sam v. wass phd · 2014. 4. 16. · sam v. wass phd medical research council cognition and brain...

WASS - PEAK LOOK TO SCREEN VS NATURALISTIC

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Comparing methods for measuring peak look duration: are individual differences observed on

screen-based tasks also found in more ecologically valid contexts?

Sam V. Wass PhD

Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK

Correspondence address: Sam Wass, MRC Cognition and Brain Sciences Unit, 15 Chaucer

Road, Cambridge CB2 7EF. Tel: +44 (0)1223 355294.

Email: [email protected]

Abstract – 240 words; Main text – 6709 words; 4 figures; 2 tables.

Acknowledgements: Thanks to Kaya de Barbaro and Emily Jones for reading drafts of the

manuscript and for helpful discussions. Thanks to Nurah Ahmad, Amada Worker, Victor Shierly,

Jaimie Bartholomew for performing the video coding on which these analyses were based. Thanks

to Ronny Geva and Hagar Harel for advice about focused attention and to Peter Watson for advice

on statistics. This work was supported by a British Academy Postdoctoral Fellowship.


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SUBSTANTIALLY CHANGED PASSAGES MARKED IN RED

Abstract

Convergent research points to the importance of studying the ontogenesis of sustained attention

during the early years of life, but little research hitherto has compared and contrasted different

techniques available for measuring sustained attention. Here, we compare methods that have been

used to assess one parameter of sustained attention, namely infants’ peak look duration to novel

stimuli. Our focus was to assess whether individual differences in peak look duration are stable

across different measurement techniques. In a single cohort of 42 typically developing 11-month-

old infants we assessed peak look duration using six different measurement paradigms (four screen-

based, two naturalistic). Zero-order correlations suggested that individual differences in peak look

duration were stable across all four screen-based paradigms, but no correlations were found

between peak look durations observed on the screen-based and the naturalistic paradigms. A factor

analysis conducted on the dependent variable of peak look duration identified two factors. All four

screen-based tasks loaded onto the first factor, but the two naturalistic tasks did not relate, and

mapped onto a different factor. Our results question how individual differences observed on screen-

based tasks manifest in more ecologically valid contexts.


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Introduction

Research is increasingly suggesting that early-developing, domain-general aspects of attentional

control may mediate subsequent skill acquisition in a variety of areas (e.g. Heckmann, 2006;

Karmiloff-Smith, 1998; Wass et al., 2012). For example, aspects of domain-general attentional

control have been shown to predict, on starting school, children’s’ subsequent learning on literacy

and numeracy tasks (e.g. Welsh et al., 2010). And research into the development of attentional

control within clinical disorders suggests that early disruption to attentional control may play a key

role in impairing early learning in social settings, for example during word learning, leading to

subsequent catastrophic developmental cascades (e.g. Karmiloff-Smith, 1998). This suggests the

importance of researching the ontogenesis of attentional control during the first few years of life.

Cohen suggested that infant attention involves at least two different mechanisms: an attention-

getting process which determines whether an individual will orient toward a stimulus presented in

his periphery, and an attention-holding process which determines how long his attention will be

maintained once he fixates (Cohen, 1972). This second phase, the attention-holding process, is

commonly described as ‘sustained attention’ (Richards, 2011). However, although individual

differences in attention are frequently reported in applied and developmental psychology, the terms

used are rarely precisely defined and are conventionally assessed using a variety of methods.

Historically, the most widely used technique for measuring infants’ looking behaviour involves

presenting static stimuli using a slide projector or computer screen across a number of discrete but

contiguous trials; the infant’s viewing behaviour is coded either live by an experimenter viewing

the infant on a video feed, or post hoc (Colombo & Mitchell, 2009). Two variables are typically

derived: peak look duration, the duration of the longest unbroken look to the screen, and habituation


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rate, i.e. the rate of change of looks over time. Colombo and Mitchell argued in favour of peak look

duration as the better metric of individual and developmental differences in visual attention during

infancy because it is more reliable, and shows more robust relationships with long-term cognitive

outcomes (Colombo & Mitchell, 1990).

Previous research has demonstrated that peak look duration to novel, static, screen-based stimuli

shows a U-shaped trajectory over the first year of life (Colombo & Mitchell, 2009; Colombo &

Cheatham, 2006; Courage et al., 2006). Research has also robustly demonstrated that peak look

duration to novel stimuli during the first year of life relates negatively with long-term cognitive

outcomes: shorter look duration during the first year is associated with better performance on later

IQ and language measures (Colombo, 1993; McCall & Carriger, 1993; Tamis-LeMonda &

Bornstein, 1989) and recognition memory (Rose et al., 2002; Rose et al., 2003a, 2003b). Shorter

looking is also associated with higher pre-existing knowledge bases and general arousal levels (de

Barbaro et al., 2011; Dixon & Smith, 2008).

An alternative technique for assessing looking durations during infancy involves presenting

dynamic stimuli on a computer screen (Courage et al., 2006; Shaddy & Colombo, 2004; see

Richards, 2010 for a review). This work has generally used either TV clips (e.g. Richards &

Anderson, 2004) or specially filmed naturalistic or semi-naturalistic dynamic scenes (Wass et al.,

2011). These techniques have been used to investigate how autonomic indices change in different

attention states (Richards, 2011; Richards & Cronise, 2000), how looking behaviour towards the

screen changes over time (Anderson et al., 1987; Richards & Anderson, 2004), and how these

changes are different in children with Attention Deficit Hyperactivity Disorder (ADHD) (Lorch et

al., 2004). To our knowledge, no research has investigated whether individual differences in look

duration are consistent across static vs dynamic looking time paradigms.


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A third paradigm that has been used to assess looking durations involves presenting a number of

unfamiliar objects consecutively or concurrently in a table-top setting, and performing video coding

post hoc to analyse looking behaviour. For example, Kannass & Oakes (2008) videoed 9-month-old

and 31-month-old infants playing with toys, in both single-object (objects presented consecutively)

and four-object (objects presented concurrently) conditions; they also measured 31-month language

performance in the same children (see also Sarid & Breznitz, 1997). They found that shorter look

durations in the single-object task correlated with larger vocabularies at 31 months (Kannass &

Oakes, 2008). For the multiple object condition, however, they found the opposite relationship:

longer durations at 9 months correlated with larger vocabularies at 31 months (see also Choudhury

& Gorman, 2000).

Despite the strong face similarities between these paradigms, no previous research has assessed

whether individual differences using one type of looking time paradigm are consistent across

different assessment techniques. A number of studies have addressed this indirectly, but none

directly. Kagan & Lewis (1965) examined the relationship between looking behaviour towards

static stimuli at 6 and 13 months and the amount of free-play locomotor activity at 13 months, and

found that infants with long fixation times at 6 and 13 months were more sedentary during free

play. Coldren found that infants’ attention to stimuli in laboratory tasks correlated with the attention

to their caregiver in face-to-face interactions at 3- and 4-month-olds but not at 6 months (Coldren,

unpublished data, described in Colombo & Mitchell, 1990). Pêcheux & Lecuyer (1983) found with

4-month-olds that fixation time towards static stimuli was positively correlated with their visual

exploration of a toy.

This gap in the literature is important for a number of reasons. As we note in Part 2, there are a

number of marked differences between these different looking time paradigms, such as: the size of

the target towards which attention is being directed, the presence or absence of movement in the


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target or periphery of the visual field of the child, and the relative luminance of the target relative to

other elements within the infants’ field of view (Figure 2). In the absence of data showing cross-

paradigm consistency, we cannot be sure how individual differences in attention as assessed using

screen-based tasks might relate to individual differences in attention in naturalistic settings. Are the

dissimilarities between screen-based and naturalistic attention tasks documented in Figure 2

incidental to the individual differences that are assessed on these tasks? Or are they central to them?

Within the habituation literature, shorter looking to static stimuli during the first year is frequently

described as an index of ‘shorter processing speed’. This is frequently posited as an explanation for

the negative correlations noted between look duration during the first year and long-term outcomes.

One question that follow from this is: does ‘faster processing’ as assessed using screen-based

attention tasks also manifest as different (‘better’, or ‘more efficient’) orienting in naturalistic

contexts? Or is shorter looking to screen-based stimuli associated with better long-term outcomes

because both measures tap some underlying, ‘pure’ aspect of cognition that is entirely independent

of naturalistic orienting? The present study is intended as a small step towards addresing these

questions.

The Present Study

As described above, there exists to our knowledge no previous research that has addressed whether

individual differences in peak look duration are consistent across different assessment techniques.

The present study was conducted in order to address this question. We presented four screen-based

assessments, namely: i) looking behaviour towards ‘interesting’ (complex) static stimuli, ii)

‘boring’ (non-complex) static stimuli, iii) mixed static and dynamic stimuli and iv) to videos under

conditions of distraction (during the recording of EEG data). We also presented two semi-


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naturalistic looking assessments involving the presentation of novel objects in a table-top setting, in

i) a single-object condition (novel objects presented one by one) and ii) a four-object condition

(four novel objects presented concurrently) (following (Kannass & Oakes, 2008). The six measures

were presented in different testing rooms and inter-leaved in order, to a single cohort of typically

developing 11-month-old infants. 11-months was chosen as the age for the present study because

this has been characterised as an age that shows the first emergence of endogenous attentional

control (Colombo & Cheatham, 2006; Courage et al., 2006).

Across all six paradigms, the single dependent variable we assessed was peak look duration. This

was selected because it has previously been argued to be the most stable assessment of looking

behaviour during infancy – in comparison for example to habituation rate (the rate of change of

looks over time), which is less reliable, and shows less robust relationships with long-term

cognitive outcomes (Colombo & Mitchell, 1990). As far as possible, peak look duration was

assessed identically across the six paradigms we administered.

From reviewing the literature we were able to find no discussions suggesting that different factors

might influence peak look duration differentially between screen-based and semi-naturalistic

settings. Therefore we predicted that individual differences in peak look duration would be

consistent across all the paradigms administered.


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Methods

a) Participants

42 typically developing 11-month-old infants participated in the study. Mean age at testing was 337

days (range 312-259, standard deviation 9). Gender ratios were 26 male/16 female. Of note, other

aspects of these data have already been published elsewhere (Wass et al., 2011; Wass & Smith,

under review). The current data contain, however, completely novel analyses which do not overlap

with previous publications.

b) Apparatus and Procedure

The six peak look assessments were administered in three sections. Section A consisted of the

‘static non-complex’ assessment, the ‘static complex’ assessment and the ‘mixed dynamic/static’

assessment. Section B consisted of the ‘structured free play’ assessment. Section C consisted of the

‘videos during EEG’ assessment.

Presentation order. All three sections were administered during a single visit, which generally

lasted c. 90 minutes with breaks. Section A was presented in two halves (‘A1’ and ‘A2’). The order

in which the sections were administered was: Section A1, then Section B, then Section A2, then

Section C. The naturalistic ‘structured free play’ assessment (Section B) was therefore presented

between the other screen-based tasks. This design was chosen in order to preclude the possibility of

order effects being responsible for the results observed.


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Testing rooms. Sections A, B and C were each presented in different rooms. Of note, therefore, the

screen-based tasks included in Section C were presented in a different room to the screen-based

tasks in Section A.

In the detailed descriptions of the methods that follows, materials are described section by section,

together with the data processing techniques that were used.

INSERT FIGURE 1 HERE


Section A - ‘static non-complex’/‘static complex’/‘mixed dynamic/static’.

Materials: For the three peak look assessments contained in section 1 infants were seated on their

caregiver’s lap while the viewing material was presented on a Tobii 1750 eyetracker subtending 24˚

of visual angle. The three assessments were presented interleaved with each other.

Static non-complex images. Two different still images were presented at different stages of

the testing protocol. The two ‘non-complex’ images were both monochromatic objects

presented against a white background (see Figure 1 for example). Trials were presented

concurrent with child-friendly music, such as songs from Sesame Street. Four different songs

were used that were paired randomly with the different images. All infants heard the same

four songs over the course of all experiments. Trials were presented using a gaze-contingent

infant-controlled habituation protocol procedure: images were presented and remained on-


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screen for as long as the infant looked to the screen. Following cessation of a look, the image

was re-presented until two successive looks had taken place that were less than 50% of the

longest look so far. In order to confirm eyetracker contact, a small (c.0.4˚) re-fixation target

was briefly presented every 15 seconds; subsequent analyses (described in the

Supplementary Materials) suggested that this did not influence the timing of peak look

duration measure. Peak look was calculated independently for each image and then

averaged.

Static complex images. The two ‘complex’ images were polychromatic scenes (see Figure 1

for example). The testing procedures used were identical to those used for the static non-

complex assessment. For practical reasons, individual trials were capped at 120 seconds; 11

out of the 152 individual trials included reached this cap (see Figure S1).

To confirm our classification of images into ‘complex’ and ‘non-complex’ feature

congestion was calculated for each image using Matlab scripts from Rosenholz et al. (2007).

Feature congestion quantifies local variability across different first-order features such as

colour, orientation and luminance; see SM for a more detailed description. For the two ‘non-

complex’ images, average feature congestion across the whole frame was found to be 1.7

and 1.6; for the two ‘complex’ images, average feature congestion was 7.6 and 5.1 (see

Figure S2). This confirmed our classification of the stimuli into ‘complex’ and ‘non-

complex’.

Mixed static/dynamic images. 3 blocks of mixed static and dynamic images were presented

at different stages of the testing protocol. Each block lasted 65 seconds. Each block consisted

of a mixture of: head shots of actors (single and in groups) reciting nursery rhymes, still

images of actors’ faces, and shots of toys and birds accompanied by background music (see


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Figure 1 for example). The individual stimuli within each 65-second block each lasted 4-12

seconds. As with the static images, a small re-fixation target was briefly presented c. every

15 seconds in order to confirm eyetracker contact (see analyses in SM).

Data processing: Infants’ looking behaviour was coded from a camera on top of the monitor. Gaze

was coded in one-second bins, as either looking at the screen or not. Total percentage looking time

and the length of each unbroken look to the target were calculated. Instances in which the

participant looked away from and then back to the screen within one second were treated as

constituting one continuous look rather than two discrete looks. Coder 1 coded 76%, coder 2 48%;

25% of the videos were double coded. Cohen’s kappa was calculated to assess inter-rater reliability

and was found to be 0.71.

Section B - ‘structured free play’

Materials: The two peak look assessments contained in section 2 were conducted in a puppet

theatre with attractive surrounds, and a stage behind which experimenter and the camera were

visible. Infants sat on their caregiver’s lap, close enough to the stage so that they could reach to and

touch the objects on it. Between each trial, the curtains of the puppet theatre were closed and new

objects were placed on the stage; reopening them marked the start of the next trial.

The two assessments were presented consecutively:

Free play – 1-object condition. In the single-object condition, five objects (an plastic figure/a

basting pipette/a glitter lamp/a lion mask/a rabbit mask) were presented in randomised order


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consecutively for 30 seconds each. Figure 1 shows an example of the objects used; Figure S3

shows images of all the objects used. The objects used varied in size from 5cm-20cm.

Free play – 4-object condition. The four-object condition was presented immediately after

the one-object condition. Four objects (a rubber duck, a plastic train, a plastic teddy bear, a

tiger finger puppet) were presented concurrently in a line across the stage, in a randomised

order, for 90 seconds. The objects used varied in size from 5cm-10cm. Data from 10

participants was unusable for the four-object condition due to changes made to the

experimental protocol during testing.

Data processing: Infants’ looking behaviour was recorded from a camera positioned behind the

stage. The coding protocol used was based on that used by Kannass & Oakes (2008). Infants’

looking behaviour was coded for whether the infant was looking at the object or not. Sections where

the object was not on the stage (because the infant had knocked or thrown it off) were excluded. All

coding was conducted in one-second bins. Data were triple coded. Coder 1 coded 70%, coder 2

50% and coder 3 24%; 40% were double coded by coders 1 and 2 and 24% by coders 1 and 3.

Cohen’s kappa was calculated to assess inter-rater agreement. This was found to be 0.72 between

coders 1 and 2 and 0.78 between coders 1 and 3.

Section C - ‘videos during EEG’

Materials: The peak look assessment contained in section 3 was presented with infants sitting on

their caregiver’s lap while viewing materials were presented on a cathode ray TV subtending 30˚ of

visual angle. Simultaneously with the administration of this task, infants were having EEG data

recorded using a 128-channel EGI hydrocel net (Wass, 2011).


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Only one assessment was presented in this section:

Videos during EEG. Three videos were presented sequentially in rotation during EEG

recording. These videos were: i) a series of actresses reciting nursery rhymes to camera; ii)

videos of toys spinning; iii) a short TV clip. Videos lasted 32-44 seconds each. Each video

was presented twice.

Data processing: Infants’ looking behaviour was recorded from a camera positioned below the

monitor. Videos were coded according to whether the infant was looking to or away from the

screen, using an identical coding scheme to that used in sections and 2. Coder 1 coded 76%, coder 2

48%; 24% were double coded. Cohen’s kappa was calculated to assess inter-rater reliability and

was found to be 0.88.


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Results

The results section is in two parts. Firstly, descriptive statistics of the looking time data obtained

from the different paradigms are presented. Secondly, analyses are presented that examine the inter-

relationships in looking time across the different assessments administered. Specifically we wished

to evaluate the hypothesis that individual differences in looking time would be consistent across the

six assessments.

Part 1 - Descriptive statistics of looking time data

Descriptive statistics for the entire data set are shown in Table 1. For each assessment, mean peak

look oberved across all infants has been reported, together with the Standard Error of the Mean

(S.E.M.) and range. Additionally, for comparison, identical data have been reported for mean look

duration (i.e. the average of all looks recorded towards the stimuli). For each assessment, the

number of participants who provided usable data is shown in the final column. With the exception

of the free-play 4 object task, for which (as described above) changes were made to the

experimental protocol during testing, drop-out rates are acceptable (maximum 4/42). These were

due to fussiness and non-compliance during testing.

INSERT TABLE 1 HERE

Figures 3a-f show histograms of all the individual looks collected on the different tasks. Figure 3g

shows plotted lognormal fittings. Lognormal distributions were calculated as these are generally


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reported to be the best fit on infant looking time data (Pempek et al., 2010; Richards & Anderson,

2004). Marked differences in the patterns of look durations observed on different tasks can be seen:

both peak and mean look duration were higher for all of the screen-based tasks than for the

structured free play tasks. Within the screen-based tasks, markedly longer peak looking times were

observed in the static complex and mixed dynamic-static categories than in the other categories.


The netween-participant distributions of peak look durations were found to be positively skewed, in

common with all looking time assessments (see e.g. Richards & Anderson, 2004); therefore all

subsequent analyses have been calculated based on log-transformed data (following e.g. Frick et al.,

1999).

Part 2 - Analyses to examine the inter-relationships in looking time across the different assessments

administered

We wished to evaluate the hypothesis that individual differences in looking time would be

consistent across the six assessments we administered. In order to examine this, two analyses were

conducted. First, zero-order correlations were calculated. Second, a factor analysis was performed.

First, histograms and scatterplots were calculated to assess whether per-participant peak look values

derived from the log-transformed data were parametrically distributed, and whether any bivariate


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relationships observed were robust. Figure 4 shows these scatterplots. All parameters were found to

be parametrically distributed.

Zero-order correlations. Figure 4 shows the zero-order bivariate correlations that were observed

between the variables entered into the factor analysis. The four screen-based tasks (static complex,

static non-complex, mixed dynamic-static, videos during EEG) all show significant correlations

(r=.33 to.56, all ps <.05) with the exception of the static complex to dynamic during EEG (r=.24,

p<.10). Inspection of the scatterplot (Figure 4) suggests that this relationship is weakened by an

outlier. In comparison the two FP tasks do not correlate with any of the screen-based tasks (negative

in 5 of the 9 comparisons conducted, and r<=.12 in the remaining 4). The scatterplots in Figure 4

suggest that this not attributable to the presence of outliers.


One explanation that was considered for the low zero-order correlations observed with the free play

data was that these data were inherently more ‘noisy’ than the screen-based looking time data. In

order to evaluate this possibility, data were examined from an overlapping dataset that has been

published previously (Wass et al., 2011; Wass, 2011). In this paper, an identical task to that

presented here was presented twice at fifteen days’ interval to a smaller cohort (N=21) of infants.

Analyses assessed the number of total attentional reorientations and attentional shifts from object to

person. Test-retest reliability between the two testing sessions was r=.53, p<.01 for total attentional

reorientations, and r=.52, p<.05 for attentional shifts from object to person. This suggests that these

measures are relatively stable as indices of individual differences.

Factor analysis. Factor analyses were conducted to examine the factorial structure underlying our

data in more detail. Our analytical approach was based on that used in previously published


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research (Rose et al., 2004, 2005). First, the sample size was examined. The ratio of participants to

variables for the factor analysis was found to be 6.3, which is above the prescribed ratio of 5

suggested by Hair and colleagues (Hair et al., 1998). To maximise the sample size for factor

analysis, missing values were imputed based on the mean of the subscale to which that item

belonged (following Blair & Razza, 2007).

The factor analysis yielded a two-factor solution (Eigenvalues > 1.0) representing 59% of the total

variance (see Table 2). These two factors were submitted to a principal axis rotation (oblimin) and

the scree plot was inspected, supporting a two-factor solution. Thresholds were set at 0.70 for

principal loading and 0.50 for secondary loading (Guadagnoli & Velicer, 1988).

INSERT TABLE 2 HERE

The first factor, which had an Eigenvalue of 2.29 and accounted for 38% of the variance, was

defined by three of the screen-based tasks, with the fourth screen-based task allotted a secondary

loading. The second factor, with an Eigenvalue of 1.23, was loaded onto by the two FP variables (4-

object as primary loading and 1-object as secondary loading), and (negatively) by the static

complex variable (primary loading).


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Discussion

Our analyses were designed to assess whether individual differences in peak look duration are

consistent across different looking time measurement paradigms. To 42 typically developing 11-

month-old infants we administered six assessments of peak look duration, including four screen-

based assessments and two free-play based assessments. We predicted that results obtained would

be consistent across all paradigms. The results were not as predicted. The factor analysis suggested

a two-factor solution. The first factor was defined by the four screen-based tasks (‘static non-

complex’, ‘mixed dynamic-static’ and ‘videos during EEG’, with ‘static complex images’ as a

secondary loading). The second factor was defined by the two free play tasks (one as a secondary

loading) and also (with a negative loading) by the static complex screen task.

The four screen-based tasks were administered across different testing rooms, and interspersed with

the free play tasks, which precludes the possibility of room or order effects being responsible for

our results. Looking time to static screen stimuli and to dynamic screen stimuli showed strong

correlations. Strikingly, we also found that looking behaviour towards a TV screen during recording

of EEG data, which has the additional variance of tightness of fit of the EEG net, reactivity to

testing and so on, mapped onto the same factor as the other three screen-based tasks, that were

administered using a different screen in a different room. In contrast the two FP assessments

mapped onto a separate factor, and showed non-significant (max r=.12) zero-order correlations with

each of the screen-based tasks. The zero-order correlations observed between the FP and screen-

based tasks were negative in 5 out of 8 comparisons. In the factor analysis, the only screen-based

task (static complex) that loaded on to the second factor loaded on negatively (higher looking time

to static complex images associated with lower looking time during structured free play). Further

analyses were conducted to assess the possibility that these findings might be attributable to other


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factors such as increased measurement error during the administration of the free play tasks, with

negative results.

There are a number of limitations to this study. The sample size was relatively small (N=42), and a

number of techniques used by other researchers to complement looking time measures (such as

heart rate measurement and focused attention coding) were not applied. Furthermore, measurements

were only taken with one age group (11-month-olds), whereas the limited data available suggests

that different results may have been observed if the experiment were repeated with younger infants

(Coldren, unpublished data; discussed in Colombo & Mitchell, 1990).

Nevertheless, our results suggest that, in 11-month-old infants, individual differences in peak look

duration are constant across different screen-based tasks but not between screen-based and semi-

naturalistic tasks. We were able to find no discussion in the literature suggesting that different

factors might influence peak look duration between screen-based and semi-naturalistic settings.

What kinds of differences might these be? The following discussion is structured around a number

of factors commonly thought to influence peak look duration.

The first factor commonly associated with peak look duration is processing speed. Sokolov argued

that the initial presentation of a novel stimulus produces a conflict between a “neural model” of the

current environment and the sensory processes occurring in the brain; prolonged exposure to that

stimulus allows the viewer to form an internal representation of it, which is why looking durations

decline over time (Sokolov, 1963). 'Faster processors' are thought to require less time to form an

internal representation; this is frequently linked to the finding that shorter peak look duration to

static stimuli during the first year correlates negatively with long-term cognitive outcomes (e.g.

Jensen, 1987; Rose et al., 2002, 2008).


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Advocates of the importance of processing speed in influencing peak look duration might predict

that individual differences would not be stable between looking time to static screen and dynamic

screen stimuli, since one involves static visual information and the other constantly changing

information. They might also suggest that individual differences might be stable between static

screen and our free play task, since both require forming internal representations of static targets

(on-screen pictures and ‘real-world’ objects). In fact we found the opposite pattern: individual

differences in peak look duration were consistent across the static screen and dynamic screen

stimuli but not with the free play task.

A second factor related to peak look duration is ease of disengaging of visual attention. Frick and

colleagues measured the relationship between experimentally assessed attentional disengagement

latencies and spontaneous looking behaviour to static screen stimuli in typically developing 3- and

4-m-os (Frick et al., 1999). They found that long-looking infants showed greater variability in their

response latencies. This suggests that, at least in younger infants than the 11-month-olds studied

here, attentional disengagement may play a role in mediating spontaneous looking behaviour.

This is one area where differences can be noted between our screen-based and semi-naturalistic

paradigms (see Figure 2). Screen-based paradigms tend to be designed with the screen occupying a

relatively large proportion of the infant’s visual field (typically c.25° of visual angle, as here),

whereas in FP paradigms the target is generally much smaller (c. 5° in our case). In screen-based

tasks the target is generally much more luminant than the surrounds (which are typically dark); in

our free play paradigms, in contrast, this was not the case (see Figure 2). Lastly, in screen-based

tasks there are sharp luminance contrasts between the edge of the screen and the surrounds; again,

these were not present in the FP task (see Figure 2). These differences may be important because

previous research has noted that viewers tend to dwell on areas of high luminance contrasts such as

object boundaries. Although this effect has been reported at all ages from 6-week-old infants


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(Bronson, 1994) through to adults (Henderson et al., 2009) its effect has been reported to decline

with increasing age (Frank et al., 2009; Karatekin, 2007). The high-contrast and prominent

luminance contrasts present in our screen-based but not in our naturalistic tasks may influence

behaviour in the current study, and perhaps more for some infants than others.

A third factor that may influence peak look duration is autonomic arousal. This can be assessed in

both phasic (i.e. event-related) and tonic contexts (De Barbaro et al., 2011; Richards, 2011).

Richards and colleagues explored phasic arousal changes by examining changes in heart rate

variability and peak look during object examination; greater phasic arousal changes were associated

with higher peak look (Richards & Casey, 1991; Richards, 2011). Of note, our screen-based tasks

(particularly the static screen stimuli) contained abrupt changes in luminance coincident with the

onset of each trial: the screen transitioned from dark to bright in an otherwise darkened room; such

changes were completely absent in the naturalistic task. It is possible that these abrupt changes in

luminance are associated with phasic changes in arousal, and that some infants are more susceptible

to these changes than others (Alkon et al., 2006). This factor would influence looking behaviour in

the screen-based but not the naturalistic looking time tasks.

Aston-Jones and colleagues suggested a role for the arousal system in shifting between attention

states; they distinguish between a ‘scanning’ mode, in which look durations are short and the focus

of visual attentiveness is wide, and a ‘focused’ mode, in which look durations are longer and the

spatial distribution of attention is narrower (Aston-Jones & Cohen, 2005; Aston-Jones et al., 1999,

2007; cf. Pannasch et al., 2008). In our semi-naturalistic tasks, other targets (objects and people) are

present within the peripheral visual field of the infant, whereas attempts were made to ‘black out’

all peripheral objects for the screen-based tasks (as is typical in other labs) (see Figure 2). The

shifting between attention states that Aston-Jones and colleagues describe may therefore be a factor

in our semi-naturalistic tasks but not in our screen-based tasks.


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A fourth factor that may relate to peak look duration is executive control. Aspects of executive

control have been reliably associated with sustained attention in older children (e.g. Reck & Hund,

2011). (Note however that sustained attention in these studies with children is assessed not using

looking time measures but with tasks such as the Continuous Performance Task, whose relationship

with peak look duration has, to our knowledge, not been studied.) Colombo and Cheatham point out

that positive correlations are observed between long-term cognitive outcomes and peak look to

static stimuli after the first year of life, whereas negative correlations are observed between the

same two variables during the first year. They suggest that this may be attributable to the emergence

of effortful control as a factor mediating behaviour at about the 12-month boundary (Colombo &

Cheatham, 2006; see also Courage et al., 2006). Note, however, that Kochanska et al. (2006) found

that focused attention (not peak look duration) during the second half of the first year correlated

positively with effortful control at 22 months.

One difference between our screen-based and our semi-naturalistic tasks may be relevant here. This

is that, for the semi-naturalistic tasks, a number of other informative gaze targets (such as the

experimenter) are within the field of view of the child - whereas the screen-based tasks were

conducted in a darkened room. There are a variety of reasons why looks away from the object may

have an adaptive value in our free play paradigm but not in our screen-based paradigm (e.g. Rueda

et al., 2005; Sheese et al., 2008). It may be therefore that executive control relates more strongly to

peak look duration in the free play than in the screen-based tasks, although future work is required

to investigate this in more detail (cf. Rothbart et al., 2003; Sheese et al., 2008).

Conclusions


23

To a cohort of typically developing 11-month-old infants we presented several assessments of peak

look duration, including some that assessed looking behaviour on screen-based tasks and others that

assessed behaviour on semi-naturalistic tasks. We found that the four screen-based tasks (looking to

static non-complex, to static complex, to mixed dynamic/static and to dynamic stimuli during EEG

recording) all mapped onto a single factor, whereas the two free play assessments mapped onto a

separate factor. In our discussion we noted a number of ways in which factors such as susceptibility

to high luminance contrasts and abrupt stimulus onset-offset changes may be key factors mediating

individual differences on screen-based tasks, but relatively unimportant in more naturalistic

contexts. Future research should exploit recent technological advances such as head-mounted

eyetrackers (e.g. Aslin, 2009) to increase our understanding of how individual differences in

naturalistic attention relate to individual differences in infant attention as assessed using screen-

based paradigms.


24

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30

Figures

Figure 1: Summary of viewing materials presented.


31

Figure 2. Comparison of paradigms presented from infant’s perspective. Top row shows the screen-

based looking tasks; bottom row shows the naturalistic looking task. In each row, the left column

shows a photo of the stimulus array from the infant’s perspective. The active target for the coding

of look durations is indicated using a red rectangle. In the central column the luminance of the

images is shown. On the right, feature congestion is shown. Feature congestion quantifies local

variability across different first-order features such as color, orientation and luminance (Rosenholz

et al., 2007) and has been shown to influence gaze allocation in contexts in which no motion is

present in the field of view (Henderson et al., 2009).


32

Figure 3 - Histograms and log normal distribution fittings. a)-f) show histograms of all peak looks

observed across the different assessments we administered. g) shows lognormal distributions of

peak looks observed.

0"

0.1"

0.2"

0.3"

0.4"

0.5"

0.6"

0"to"5" 5"to"10" 10"to"15" 15"to"20" 20"to"25" >25"

Sta$c&non)complex&

0"

0.1"

0.2"

0.3"

0.4"

0.5"

0.6"

0"to"5" 5"to"10" 10"to"15" 15"to"20" 20"to"25" >25"

Sta$c&complex&

Look duration (secs)

pdf

0"

0.05"

0.1"

0.15"

0.2"

0.25"

0.3"

0.35"

0.4"

0"to"5" 5"to"10" 10"to"15" 15"to"20" 20"to"25" >25"

Mixed&dynamic,sta/c&

0"

0.05"

0.1"

0.15"

0.2"

0.25"

0.3"

0.35"

0.4"

0.45"

0"to"5" 5"to"10" 10"to"15" 15"to"20" 20"to"25" >25"

Videos'during'EEG'

0"

0.1"

0.2"

0.3"

0.4"

0.5"

0.6"

0"to"5" 5"to"10" 10"to"15" 15"to"20" 20"to"25" >25"

Free$play$1$object$

0"

0.1"

0.2"

0.3"

0.4"

0.5"

0.6"

0.7"

0.8"

0.9"

0"to"5" 5"to"10" 10"to"15" 15"to"20" 20"to"25" >25"

Free$play$4$objects$

Look duration (secs)Look duration (secs)

Prop

. tot

. loo

ksPr

op. t

ot. l

ooks

Prop

. tot

. loo

ks

a) b)

c) d)

e) f)

g)

Saturday, 7 September 13


33

Figure 4 - Correlation matrix showing the relationship between the dependent variables entered

into the factor analyses. Histograms showing the distribution of each variable are shown

diagonally on the 1:1 line. Below this line, scatterplots show the relationships between variables.

For those variables showing a bivariate correlation at p(2-tailed)>.05, a linear regression line has

been drawn in black. Above the 1:1 line, the Pearson’s product moment correlation shows the

relationship between the two variables. The stars show the significance levels of the bivariate

correlation: ** - p(2-tailed)<.01, * - p<.05, (*) - p<.10.

Free

pla

y 4

obje

ct

Free play 1 object

Videos during EEG

Mixed dynamic-static

Static non-complex

Static complex

Free

pla

y 1

obje

ctV

ideo

s du

ring

EEG

Mix

ed d

ynam

ic-s

tatic

Stat

ic

non-

com

plex

0.43** 0.33* 0.24(*) -0.28(*) -0.20

0.120.41* 0.49** -0.04

0.050.56** -0.09

0.11-0.14

0.03

Stat

ic

com

plex

Free play 4 object


34

Tables

Table 1 - Descriptive statistics.


35

Table 2: Two-factor solution. Bold indicates principal loading. Italicized bold indicates secondary

loading


36

Supplementary materials

Supplementary methods

Section 1 – static non-complex, static complex and mixed static/dynamic assessments

In order to confirm eyetracker contact, a small re-fixation target subtending c. 0.4˚ of visual angle

was briefly presented every 15 seconds. In order to assess the possibility that the presence of this re-

fixation stimulus might have influenced our results we plotted a cumulative frequency distribution

of the peak look duration of all of the individual trials (four per infant - two complex, two non-

complex) that were recorded. We reasoned that, if the presence of this target had influenced the

peak look duration measure, then peaks would be observable in this frequency distribution

immediately after the target was presented (i.e. at 15, 30, 45 seconds and so on). Indeed, small

peaks are detectable at these times, consistent with this hypothesis. However, it can also be seen

that these peaks are small relative to the range of response times observed. We concluded therefore

that there was no evidence to suggest that the presence of this re-fixation target invalidates our use

of this measure as an assessment of peak look duration to static images.


37

Figure S1: Cumulative frequency distribution of all trials presented during the assessment of

looking time to static images.

Feature congestion calculations

Feature congestion was calculated using Matlab scripts that are described in detail by Rosenholz

and colleagues (Rosenholtz et al. 2007). These were chosen in preference to the more widely used

salience models (e.g. Itti & Koch, 2001) since these models make a lot of assumptions about how

attention is controlled, such as the inclusion of inhibition of return and winner takes all in the

salience computation. Briefly, the input image was converted into the CIELAB color space and then

processed at three scales by creating a Gaussian pyramid by alternately smoothing and subsampling

the image (Burt & Adelson, 1983). Features were then identified based on luminance contrasts (by

filtering the luminance band by a center-surround filter formed from the difference of two

Gaussians and squaring the outputs); color, performed at each scale by pooling with a Gaussian

filter; and orientation (using a two-vector, (k cos(2θ), k sin(2θ)), at each image location and scale,

where θ is the local orientation and k is related to the extent to which there is a single strong


38

orientation at the given scale and location. Then the local (co)variance for each feature is

calculated; these are then combined, scaling the clutter value in each feature dimension by the range

of possible clutter values for that feature (Rosenholtz et al., 2007).

Figure S2: Feature congestion calculations used to quantify simulus complexity. The images on the

left show the original image. From top to bottom, the top two images were selected as ‘non-

complex’ and the bottom two images were selected as ‘complex’. The images on the right are those

outputted by scripts from Rosenholz and colleages. Areas drawn white are areas with relatively

high feature congestion, defined via differentials of a combination of first-order stimulus features

such as luminance, colour and edge density (see text).


39

Section 2 - Structured free play

Figure S3: Stimuli used in the free play tasks. Stimulus c) is similar but not identical to that used. a)

lion mask, c.25cm x 20cm. b) rabbit mask, c.30cm x 20cm. c) plastic figure, c. 10cm x 8cm. d)

basting pipette, c. 25cm x 5cm. e) glitter lamp, c. 15cm x 5cm.

sam v. wass phd · 2014. 4. 16. · sam v. wass phd medical research council cognition and brain...

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