus (STS), with other voice sensitive
regions in frontal and parietal cortex (Belin, Zatorre, Lafaille, Ahad, & Pike, 2000;
Gervais et al., 2004; Linden et al., 2011). Recent studies also show that voice selective
perceptual abilities arise early in human development – around seven months of age –
(Grossman, Oberecker, Koch, & Friederici, 2010). This is well before speech perception
has been fully established (Belin & Grosbras, 2010), suggesting early development of
cortical voice processing (Latinus & Belin, 2011b).
A model of human voice processing
Based on Bruce and Young’s (1986) model of face perception, current models of human
voice processing suggest a functional organisation of voice perception whereby speech,
affect and identity information are processed in partially segregated, parallel cortical
pathways (Belin et al., 2011; Warren, Scott, Price, & Griffiths, 2006) (see Figure 1).
According to this model, initial low-level sensory processing of acoustic input takes
place in sub-cortical nuclei and core regions of the auditory cortex, wherein three main
types of vocal information are extracted and further processed in somewhat segregated
functional pathways: (1) a speech analysis pathway involving the anterior and posterior
STS as well as inferior prefrontal regions and pre-motor cortex predominantly in the left
hemisphere; (2) a vocal affect analysis pathway, involving the anterior insula and
amygdala, temporo-medial regions, and inferior prefrontal regions predominantly in the
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right hemisphere; and (3) a vocal identity analysis pathway, involving ‘voice
recognition units’ in regions of the right anterior STS (Belin et al., 2004; Belin et al.,
2000; Fecteau, Armony, Joanette, & Belin, 2005; Formisano, De Martino, Bonte, &
Goebel, 2008).
Figure 1. A model of human voice perception displaying three dissociable functional
pathways which interact with equivalent functional pathways in facial processing.
Reproduced from Belin et al. (2004).
These voice processing pathways interact, but as a result of their parallel
organisation, functional dissociations have also been revealed, indicating they can also
be affected somewhat independently. Support for this model of segregated pathways has
been provided from clinical studies (Garrido et al., 2009; Hailstone, Crutch,
Vestergaard, Patterson, & Warren, 2010; Van Lancker & Kreiman, 1987; Van Lancker,
Cummings, Kreiman, & Dobkin, 1988), as well as behavioural studies of healthy
participants (Kreiman & Gerratt, 1998). For example, studies reported in the
phonagnosia literature have identified specific deficits in the recognition of voice
identity in those affected by this disorder, in the presence of preserved recognition of
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vocal emotions (Garrido et al., 2009; Hailstone et al., 2010). Conversely, patients with
ventro-frontal damage who are impaired in vocal emotion processing, are not
necessarily impaired in voice discrimination (Hornak, Rolls, & Wade, 1996).Within the
voice identity pathway, distinctions between voice recognition (of familiar voices) –
which is impaired – and voice discrimination (distinguishing between two or more
unfamiliar voices) – which is intact – have also been revealed in individuals with
phonagnosia (Van Lancker & Kreiman, 1987; Van Lancker et al., 1988), pointing to
possible differences in the analysis of vocal structure as a function of voice familiarity
(see also Latinus, Crabbe, & Belin, 2011).
Voice affect perception
Perception of emotional information in voice is typically studied in the context of
recorded speech with different emotional intonation, meaningless sentences spoken in
various emotional tones, non-linguistic verbalisations – such as laughter or screams of
fear – or using adaptation paradigms (wherein continuous stimulation leads to a biased
perception towards opposite features of the adapting stimulus) (see Belin et al., 2011,
for a review). A listener can infer much of a speaker’s affective state from emotional
prosody – a set of acoustic parameters of speech directly influenced by affect such as
mean amplitude, segment and pause duration, mean F0, and F0 variation (Belin et al.,
2004).
Voice identity perception
Voice identity perception is typically studied using stimuli with no emotional intonation
(or sometimes, no emotional content), at separate levels including: (1) recognition of
familiar voices or distinguishing familiar from unfamiliar voices – although this design
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also involves a memory component, which may be a confounding factor,
(2) discriminating unfamiliar voices, and (3) differentiating vocal sounds (speech or
non-speech) from control sounds (e.g. modulated noise, animal sounds) (Belin et al.,
2011; Belin et al., 2004). The cognitive and neural bases of voice identity perception are
still being uncovered, however psychoacoustic evidence suggests that the extraction of
particular paralinguistic features of a voice (such as F0) is required for speaker
identification (Latinus & Belin, 2011a). Identity information is also carried in ‘static’
features of voice such as timbre (which includes very different aspects of phonation,
such as the amount of phonation noise, or an individual’s particular repetition of
acoustical energy across frequency). That is, directly influenced by physical factors
such as age and gender, and ‘dynamic’ information, such as patterns of pronunciation
(accent) specific to a region or person (Belin et al., 2004).
Multidimensional scaling (MDS) is one technique that has often been used to
examine the representation of voice identity (e.g., Bestelmeyer et al., 2011; Kreiman,
Gerratt, Precoda, & Berke, 1992; Murray & Singh, 1980). At the heart of this approach
lies the analysis of judgments of the perceived identity similarity/dissimilarity of a set of
voices. This technique allows individual voices to be represented in common
dimensions in acoustical space. Voices depicted closer to each other in this “voice
space” are perceived as more similar in identity, while voices further apart are perceived
as being relatively different in identity. The dimensions of this voice space are
interpreted by examining how they correlate with basic acoustic cues (e.g., F0, formant
frequencies, timbre etc). The result of this process is a description of the variability in
voice characteristics associated with different voices (Latinus & Belin, 2011b). For
example, Baumann and Belin (2010) found that voices could be represented in a
minimally multidimensional voice space with only two dimensions, reflecting
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contributions of the source and the filter respectively. However, Baumann and Belin
(2010) examined speaker recognition of vowel sounds which may not generalise to
more naturalistic situations involving whole words or sentences, where several other
cues contribute to recognition of the voice.
In sum, whilst the perception of voice identity involves a variety of low-level
acoustic features, it typically can be adequately described using a two-dimensional
voice space. Current models of voice perception (e.g., Latinus & Belin, 2011a) provide
a useful, empirically-supported framework to guide research of clinical groups with
abnormal voice perception, such as schizophrenia.
Voice processing in schizophrenia and its link to auditory hallucinations
There is a large body of literature documenting deficits in vocal affect perception – that
is, emotional prosody – in schizophrenia (see Edwards, Jackson, & Pattison, 2002;
Hoekert, Kahn, Pijnenborg, & Aleman, 2007, for reviews), whilst very few studies have
specifically examined the perception of voice identity. Furthermore, deficits in vocal
affect processing have been proposed as a significant contributory factor to the
development of auditory hallucinations (AH) (for a review, see Alba-Ferrara,
Fernyhough, Weis, Mitchell, & Hausmann, 2012; for experimental evidence, see: Kang
et al., 2009; Rossell & Boundy, 2005; Shea et al., 2007). Emotional prosodic processing
deficits clearly depend to some degree on deficits processing more basic acoustic
characteristics, including pitch-based cues such as mean and variability of fundamental
frequency (Kantrowitz et al., 2011; Leitman et al., 2005; Leitman et al., 2007; Leitman
et al., 2010), highlighting the importance of both a lower-level cue-based approach, as
well as a higher-level analysis of the auditory system, to the assessment of dysfunction
in voice perception in schizophrenia. Since this literature on vocal affect in
144
schizophrenia and AH has been extensively reviewed, it will not be considered further
here.
It is important to note that – given partial segregation of affect and identity
information in current models of voice perception reviewed above (see Figure 1) – one
possibility is that whilst vocal emotion is impaired in patients with AH, recognition of
voice identity may be spared. However, recent neural studies provide evidence of
disturbed activation to voices in the voice specific network in schizophrenia (Koeda et
al., 2006), and prosodic features are important cues for differentiation of voices (Belin
et al., 2004), hence it seems likely that patients with schizophrenia (including those with
AH in particular) could have difficulties in perception and recognition of voice identity
(Shea et al., 2007).
Voice identity perception
There is a small, but growing, body of evidence of difficulties in voice identity
processing for familiar voices in AH in schizophrenia. For example, using functional
resonance magnetic imaging, Zhang and colleagues (2008) reported that schizophrenia
patients (both with and without AH) were particularly impaired in voice recognition in
response to familiar (i.e., voices of their closest friends or personal acquaintances),
versus unfamiliar voices, when compared to healthy controls. Additionally, Waters and
Badcock (2009), using a gender-identity (male/female) recognition task, found that,
when compared to controls, patients with schizophrenia (with and without AH)
demonstrated greater impairments in recall of previously-presented female, compared to
male, unfamiliar voices. However, there are uncertainties regarding the interpretation of
these voice recognition deficits. For example, an impairment distinguishing
familiar/unfamiliar voices may reflect non-specific/broader difficulties of memory often
145
reported in schizophrenia (Drakeford et al., 2006) or it could arise specifically from
difficulties differentiating between speaker identities, linked to more basic perceptual
abilities. That is, the problem may arise at several different levels within Belin and
colleague’s (2004) model of healthy voice perception (see Figure 1). In addition, the
study by Zhang et al. (2008) failed to include signal detection procedures; consequently
it is possible that patients' performance reflected a bias to classify voices as unfamilar,
rather than a difference in sensitivity to detect familiar from unfamiliar voices.
Furthermore, very few studies have disambiguated the role of voice specifically, from
that of speech and language activation (see Koeda et al., 2006, for an example of how
this was approached).
The experimental results reported so far in this thesis also highlight the potential
importance of voice identity abilities since: (1) binding speech and voice in
schizophrenia appears to depend on how well individual voices are encoded, and (2)
poor sensitivity to voices distinguished performance of patients with psychosis but not
healthy voice hearers from controls. There are several further reasons why further
research on voice identity processing in schizophrenia should be conducted. First,
impairments in voice processing have been thought to contribute to the psychosocial
functioning difficulties observed in individuals with schizophrenia (Brekke, Kay, Lee,
& Green, 2005; Hoekert et al., 2007), Second, as noted in Section One, a prominent
feature of AH is the perception of a voice whose identity is separate from the voice
hearer (Jones & Fernyhough, 2007). For example, a schizophrenia patient with AH may
identify the hallucinated voice as belonging to a famous newsreader, who is angry at
him/her (Chadwick & Birchwood, 1994; Nayani & David, 1996). (Mis)attribution of
voice/speech to a specific other identity is a challenge to existing models based on
failures of self-recognition (see Aleman & Laroi, 2008; Waters, Woodward, Allen,
146
Aleman, & Sommer, 2010 for reviews). This impairment could stem from a broader or
additional deficit in discriminating the identity of speakers.
Third, approximately 75% of the voices heard in schizophrenia patients with AH
are reported to be male in gender (i.e., not a specific identity, but an enduring feature
related to identity), and this is irrespective of the gender of the voice hearer (Nayani &
David, 1996). Gender is a key cue to vocal identity (Burton & Bonner, 2004), so the
preponderance of male voices may indicate a bias in identity processing or in the
representations of voices in voice space. Finally, phenomenological research has
confirmed the importance of attribution of identity in producing emotional and
behavioural responses to AH in schizophrenia. In particular, voices appraised as
dominant, malevolent, and of personal acquaintance to the individual, have been found
to result in increased distress in voice-hearers (David, 2004; Mawson, Cohen, & Berry,
2010; Sorrell, Hayward, & Meddings, 2010). In sum, there is sufficient unknown about
voice identity processing in schizophrenia patients with AH.
Voice processing in relation to AH in the general population
Research into voice affective processing has revealed mixed findings in individuals
prone to psychosis or predisposed to AH. Some studies have shown deficits in affective
prosody, such as impairments in the recognition of fear, anger and sadness in voices
(e.g., Amminger et al., 2011), while others have failed to identify deficits in emotional
prosody perception in these individuals (for a review, see Phillips & Seidman, 2008).
These inconsistencies in the literature have been thought to result, at least in part, from
the differences in stimuli and methodology utilised between studies, again drawing
attention to the need for consistent methodology to be used across studies.
147
On the other hand, as with the literature in schizophrenia, there has been
remarkably little research into voice identity perception that has been addressed from
the point of view of the continuum model of psychotic symptoms. In their comparison
of clinical and non-clinical AH, Daalman and colleagues (2011) found that these groups
were similar in the reality and personification aspect of AH (i.e., similar attributions to
real and familiar identities), providing some evidence in support of the continuum
model of psychotic symptoms. Similarly, from a neurimaging perspective, Diederen et
al (2011) found evidence suggesting that similar neural networks appear to be activated
in clinical and non-clinical AH, though the authors caution that this result may still
reflect different mechanisms that culminate in a final common pathway. Together with
the findings from cognitive data in this thesis indicating that individuals with high levels
of hallucination proneness had no differences in sensitivity to voice or voice-binding
difficulties compared to controls, this opens up the possibility that there are some
differences in voice identity perception in clinical and non-clinical AH groups.
Uncovering the vocal processes that are/are not involved in the predisposition to
hallucinate in non-clinical groups would provide valuable information for the
continuum model of psychotic symptoms. Clearly, more research into these vocal
processes is required using similar tasks to those used in patient studies.
Specific aims and hypotheses
The following experimental chapters investigated voice identity perception in relation to
clinical and non-clinical AH. Chapters 6 and 7 describe two experimental studies, with
an overall aim to investigate voice identity discrimination using an identical task in
separate studies of; (1) individuals with schizophrenia (both with and without AH) and
healthy age-matched controls (Chapter 6), and (2) healthy individuals with and without
148
a tendency to hallucinate (Chapter 7). In Chapter 6, if individuals with schizophrenia
demonstrate limited discrimination of unfamiliar voices compared to healthy controls,
this would indicate atypical voice identity perception. On the other hand, similar voice
identity discrimination in schizophrenia patients and controls would suggest intact
lower-level perception of speaker identity in individuals with schizophrenia. Atypical
voice identity discrimination in only schizophrenia patients with, as opposed to without
current AH, would imply specificity of atypical voice identity perception to AH. For
Chapter 7, if healthy individuals highly predisposed to hallucinations demonstrate
similar atypical discrimination of voice identity to schizophrenia patients with AH when
compared to controls; this would imply continuity of voice processing deficits in
clinical and nonclinical impairments. Conversely, similar voice identity discrimination
in high and low hallucination-prone groups would add to the challenges on the
continuum model of psychotic symptoms.
149
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Chapter Six
Voice identity discrimination in Schizophrenia1
Abstract
Voices provide a wealth of socially-relevant information, including cues to a speaker's
identity and emotion. Deficits recognizing emotion from voice have been extensively
described in schizophrenia, and linked specifically to auditory hallucinations (AH), but
relatively little attention has been given to examining the ability to analyse speaker
identity. Hence, the current study assessed the ability to discriminate between different
speakers in people with schizophrenia (including 33 with and 32 without AH) compared
to 32 healthy controls. Participants rated the degree of perceived identity similarity of
pairs of unfamiliar voices pronouncing three-syllable words. Multidimensional scaling
of the dissimilarity matrices was performed and the resulting dimensions were
interpreted, a posteriori, via correlations with acoustic measures relevant to voice
identity. A two-dimensional perceptual space was found to be appropriate for both
schizophrenia patients and controls, with axes corresponding to the average
fundamental frequency (F0) and formant dispersion (Df). Patients with schizophrenia
did not differ from healthy controls in their reliance on F0 in differentiating voices,
suggesting that the ability to use pitch-based cues for discriminating voice identity may
be relatively preserved in schizophrenia. On the other hand, patients (both with and
without AH) made less use of Df in discriminating voices compared to healthy controls.
This distorted pattern of responses suggests some form of deficient voice identity
processing in schizophrenia. Formant dispersion has been linked to perceptions of
dominance, masculinity, size and age in healthy individuals. These findings open some
interesting new directions for future research.
1 A revised version of this chapter has been accepted subsequent to thesis submission: Chhabra, S.,
Badcock, J. C., Maybery, M. T., & Leung, D. (2012). Voice identity discrimination in schizophrenia.
Neuropsychologia, 50, 2730-2735.
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1. Introduction
Voices carry a wealth of important information relevant to social cognition (Belin,
Fecteau, & Bedard, 2004; Ko, Judd, & Blair, 2006). This includes information about
speaker identity (e.g., physical characteristics like gender, age and size) as well as
speaker affect (i.e., emotional state). In schizophrenia, cerebral activation to human
voices is disturbed, including dysfunctional activation of the voice-specific network in
the right hemisphere (Koeda et al., 2006), whilst an extensive amount of cognitive
research also points to sizeable deficits recognising emotion from voice in people with
schizophrenia (for a review, see Hoekert, Kahn, Pijnenborg, & Aleman, 2007). Such
deficits in affective prosody perception are thought to be important contributors to the
observed psychosocial functioning difficulties in this group of individuals (Brekke,
Kay, Lee, & Green, 2005; Hoekert et al., 2007; Leitman et al., 2010) and to the
formation of positive symptoms such as auditory hallucinations (AH) (Leitman et al.,
2005) and delusions (Rossell & Boundy, 2005). A series of elegant studies shows that
these difficulties with vocal affect recognition stem, at least in part, from deficits in
processing basic acoustic characteristics, including pitch-based cues such as mean and
variability of fundamental frequency (Kantrowitz et al., 2011; Leitman et al., 2005;
Leitman et al., 2007; Leitman et al., 2010), highlighting the value of a cue-based
approach to assessment of dysfunction in voice perception in schizophrenia.
In contrast, markedly less attention has been paid to the ability to analyse
speaker identity in schizophrenia. So far, the research conducted in this area has focused
largely on cortical activation to voices (Koeda et al., 2006) and deficits in recognition of
familiar voices. For example, using functional magnetic resonance imaging, Zhang and
colleagues (2008) reported impaired voice recognition in response to familiar versus
unfamiliar voices in schizophrenia. This impairment was found to be particularly
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relevant to patients with AH. Additionally, Waters and Badcock (2009) found that
patients with schizophrenia demonstrated greater impairments in recall of female
compared to male unfamiliar voices. It is currently unknown whether these vocal recall
and recognition deficits reflect the well-documented memory problems present in
schizophrenia (Drakeford et al., 2006) or arise from difficulties differentiating between
speaker identities, linked to more basic perceptual abilities. This basic ability to
discriminate between different speakers is likely to contribute to the higher-level ability
to recognise familiar voices.
Current neural models of human voice processing (Belin, Bestelmeyer, Latinus,
& Watson, 2011; Belin et al., 2004) provide further incentive to investigate voice
identity discrimination in schizophrenia. These models suggest that, following a
preliminary stage of structural encoding, affect and identity information conveyed in
speech are processed in partially segregated pathways (see also Warren, Scott, Price, &
Griffiths, 2006). These functionally separable, parallel pathways are interrelated, but
also reveal dissociations between vocal affect and identity. For example, phonagnosia
patients exhibit specific deficits in voice identity recognition, whilst recognition of
vocal emotions is relatively preserved (Garrido et al., 2009; Hailstone, Crutch,
Vestergaard, Patterson, & Warren, 2010). This model of voice perception leaves open
the possibility that people with schizophrenia are deficient in vocal emotion processing
but not necessarily impaired in voice discrimination.
One common approach to examining how unfamiliar voices are discriminated is
multidimensional scaling (MDS) (Baumann & Belin, 2010; Latinus & Belin, 2011), a
technique which analyses ratings of perceived differences for pairs of speakers and
allows individual voices to be represented in common dimensions. For example,
Baumann and Belin (2010) asked participants to rate the dissimilarity between a large
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number of voice pairs for vowel sounds. They found a two-dimensional voice space
reflecting contributions from different acoustic features in the voice defined by the
average fundamental frequency of phonation (F0) and the average first formant
frequency (F1). In this space, voices further apart are perceived as more different in
identity. Using MDS, it would be possible to examine whether this perceptual voice
space is different in people with schizophrenia compared to healthy controls, which
would indicate differences in the perception of voice identity. To our knowledge, this
has not previously been attempted.
In sum, the unique aim of this study was to assess the perception of voice
identity in individuals with schizophrenia compared to healthy controls using MDS of
similarity-dissimilarity judgments for pairs of unfamiliar speakers. Based on established
acoustic research, we also obtained acoustic measures (pitch, pitch variability, and
resonance) relevant to voice identity (rather than voice emotion perception) for our
speakers in order to explore the acoustic correlates of the dimensions found. Studies by
Bachorowski and Owren (1999) and Baumann and Belin (2010) have demonstrated that
pitch (F0) is a key acoustic variable relevant to voice identity discrimination for both
male and female vowel sounds in healthy individuals. In addition, F0, pitch variability
(F0SD), and resonance (Df) have been shown to be associated with perceptions of
femininity in healthy individuals (Ko et al., 2006). Similarly, F0 and Df have been
linked to perceptions of dominance in healthy individuals (Puts, Hodges, Cárdenas, &
Gaulin, 2007). The acoustic attributes selected should adequately tap cues from the
larynx and supra-laryngeal vocal tract, which according to source-filter theory are
relatively independent components of voice production (Latinus & Belin, 2011). We
also obtained acoustic measures relevant to voice identity for our speakers in order to
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explore the acoustic correlates of the dimensions found. This is the first study to use an
acoustically-characterised stimulus set in voice-identity research in schizophrenia.
Given previous evidence that impairments in voice perception may be
particularly relevant to AH, we planned follow-up comparisons of patients currently
experiencing AH and those without AH. Based on findings from Baumann and Belin
(2010), we hypothesised that the MDS of dissimilarity judgments on voices in healthy
controls would result in a two-dimensional voice space. If individuals with
schizophrenia show a distorted pattern of voice dissimilarity judgments compared to
healthy controls, this would indicate a deficit in vocal identity perception. Alternatively,
if both patients with schizophrenia and controls demonstrate similar voice
discrimination, this might suggest that lower-level perception of speaker identity is not
impaired in schizophrenia. Finally, distortions in voice similarity judgments found only
in currently hallucinating patients with schizophrenia would suggest that voice identity
processing deficits may be specific to the presence of AH.
2. Method
2.1. Participants
Seventy patients with schizophrenia (34 with current AH, 36 without current AH) and
34 healthy comparison controls participated in this study. The groups did not differ
significantly in gender ratio (patients: n = 25 female, 45 male; controls: n = 12 female,
22 male). The patient sample met the Diagnostic and Statistical Manual of Mental
Disorders Fourth Edition criteria and/or International Classification of Diseases (10th
Revision) criteria for a lifetime diagnosis of schizophrenia or schizophrenia-spectrum
disorder (F20, N = 52; F22, N = 2; F25.0, N = 3; F25.1, N = 4; F25.2, N = 4; F28, N = 5)
as determined by the Diagnostic Interview for Psychosis.(Castle et al., 2006) The
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Psychotic Symptom Rating Scales (Haddock, McCarron, Tarrier, & Faragher, 1999)
were used to assess more detailed characteristics of AH for those currently experiencing
them. Patients were recruited from community mental health centres and inpatient
services of Graylands Hospital (Perth, Australia) and were receiving their usual
medication (mean chlorpromazine [CPZ] equivalents = 614) at the time of testing (n =
51 atypical antipsychotics, n = 8 typical antipsychotics, n = 10 anxiolytics, n = 27
antidepressants, n = 17 mood stabilisers). Exclusion criteria included the presence of
neurological disorders, loss of consciousness > 15 minutes, and poor English fluency.
Individuals with hearing levels poorer than 30 dB at the frequencies tested were also
excluded (Waters, Price, Dragović, & Jablensky, 2009).
Healthy controls were recruited from the local community through email
advertisements in health department and university networks. Exclusion criteria were as
for patients, except that individuals with a current diagnosis/treatment for a mental
illness (as determined via screening using the Mini International Neuropsychiatric
Interview for Schizophrenia and Psychotic Disorders Studies (Sheehan et al., 1998),
diagnosis of schizophrenia in a first-degree relative, or treatment for substance-use
disorder were also excluded. Following exclusion criteria, 65 individuals with
schizophrenia (33 with current AH, 32 without current AH) and 32 healthy controls
remained in the study. Each participant provided informed consent, and all procedures
were approved by the Human Research Ethics Committees of the University of Western
Australia, and the North Metropolitan Area Mental Health Service (Perth).
Patients and controls did not differ in age (patients: M = 41.36 years, SE = 1.13
years, controls: M = 40.34 years, SE = 1.61 years, t = .52, p > .05) or level of education
(patients: M = 11.88, SE = .26, controls: M = 12.02, SE = .32, t = .33, p > .05); however
patients obtained lower IQ scores on the Wechsler Abbreviated Scale of Intelligence
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(WASI) than controls (patients: M = 103.59, SE = 1.98, controls: M = 116.22, SE =
1.72, t = 4.13, p < .001).
2.2. Similarity Ratings Task
2.2.1 Stimuli
Stimulus presentation was controlled by a laptop computer. Participants listened to
recordings of spoken words which were presented via Sennheiser HD 205 headphones
at 69.22 dB. We utilised words, rather than vowels, as they represent more “real world”
stimuli (Ko et al., 2006) in being more similar to what listeners hear every day. Stimuli
were 96 spoken words, consisting of eight different three-syllable words (abundance,
commencement, discretion, equity, bereavement, impetus, paradox, resumption),
spoken in 12 different voices. The 12 individuals providing the voice samples (half
male; age range 18-30 years) were native Australian-English speakers unknown to
participants. Stimuli comprised an emotionally-neutral reading of the words, recorded in
16-bit mono format at a sampling rate of 44.1 kHz using a Shure Professional Dynamic
microphone (SM57) and the audio recording program Sound Forge (version 4.5). The
words spoken were matched in terms of frequency (Kucera & Francis, 1967),
familiarity, and imagability (Coltheart, 1981), and were 1000 ms in duration. The
amplitude of the individual words was equalised across speakers to approximately 75
dB to ensure there was no variation in sound intensity between the same words spoken
in different voices.
2.2.2. Procedure
On each trial, participants heard two speakers saying the same word in sequence, with a
stimulus onset asynchrony of 2000 ms. Then, 1000 ms after the onset of the second
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word, a seven-point rating scale appeared on a touch screen, ranging from very similar
(1) to very dissimilar (7). Participants were instructed to focus on the voice qualities of
the two words and to make a judgment as to how similar/dissimilar the two voices
sounded by recording their rating on the touch screen. Participants were advised to use
the full range of the scale when making their voice ratings throughout the task. The
word used for each trial was selected at random. There were two blocks of testing, with
each block including 4 practice and 66 test trials. Each of the 66 possible pairings of the
12 voices occurred once in each block, with the order of the pairings randomized. The
order of the two voices in each pairing was randomized for the first block and then
reversed for the second block. Stimuli were selected anew for each participant. Four rest
breaks were provided throughout the task. Total task duration was approximately 15
minutes.
2.2.3. Acoustic analysis of voices
We selected acoustic characteristics of pitch, pitch variability, and resonance for
analysis, based on results from established acoustic research highlighting these cues as
relevant for voice identity perception specifically (rather than voice emotion perception)
in both healthy individuals as well as patients with schizophrenia (Bachorowski &
Owren, 1999; Baumann & Belin, 2010; Ko et al., 2006; Leitman et al., 2010). We also
included the frequency of the first formant in our analyses as this allowed a direct
comparison with the voice space described by Baumann and Belin (2010). We based
our acoustic measurements on stimuli comprising all eight words (in a consistent order)
spoken by each of the 12 speakers. PRAAT 5.0.32 software (Boersma, 2001) was
utilised to compute the chosen acoustic characteristics:
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Pitch. The pitch of each voice stimulus was measured by its mean fundamental
frequency (F0) in Hertz (Hz).
Pitch variability. The standard deviation of pitch (F0-SD) across the vocal sample for
each voice was calculated to assess the momentary variability in pitch during speech,
which is an indicator of intonation.
First formant. We obtained the peak frequency of the first formant (F1) (derived as in
Baumann & Belin (2010).
Resonance. The resonance of each voice was represented by the formant dispersion (Df)
(Fitch, 1997), measured in Hz. This involved averaging the distance between adjacent
pairs of the first five formant frequencies. The maximum formant frequency for female
voices was set to 6500 Hz (Baumann & Belin, 2010). All other parameters were default
values recommended by the authors of PRAAT.
2.3. Data Analysis
2.3.1 Multidimensional scaling (MDS) of similarity judgements
The mean dissimilarity rating (out of 7, where 1 = very similar and 7 = very dissimilar)
for each pair of the 12 voices was calculated for each participant. Matrices of these
dissimilarity ratings were then entered into MDS analyses that were conducted using an
Individual-Squares Scaling (INDSCAL) model, with interval scaling and Euclidean
distances. Analyses were conducted in two phases. First, group dissimilarity matrices
(i.e., one for the patient group and one for the control group) were submitted to MDS to
check whether our data maps onto a two-dimensional space, as previously described in
the literature (Baumann & Belin, 2010; Leitman et al., 2010). The resulting dimensions
were then interpreted a posteriori by correlating values on each dimension for the 12
voices with the acoustic measures for those voices (using Pearson correlation
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coefficients) to determine the nature of the dimensions that participants used to make
the dissimilarity judgements.
Second, matrices from individual participants were included within the same
INDSCAL model (i.e., 65 patient and 32 control dissimilarity matrices included in one
model) to enable testing of whether there were any differences between the patient and
control groups in the processes used to differentiate voices. The critical parameter
produced by INDSCAL, which accounts for individual variation in the perceptual
processes when performing the rating task, is a weight for each individual on each
dimension. The higher the weight, the more important that dimension was to that
individual (Kring, Barrett, & Gard, 2003). The weights for each dimension were then
compared for the patient and control groups using independent-samples t tests. Any
participant weights that were three or more standard deviation units away from the
respective group mean were excluded prior to these comparisons. To check for possible
confounds of IQ and medication dosage (CPZ equivalents), individual subject weights
which differed between patients and controls for any dimension were correlated with
WASI IQ scores (for the full sample) and CPZ equivalents (for the patients). If these
correlations were not significant, no further action was taken to control for them.
3. Results
3.1. MDS of group dissimilarity matrices within the same model
Based on recommendations from the literature (Borg & Groenen, 1997; Kruskal &
Wish, 1978) and on the interpretability, uniqueness, and percentage of variance
accounted for (Baumann & Belin, 2010), a two-dimensional solution was found to be
most appropriate. This solution yielded a good fit to the dissimilarity ratings for the
schizophrenia-patient and control groups (proportion of variance accounted for (R2) =
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0.86; stress = 0.17). Figure 1 shows the two-dimensional scaling solution for the 12
voices (the greater the distance between voices, the lower their perceived similarity) for
patients and controls together.
Figure 1. The two-dimensional INSCAL voice space for patients and controls together,
derived from dissimilarity ratings for 12 voices. Interpretations of the dimensions (F0 &
Df) are based on correlational evidence (see text for details).
Correlations between values on the two dimensions taken from this analysis and
the acoustic measures for the 12 voices are presented in Table 1. Values for the first
dimension correlated significantly with F0 (pitch) only. Values for the second
dimension correlated significantly with Df (resonance) only. Overall, it seems that
participants mainly used the speaker attributes of F0 and Df in making the
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similarity/dissimilarity judgements. This is consistent with dimensions found in other
studies that have examined voice similarity using MDS (Baumann & Belin, 2010; Goh,
2005; Leitman et al., 2010). From Figure 1, a clear distinction is obvious between
female and male voices along the F0 but not the Df dimension.
Table 1
Correlations calculated across the 12 voices between values on MDS Dimensions 1 and
2, and acoustic measures, for the schizophrenia patients and controls together.
Acoustic measures Dimension 1 Dimension 2
Pitch (F0) .96** .49
Pitch variability (F0SD) .57 .22
First formant (F1) .10 .15
Formant dispersion (Df) .23 .64*
*p < .05, ** p < .01
3.2. MDS using dissimilarity matrices for individual participants
One outlier was eliminated from the patient sample. To provide a visual comparison of
the distributions of voices in two-dimensional space for the patients and controls,
dimension values were averaged for the participants in each group. Figure 2 shows that
the voices were distributed similarly for the patient and control groups.
As indicated earlier, the critical output from this INDSCAL analysis concerns
the estimated weights for individuals for each of the two dimensions. These weights
were compared for patients and controls to assess whether the two groups differed in the
extent to which their judgments relied on either dimension. The control and
schizophrenia patient groups did not differ significantly in their weights for Dimension
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1 (F0), t(1,94) = 1.05, p = .29. However, the group difference was significant for the
Dimension 2 weights, with patients appearing to be less sensitive to variability in Df
compared to controls, t(94) = 2.58, p < .05, Cohen’s d = .57 (see Figure 2, Table 2).
It is unlikely that this group difference for Dimension 2 reflects a confound of
IQ since individual subject weights (from patients and controls) on Dimension 2 were
not correlated with WASI IQ scores, r(96) = .07, p = .49. Additionally, to determine
whether antipsychotic medication influenced the performance of patients, the
relationship between medication dosage (CPZ) and subject weights on Dimension 2 was
examined. CPZ equivalents did not correlate with Dimension-2 subject weights, r(64) =
-.02, p = .86. Given these non-significant correlations, no further action was taken to
account for these variables.
Table 2
Descriptive statistics for subject weights as a function of dimension in schizophrenia
patients and healthy controls.
Controls Patients
Mean SD Mean SD
Dimension 1 .37 .21 .32 .24
Dimension 2 .26 .10 .20 .11
Further analysis of the patient sample was conducted to determine whether there
was a specific influence of the presence/absence of hallucinations on the perception of
voices in schizophrenia. Patients with AH and patients without AH did not differ in
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weights for either Dimension 1, t(62) = 1.10, p = .27, or Dimension 2, t(62) = .45, p =
.65.
Figure 2. Super-imposition of the two-dimensional INDSCAL solutions obtained for
individual patients and controls, derived from dissimilarity ratings for the 12 voices.
4. Discussion
The ability to analyse speaker identity plays an important role in social cognition (Belin
et al., 2004). The present study is the first to use MDS to examine the discrimination of
voice identity in schizophrenia, and its relation to AH. The main findings reveal that,
when compared to healthy controls, individuals with schizophrenia (both with and
without AH) differentiated voices based on pitch (F0) to a similar extent, suggesting
that the ability to use pitch-based cues in identifying voices is relatively preserved in
171
schizophrenia. Conversely, patients demonstrated reduced reliance on formant
dispersion (Df) in discriminating voices compared to controls, suggesting that some
aspects of voice identity processing in schizophrenia are impaired.
In accordance with previous research utilising MDS in healthy individuals and
patients with schizophrenia (Baumann & Belin, 2010; Leitman et al., 2010), a two-
dimensional MDS perceptual space organised along F0 and Df dimensions was found to
be most appropriate for both schizophrenia patients and controls. Schizophrenia patients
and healthy controls did not differ in their sensitivity to variability in voices based on
Dimension 1 (F0), suggesting that, in patients, the use of pitch cues to discriminate
between voices may be intact despite evidence of pitch perception deficits in affective
prosody tasks (Leitman et al., 2005; Leitman et al., 2010). There are several potential
interpretations of these different outcomes. First, individuals with schizophrenia may be
impaired in the ability to use pitch-based cues to process vocal emotions, but their
ability to use these cues to process vocal identity may be intact. This interpretation is
broadly compatible with current models of voice perception based on functionally
dissociable pathways for vocal affect and identity information (Belin et al., 2004), and
evidence that such pathways can be disrupted somewhat independently of each other
(Garrido et al., 2009; Hailstone et al., 2010). However, it is also possible that pitch-
based cues play a more important role in the discrimination of vocal affect, compared to
vocal identity, due to the greater range and variability in pitch when conveying
emotions in speech (Laukka, 2005; Leitman et al., 2010). Consequently, a modified, and
more cautious interpretation of our findings is that the ability to use pitch as a cue to
voice identity may be compromised in schizophrenia but is sufficient to meet the current
task requirements (e.g. to judge whether voice pairs are similar or different in identity
for word stimuli). This interpretation is consistent with evidence of pitch perception
172
deficits in schizophrenia patients for basic auditory stimuli (Javitt, Shelley, & Ritter,
2000; Javitt, Strous, & Cowan, 1997).
Alternatively, previous studies that have documented deficits in voice identity
perception in schizophrenia have tested recognition of familiar voices (Zhang et al.,
2008) or recall of previously-presented voices (Waters & Badcock, 2009). There is a
wealth of evidence pointing to memory impairments in schizophrenia (Drakeford et al.,
2006), consequently it is possible that impairments in voice identity perception - using
pitch-based cues - may only be revealed when patients are required to remember voices
rather than simply discriminate between them. In support, studies in the phonagnosia
literature have revealed dissociations between voice recognition (of familiar
individuals) and voice discrimination (of two unfamiliar voices) (Van Lancker &
Kreiman, 1987; Van Lancker, Cummings, Kreiman, & Dobkin, 1988). Future research
in schizophrenia should examine the mechanisms underlying discrimination of voices
versus memory (recognition or recall) for voices.
On the other hand, when compared to controls, patients with schizophrenia
showed a reduced weighting for Dimension 2 (Df) when making their similarity-
dissimilarity ratings. That is, patients appeared to be less sensitive to differences in
voices on the basis of Df, a cue to resonance. This new finding is consistent with
previous evidence of abnormal cortical response in the human voice area in the superior
temporal sulcus in people with schizophrenia (Koeda et al., 2006). It also adds to the
growing literature on deficits in voice recognition (Waters & Badcock, 2009; Zhang et
al., 2008) and suggests that schizophrenia patients discriminate between voices in a
somewhat different way to controls. This difference in discrimination of vocal identity,
showing a moderate effect size, may contribute to the social cognition difficulties
observed in schizophrenia (Hoekert et al., 2007; Leitman et al., 2010). Recent research
173
has indicated that Df is linked to perceptions of masculinity (Ko et al., 2006) and
dominance (Puts, Hodges, Cárdenas, & Gaulin, 2007) of the speaker in healthy
individuals. It is thus possible that individuals with schizophrenia perceive dominance
and masculinity of voices in an altered way to healthy individuals. These findings
indicate the need for more detailed investigation into the perception of masculinity and
dominance in schizophrenia. However, it must also be noted that F0 has also been
linked to perceptions of masculinity and dominance in healthy individuals (Hodges-
Simeon, Gaulin, & Puts, 2010). Clearly, this is a rapidly evolving area of research in
healthy individuals that may provide new and informative approaches to understanding
the use of acoustic cues in voice identity discrimination in schizophrenia.
4.1. Symptom-level analysis
Further analysis of the data revealed no differences in voice identity discrimination
between hallucinating and non-hallucinating patient groups on the basis of either F0 or
Df. This contrasts with findings of specific links of emotion recognition deficits to AH
(e.g., Leitman et al., 2005). Difficulties using resonance-based voice cues such as Df
may therefore be a general vulnerability factor for psychosis (i.e., increasing the risk of
AH as well as other psychotic symptoms). Along these lines, several recent authors
have speculated that biases in the perception of social power in real voices may be
directly related to perceptions of dominance and power in hallucinated voices
(Birchwood, Meaden, Trower, Gilbert, & Plaistow, 2000; Hayward, 2003).
In conclusion, this study demonstrated, for the first time, that the lower-level
ability to detect and use pitch in making dissimilarity judgments of speaker identity was
not impaired by the presence of schizophrenia. This finding has important clinical
174
implications. It suggests that the ability to use some low-level acoustic properties for
discriminating voice identity may be, at least partially, preserved in schizophrenia. On
the other hand, schizophrenia patient and control groups appear to be utilising Df in
different ways to discriminate between speakers, suggesting some impairment in voice
identity perception in schizophrenia.
4.2. Limitations
There were several limitations of the current study. First, the healthy control group was
smaller in size compared to the total patient group. Future research should target a
larger control sample. Second, all patients were receiving antipsychotic medication at
the time of testing. As such, we cannot rule out the possibility that medication impacted
on the ability of individuals with schizophrenia to discriminate between voices.
However, patient weightings on voice dimensions did not correlate significantly with
medication dosage (CPZ equivalents). Third, we did not directly test the possibility that
the observed deficits in discrimination of vocal identity relate to the social cognition
difficulties observed in schizophrenia. A test of social cognition should be included in
any similar research on voice identity processing in this group of individuals. Finally,
patient groups in this study differed on symptoms other than hallucinations. The role of
voice identity perception in other symptoms of psychosis (e.g., disorganised thought)
should be explored.
Ethical Statement
All research described within this manuscript conformed to the ethical guidelines
recommended by the Declaration of Helsinki and was approved by the Human Research
Ethics Committees of the University of Western Australia, and the North Metropolitan
175
Area Mental Health Service (Perth). Written informed consent was obtained from each
participant prior to testing.
Acknowledgements
This work was supported by the Australian Schizophrenia Research Bank (ASRB),
which is supported by the National Health and Medical Research Council of Australia
(NH&MRC Enabling Grant 386500), the Pratt Foundation, Ramsay Health Centre, the
Viertel Charitable Foundation, the Schizophrenia Research Institute, and an Australian
Research Council Discovery Grant (DPO773836).
176
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Chapter Seven
Voice identity discrimination and hallucination-proneness in healthy young
adults: A further challenge to the continuum model of psychosis1
Abstract
Auditory hallucinations are common in schizophrenia and also occur in the general
population. However, several lines of evidence point to both similarities and differences
in the nature and mechanisms of clinical and non-clinical hallucinations, challenging the
dominant assumption that they represent the same phenomenon. The current study
extended this evidence by examining the perception of voice identity in healthy
individuals predisposed to hallucinations. In schizophrenia, deficiencies in
discriminating between real (external) voices have been linked to basic acoustic cues,
but voice discrimination has not yet been investigated in non-clinical hallucinations.
Using a task identical to that used in patients, we examined speaker discrimination in
samples of healthy young adults selected to differ in the predisposition to hallucinate
(30 high and 30 low scorers on the Launay-Slade Hallucination Scale-Revised).
Multidimensional scaling was conducted on dissimilarity judgements of pairs of
unfamiliar voices pronouncing three-syllable words. The resulting dimensions were
interpreted, a posteriori, in terms of acoustic measures relevant to voice identity. A two-
dimensional perceptual voice space, with axes corresponding to the average
fundamental frequency (F0) and formant dispersion (Df), was derived for both the high
and low hallucination-prone groups. No significant differences were found in speaker
discrimination for high versus low hallucination-prone individuals on the basis of
weightings from either F0 or Df. These findings suggest that the perception of voice
identity is not impaired in healthy individuals predisposed to hallucinations, adding a
further challenge to the continuum model of psychotic symptoms.
1 This article by: Chhabra, S., Badcock, J. C., Maybery, M. T., & Leung, D. is ready for submission to
Personality and Individual Differences.
182
1. Introduction
Although auditory hallucinations (AH) are typically associated with schizophrenia
(American Psychiatric Association, 2000; Blashki, Rudd, & Piterman, 2007; Sartorius
et al., 1986; Sartorius, Shapiro, & Jablensky, 1974), they have also been found to be
relatively frequent in the general population, with prevalence rates averaging 3-4% and
rising to 14-71% in student populations (see Sommer et al., 2010; Stip & Letourneau,
2009; van Os, Linscott, Myin-Germeys, Delespaul, & Krabbendam, 2009 for reviews).
While many healthy individuals will experience hallucinations with no other
consequences, for some, hallucinations will progress to full psychosis (De Loore et al.,
2011; Dominguez, Wichers, Lieb, Wittchen, & van Os, 2011; Johns & Van Os, 2001).
These observations fit with the dominant “continuum” model of psychotic symptoms
(Choong, Hunter, & Woodruff, 2007; Linscott & Van Os, 2010; Meehl, 1989; Shevlin,
Murphy, Dorahy, & Adamson, 2007; van Os et al., 2009) which rests on the assumption
that AH in clinical and non-clinical populations are the same both phenomenologically,
and in their underlying mechanisms. However, several recent reviews of the literature
have challenged this view, showing both similarities and differences in the nature
(Daalman et al., 2011; Linscott & Van Os, 2010) and mechanisms (Badcock &
Hugdahl, 2012; Diederen et al., 2011; Linden et al., 2011) of clinical and non-clinical
hallucinations. As a result, there is a growing call for more debate and research on the
continuum model (David, 2010; Kaymaz & van Os, 2010; Sommer et al., 2010).
Uncovering the processes that are/are not involved in the predisposition to AH in non-
clinical groups would be theoretically useful as well as providing valuable information
for identifying those at risk of transitioning to schizophrenia.
AH typically involve voices (Beck & Rector, 2003; Wible, Preus, & Hashimoto,
2009), and both hallucinated and real (external) voices often convey information about
183
the identity (age, gender, and size) and emotional state of the speaker (Belin, Zatorre,
Lafaille, Ahad, & Pike, 2000). For example, a person with AH may identify the
hallucinated voice as belonging to a famous newsreader, who is angry at them
(Chadwick & Birchwood, 1994; Hayward, 2003; Sorrell, Hayward, & Meddings, 2010).
Consistent with the continuum model, there is growing evidence for emotional prosody
deficits in both clinical (Rossell & Boundy, 2005; Shea et al., 2007) and non-clinical
AH (Phillips & Seidman, 2008), which arise (at least in part) from difficulties
processing pitch-related cues in voice (Kantrowitz et al., 2011; Leitman et al., 2005;
Leitman et al., 2007; Leitman et al., 2010). On the other hand, much less is known
about the perception of voice identity, though preliminary evidence in schizophrenia
points to impairments in voice recognition (Zhang et al., 2008) and recall (Waters &
Badcock, 2009) associated with AH. In addition, we recently reported a deficit
discriminating speaker identity – linked to resonance-based voice cues – in
schizophrenia patients, using multidimensional scaling (MDS) (Chhabra, Badcock,
Maybery, & Leung, 2012b). This deficit was present both in patients with, and without
AH, suggesting it might be a general vulnerability factor for psychosis. Consequently,
similar difficulties in perception of voice identity might be expected in healthy
individuals prone to experiencing schizophrenia-like symptoms such as AH. The aim of
the current study, therefore, was to examine voice identity discrimination in healthy
(non-clinical) voice-hearers.
The adoption of identical tasks in studies of AH in different groups is rare
(Amminger et al., 2011), resulting in inconsistencies in the literature which could
simply reflect differences in stimuli and other aspects of methodology. In order to avoid
this problem, the task used in the current study to measure perception of voice identity
in relation to hallucination-proneness, was identical to that previously employed in
184
patients with schizophrenia (Chhabra et al., 2012b). If individuals high in the
predisposition to hallucinate show difficulties differentiating speaker identity compared
to individuals low in the predisposition to hallucinate, this would support the continuity
of vocal identity discrimination deficits in clinical and non-clinical hallucinations. More
specifically, based on the results of Chhabra et al (2012b), support for the continuum
model would take the form of individuals highly predisposed to hallucinations showing
reduced use of Df to discriminate voices. Alternatively, if the high and low
hallucination-prone individuals demonstrate similar speaker discrimination, this would
be in keeping with a discontinuity of mechanisms underlying clinical and non-clinical
hallucinations.
We also examined individual differences in intelligence to check the specificity
of any significant results that may be obtained in comparing groups. Furthermore, given
recent evidence that hallucinatory and delusional tendency tend to co-occur in healthy
individuals (Sommer et al., 2010), we measured delusional experiences to explore any
potential relationship between voice identity discrimination and this symptom.
2. Method
2.1Participants
Five hundred and 22 undergraduate psychology students completed the Launay-Slade
Hallucination Scale-Revised (LSHS-R) questionnaire (Bentall & Slade, 1985), a 12-
item measure which assesses a range of visual and auditory experiences (M = 14.08;
range = 0–40). Individuals scoring in the upper (scores of 28 and above) and lower
(scores of 6 and below) quartiles were invited to participate in the study. Thirty high
scorers (23 female) and 30 low scorers (22 female) responded to this invitation (see
Table 1 for demographic information). Participants were free from current/previous
185
history of psychosis (assessed using the Mini International Neuropsychiatric Interview
for Schizophrenia and Psychotic Disorders; Sheehan et al., 1998) and all had normal
hearing acuity (assessed with standard audiometry). IQ was estimated using the
vocabulary and matrix reasoning subtests of the Wechsler Abbreviated Scale of
Intelligence (WASI; Wechsler, 1999). The yes/no version of the Peter’s Delusion
Inventory (PDI; Peters, Joseph, Day, & Garety, 2004) was used to assess delusional
thinking. Each participant provided informed consent using procedures approved by the
Human Research Ethics Committee of the University of Western Australia.
2.2 Similarity Ratings Task (Chhabra et al., 2012b)
Stimuli consisted of eight three-syllable words, matched in amplitude and spoken in 12
different voices (half male), presented via Sennheiser HD 205 headphones at 69 dB.
On each trial, participants heard two speakers saying the same word in sequence and
were instructed to make a judgment as to how similar/dissimilar the two voices
sounded, by focusing on the qualities of the speakers’ voices. One second after the onset
of the second word, a seven-point rating scale appeared on a touch screen, ranging from
very similar (1) to very dissimilar (7), which participants used to record their responses.
Participants were encouraged to use the full-range of the scale when making their voice
ratings throughout the task. Testing was conducted in two blocks, each of which
included all possible voice pairings in random order. The word used on each trial was
randomly selected. Participants were provided with four rest breaks throughout the task.
Four practice trials were administered prior to commencing each block of testing, with
132 test trials in total. Stimuli were selected anew for each participant of each group.
Total task duration was approximately 15 minutes (see Chhabra et al., 2012b for further
details of the stimuli and procedure).
186
2.3 Analysis of acoustic characteristics
We selected identical acoustic characteristics as in (Chhabra et al., 2012b) since these
have been highlighted as specifically relevant for voice identity recognition
(Bachorowski & Owren, 1999; Baumann & Belin, 2010; Ko, Judd, & Blair, 2006;
Leitman et al., 2010). Acoustic measurements were based on stimuli comprising a
consistent order of all eight words spoken by each of the 12 speakers. PRAAT 5.0.32
software (Boersma, 2001) was used to compute the following acoustic characteristics:
Pitch. The pitch of each voice was measured by its average fundamental frequency (F0)
in Hertz (Hz).
Pitch variability. As an indicator of intonation, the standard deviation of pitch (F0-SD)
for each voice was calculated to assess the momentary variability in pitch during
speech.
First formant. The peak frequency of the first formant (F1), in Hz was obtained.
Resonance. The formant dispersion (Df )(Fitch, 1997), in Hz, represented the resonance
of each voice. This involved averaging the distance between adjacent pairs of the first
five formant frequencies (derived as in Baumann & Belin, 2010). The maximum
formant frequency for female voices was set to 6500 Hz (Baumann & Belin, 2010). All
other parameters were default values recommended by the authors of PRAAT.
2.4 Data Analysis
2.4.1 Multidimensional scaling (MDS) of similarity judgements.
The average listener dissimilarity rating (out of 7, where 1 = very similar and 7 = very
dissimilar) for each pair of the 12 voices was calculated for each participant, and
187
matrices of these mean dissimilarity ratings were entered into MDS analyses which
were conducted via an Individual-Squares Scaling (INDSCAL) model, with Euclidean
distances and interval scaling. There were two phases of analysis; first, dissimilarity
matrices from the two groups (i.e., one high LSHS-R group matrix and one low LSHS-
R group matrix – available in Appendices C and D) were submitted to MDS to verify
whether the data map onto a two-dimensional space as described in our recent study
with schizophrenia patients (Chhabra et al., 2012b) and in previous studies of both
healthy (non-clinical) and schizophrenia populations (Baumann & Belin, 2010; Leitman
et al., 2010). To determine the nature of the dimensions that participants perceived as
important in making their dissimilarity judgements, the resulting dimensions were then
interpreted a posteriori via Pearson correlation coefficients between values on each
dimension for the 12 voices and the acoustic measures for those voices.
Next, we included matrices from individual participants (i.e., 30 high LSHS-R
and 30 low LSHS-R dissimilarity matrices) within the same INDSCAL model in order
to assess whether there were any differences in the processes used to differentiate voices
between high and low LSHS-R groups. INDSCAL produces a critical parameter in the
form of a weight for each individual on each dimension, which accounts for individual
variation in the perceptual processes when performing the rating task. The higher the
weight, the more important that individual gave to that dimension (Kring, Barrett, &
Gard, 2003). The weights for each dimension were then compared for the high and low
LSHS-R groups using independent-samples t tests. Prior to these comparisons,
participant weights were excluded if they were three or more standard deviation units
away from their respective group means. To check for the possible influence of
confounds, individual subject weights for both high and low LSHS-R groups on each
dimension were correlated with any of the control variables (IQ or PDI scores) on which
188
the high and low LSHS-R groups differed. No further action was taken if these
correlations were not significant.
3. Results
3.1 Descriptive statistics
A summary of cognitive and schizotypy measures for the high and low LSHS-R groups
is provided in Table 1. Substantial group separation was obtained on the LSHS-R, as
expected. No significant group differences were observed in age or WASI IQ scores,
however high LSHS-R scorers obtained significantly higher scores than low LSHS-R
scorers on the PDI.
Table 1
LSHS-R group means, standard errors (SE), and t-tests for the age, PDI and WASI
data.
Low LSHS-R High LSHS-R
Mean SE Mean SE t
LSHS-R 3.53 .32 31.37 .74 34.56**
AGE (years) 17.93 .18 17.80 .16 .55
PDI 3.97 .44 8.43 .51 6.64**
WASI 109.23 1.37 109.87 1.39 .33
** p < .001
3.2 MDS of group dissimilarity matrices
A two-dimensional solution was found to be most appropriate for the MDS analysis
conducted using the group dissimilarity matrices for the high and low LSHS-R samples
189
(proportion of variance accounted for (R2) = 0.78; stress = 0.19) based on the
interpretability, uniqueness, and percentage of accounted-for variance (Baumann &
Belin, 2010), as well as on other recommendations from the literature (Borg & Groenen,
1997; Kruskal & Wish, 1978). This two-dimensional solution for the 12 voices for the
high and low LSHS-R scorers combined is shown in Figure 1. In this voice space, the
greater the distance between voices, the lower their perceived similarity.
Figure 1. The two-dimensional INSCAL voice space for high and low LSHS-R scorers
combined, derived from dissimilarity ratings for the 12 voices. Dimensional
interpretations (F0 & Df) are derived from correlational evidence (see text for details).
Table 2 demonstrates correlations between values on the two dimensions
derived from this analysis and the acoustic measures for the 12 voices. Values for
190
Dimension 1 correlated strongest with the F0, and also correlated with the F0-SD. The
correlation between values for Dimension 2 and Df was marginally significant (p = .06).
Overall, it seems that participants mainly used the speaker attributes of F0 and Df in
making the similarity/dissimilarity judgements. This is consistent with MDS dimensions
identified using the identical task in patients with schizophrenia (Chhabra et al., 2012b),
as well as with those found in other studies that have examined voice dissimilarity using
MDS (Baumann & Belin, 2010; Goh, 2005; Ko et al., 2006; Leitman et al., 2010).
Table 2
Correlations between scores on MDS Dimensions 1 and 2, and acoustic measures, for
high and low LSHS-R groups.
Acoustic measures Dimension 1 Dimension2
Pitch (F0) .93** .43
Pitch variability (F0-SD) .60* .17
First formant (F1) .05 .04
Formant dispersion (Df) .24 .55*-
*p < .05, ** p < .01, *- p = .06
3.3 MDS using dissimilarity matrices for individual participants
The distribution of weightings within each subgroup was normal. No outliers were
identified. To provide a visual comparison of the distributions of voices in two-
dimensional space for the high and low LSHS-R groups, dimension values were
averaged for the participants in each group. Figure 2 illustrates the similar distribution
of voices for the high and low LSHS-R groups.
191
Figure 2. Super-imposition of the two-dimensional INDSCAL solutions obtained for
individual high and low LSHS-R scorers, derived from dissimilarity ratings for the 12
voices
The subject weights for each dimension (1 and 2) were then compared for high and low
LSHS-R scorers to assess whether the two groups differed in the extent to which their
judgments relied on either dimension. Although the high LSHS-R group appeared to
assign lower weightings (i.e., be less sensitive to variability between voices) compared
to the low LSHS-R group (see Figure 2, Table 3), independent-samples t tests did not
reveal any significant differences between groups in terms of their weightings for either
Dimension 1, t (58) = 1.70, p > .05, or Dimension 2, t (58) = 1.43, p > .05.
192
It is unlikely that delusional tendency influenced the performance of high and
low LSHS-R scorers since PDI scores did not correlate with individual subjects weights
on either Dimension 1, r (30) = -.19, p > .05, or Dimension 2, r (30) = -.14, p > .05.
Given these non-significant correlations, no further action was taken to account for PDI
scores.
Table 3
Descriptive statistics for subject weights as a function of dimension in high and low
LSHS-R groups.
Low LSHS-R High LSHS-R
Mean SD Mean SD
Dimension 1 .37 .18 .29 .19
Dimension 2 .28 .10 .24 .12
4. Discussion
To our knowledge, this is the first study to use MDS to examine the
perception/discrimination of voice identity in young adults highly predisposed to
hallucinations. The main finding of this study was that individuals with high and low
levels of hallucination-proneness showed a similar pattern of discrimination between
voices, using both pitch (F0) and formant dispersion (Df) cues. These results suggest
that the discrimination of voice identity is unimpaired in healthy (non-clinical)
individuals who are prone to hallucinate, and contrast with those recently documented
for patients with schizophrenia, which showed significant impairments in some aspects
of speaker discrimination. It is unlikely that this difference in outcome can be explained
simply as a result of differences in stimuli or method since an identical task was used in
193
both studies (Chhabra et al., 2012b). Consequently our findings present a further
challenge to the continuum model of psychosis.
The pattern of voice dissimilarity judgements obtained for both high and low
hallucination-prone groups was well captured by representing individual voices in a
two-dimensional space, defined by the average F0 and Df. This characterization of a
"perceptual voice space" (see Latinus & Belin, 2011) is consistent with the MDS space
previously described in normal (i.e. non-clinical) individuals listening to brief vowel
sounds (Baumann & Belin, 2010), and in patients with schizophrenia and their controls,
using three-syllable words (Chhabra et al., 2012b). Statistical testing revealed no
significant difference between high and low LSHS-R groups in the weightings assigned
to either Dimension 1 (F0) or Dimension 2 (Df), pointing to similar sensitivities to
differences in speaker identity arising from lower level acoustic cues (pitch and
resonance) in healthy, non-clinical hallucinators and non voice hearers. This latter result
in particular contrasts with that of our recent study comparing schizophrenia patient and
healthy control samples (Chhabra et al., 2012b), whereby patients appeared to be less
sensitive to differences in voices based on formant dispersion, and therefore, less able to
differentiate between them on this basis. Of note, this deficiency in voice identity
discrimination in schizophrenia appeared to be relevant to other psychotic symptoms as
well as to AH (i.e., to be a general vulnerability factor for psychosis). Thus, one
possible interpretation of the apparent discontinuity in the perception of voice identity
in clinical and non-clinical AH is that deficits in voice identity discrimination may only
emerge further along the continuum of psychosis when early hallucinatory experiences
become complicated with other symptoms such as delusional ideation (Smeets et al.,
2010). However, PDI scores – although relatively low – did not correlate with
individual subject weights on either dimension, suggesting that there does not seem to
194
be a relationship between delusional tendency and discrimination of voice identity in
the current task. Alternatively, voice identity discrimination deficits may only arise as
psychosis fully develops (Badcock, Chhabra, Maybery, & Paulik, 2008; Chhabra,
Badcock, & Maybery, 2012a; Chhabra, Badcock, Maybery, & Leung, 2011; Waters &
Badcock, 2009; Zhang et al., 2008). Importantly, AH in psychosis are more frequently
experienced, are more intrusive and distressing, and have a different average age of
onset than AH in the general population (Badcock et al., 2008; Choong et al., 2007).
Thus a different explanation is that non clinical hallucinations represent a different
phenomenological subtype – rather than different points on a continuum – stemming
from different aetiological mechanisms.
Overall, the current results appear to indicate a discontinuity in the perception
of voice identity in individuals experiencing clinical versus non-clinical hallucinations.
In contrast, recent evidence indicates that the perception of vocal emotion (affective
prosody) is impaired across the continuum of psychosis, that is, is present in patients
with schizophrenia and AH as well as in individuals at risk of developing schizophrenia
(Hoekert, Kahn, Pijnenborg, & Aleman, 2007; Phillips & Seidman, 2008). If correct,
this difference may reflect the underlying separability of neural pathways specialized
for processing vocal affect and vocal identity information in human voices (Belin,
Fecteau, & Bedard, 2004; Garrido et al., 2009; Hailstone, Crutch, Vestergaard,
Patterson, & Warren, 2010). Specific abnormalities within these pathways may result in
differential contributions to the symptoms of schizophrenia: consequently continuity of
deficits for healthy individuals prone to AH and individuals with psychosis may arise in
one pathway, but not necessarily in the other. As such, hallucination-prone individuals
may be impaired in the ability to process vocal emotions (van't Wout, Aleman, Kessels,
Larøi, & Kahn, 2004), but unimpaired in their ability to process vocal identity.
195
Alternatively, it is possible that emotion prosody tasks may simply be more demanding,
and that impairments may be present in both vocal affective and identity pathways in
healthy individuals predisposed to hallucinations, but not revealed within the
requirements of the current task (i.e., to judge whether voice pairs are similar or
different in identity) (Chhabra et al., 2012b). Additional investigation of this proposal is
warranted.
4.1 Limitations
The current study was limited by a modest sample size of hallucination-predisposed
individuals. In addition, these individuals were all undergraduate university students,
and hence, not necessarily representative of the general population, although young
adults are the peak group in which hallucinations are reported (Stip & Letourneau,
2009). Future research should test a larger, more varied sample. Furthermore, Laroi
(2012) distinguishes between two types of non-patient (healthy) AH: type i, in which
AH are infrequent, and not very similar to patient AH; and type ii, in which frequent
AH are experienced, which are very similar on a number of levels to those in patients
with psychosis. As is the tendency in research into non-patient AH, the hallucination-
predisposed sample (i.e., high LSHS-R scorers) in this study are likely to have
comprised non-patient individuals with type i AH (though this was not formally
assessed). Hence one approach would be to design a study employing the same task in
comparisons of a clinical group of individuals experiencing AH to a group of healthy
individuals who are selected because they experience phenomenologically similar AH
(i.e., similar in frequency, form, and severity) to those experienced by patients (i.e., type
ii AH). This approach would help to make firm conclusions about whether voice
identity processing is or is not impaired in non-patient compared to patient AH.
196
Acknowledgement
This research was partially supported by an Australian Research Council Discovery
Grant (DPO773836).
197
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Section Four
General Discussion
206
207
General Discussion
Synopsis
This chapter presents a summary and critical analysis of the main findings pertaining to
the two central aims of this thesis. Methodological considerations of the completed
studies are noted and new directions for future research are highlighted. Implications of
the findings for the continuity model of psychotic symptoms are discussed throughout,
in light of the overarching goal of the thesis. Clinical implications of the key results
from the thesis are then briefly noted. Finally, general conclusions are presented.
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Auditory hallucinations (AH) are one of the most prevalent, heterogeneous, distressing,
and functionally disabling symptoms in schizophrenia (Kuhn & Gallinat, 2011). These
experiences also occur relatively frequently in healthy individuals from the general
population, supporting a continuum model of psychotic symptoms (Eysenck &
Eysenck, 1976; Meehl, 1989; Shevlin, Murphy, Dorahy, & Adamson, 2007; Strauss,
1969; van Os, Linscott, Myin-Germeys, Delespaul, & Krabbendam, 2009). Driven by
this model, it is usually assumed that the same phenomenology and mechanisms
underlie clinical and non-clinical AH. However, despite its pervasive influence, there
has been renewed debate concerning the nature of this continuum (David, 2010;
Kaymaz & van Os, 2010; Sommer, 2010). Very few studies have directly compared
clinical and non-clinical AH in terms of their underlying mechanisms (Laroi, 2012),
thus leaving us with a limited understanding of AH in clinical and non-clinical groups.
The current thesis outlines a program of research that was conducted to
investigate the commonalities and differences in cognitive and perceptual processes for
clinical versus non-clinical AH symptoms. The research had two aims:
1. To disentangle the exact nature of context memory deficits in clinical and
non-clinical AH (Chapters 2, 3, and 4); and
2. To explore the particular contribution of voice identity processing in clinical
and non-clinical AH (Chapters 6 and 7).
To address these aims, a series of studies investigated context memory binding and
voice identity processing in both non-clinical (healthy individuals) and psychotic
(individuals with schizophrenia) AH. The specific findings from these studies are
outlined below.
209
Findings_____________________________________________________
What is the nature of context memory deficits in clinical and non-clinical AH?
Summary of findings and interpretation
One of the core features of AH involves them being experienced as separate from one’s
own mental processes (e.g., Nayani & David, 1996). As a means of explaining these
experiences, the literature has predominantly focused on failures in remembering
oneself as the source of spoken events and misattributing self-generated events to
external sources (i.e., ‘failures of self-recognition’) in AH (Bentall, 1990; Bentall &
Slade, 1985a; Frith & Done, 1988; Laroi, de Haan, Jones, & Raballo, 2010). However,
several methodological and theoretical critiques of this account have recently appeared
(as reviewed in Chapter 1), pointing to the existence of a broader range of context
memory impairments in individuals with AH (Achim & Weiss, 2008; Johnson,
Hashtroudi, & Lindsay, 1993; Waters, Badcock, Michie, & Maybery, 2006a;
Woodward, Menon, & Whitman, 2007). As such, this thesis focused systematically on
context memory binding of external sources of information (i.e., avoiding self versus
other comparisons) in clinical and non-clinical hallucinatory experiences.
Chapter 2 explored whether psychometrically identified hallucination-
predisposed university students – as measured by a modified version of the Launay
Slade Hallucinaton Scale-Revised (LSHS-R; Bentall & Slade, 1985b) – experience
difficulties binding two external, contextual features of information in memory
(assessed using a voice-location binding task). The results showed that healthy young
adults highly predisposed to hallucinations experienced markedly less frequent AH
(modal frequency of only once a year) compared to individuals with schizophrenia
(modal frequency of at least once a day; e.g., Steel et al., 2007). This observation points
to potentially important differences in the phenomenology of clinical and non-clinical
210
AH, consistent with similar evidence in the literature (Choong, Hunter, & Woodruff,
2007; Daalman et al., 2011; Honig et al., 1998; Tien, 1991). Significantly, students
highly predisposed to hallucinations were not impaired in binding voice and location
information in memory compared to controls. What is more, there was no association
between hallucination frequency and context binding difficulties in these individuals.
However, it is possible that this non-significant correlation reflects the restricted range
of frequency of AH experiences reported by high LSHS-R scorers in this study. These
results can be set against those from studies that have revealed context binding
impairments linked to AH in schizophrenia (e.g., Waters, Badcock, & Maybery, 2006b;
Woodward et al., 2007), suggesting the possibility of some partially distinct cognitive
mechanisms underlying hallucinations in patient and non-patient (healthy) groups.
There are suggestions that schizophrenia patients with AH may be more
impaired on memory binding tasks involving intentional (conscious), as opposed to
automatic (incidental), encoding of context (Luck, Foucher, Offerlin-Meyer, Lepage, &
Danion, 2008). As such Chapter 3 aimed to clarify the findings of a lack of deficit from
Chapter 2 – which only assessed an automatic form of context binding – in order to
investigate whether healthy undergraduates predisposed to hallucinations may reveal
impairment on an intentional form of binding of the same features of information. Both
automatic and intentional versions of a voice-location binding task were applied to high
and low LSHS-R scorers in this study. In short, healthy individuals highly predisposed
to hallucinations demonstrated no difficulties in either version of the task compared to
healthy individuals low in the predisposition to hallucinate. The findings from Chapters
2 and 3 might suggest that some memory deficits emerge only as psychosis fully
develops. Clinical and non-clinical AH may therefore represent two distinct subtypes
(i.e., categorically different experiences). Some important dissimilarities in
211
hallucinatory experiences in the general population versus in psychosis may thus be
overlooked by uncritical acceptance of the continuum model.
Laroi (2012) makes the distinction between a continuum of proneness to AH
experiences in the general population, versus a continuum indexing level of risk to
develop “clinical” AH. He describes two types of non-patient (healthy) AH: type i, in
which AH are infrequent and not very similar to patient AH (i.e., low proneness), with
low risk of transition to clinical AH; and type ii, in which AH are frequent and very
similar on a number of levels (e.g., form and severity) to those experienced in patients
with psychosis (i.e., high proneness) with a very high risk to transition to clinical AH.
The majority of research into non-patient AH, including that described in this thesis, is
likely to have examined type i AH (though this was not specifically assessed). Thus, an
alternative possible explanation of the results reported in Chapters 2 and 3 is that
memory binding deficits may only be observed in healthy individuals with both a very
high proneness and associated risk of developing clinical AH.
Studies in this field have rarely utilised the same tasks in both clinical and non-
clinical samples, resulting in inconsistencies in the literature, which leave us unable to
unambiguously tease out the similarities and/or differences in cognitive mechanisms
underlying clinical and non-clinical AH (Badcock & Hugdahl, 2012; Laroi, 2012).
Much like the existing research, Chapters 2 and 3 relied on an indirect comparison of
performance in non-clinical AH to previous results in clinical AH. Consequently, in
order to make firm conclusions about whether context memory binding impairments are
present in non-clinical compared to clinical AH, the research reported in Chapter 4
utilised an identical task to assess binding of word and voice information in memory in
two separate studies of: (1) hallucination-prone individuals and controls (high and low
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scorers on the LSHS-R), and (2) schizophrenia patients (with and without AH) and non-
psychiatric controls.
The main findings of these studies revealed no evidence of impaired binding in
high versus low hallucination-prone individuals. However, when compared to controls,
patients with schizophrenia (both with and without AH) demonstrated difficulties
binding words and voices (i.e., remembering ‘who said what’) alongside difficulties
remembering individual words and voices. A more direct test of the continuum model
via inclusion of schizophrenia patients with AH and healthy hallucination-prone
individuals (i.e., high LSHS-R scorers) within the same statistical analysis confirmed
that any deficits in binding were specific to the hallucinating patient group and not the
healthy hallucination-prone group. The overall pattern of results indicates that some
different cognitive mechanisms may exist in clinical and non-clinical hallucinators,
adding further to the challenges to the continuum model of psychotic symptoms (David,
2010; Kaymaz & van Os, 2010; Sommer, 2010).
The context binding impairment reported in this thesis in both currently and not
current hallucinating patients with schizophrenia, is distinct from, but adds to the
commonly reported findings of self-recognition difficulties in AH (see Aleman & Laroi,
2008; Waters, Woodward, Allen, Aleman, & Sommer, 2010, for reviews), suggesting
that the extent of impairment is more wide-ranging than simply a deficit in identifying
the self as source of mental events. Context binding deficits could – at least in part –
explain why AH involve an identity separate from the self, often with different acoustic
characteristics (which self-recognition models fail to account for). It is also interesting
to consider these cognitive results in light of recent neuroimaging studies demonstrating
de-activation of the hippocampal/parahippocampal areas of the medial temporal lobes
(areas which have been associated with context memory binding) immediately prior to
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the onset of AH (Diederen et al., 2010; Hoffmann, Anderson, Varanko, Gore, &
Hampson, 2008; Silbersweig et al., 1995), pointing to the possibility that context
memory binding difficulties could play a triggering role in the production of AH.
It is important to note that the deficits in context memory binding were present
in schizophrenia patients in general, and not specifically to patients with current AH.
This may mean that deficits in context memory binding may be essential, but not
sufficient for AH to occur. There is now widespread acceptance that a single cognitive
deficit is unlikely to result in such a complex event, and that a combination of deficits
might be needed to explain AH in schizophrenia (Badcock, Waters, Maybery, &
Michie, 2005; Waters et al., 2012). Therefore, it is possible that the symptom of AH
may only emerge when a combination of impairments in both self-recognition as well as
in broader forms of context memory (such as binding of word and voice information),
and also in inhibition occur.
Context binding difficulties may also be relevant to other (particularly positive)
symptoms of schizophrenia. For example, they could provide a complementary
explanation of some aspects of delusion maintenance, with individuals being unable to
disconfirm their beliefs due to being incapable of binding new contextual information
with stored information relevant to this context, in memory. That is, individuals with
delusions may not benefit from stored experience, which has some basis in fact (i.e.,
contradictory to their delusion), because they fail to compare these past experiences
with the actual context of an event (Corlett, Krystal, Taylor, & Fletcher, 2009). This
processing abnormality could explain, for example, why these individuals might
interpret a conversation they hear as being directed to them, despite not being active
participants in this conversation (Boyer, Phillips, Rousseau, & Ilivitsky, 2007).
Additionally, the ability to distinguish external cues in context memory may be relevant
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to research on the sense of agency in delusions of control (Gallagher, 2004). However,
this research included a measure of delusional ideation, with no association between
context binding difficulties and delusional ideation being revealed (for further critical
analysis, see “methodological considerations and implications for future research”
below).
Interestingly, the binding deficit observed in schizophrenia patients was
eliminated in follow-up comparisons of subsets of patients and controls matched on
memory for individual stimulus features. These results suggest that the binding deficits
observed in schizophrenia might reflect – at least in part – difficulties encoding
individual stimulus features (voices and words). Similarly, other research has
emphasized the importance of perceptual-encoding deficits in schizophrenia (e.g., Javitt,
Strous, & Cowan, 1997; Mathes et al., 2005; Ragland et al., 2005). In the research
reported in Chapter 4, when compared to controls, patients with schizophrenia found
the voice exemplars much more difficult to discriminate than words. This is possibly
due to the fact that the voices were unfamiliar and carried more complex information
(Belin, Fecteau, & Bedard, 2004) compared to the familiar words. The potentially
important role of voice recognition in patients with current AH was further emphasised
in other results, in that both sensitivity to binding and sensitivity to new voices
decreased as the loudness of hallucinated voices increased. Hallucinated voices and real
(external) speech sounds have been proposed to draw on similar neural substrates in the
temporal lobe (Hugdahl et al., 2008; Vercammen, Knegtering, Bruggeman, & Aleman,
2011), therefore fewer resources may have been available to recognise and integrate real
voices in memory as the loudness of hallucinated voices increased.
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General comments regarding context memory and AH
Overall, the results pertaining to the first aim of the thesis showed that contextual
memory binding is impaired in individuals with schizophrenia, irrespective of whether
they report recent AH experiences or not. AH are typically conceptualised as involving
a two-step cognitive process: (1) failures of self-recognition, and (2) (mis)attributions of
source. The findings from this thesis relate more to the second step of this process.
There are two alternative interpretations of the data. First, context binding difficulties
may contribute to the misattribution of self-generated information to an external agent.
On the other hand, at least on some occasions, hallucinated words or voices may indeed
derive from externally-generated information. That is, patients may, in some sense, be
making a correct attribution. It has previously been argued that bound memories may be
fragmented or incomplete in AH, consisting of highly familiar information for which
specific contextual details have not been recollected correctly (Badcock et al., 2005;
Waters et al., 2006a). One possibility is that during AH, detailed acoustic information to
identify a speaker is missing, leaving only sufficient information about the gender of the
hallucinated voice. Alternatively, hallucinated voices are often familiar (e.g., Chadwick
& Birchwood, 1994), which may reflect correct recollection of voice identity
information, but incorrect binding of this information to the content of an event. That is,
the AH involves a familiar voice saying things that person would not typically say.
These wide-ranging contextual memory deficits may also help to explain the variation
in experiences of AH. Further investigation of this proposal is warranted.
The findings from the healthy hallucination-prone undergraduates in Chapters 2,
3, and 4 indicate that these individuals with non-clinical AH demonstrate intact
memory-binding of both voice and location information (Chapters 2 & 3) and word and
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voice information (Chapter 4). These results are consistent with evidence of different
cognitive mechanisms associated with the predisposition to hallucinate in healthy
individuals compared to those underpinning active clinical hallucinations (Kaymaz &
van Os, 2010). The dominant continuum model may need to be refined. See Table 1 for
a summary of the key results for schizophrenia patients (with and without AH) and for
healthy undergraduates predisposed to hallucinations.
The strengths of the research reported in the first part of this thesis include the
fact that it focused on memory for information supplied from different external sources,
as compared to previous research which has primarily investigated self-generated versus
other information. Thus, results provided new and much-needed information about the
broader cognitive processes involved in clinical AH. Further, all three studies involved
voice being bound to something else (i.e., location or word), thus enabling a systematic
examination of context memory deficits. These findings also emphasize the importance
of using an identical task in clinical and non-clinical samples, in order to establish
similarities and differences in cognitive processes between clinical and non-clinical AH.
A limitation of this research is that it did not establish the stage(s) in processing
at which the memory binding difficulties linked to schizophrenia occur. To address this
shortcoming, future research could employ magnetic resonance imaging techniques in
combination with context memory tasks to explore the stages in processing (i.e., at
encoding, at recall, or both) at which memory binding deficits are present in patients
with schizophrenia (see Boyer et al., 2007, for a review of studies that have conducted
such research in schizophrenia; see Ryan & Cohen, 2004, for an example of how this
was conducted in individuals with amnesia). A combination of methodological
approaches would help to link the phenomenological, cognitive and neurological
aspects of hallucinatory experiences.
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Table 1
Key findings for schizophrenia patients (with and without AH) and healthy
undergraduates predisposed to hallucinations (non-clinical AH) from the various
experiments in this thesis.
Mechanism Non-clinical (healthy) AH Schizophrenia patients
(with and without AH)
Automatic context-context
binding (voice, location)
Intact not assessed
Intentional context-context
binding (voice, location)
Intact not assessed
Content-context binding
(voice, word)
Intact Impaired binding of “who”
to “what”
Memory for individual
features (word, voice)
Intact Impaired memory for
individual words and voices
(particularly voices)
Voice identity
discrimination
Intact Difficulties using
resonance-based cues to
discriminate voice identity
What is the particular contribution of voice identity processing to clinical and non-
clinical AH?
Summary of findings and interpretation
The findings from Chapter 4 highlight the significance of voice recognition difficulties
in schizophrenia, and their relevance to AH in particular. These results converge with a
small but growing literature demonstrating deficits processing voices in individuals with
schizophrenia (e.g., Hirano et al., 2010; Koeda et al., 2006; Zhang et al., 2008) and
highlight the need for investigation into the perception of real (external) voices in AH.
Consequently, two separate studies (Chapters 6 & 7) were designed to assess voice
identity discrimination, using identical methodology in: (1) individuals with
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schizophrenia (with and without AH) and healthy age-matched controls and (2) healthy
undergraduates with (high LSHS-R) and without (low LSHS-R) a tendency to
hallucinate. Multidimensional scaling (MDS) of voice similarity judgments was used to
examine how these groups of individuals distinguished pairs of unfamiliar voices. The
resulting dimensions were interpreted via correlations with acoustic measures
previously determined to be relevant to voice identity. In both studies a two-
dimensional “voice space” defined by the average fundamental frequency (F0;
perceived as pitch) and formant dispersion (Df; a resonance-based cue) best captured the
pattern of dissimilarity judgments made. This outcome is comparable with those
reported in other studies that have examined voice similarity using MDS (Baumann &
Belin, 2010; Goh, 2005; Leitman et al., 2010).
Findings from Chapter 6 demonstrated, for the first time, that patients with
schizophrenia (both with and without AH) made less use of Df (but not F0) in
discriminating between voices compared to healthy controls. These results appear to
suggest a relatively preserved ability to use pitch-based cues to discriminate voice
identity in schizophrenia, which contrasts with an extensive body of evidence of pitch
perception deficits in affective prosody tasks (Leitman et al., 2005; Leitman et al.,
2010). This outcome could reflect the fact that voice identity and vocal emotion are
processed in separable neural pathways. However, it is conceivable that if the voices
employed in the current task had covered a greater pitch range, then a deficit in pitch
perception may have been observed. Determining which of these interpretations is
correct will demand further investigation. On the other hand, the results indicated
limited processing of resonance-based cues to vocal identity in patients with
schizophrenia, suggesting that some aspects of voice discrimination are “atypical” in
schizophrenia. These findings converge with the conclusion of several recent reports of
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deficits in voice recognition using very different tasks (Waters & Badcock, 2009; Zhang
et al., 2008), together with evidence of abnormal cortical responses to human voices
(Koeda et al., 2006) in people with schizophrenia. Our finding raises the question of the
role of Df in schizophrenia and in the voice-hearing experience. In healthy individuals,
Df has been linked to perceptions of dominance (Puts, Hodges, Cárdenas, & Gaulin,
2007) and masculinity (Ko, Judd, & Blair, 2006) of speakers. Consequently one
possibility arising from these data is that individuals with schizophrenia may perceive
dominance and masculinity of real, external voices in an altered way to controls, a
possibility that merits further investigation. In keeping with this proposal, several
authors have argued that the manner of ongoing relating to real voices might influence
the relationship to hallucinated voices, thus biases in the perception of social power in
external voices may directly influence perceptions of dominance and power in
hallucinated voices (Birchwood, Meaden, Trower, Gilbert, & Plaistow, 2000; Hayward,
2003; Vaughan & Fowler, 2004). Similarly, cognitive models of AH emphasize that the
appraisal of voice dominance of the hallucinated voice is an important predictor of the
distress experienced (Mawson, Cohen, & Berry, 2010).
Previous evidence indicates that emotion recognition deficits play a specific,
contributory role in AH (e.g., Alba-Ferrara, Fernyhough, Weis, Mitchell, & Hausmann,
2012; Leitman et al., 2005). In contrast, atypical voice identity discrimination reported
in Chapter 6 characterised patients both with and without AH (i.e., was not specific to
patients with current AH). This result can be explained by two alternative possibilities:
(1) voice identity deficits could be relevant to something else altogether (i.e., a third
factor) in schizophrenia, or (2) voice identity deficits may be present even when AH are
not (i.e., the deficits may represent a general vulnerability factor for psychosis that
increases the risk of AH as well as other psychotic symptoms). Consequently, it might
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also be pertinent to consider how Df and perceptions of power and dominance might
relate to other symptoms of schizophrenia. For example, current models of delusions
(Freeman, 2007; Freeman, Garety, Kuipers, Fowler, & Bebbington, 2002) emphasize
the role of perceived threat. It thus seems conceivable that voices perceived as more
dominant may be construed as more threatening, which may then contribute to the
experience of a delusion.
However, it is still necessary to uncover the exact nature of the problems
schizophrenia patients have with voices. A wealth of evidence points to memory
impairments in schizophrenia (Drakeford et al., 2006), consequently it could be argued
that atypical use of Df to discriminate voice identity in patients with schizophrenia
could reflect memory problems. In the voice identity discrimination task employed in
Chapters 6 and 7, participants were required to judge how similar two voices presented
in sequence were, thus they had to remember the first voice heard in order to compare it
to the second. However, if memory is the sole explanation for the deficit in voice
identity discrimination, one would expect schizophrenia patients to have problems
remembering pitch-based cues as well; though it might be the case that some features
are easier to remember than others. For example, Clement, Demany, and Semal (1999)
argued that memory for pitch decays more slowly than memory for loudness (with
artificial sounds). More basic, specific perceptual tasks – at the level of voice structural
analysis in Belin et al.’s (2004) model of voice perception – may need to be employed
to explore whether the identified atypical voice identity discrimination in schizophrenia
reflects perceptual difficulties, as opposed to memory difficulties. For example, future
studies could manipulate acoustic characteristics (e.g., F0 or Df) and examine the effects
of this manipulation on participants’ ratings of more fine-grained (lower-level)
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impression aspects of vocal identity, such as speakers’ age, attractiveness, masculinity,
dominance, and size (as in Puts et al., 2007).
Results from Chapter 7 revealed no significant differences in speaker
discrimination in high versus low hallucination-predisposed individuals on the basis of
either F0 or Df. These findings are in contrast with those from Chapter 6 involving
schizophrenia patient and control samples (see Table 1), suggesting that the perception
of voice identity is not atypical in healthy individuals predisposed to hallucinations.
Again, it is possible that atypical voice identity discrimination may only be revealed
later in the development of psychosis. Alternatively, as outlined earlier, it is possible
that the healthy hallucination-prone individuals in this study experienced type i AH,
which are quite dissimilar to patient AH, and were thus at low risk of developing
clinical AH (Laroi, 2012). Atypical voice identity discrimination may only emerge in
healthy individuals with type ii AH, that are similar to patient AH, and much more
likely to transition to clinical AH. Longitudinal studies following up healthy individuals
with type ii AH could be employed to explore this possibility.
On the other hand, previous literature reports impaired perception of affective
prosody in individuals at risk of developing schizophrenia, though this is not always the
case (see Hoekert, Kahn, Pijnenborg, & Aleman, 2007; Phillips & Seidman, 2008, for
reviews). As previously mentioned, current models of voice perception recognize that
processing of vocal affect and vocal identity information occurs in partially segregated
neural pathways (Belin et al., 2004; Garrido et al., 2009; Hailstone, Crutch,
Vestergaard, Patterson, & Warren, 2010). Consequently, one possible interpretation of
the current data is that healthy, hallucination-prone individuals have a selective deficit
in the neural pathway underpinning vocal emotions, whilst the pathway supporting
voice identity processing is relatively intact. Alternatively, given the relatively limited
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and somewhat inconsistent data in this area, a more conservative conclusion is that
voice perception impairments have not been convincingly supported in non-clinical AH.
Importantly, our results again point to a discontinuity in the mechanisms involved in
clinical and non-clinical hallucinators, this time related to the perception of voice.
General comments on voice identity processing and AH
In summary, the findings pertaining to the second aim of the thesis suggest that
individuals with schizophrenia (both with and without AH) demonstrate atypical
discrimination of voice identity. Voice identity processing may involve several different
levels of precision: (1) voice detection, (2) voice discrimination, and (3) voice
identification. The highest level of precision would enable schizophrenia patients to
correctly and accurately identify familiar speakers. Patients with schizophrenia may be
biased in their perception of voice quality (or resonance) towards the discrimination of
speaker characteristics at a more general level, in addition to the specific identity level.
The atypical voice identity discrimination may be able – at least in part – to
explain one of the main phenomenological features of AH, namely the perception of
voices with a specific identity (Stephane, Thuras, Nasrallah, & Georgopoulos, 2003),
and perceived as other than the self. Difficulties with voice identity discrimination
would also reasonably be expected to apply when hearing one’s own recorded voice.
This may explain some existing literature which required participants to distinguish
their own voice from that of others, finding that patients with AH tended to misattribute
their own speech to others (Allen et al., 2004). If their own voice is not recognised, via
incapacity to use vocal cues to estimate the origin of the stimuli, then the only option is
to attribute it to someone else. Therefore, there may be a more general problem with
voice (i.e., both internally- and externally-generated voices) in schizophrenia.
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Alternatively, atypical voice processing could contribute to the somewhat correct, but
fragmentary representation of events in AH. For example, as outlined previously,
patients with AH may be unable to extract detailed acoustic information to identify a
speaker, leaving only sufficient information about the gender of the hallucinated voice.
These voice identity deficits do not appear to be present in healthy individuals
experiencing non-clinical AH, indicating a discontinuity in the perception of voice
identity in individuals experiencing clinical versus non-clinical AH.
Given the significance of voice identity in producing distress in voice hearers
(David, 2004; Mawson et al., 2010; Sorrell, Hayward, & Meddings, 2010), future
studies should examine the potential interaction between voice identity perception and
emotional response in AH – that is, explore whether certain voices (e.g., those that
“sound” more dominant, more negative, or more powerful) are more likely to result in
increased distress in schizophrenia patients with AH. For example, participants (with
and without AH) could be asked to rate their level of distress experienced, in addition to
providing ratings of perceptions of dominance and/or masculinity of the speakers, when
listening to different voices. This would allow the relationships between powerful and
negative voices and distress to be explored, which would in turn enable a better
understanding of the findings of atypical voice identity processing in Chapter 6.
The studies reported in Chapters 6 and 7 were limited in that we did not collect
dominance ratings for the voices used, and so had to rely on appealing to other data
linking Df to dominance. It would be valuable to collect a greater range of ratings of the
voices to see to what extent perceived characteristics of the voices (such as dominance)
correlate with both Df and F0 in samples of both patients with schizophrenia (with and
without AH) and healthy individuals predisposed to hallucinations. It must also be noted
that, as well as Df, F0 has also been linked to perceptions of masculinity and dominance
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in healthy individuals (Hodges-Simeon, Gaulin, & Puts, 2010). This area of research is
still in the early stages; hence further research is required to increase our understanding
of the use of acoustic cues in the discrimination of voice identity in schizophrenia.
Methodological considerations and implications for future research
For the research reported in this thesis, although we included measures of delusional
ideation and attempted to co-vary for this when necessary, we did not control for
variation in other symptoms of schizophrenia when focusing on AH. However, it is
generally very difficult to find “pure” AH symptom groups. For example, AH tend to
co-occur with delusions. One way to address this issue may be for future experiments to
select individuals with schizophrenia who experience delusions on their own (a more
likely scenario), without a history of AH (see Johns, Gregg, Allen, & McGuire, 2006 for
an example). Alternatively, another approach could be the use of longitudinal studies
that involve following up individuals from the general population with sub-clinical
psychotic experiences (or early stages of psychosis) over time (as in Smeets et al.,
2010). Such investigations into the developmental process of psychotic symptom
clusters may help to understand the processes responsible for transition from non-
clinical to clinical AH, as well as provide information about protective factors which
prevent individuals from relapsing or even from developing clinical AH to begin with.
It must also be noted that the schizophrenia patients in the no AH sub-group in the
research reported in this thesis had hallucinated in the past, and thus, were vulnerable to
future hallucinations. Future studies should test patients with no history of AH to find
out more about symptom associations.
Second, it is necessary to consider exactly what the healthy individuals
predisposed to hallucinations involved in this thesis were at risk for. Specifically, it
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could be argued that the high LSHS-R scorers tested in Chapters 2, 3, 4, and 7 may
have been predisposed to schizophrenia more generally (i.e., high schizotypy), rather
than AH more specifically. A limitation of the LSHS-R as a measure of hallucination
predisposition is that it contains only four items that are clear auditory hallucination
items (Allen, Freeman, Johns, & McGuire, 2006), and high scores on this scale may be
achieved by endorsing a wide range of experiences (including visual experiences) –
although it must be noted that out of the 12 items, only one is visual, and one does not
specify a modality. Further research should also examine the cognitive and perceptual
underpinnings of hallucinations in other modalities (e.g., visual, tactile, olfactory, and
gustatory). It is important to note, however, that the design of comparing high and low
LSHS-R scorers on cognitive tasks has been shown to reveal impairments on intentional
inhibition in healthy individuals with hallucination-like experiences (e.g., Paulik,
Badcock, & Maybery, 2007), illustrating that this design is potentially sensitive to
cognitive differences. Yet differences in context binding and voice identity
discrimination could not be detected in the experiments in this thesis. Nevertheless,
regardless of whether the high LSHS-R groups were high in the predisposition to
hallucinate, or high in more general schizotypy, the evidence from this thesis still
supports discontinuity of mechanisms in clinical and non-clinical symptomatology.
There are several interesting possibilities that could explain the findings of
discontinuity between clinical and non-clinical groups. First, as highlighted earlier, the
hallucination-predisposed samples recruited for the research reported in this thesis are
likely to have experienced type i non-patient AH, which are unlikely to transition to
clinical AH (Laroi, 2012). The selection criteria for clinical and non-clinical groups
need to be carefully considered in future research, with a need to articulate much more
clearly which type of non-clinical AH group (i or ii) researchers are working with. For
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example, a recent well-designed study into AH in healthy individuals by Sommer and
colleages (2010) recruited non-clinical individuals who experienced a frequency of
hallucinatory experiences more similar to the phenomenology of clinical AH (voices
had to be experienced at least once a month, unlike the finding of the modal frequency
in hallucination-prone individuals in Chapter 2, which was once a year). These stringent
selection criteria would help to make firm conclusions about whether context memory
binding and voice identity processing are or are not impaired in type ii non-patient AH
(which are likely to transition to clinical AH) compared to patient AH.
Second, AH in patient and non-patient (healthy) groups may be subserved by
both shared and unique mechanisms. Some mechanisms may be continuous (e.g.,
intentional inhibition, Paulik et al., 2007; vocal emotion perception, van't Wout,
Aleman, Kessels, Larøi, & Kahn, 2004) and some discontinuous (e.g., context memory
binding; voice identity discrimination). Alternatively, it is possible that non-clinical
hallucinations may represent a different phenomenological subtype – rather than a
different point on a continuum – to clinical hallucinations, stemming from different
aetiological mechanisms. In keeping with this proposal, Werbeloff and colleagues
(2012) conducted a longitudinal cohort study to assess the link between self-reported
attenuated psychotic symptoms and subsequent psychiatric hospitalisation for psychotic
illness. They found that attenuated psychotic symptoms in the general population signal
risk for later psychotic disorders, but are not clinically useful in predicting who will
actual transition to a full-blown psychotic disorder. Taking into account Laroi’s (2012)
recent suggestions, an alternative, more likely, possibility is that the individuals with
attenuated psychotic symptoms studied in the Werbeloff (2012) study may have been
low in the proneness, and consequently, risk to develop a psychotic disorder. The
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outcome for transition to psychotic disorder could have been different if individuals
high in the proneness to develop a psychotic disorder were studied instead.
Clinical implications
The results of this thesis have several practical implications. First, together with recent
evidence of low rates of conversion of psychotic-like symptoms in the general
population to psychotic illness (e.g., Werbeloff et al., 2012), the differences in cognitive
and perceptual mechanisms identified between AH in schizophrenia and AH in the
general population have implications for proposals to include an “attenuated psychotic
syndrome” diagnostic class (Carpenter & van Os, 2011) in the fifth revision of the
Diagnostic and Statistical Manual of Mental Disorders (DSM-V; American Psychiatric
Association, 2012). Caution needs to be taken when assuming similarities in features of
clinical and non-clinical psychotic symptoms, with the potential risk of administration
of ineffective treatments to individuals with schizophrenia, or even worse,
administration of unnecessary preventative medication with harmful side-effects to
individuals predisposed to psychotic experiences who may never transition to clinical
psychosis.
Second, the impairment in context memory binding and atypical voice identity
processing identified in patients with schizophrenia (both with and without AH) in this
thesis could contribute to deficits in social functioning and interaction. However, future
studies would be needed to explore this possibility as the studies in this thesis did not
include an assessment of social or other functional outcomes. Integrating insights from a
social-psychological approach may help to provide a more complete understanding of
the maintenance process of AH. Further, given that sensitivity to voice identity emerges
early in development (Belin & Grosbras, 2010), one might speculate that abnormal
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voice processing could be observed in individuals in the very early stages of the
development of schizophrenia. One possibility could thus be to introduce measures of
voice processing (i.e., assessment of the experience of real voices) as a useful
supplement to existing diagnostic assessment of schizophrenia, which emphasizes
phenomenology. This would provide an innovative approach to assess a functional
capacity that has a real impact in daily life, and would potentially contribute to early
diagnosis and treatment of vocal communication difficulties (see Belin & Grosbras,
2010, for a similar idea expressed for autism).
Overall, it is important to target clinically-relevant hallucinatory experiences for
intervention. Type i non-patient AH may have little or no long-term concern since these
individuals typically continue to hallucinate only infrequently. It is thus important for
treatment to target type ii non-patient AH, which are more similar to clinical AH, and
therefore much more likely to transition to severe psychosis. This strategy is likely to
guide the best approaches for clinical diagnosis and treatment.
Final comments
As Laroi (2012) has recently stated, far too few studies have directly compared
clinical and non-clinical AH in terms of underlying cerebral correlates, and none has
done so in relation to cognitive mechanisms. This thesis has explicitly addressed this
gap and made a substantial contribution to our understanding of AH in clinical and non-
clinical groups. The results of these investigations showed that clinical, but not non-
clinical AH are associated with difficulties binding contextual information in memory
(i.e., remembering ‘who said what’), as well as being linked to atypical voice identity
processing. These novel findings open many interesting directions for new theoretical
formulations of the genesis of AH.
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For example, one possibility that arises from the current research is that
individuals with clinical AH may be less precise when encoding or recollecting
individual voices. Consequently, they may be able to determine the gender and
character of a voice, but may not have access to more fine-grained information required
to identify the voice. This formulation may help to connect neural, cognitive and
phenomenological levels of understanding hallucinations. Thus, as a result of
underlying perceptual abnormalities, a voice hearer might be unable to say they are
hearing the voice of a close friend, but ‘know’ that it is the voice of a male they can
trust, that is a benevolent protector.
Overall, the findings from this thesis add to the challenges to the continuity
model of psychotic symptoms (Daalman et al., 2011; David, 2010; Kaymaz & van Os,
2010; Linscott & Van Os, 2010). Nevertheless, as raised in the comments above, there
is considerable variation in how samples of individuals with non-clinical AH are
selected. Current studies of non-clinical AH tend to ignore or at least downplay the
heterogeneity in this group. Some groups (e.g., Sommer et al., 2010) test voice hearers
with very frequent AH - which may place them much further along the continuum of
risk (i.e., makes them much more similar to patients with AH), whereas the majority of
studies which rely on instruments like the LSHS-R may be selecting people with
phenomenological characteristics that are substantively different from clinical AH.
One big, unresolved question in this literature is why the age of onset of AH in
clinical and non-clinical groups is so different. The average age of onset of non-clinical
AH is approximately 12 years, compared to around 21 years for the onset of clinical AH
(Daalman et al., 2011; Laroi, 2012). One possibility is that the differences in age of
onset are somehow connected to the presence of memory impairment in the clinical (as
opposed to non-clinical) AH group only, as revealed in this thesis. Hunter and
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colleagues (2006) propose a normal distribution of spontaneous activation of Temporal
Voice Areas (TVA) in the general population. Some healthy individuals may experience
this activation in the form of infrequent voices that could appear from an early age. On
the other hand, it is possible that clinical AH would only emerge when activation of the
TVA occurs in combination with memory problems, which might only arise at a later
age. Developmental studies involving separately mapping individuals with type i and
type ii non-clinical AH longitudinally on their performance on memory tasks are
warranted to explore this proposal.
In conclusion, understanding the processes underlying AH remains an important
priority for numerous reasons including health care and economic costs attributable to
schizophrenia, as well as the suffering of those affected with the illness and their
families. It is hoped that the findings presented in this thesis provide a solid base upon
which future research, and ultimately, clinical interventions for AH can build.
231
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