attention and decision making - au...

ATTENTION AND DECISION-MAKING:SEPARATING TOP-DOWN FROM BOTTOM-UP

COMPONENTS

PhD dissertation

Martin Petri Bagger

Aarhus BSS, Aarhus UniversityDepartment of Economics and Business Economics

2016

"In God we trust. All others must bring data.”

- W. Edwards Deming

PREFACE

This dissertation was written in the period September 2012 to September 2016 and

it is the tangible outcome of my time as a PhD student at Aarhus University. During

this period I have been enrolled as a PhD student at the Department of Business

Administration and at the Department of Economics and Business Economics. I

am grateful to both departments for providing stimulating and positive research

environments and for generous financial support.

I would like to thank my supervisors Joachim Scholderer and Hans Jørn Juhl for

helping and guiding me in the pursuit of my goals. It has been a pleasure working

with both of you, and I hope to continue doing so in the years to come.

During my time as a PhD student I have met a lot of amazing, brilliant, and lovely

people and every single one of them deserves my gratitude. Some even deserve extra

thanks for being part of my journey. My fellow K.A.G.E. Consortium management

team (Anne, Morten and Lina) should receive huge thanks, not only for spamming

my mailbox with "very important" information, but mostly for just creating a fun and

relaxing atmosphere. It has been amazing sharing an office with Anne and Morten,

and I hope that we can continue to do so at this ever changing institution.

I would like to thank Dan and Josh for their work and help in the lab and for

ensuring high quality coffee. Thanks also for good talks on nerdy topics.

Special thanks go to Jacob Lund Orquin who has been a friend, great co-author

and mentor during my time as a PhD student. I have learned a lot from working

closely with you and I hope that we can continue cooperating on challenging and

exciting projects in the future.

I would like to thank Sarah and Kasper for being great friends with whom I can

talk about small and large things related to our both academic and our personal lives.

Meeting you has been a great added bonus of these four years. I also wish to thank

Jacob Rosendahl who has been part of my academic journey ever since we started

our bachelor studies in 2008. Thank you for being part of my and my family’s lives.

I am especially indebted to my family who have endured my ups and downs for

the past four years. I wish to thank my wife Marie for her endless support and for

always believing in me. I hope that I can repay you with just a fraction of what you

have given me. And last but not least I wish to thank my three boys Samuel, Vilhelm

and Hector for lightening up my life and showing me that life is great; no matter what

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problem one might have they fade away in your presence. I love you.

Martin Petri Bagger

Aarhus, September 2016

UPDATED PREFACE

The predefence took place on October 26, 2016. I would like to thank the members

of the assessment committee, Martin Meißner, Anderas Glöckner, and Julia Nafziger

(chair) for their very constructive and insightful comments. Some of the suggestions

have already been incorporated into the dissertation, and more will follow in the near

future.

Martin Petri Bagger

Aarhus, November 2016

iii

CONTENTS

Summary vii

Danish summary xi

1 Learning affects top-down and bottom-up modulation of eye movementsin decision-making 1

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

1.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.5 Implications and future research . . . . . . . . . . . . . . . . . . . . . 24

1.6 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

1.7 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

2 Efficiency gains in repeated binary choice: Adaptation to the task envi-ronment 35

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.2 Overview of experimental procedure . . . . . . . . . . . . . . . . . . . 43

2.3 Treatment 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

2.4 Treatment 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

2.5 Treatment 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

2.6 Treatment 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

2.7 Cross-treatment analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 62

2.8 General discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

2.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

2.10 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

2.11 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

3 The Perceptual Pull in Decision Making 81

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

3.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

v

vi CONTENTS

3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

3.5 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

3.6 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

SUMMARY

This thesis consists of three independent chapters, dealing with decision making and

visual attention. The three chapters each examine different aspects of the interaction

between the visual environment and goals in decision-making situations, and the

thesis contributes with insights into human decision making processes. All chap-

ters are based on experimental data which has been collected through controlled

experiments in computer labs. Data for the second and third chapters were collected

in the facilities of the Aarhus University Cognition and Behavior (COBE) Lab. Data

for the first chapter were collected prior to the establishment of the COBE Lab on

equipment at the Department of Business Administration (now the Department of

Management).

A large part of the analyses in the thesis are based on eye-tracking, which is a

process tracking method where the participants’ eye movements are recorded and

mapped. The method provides a variety of opportunities to examine how people col-

lect information and how external factors can affect this process (e.g. the information

gathered by the participants and the order in which the information was collected).

Eye-tracking is a powerful and excellent tool for studying decision-making because

the method imposes few restrictions on the participants compared to other process

tracking tools (e.g., "think aloud" and "information search displays"). Eye-tracking as

process a tracking tool has been strongly recommended (e.g. Bialkova & van Trijp,

2011; Glaholt & Reingold, 2011), and for an in-depth introduction to eye-tracking the

books by Holmqvist, Nyström, Andersson, Dewhurst, Jarodzka & van de Weijer (2011)

and Duchowski (2007) are highly recommendable.

The first chapter, Learning affects top down and bottom up modulation of eye

movements in decision making, explores the perceptual processes that drive temporal

dynamics in decision making. Under the framework of "top-down" and "bottom-up"

processes, we examine the information reduction hypothesis (Haider & Frensch,

1999). We conduct a repeated measures binary choice experiment under three dif-

ferent presentation formats. We operationalise top-down and bottom-up control as

the effect of individual usefulness and presentation formats, ability to capture the

participants’ visual attention. We examine the prediction that experience increases

the effect of top-down control of attention, which in turn leads to a reduction in

the effect of the bottom-up factors, ability to capture attention. The results show an

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viii SUMMARY

increase in top-down control of eye movements over time and that decision-makers

learn to direct their attention to stimuli with high utility while ignoring the stimuli

with low utility. Furthermore, we find that the influence of the presentation format on

attention decreases over time indicating decreasing bottom-up control. The chapter

is written in collaboration with Jacob Lund Orquin and Simone Mueller Loose and

was published in Judgment and decision making in 2013.

The second chapter, Efficiency gains in repeated binary choice: Adaptation to

the task environment, deals with the conditions that lead to improvements in terms

of choice speed and accuracy. Previous research has shown that decision makers

become faster and make more accurate decisions through experience with a task

(Orquin, Bagger & Mueller Loose, 2013; Meißner, Musalem & Huber, 2016). In this

chapter I examine how the structure of the environment modulates the changes. I

conduct four experiments in which I manipulated the relevance of visual salience.

Relevance was manipulated as the inter-relationship (correlation) between the at-

tribute value and visual salience of an attribute. The findings lead to the conclusion

that decision makers in general become faster, but accuracy only improves if visual

salience has a high degree of relevance (i.e. high correlation). Decision makers rapidly

learn to utilize relevant information and to discard irrelevant information, and I

conclude that these changes in task performance are controlled by changes in cue

reactivity and other attention control processes that depend on the structure of the

task environment. The chapter has been submitted to Organizational Behavior and

Human Decision Processes.

The third and final chapter, The perceptual pull in decision making, is written in

collaboration with Jacob Lund Orquin, Erik Lahm, George Tsalis and Klaus Grunert

and has been submitted to Acta Psychologica. In this chapter, we examine the balance

between top-down and bottom-up control in decision-making. We investigate it in

relation to both the effect on attention and the effect on the final decisions. In order

to compare the relative importance of the two processes, we conduct an eye-tracking

study in which we manipulate bottom-up and top-down processes in an orthogonal

design. We manipulate bottom-up processes by varying the size, the position, and

salience of target attributes and the number of distractor attributes. Top-down pro-

cesses are manipulated by varying the relevance of target attributes through three

different tasks: preference-based choice, inferential choice and preference-based

choice with priming. The results show that bottom-up processes play a greater role

than top-down processes in determining eye movements, and the chapter opens up

the question about when bottom-up and top-down processes have the stronger effect

on attention. At the same time we find that the top-down manipulation influences

the use of information associated with the final decisions and that relevant and fixed

information has the greatest influence on decisions.

ix

References

Bialkova, S. & van Trijp, H. C. M. (2011). An efficient methodology for assessing

attention to and effect of nutrition information displayed front-of-pack. Food

Quality and Preference, 22(6), 592–601.

Duchowski, A. (2007). Eye tracking methodology: Theory and practice, volume 373.

Springer Science & Business Media.

Glaholt, M. G. & Reingold, E. M. (2011). Eye Movement Monitoring as a Process Trac-

ing Methodology in Decision Making Research. Journal of Neuroscience, Psychology,

& Economics, 4(2), 125–146.

Haider, H. & Frensch, P. A. (1999). Eye movement during skill acquisition: More

evidence for the information-reduction hypothesis. Journal of Experimental Psy-

chology: Learning, Memory, and Cognition, 25(1), 172–190.

Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., & van de

Weijer, J. (2011). Eye Tracking : A Comprehensive Guide to Methods and Measures.

Oxford: Oxford University Press.

Meißner, M., Musalem, A., & Huber, J. (2016). Eye Tracking Reveals Processes That

Enable Conjoint Choices to Become Increasingly Efficient with Practice. Journal of

Marketing Research (JMR), 53(1), 1–17.

Orquin, J. L., Bagger, M. P., & Mueller Loose, S. (2013). Learning affects top down

and bottom up modulation of eye movements in decision making. Judgment and

Decision Making, 8(6), 700–716.

DANISH SUMMARY

Denne afhandling består af tre selvstændige kapitler, som alle beskæftiger sig med

beslutningsadfærd og visuel opmærksomhed. De tre kapitler undersøger hver især

forskellige aspekter af samspillet mellem det visuelle miljø og menneskers mål i

beslutningssituationer, og afhandlingen bidrager med ny indsigt i menneskelige be-

slutningsprocesser. Alle afhandlingens kapitler bygger på eksperimentelle data, som

er blevet indsamlet gennem kontrollerede eksperimenter i computer-laboratorier.

Data til andet og tredje kapitel blev indsamlet i faciliteterne i Aarhus Universitets

Cognition and Behaviour (COBE) Lab. Data til første kapitel blev indsamlet inden

etableringen af COBE Lab på udstyr udlånt af daværende Institut for Marketing og

Organisation (nu Institut for Virksomhedsledelse).

En stor del af afhandlingens analyser bygger på eye-tracking, som er en metode

proces-sporing, hvor deltageres øjenbevægelser optages og kortlægges. Metoden gi-

ver mange muligheder for at undersøge, hvordan mennesker indsamler information,

og hvordan eksterne faktorer kan have indvirkning på denne proces (eksempelvis

hvilke informationer deltagere har indsamlet, og i hvilken rækkefølge informatio-

nerne er blevet indsamlet). Eye-tracking er et kraftfuldt og fremragende redskab til

undersøgelse af beslutningsprocesser, da deltageren stort set ikke er begrænset af

metoden, hvilket er i modsætning til andre metoder (for eksempel "think aloud"og

"information search displays"). Eye-tracking som proces metode er blevet kraftigt

anbefalet (eksempelvis af Bialkova & van Trijp, 2011; Glaholt & Reingold, 2011), og

hvis en mere dybdegående indføring i eye-tracking ønskes, kan bøgerne af Holmqvist

et al. (2011) og Duchowski (2007) varmt anbefales.

Afhandlingens første kapitel, Learning affects top down and bottom up modu-

lation of eye movements in decision making, undersøger de perceptuelle processer,

som driver temporale dynamikker i beslutningstagning. Under rammerne af "top-

down"og "bottom-up"processer undersøger vi informationsreduktions-hypotesen

(the information reduction hypothesis) (Haider & Frensch, 1999). Vi gennemfører

et "repeated measures"eksperiment med binære beslutninger under tre forskellige

præsentationsformater. Vi operationaliserer top-down og bottom-up kontrol som

effekten af individuelle nytteværdi og præsentationsformaters evne til at fange delta-

gernes visuelle opmærksomhed. I kapitlet undersøger vi en hypotese om, at erfaring

med en opgave øger effekten af top-down kontrol på opmærksomhed, som igen fører

xi

xii DANISH SUMMARY

til en reduktion i effekten af bottom-up faktorenes evne til at fange opmærksomhed.

Studiet viser en stigning i top-down kontrol af øjenbevægelser over tid, og vi finder

ligeledes, at beslutningstagerne lærer at rette deres opmærksomhed mod stimuli med

høj nytteværdi og samtidig ignorere stimuli med lave nytteværdi. Desuden finder vi,

at indflydelsen af præsentationens format på opmærksomhed daler over tid, hvilket

indikerer aftagende bottom-up kontrol. Kapitlet er skrevet i samarbejde med Jacob

Lund Orquin and Simone Mueller Loose og udgivet i Judgment and decision making i

2013.

Afhandlingens andet kapitel, Leveraging choice efficiency depends on the environ-

ment, omhandler de omstændigheder, som fører til, at beslutningstagere bliver hurti-

gere og tager bedre beslutninger. Tidligere forskning har vist, at mennesker gennem

erfaring bliver hurtigere og mere præcise i deres beslutninger (Orquin et al., 2013;

Meißner et al., 2016). I dette kapitel undersøger jeg, hvordan visuelle strukturer mo-

dulerer disse ændringer. Jeg gennemfører fire eksperimenter, hvor jeg manipulerer

relevansen af visuel saliens. Jeg manipulerer relevans som den indbyrdes sammen-

hæng (korrelation) mellem en attributs værdi og dens visuel saliens. Resultaterne

fører til konklusionen, at beslutningstagerne i almindelighed bliver hurtigere, men

øget præcision finder kun sted i situationer, hvor visuel saliens har en høj grad af

relevans (dvs. høj korrelation). Beslutningstagerne lærer hurtigt at udnytte relevante

oplysninger og se bort fra irrelevante oplysninger, og jeg konkluderer, at disse æn-

dringer i opgaveløsningen styres af ændringer i opmærksomheds-kontrolprocesser.

Kapitlet er sendt til Organizational Behavior and Human Decision Processes.

Afhandlingens tredje kapitel, The perceptual pull in decision making, er skrevet

i samarbejde med Jacob Lund Orquin, Erik Lahm, George Tsalis og Klaus Grunert.

I dette kapitel undersøger vi balancen mellem top-down og bottom-up kontrol i

beslutningsprocesser både i forhold til effekten på opmærksomhed og effekten på

de endelige beslutninger. For at kunne sammenligne den relative betydning af de to

processer, gennemfører vi et eye-tracking studie, hvor vi manipulerer bottom-up og

top-down processer i et ortogonalt design. Vi manipulerer bottom-up processer ved

at variere størrelsen og saliensen af ”target”-attributter samt positionen og antallet

af ”distractor”-attributter. Top-down processerne manipulerer vi ved at varierede

relevansen af ”target”-attributter gennem tre forskellige opgaver: præferencebaseret

valg, inferentielt valg og præferencebaseret valg med priming. Resultaterne viser at

bottom-up processer spiller en større rolle end top-down processer i at kontrollere

øjenbevægelser, og kapitlet åbner op for spørgsmålet om hvornår bottom-up eller

top-down processer har den største effekt på opmærksomhed.Samtidig finder vi, at

top-down manipulation påvirker brugen af oplysninger i forbindelse med de endelige

beslutninger, og at relevante og fikserede informationer har den største indflydelse

på beslutningerne.

xiii

Litteratur




Duchowski, A. (2007). Eye tracking methodology: Theory and practice, volume 373.

Springer Science & Business Media.

Glaholt, M. G. & Reingold, E. M. (2011). Eye Movement Monitoring as a Process

Tracing Methodology in Decision Making Research. Journal of Neuroscience, Psy-

chology, & Economics, 4(2), 125–146.

Haider, H. & Frensch, P. A. (1999). Eye movement during skill acquisition: More eviden-

ce for the information-reduction hypothesis. Journal of Experimental Psychology:

Learning, Memory, and Cognition, 25(1), 172–190.










CH

AP

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1LEARNING AFFECTS TOP-DOWN AND BOTTOM-UP

MODULATION OF EYE MOVEMENTS IN

DECISION-MAKING1

PUBLISHED IN JUDGMENT AND DECISION-MAKING, VOL. 8, NO. 6 (2013), 700-716

Jacob Lund Orquin

Aarhus University

Martin Petri Bagger

Aarhus University

Simone Mueller Loose

Aarhus University and University of South Australia

Abstract

Repeated decision-making is subject to changes over time such as decreases

in decision time and information use and increases in decision accuracy. We show

that a traditional strategy selection view of decision-making cannot account for

these temporal dynamics without relaxing main assumptions about what defines

a decision strategy. As an alternative view we suggest that temporal dynamics

in decision-making are driven by attentional and perceptual processes and that

this view has been expressed in the information reduction hypothesis. We test

the information reduction hypothesis by integrating it in a broader framework

of top-down and bottom-up processes and derive the predictions that repeated

1Acknowledgments: We thank Jana Jarecki for her helpful comments to the introduction.

1

2 CHAPTER 1. LEARNING AFFECTS EYE MOVEMENTS IN DECISION-MAKING

decisions increase top-down control of attention capture which in turn leads to a

reduction in bottom-up attention capture. To test our hypotheses we conducted

a repeated measures discrete choice experiment with three different information

presentation formats. We thereby operationalized top-down and bottom-up

control as the effect of individual utility levels and presentation formats on

attention capture on a trial-by-trial basis. The experiment revealed an increase in

top-down control of eye movements over time and that decision-makers learn to

attend to high utility stimuli and ignore low utility stimuli. We furthermore find

that the influence of presentation format on attention capture reduces over time

indicating diminishing bottom-up control.

1.1 Introduction

Human decision behavior is consistently inconsistent in its tendency to change

over time and over repeated decisions, yet these changes are mostly seen as a nui-

sance factor or even treated as a theoretical anomaly. In economics the static view of

decision-making is reflected in the assumption about stability of preferences (McFad-

den, 2001) while in psychology a similar assumption is often made about the stability

of decision strategies over time (Riedl, Brandstätter, & Roithmayr, 2008). While both

assumptions have been challenged on different occasions (Kahneman, 2003; Svenson,

1979) many studies implement them implicitly by aggregating choice and process

data over time. In this paper we propose that not only are temporal dynamics in

decision-making much more than a nuisance factor, in fact they are informative to

decision research out of two reasons. First of all, temporal dynamics in decision-

making pose a theoretical challenge to strategy selection models of decision-making

which in itself makes it a topic worthy of study and second, understanding temporal

dynamics calls for a previously neglected perspectives on decision-making. We re-

cently argued that decision research to a large extent has ignored attention processes

and that a better integration of visual cognition into decision research could help

account for a large number of observations (Orquin & Mueller Loose, 2013). Here we

expand our argument by examining temporal dynamics in decision-making, more

particularly how decision-making changes over the course of repeated decisions. We

explore two competing explanations of temporal dynamics in decision-making one

derived from strategy selection theory and one derived from vision research.

Can strategy selection account for temporal dynamics?

Among the many findings on temporal dynamics three have emerged as particularly

robust: Over time decision-makers become faster in making decisions (Meißner &

Decker, 2010; Mueller Loose & Orquin, 2012), use less information in making their

decisions (Payne, Bettman, & Johnson, 1988), and at the same time increase the

accuracy of their decisions (Carlsson, Mørkbak, & Olsen, 2011; Hess, Hensher, &

Daly, 2012; Payne, et al., 1988). The simultaneous reduction in decision time and

1.1. INTRODUCTION 3

information use with an increase in decision accuracy seems counter-intuitive at first,

but can only mean that decision-makers become better or more efficient at making

decisions over time. According to a strategy selection view of decision-making, which

in general terms posits that decision-makers first select a decision strategy and then

implement it in a given decisions task (Glöckner & Betsch, 2008), the increased effi-

ciency could result either from a more efficient application of one particular decision

strategy or from selecting a decision strategy that is more efficient in the given deci-

sion environment. If decision-makers become more efficient in applying a decision

strategy we would expect a decrease in decision time and perhaps also a reduction

in the amount of information that is re-fixated. One could, for instance, imagine

that decision-makers become faster in reading and remembering information which

would lead to shorter fixation durations and fewer re-fixations. On the other hand,

we would not expect any changes as to what or how the information is searched since

the decision strategy itself specifies what information is needed and in what order it

should be acquired (Costa-Gomes, Crawford, & Broseta, 2001). However, this account

of decision efficiency is in conflict with studies showing that decision-makers often

change their search pattern over the course of repeated decisions (Meißner & Decker,

2010; Patalano, Juhasz, & Dicke, 2010). Even though decision-makers may become

more efficient over time in applying one particular decision strategy, this merely

accounts for some of the observations on temporal dynamics. The change in search

pattern could, on the other hand, indicate that decision-makers are likely to change

their decision strategy over time.

If decision-makers learn over repeated decisions to select strategies that are more

efficient to the decision environment we would expect a reduction in decision time,

an increase in decision accuracy, and a change in the information search pattern

because each decision strategy predicts qualitatively different search patterns (Riedl,

et al., 2008). Although this view seems promising as it could potentially explain the

general observations from studies using repeated decision trials there is one major

problem: There is most likely no order for which decision-makers could select their

decision strategies so that they over time would decrease in information use, decision

time and increase in accuracy. In Figure 1 we compare a typical pattern of observed

decision time and accuracy (compare this to Appendix 2) with the predicted deci-

sion time and accuracy of nine different decision strategies. The predicted decision

times and accuracies are borrowed from Payne and colleagues (Payne et al., 1988,

Table 1 column 4) who simulated the performance of nine decision strategies under

different environments. The simulation reports the number of operations which we

use as a proxy for decision time following (Johnson & Payne, 1985). The decision

time and accuracy measures are fitted to the same scale for the sake of comparison.

The figure illustrates that there is no ordering of decision strategies that can produce

or approximate the observed pattern. The predicted decision times and accuracies

are positively correlated across decision strategies while the observed pattern indi-


Figure 1.1. A: Typical pattern of observed decision time and accuracy in a repeated choicetask. B: Ordering of decision strategies in accordance with their predicted decision times andaccuracies (Payne et al., 1988). The y-axis represents normalized values for both decisiontime and accuracy. WADD = weighted additive, EQW = equal weight, EBA = Elimination byaspects, MCD = majority of confirming dimensions, SAT = satisficing, LEX = lexicographic,LEXSEMI = lexicographic semi-order, EBA+WADD elimination-by-aspects plus weightedadditive, EBA+MCD = elimination-by-aspects plus majority of confirming dimensions.

0.4

0.6

0.8

1.0

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t trial

Last

trial

A

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Accuacy

Decision time

0.4

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1.0

WADDMCD

EBA+WADD

EBA+MCDEBA

LEXSEMIEQW LEX

SAT

B

cates a negative correlation. Although the comparison is neither a mathematical nor

empirical proof, it does point us to a theoretical challenge to the strategy selection

view of decision-making: It is impossible to account for the development of decision

time and accuracy in repeated decisions by switching between decision strategies. To

account for temporal dynamics through strategy selection one could, for instance,

relax the assumptions about what defines a decision strategy or about how deci-

sion strategies are mapped to process measures such as information acquisition,

decision time and choice accuracy (see the discussion). In the following section we

pursue an alternative view of temporal dynamics which accounts for the behavioral

observations mainly through attentional and perceptual processes.

Temporal dynamics and perceptual efficiency

In the previous section we examined whether the strategy selection paradigm could

account for temporal dynamics in decision-making such as the development in

decision time, information search patterns, and decision accuracy. The compari-

son between observed and predicted decision time and accuracy suggested that

the strategy selection paradigm cannot account for temporal dynamics in decision-

making except by relaxing the assumptions about what defines a decision strategy

or by introducing new strategies. As an alternative account, we propose that at least

part of the development in decision time, information search, and decision accu-

racy could be driven by increased efficiency in attentional and perceptual processes.

1.1. INTRODUCTION 5

Such a view has previously been expressed in the information reduction hypothesis

(Haider & Frensch, 1999) which accounts for expertise across different domains as

a consequence of perceptual efficiency. The theory posits that experts compared

to beginners are more efficient because they have learned to fixate task relevant

information and ignore task redundant information — hence the term "information

reduction". Information reduction effects has been demonstrated in various domains,

and a recent meta-analysis on the effect of expertise on attention to visualizations

(Gegenfurtner, Lehtinen, & Säljö, 2011) concludes that experts have more fixations

to task-relevant areas and fewer fixations to task-irrelevant areas. Information re-

duction has also been demonstrated in repeated-measures experiments showing

that practice increases fixation likelihood and fixation duration to important stimuli,

and reduces fixation likelihood and duration for irrelevant stimuli (Droll, Gigone,

& Hayhoe, 2007; Hegarty, Canham, & Fabrikant, 2010; Jovancevic-Misic & Hayhoe,

2009; Lee & Anderson, 2001). In decision-making similar observations have emerged,

indicating that decision-makers become more likely to fixate high utility attributes

over the course of repeated decision trials (Meißner & Decker, 2010; Mueller Loose

& Orquin, 2012). decision-makers also reduce the overall number of fixations per

trial over the course of repeated-measures experiments (Fiedler & Glöckner, 2012;

Knoepfle, Tao-yi Wang, & Camerer, 2009; Toubia, de Jong, Stieger, & Füller, 2012).

Although none of these studies address information reduction directly, the increased

attention to high utility information and the overall reduction in information search

indicates that information reduction is likely to happen in repeated decision-making.

If decision-makers reduce information in a manner predicted by the information

reduction hypothesis this could potentially explain the development in decision time,

information search, and accuracy. Whereas the strategy selection theory shows a

positive correlation between decision time, information search, and accuracy across

decision strategies the information reduction hypothesis posits a negative correlation,

i.e. the more accurate you are the less information you look at, and the faster you are.

However, even if it is the case that decision-makers reduce information as sug-

gested above there is still one problem; the information reduction hypothesis does

not account for the underlying cognitive mechanism that leads to information re-

duction. Accounting for temporal dynamics by information reduction is therefore

no different than giving the problem a new name. To avoid this logical loop the fol-

lowing section attempts to integrate information reduction in a broader theoretical

framework and derive hypotheses concerning the development of visual attention in

repeated decision tasks.

Top-down and bottom-up control of attention

An alternative way of viewing information reduction/perceptual efficiency is to see

it as a consequence of top-down and bottom-up processes, i.e. goal and stimulus

driven processes (Corbetta & Shulman, 2002; Theeuwes, 2010). According to this


terminology, the findings above strongly suggest that practice, whether in the form

of years of expertise in a particular field or as practice in repeated measures exper-

iments, increases top-down control of attention. The claim follows logically from

the propositions that top-down control is defined as attention to task relevant stim-

uli, and that practice in repeated measures experiments increases attention to task

relevant stimuli. However, the process through which practice increases top-down

modulation is by no means clear.

One possible explanation is that increasing top-down control is a consequence

of perceptual learning, i.e. an improved ability to identify and discriminate between

sensory inputs. It has, for instance, been demonstrated that playing certain video

games can improve spatial resolution (Green & Bavelier, 2007) and target detection

(Green & Bavelier, 2006) so that experienced video game players become better at

identifying objects in visually cluttered environments. This may lead to enhanced

top-down control of eye movements (West, Al-Aidroos, & Pratt, 2013), and reduced

bottom-up attention capture by distractors (Chisholm, Hickey, Theeuwes, & King-

stone, 2010). Perceptual learning could therefore explain perceptual efficiency in

situations where the target stimulus is difficult to identify or categorize, such as in

comprehending visualizations (e.g. Gegenfurtner, et al., 2011) or when performing

tasks under time pressure, such as in reaction time experiments (e.g. Chisholm, et

al., 2010; West, et al., 2013). However, in the walking experiment by Jovancevic-Misic

and Hayhoe (2009) and in the choice experiment by Meißner and Decker (2010), the

stimuli were easy to discriminate and categorize, and the participants were not under

time pressure, which questions the role of perceptual learning.

Another perspective on increasing top-down control would be the reward-based

model of gaze allocation advocated by Hayhoe and colleagues (Hayhoe & Rothkopf,

2011; Tatler, Hayhoe, Land, & Ballard, 2011). According to their theory, gaze allocation

is crucially dependent on reward systems so that eye movements are guided by the

reward value of gazing at a particular stimulus. Reward value is understood here both

as primary reinforcers such as foods and as secondary reinforcers such as money or

system feedback. It has, for instance, been demonstrated that monkeys are willing to

trade-off food rewards for visual information about members of their social group

(Deaner, Khera, & Platt, 2005), and studies on humans indicate similar trade-off

patterns (Dai, Brendl, & Ariely, 2010). Trommershäuser and colleagues have also

pointed to the fact that most brain areas dedicated to the control of eye movements

are sensitive to rewards, and that neural computations during visual search in humans

and primates are similar to those activated when eye movements are extrinsically

rewarded (Trommershäuser, Glimcher, & Gegenfurtner, 2009). According to this

view, top-down control develops in a feedback loop between the agent and the

environment. Certain gaze behaviors are selected because they lead to rewarding

outcomes such as avoiding collisions with other pedestrians or completing a decision

task successfully.

1.1. INTRODUCTION 7

The question is, of course, what type of feedback decision-makers can rely on in a

repeated decision task in which no explicit feedback is given? One possibility is that

decision-makers monitor their own decision process in terms of how effortful the

decision is and how confident they feel about it (Anzai & Simon, 1979; Payne, et al.,

1988). Such process feedback could potentially serve to guide learning of top-down

control both within and across decision trials. Given that decision-makers generate

some form of process feedback we therefore hypothesize the following, in accordance

with the reward-based model of gaze allocation (Hayhoe & Rothkopf, 2011; Tatler, et

al., 2011), and the information-reduction hypothesis (Haider & Frensch, 1999):

H1: Learning during repeated decision trials increases top-down modulation of

attention leading to higher fixation likelihood for important attributes and

lower fixation likelihood for unimportant attributes.

The hypothesis poses another important question: What is the role of bottom-up

modulation during the development of top-down modulation? In line with the biased

competition theory of selective attention (Desimone & Duncan, 1995) we suggest

that, in a situation with weak top-down modulation, the competition between stimuli

will be based on bottom-up processes. A similar view is proposed by Theeuwes

(2010), who argues that selective attention is initially completely driven by bottom-

up processes and only later (a few hundred milliseconds after stimulus onset) by

top-down processes. Both theories suggest that in the absence of top-down control

we should expect a stronger influence of bottom-up control. If H1 is correct and

top-down control increases over repeated decisions we would expect that bottom-up

control has a relatively larger influence in the beginning of the experiment when

top-down control is still relatively weak.

However, the important question is what will happen later in the learning process

when top-down modulation becomes relatively stronger? One possibility is that

increasing top-down modulation will diminish bottom-up control. It has, for instance,

been shown that top-down factors, such as semantic or contextual cues about a

visual scene, feature based attention, object representations, task demands, and

rewards for task performance, all override the effect of visual saliency (Kowler, 2011).

Alternatively, it has been suggested that changes in the balance between the two

processes over time will favor the process that makes more efficient use of cognitive

resources (Nyamsuren & Taatgen, 2013; Salvucci & Taatgen, 2008). According to

this view, both top-down and bottom-up modulation could in fact increase over

time if both processes contributed to higher perceptual efficiency. Such interaction

effects between top-down and bottom-up processes have been demonstrated on

attention capture (Nyamsuren & Taatgen, 2013) and encoding to short term memory

(Nordfang, Dyrholm, & Bundesen, 2013). Although interactions between top-down

and bottom-up control are theoretically possible in laboratory experiments, studies


on naturalistic tasks often show a limited role of bottom-up and interaction processes

in gaze allocation.

According to our previous proposition, strong top-down modulation should re-

duce bottom-up modulation except in the special case in which an interaction be-

tween the two processes leads to higher perceptual efficiency (Nyamsuren & Taatgen,

2013). Given that there is no interaction or that the interaction between top-down

and bottom-up processes remains constant, we therefore hypothesize the following:

H2: bottom-up modulation of attention is stronger in the beginning of the exper-

iment and diminishes over time as a consequence of increasing top-down

modulation.

Experimental approach

In order to examine Hypotheses 1 and 2, we decided for an experimental approach

combining measured within-subjects and manipulated between-subjects indepen-

dent variables. top-down factors were operationalized as individual level attribute

importance, while bottom-up factors were operationalized through information pre-

sentation formats. Combining measured and manipulated independent variables

has the main advantage that it disentangles top-down and bottom-up modulation.

Earlier studies have shown that important attributes gain higher fixation likelihood

over time (Meißner & Decker, 2010), but it is in principle impossible to rule out that

the effect could have been caused by bottom-up factors or interactions between

top-down and bottom-up factors, i.e. the important attributes could have been more

salient than the less important attributes.

The importance of attributes can also be directly manipulated through task in-

structions, which, for instance, increases the utility of the attribute in one situation

but lowers it in another (Bialkova & van Trijp, 2011; van Herpen & Trijp, 2011; Viss-

chers, Hess, & Siegrist, 2010), however, a more subtle approach is to derive it from

individual level estimates of part-worth utilities. By taking a measurement approach

to attribute importance it should be possible to show that participants who assign a

higher level of importance to an attribute will increase their fixation likelihood for

that attribute, compared with participants who assign a lower importance.

Regarding bottom-up factors, one common approach in decision research has

been to manipulate the format in which the information is presented using, for

instance, verbal matrices or more naturalistic product representations (Huang & Kuo,

2011; Smead, Wilcox, & Wilkes, 1981; Söllner, Bröder, & Hilbig, 2013; Van Raaij, 1977).

Although this method involves less control over individual bottom-up factors, such

as saliency, size and position of information elements, one can think of all these

factors as captured across presentation formats. In this experiment, the product

representation format varies in, for instance, the saliency and size of attributes relative

to a verbal or visual matrix presentation. Using this approach, the strength of bottom-

1.2. METHOD 9

up modulation on gaze allocation is observable as the difference in fixation likelihood

between the presentation formats as well as in the effect size of the presentation

format model terms. If an increasing top-down modulation competes with bottom-

up modulation, we therefore expect that differences in fixation likelihood across

presentation formats diminish over the course of repeated decisions.

In line with H1, we expect that learning over the course of the experiment will

increase top-down modulation, leading to a larger effect size of attribute importance

over time and to increasing fixation likelihood when attributes are high, rather than

low, in importance. We also expect, in line with H2, that increasing top-down mod-

ulation will diminish the effect of bottom-up modulation, leading to diminishing

differences in fixation likelihood between the presentation formats and a smaller

effect size of presentation format over time.

1.2 Method

Participants

Sixty eight participants were recruited on campus, thereof 62 percent male. The

average age was 25.6 years. To qualify, participants had to be of normal sight and had

to buy and eat fruit yoghurt at least once a month.

Experimental design

We conducted a discrete choice experiment in which participants made choices

between four alternative fruit yoghurts and a no-choice alternative. Each participant

saw 48 choice sets in which six product attributes varied on four levels according

to a D-optimal design, which maximizes the differences in attribute levels between

choice alternatives (Street & Burgess, 2007). Accordingly, all four choice alternatives

in a set differed in those attributes with four levels (brand, flavor, fat percentage, and

price), while attributes with two levels (organic and health claim) were present twice

in each choice set. The presentation order of the choice sets was randomized across

participants. As an additional between-subjects factor, the choice set presentation

format varied between a verbal information matrix (N = 22), a visual information

matrix (N = 24), and a realistic product representation (N = 22) (Mueller, Lockshin, &

Louviere, 2010).

Materials and measures

The three stimulus presentation formats were operationalized as follows: For the

verbal and visual matrix formats the attributes were presented in six rows and the

alternatives as four columns within the rows. The attributes were, from top to bottom:

brand, flavor, fat percentage, organic claim, health claim, and price. The product


representation format was operationalized as individual products presented next to

each other. The attributes were inserted on the products with the brand at the top of

the product followed by the flavor, fat percentage, organic claim in the lower right

and health claim in the lower left of each product, and price at the very bottom below

each product. The verbal information matrix was based on written descriptions of

the attribute levels. Each attribute description was kept to a minimum number of

letters stating only the name of the attribute level, such as "strawberry" or "peach"

for the flavor attribute or "Arla" or "Cultura" for the brand attribute.

The position of the attributes remained constant throughout the experiment. Two

of the attributes, organic and health claim, had two levels (absent or present). The

absence of either the organic or health claim on an alternative was operationalized

as an empty cell in the verbal and visual matrices or as empty space in the product

representation format. A pre-test ensured that all attribute levels were sufficiently

large to be easily readable in all three presentation formats at a distance of 60 cm

from the screen which is the optimal distance for the Tobii 2150 eye-tracker system

used in the study.

Assuming that participants remained at a distance of 60 cm to the screen, the

individual attributes were separated by an average angle of 2.3 degrees for the verbal

and visual matrices and an average angle of 2 degrees for the product representation

format. The spacing of attributes was chosen so that it would be impossible for

participants to foveate more than one attribute at the time.

The yellow highlighted areas in Figure 2 represent the rank of product attributes

by visual saliency as assessed by the Itti-Koch algorithm (Itti & Koch, 2001). The

algorithm predicts a visual scanpath based on a computation of visual saliency,

i.e. the color, intensity, and orientation of stimuli, and gives an impression of how

attention would be distributed in the absence of top-down modulation. There were

no systematic differences in visual saliency between product attributes in the verbal

information matrix (lower left of Figure 2). In the visual information matrix (lower

middle of Figure 2), the health claim had the highest visual saliency followed by brand

(top row), flavor (second row), and organic claim. In the product representation

format, the attribute flavor had the highest visual saliency followed by brand. The

relative size of the attributes also differed between the product representation and

the two information matrices.

Eye movements were recorded using a Tobii 2150 eye-tracker (21 inches, 50 frames

per second). Respondents’ choices were recorded as mouse clicks on the chosen

product.

Procedure

Upon entering the laboratory, participants were seated in front of the eye tracker and

randomly assigned to one of the three presentation format conditions. After calibra-

tion, each participant completed 48 choice sets. Before each choice set, respondents

1.2. METHOD 11

Figure 1.2. First column from top to bottom: Examples of experimental stimuli for the verbalinformation matrix, visual information matrix and product representation format. Secondcolumn: Examples of the visual saliency of attributes for the three presentation formats.

had to click on a calibration cross that centered their gaze between the two middle

choice alternatives. The first fixation of each choice set was discarded from further

analysis as this fixation is a direct consequence of having fixated on the fixation cross

immediately before stimulus onset. The first fixation is therefore driven neither by

top-down nor bottom-up processes which makes it of little interest to the analysis.


Analytical plan

The analysis unfolded in four steps: First we assessed the stability of preferences over

time. This is an important prerequisite as any conclusion regarding learning effects

on attention would only be valid if the participants did not change their preferences

during the experiment. The first step was carried out by splitting the choice sets into

three bins of 16 choice sets, separately estimating individual level choice models for

each of the bins and checking if there were any differences within participants across

the three bins. In the second step we modelled choices across all 48 choice sets for

each participant at a time, thus providing individual level estimates of part-worth

utilities and attribute importance. In the third step we merged the individual level

choice data with the attention data and analysed attention selection as a function of

trial order, attribute importance, and presentation format. To interpret the model we

computed the correlation between fixation likelihood and attribute importance for

each trial which reflects changes in top-down modulation. To interpret the model

with regards to bottom-up modulation we plotted the fixation likelihood for all

six attributes across the presentation formats. In the fourth step of the analysis

we calculated effect sizes for top-down, bottom-up, and interaction components

separately for each of the 48 trials to assess changes in modulatory strength over time.

1.3 Results

Step 1. Analysis of stability of preferences

In the first step of the analysis, participants’ choices were analyzed individually for

three consecutive bins of 16 choice sets based on random utility theory (Louviere,

Hensher, & Swait, 2000), according to which subjects choose the alternative that

maximizes their subjective utility. Utility is defined as:

Ui =Vi +εi (1.1)

where Ui is the utility of the choice alternative i , Vi is the observable or systematic

utility component, which is a function of its attributes, and εi is the random utility

component. The systematic component Vi is assumed to be an additive and linear

function in the attributes X . The systematic component is defined as:

Vi =∑

sβs Xi s (1.2)

where Xi s is the value of alternative i with attributes s(s = 1, . . . ,6), and βs are

part-worth utilities to be estimated. Under the assumption that the random error

terms εi are independently and identically extreme value distributed, the choice

probability of alternative i being chosen from all the alternatives in choice set T

follows the closed form expression of the multinomial logit (MNL) model

1.3. RESULTS 13

P (i ) = exp(Vi )∑i ′∈T exp(Vi ′ )

(1.3)

Parameters are estimated with maximum likelihood where likelihood is given by:

L =N∏

n=1

∏i∈Cn

Pn(i ) fi n (1.4)

where N represents the number of choice observations and fi n is a dummy

variable such that fi n = 1 if alternative i is chosen and fi n = 0 if an alternative is not

chosen from the choice set.

Attribute importance was approximated for each participant and each choice

set bin with the share of variance explained by each attribute, assuming that the

presented attributes determine 100 % of the choice process (Lancsar, Louviere, &

Flynn, 2007; Louviere & Islam, 2008). All choice models were run in Latent Gold

Syntax 4.5 (Statistical Innovations Corp.).

Differences in attribute importance were calculated on an individual level be-

tween the first and second as well as the second and third bin. T-tests were per-

formed to test if these changes in attribute importance differed significantly from

zero. Only brand differed significantly between the first and second choice set bin

(t =−3.038, p = 0.003), while all other attributes were not significantly different from

zero. Accordingly, results overall suggested that participants did not change prefer-

ences over the course of the experiment.

Step 2. Analysis of choice data

Because preferences were confirmed to be stable during the experiment, participants?

choices were analyzed individually for all 48 choice sets according to equations (1)

to (4), resulting in individual level importances [0;100] for all six product attributes. A

summary of average attribute importance is provided in Table 1.

Step 3. Analysis of fixation likelihood

Before analyzing fixation likelihood we first inspected the proportion of attributes

fixated per trial as a complete or very high degree of attendance or non-attendance

would go against the purpose of the analysis. The inspection revealed that roughly

50 % of the attributes are fixated in the beginning of the experiment for the verbal and

visual information matrices and less than 40 % are fixated in the product representa-

tion format. The proportion of attributes fixated furthermore declines throughout

the experiment (see Figure 3).

In the third step of the analysis, individual level attribute importances were

merged with the eye tracking data. We estimated fixation likelihood by means of

a generalized linear mixed model (GLMM), using fixation selection as the dependent


Table 1.1. Average attribute importance in percent (N = 68).

Attribute Mean SD

Flavor 36.63 26.83

Price 19.48 19.20

Fat 14.54 14.19

Organic claim 9.93 11.04

Brand 9.90 10.42

Health claim 5.58 7.94

Note: Attributes are sorted by decreasing average im-portance.

Figure 1.3. Proportion of AOI’s fixated across presentation formats.

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Condition ● ● ●Verbal Visual Product

variable (0 indicating no fixation to the attribute and 1 indicating that the attribute

was fixated at least once during a trial). Four independent variables were included

in the model: presentation format (verbal information matrix, visual information

matrix, and product representation), attribute (flavor, price, brand, fat percentage,

organic claim, and health claim), attribute importance derived from individual level

1.3. RESULTS 15

estimates, and experimental trial order.

Data from all 68 participants and 48 choice sets were used to estimate the model.

The GLMM assumed a binomial distribution for the dependent variable with a log link

function and a random intercept for participants to capture individual differences in

fixation likelihood. The model was estimated by means of maximum likelihood esti-

mation with quadrature approximation. This approximation was used to obtain log

likelihood values for model comparison and effect size measurement (Schabenberger,

2007; Stroup, 2013).

We estimated a full factorial model and compared it to reduced models. Model

comparison (LR tests) revealed that the full factorial model provided the best fit, and

that model was therefore used for interpretation. Table 2 shows the type III test of

fixed effects.

All effects in the final model were significant indicating that trial order, presen-

tation format, attribute importance, and attribute type as well as their interactions

contribute to explain fixation likelihood. In relation to the hypotheses we were mainly

interested in the interaction terms between trial and importance and between trial

and presentation format which would indicate changes in top-down and bottom-up

attention capture over time.

Table 2 shows that the model terms trial×importance and trial×importance×attribute

are significant which means that the influence of attribute importance on fixation

likelihood changes over time. Similarly, the significance of the interaction terms

trial×format and trial×format×attribute means that the influence of presentation

format on fixation likelihood changes over time. It is important to observe that the

interpretation of the interaction terms between importance and format is limited

because the two main effects might not be causally independent.

In order to interpret the changes in top-down processes over time we computed

the observed correlation between fixation likelihood and attribute importance for

each trial across participants, attributes, and presentation formats. The correlations

are plotted in Figure 4. We also fitted a linear function across the correlation values

showing that the development was significantly different from zero (t = 6.212, p <0.001). The figure shows that the observed correlation between fixation likelihood

and importance increases over time, which in relative terms means that important

attributes are more likely and unimportant attributes are less likely to be fixated

in the end, rather than in the beginning, of the experiment in accordance with the

information-reduction hypothesis. The predicted fixation likelihood as a function of

attribute importance is shown in the Appendix 1.

To interpret the change in bottom-up processes over time we plotted the pre-

dicted fixation likelihood for each attribute per presentation format across trials (see

Figure 5). The plots revealed that for the attributes brand, fat percentage, organic

claim, and health claim the slopes are fanning in over the course of the experiment,

while for flavor the slopes fan out and for price there is no clear development. One


Table 1.2. Fixation likelihood as a function of presentation format (format), attribute, trial, andimportance.

Effect Num D f Den D f F -Value Pr > F

Format 2 3125 14.41 < .0001

Attribute 5 3125 154.31 < .0001

Trial 1 3125 362.63 < .0001

Importance 1 3125 134.48 < .0001

Format×Attribute 10 3125 14.09 < .0001

Trial×Format 2 3125 4.57 0.0105

Trial×Attribute 5 3125 29.20 < .0001

Importance×Attribute 5 3125 47.58 < .0001

Trial×Importance 1 3125 35.94 < .0001

Trial×Format×Attribute 10 3125 7.73 < .0001

Trial×Importance×Attribute 5 3125 4.89 0.0002

Importance×Format 2 3125 23.25 < .0001

Importance×Format×Attribute 10 3125 9.69 < .0001

Trial×Importance×Format 2 3125 4.34 0.0132

Trial×Importance×Format×Attribute 10 3125 5.05 < .0001

Notes: Variance random intercept = 0.7439 (std. err. 0.1341). −2 Log Likelihood =78043.17, R2 = 0.1624, Generalized χ2 = 79207.63, Generalized χ2/DF = 1.01.

way to interpret the plots is that the fanning in of slopes means that the presentation

formats become more similar over time with regards to fixation likelihood while

slopes fanning out means that the presentation formats become more dissimilar.

With the exception of flavor and price, the plots suggest that the three presentation

formats become more similar over time. The diminishing difference between the

presentation formats could indicate a reduced modulatory influence of bottom-up

control over time.

Step 4. Analysis of top-down and bottom-up modulatory strength over

time

To determine how the modulatory strength of top-down and bottom-up processes

change over time we computed the effect sizes of top-down and bottom-up compo-

nents across trials. The idea is that if top-down and bottom-up modulation changes

over time this would be reflected in the effect size of the model terms corresponding

1.3. RESULTS 17

Figure 1.4. Observed correlation between fixation likelihood and attribute importance overtime.

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to the two cognitive processes. Separate models were estimated for each of the 48

trials and effect sizes were determined with partial generalized R-Squares by stepwise

integration of parameters into the model (Aerni, Scholderer, & Ermen, 2011). An

example of the computation is provided in Table 3.

The model terms were divided into three factors; top-down factors (importance

and attribute×importance), bottom-up factors (format and format×attribute), and in-

teraction factors (importance×format and importance×format×attribute). By adding

up the partial effect sizes for the top-down, bottom-up, and interaction factors, total

effect size components were calculated for the top-down, bottom-up, and interac-

tion components in each trial. The total effect size components were analyzed by

regressing trial order on each component. The slope of the top-down component

was significantly different from zero (t = 2.212, p = .032), meaning that over time

the effect of top-down modulation on fixation likelihood increases. The slope of the

bottom-up component was not significantly different from zero (t =−1.192, p = .239),

which suggests that the modulatory strength did not change over time2.

2In order to test if the significance level could be due to low statistical power (48 observations) webootstrapped the data increasing the number of observations to 200. By bootstrapping the slope became


Table

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1.3. RESULTS 19

Figure 1.5. Fixation likelihood for the six attributes across presentation formats.

Brand Flavour

Fat Organic claim

Health claim Price

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tors was not significantly different from zero (t = −1.040, p = 0.304). As for step 3

of the analysis, it is important to observe that the interpretation of the interaction

component is limited since we cannot assume that the two main effects are causally

independent.

significantly different from zero.


Figure 1.6. bottom-up, top-down and interaction effect sizes over time.

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1.4 Discussion

Summary of results

In line with the reward-based model of gaze allocation (Hayhoe & Rothkopf, 2011;

Tatler, et al., 2011) we hypothesized that top-down modulation is learned through

interaction with the environment and that modulatory strength increases as partici-

pants become more experienced with a task or situation. The modulatory increase

will lead to higher fixation likelihood for task relevant stimuli and lower fixation

likelihood for task redundant stimuli consistent with the information reduction hy-

pothesis (Haider & Frensch, 1999). This prediction was expressed in H1. Furthermore,

we hypothesized that an increase in top-down modulatory strength would reduce

bottom-up attention capture as the two processes have been shown to compete

for control over eye movements (Desimone & Duncan, 1995; Theeuwes, 2010). This

prediction was expressed in H2.

In order to examine the hypotheses we conducted a repeated measures discrete

choice experiment manipulating three different presentation formats. In the first

step of the analysis we compared the choice models based on bins of the first, middle

and last groups of choice sets. The analysis revealed that participants were largely

1.4. DISCUSSION 21

stable in their preferences throughout the experiment, which is an important point

when drawing conclusions about learning effects. In the second step of the analysis

we modeled individual level estimates of attribute importance. In the third step we

merged the individual level estimates with the attention data to model and analyze

the effect of top-down and bottom-up factors over time. The analysis revealed signifi-

cant interaction effects between trial order and attribute importance and trial order

and presentation format, indicating a change over time in top-down and bottom-up

processes. To interpret the direction of effects, we computed the correlation across

trials between fixation likelihood and importance. Plotting the correlations revealed

a positive slope demonstrating that fixation likelihood increases over time when an

attribute is of high importance to the decision-maker relative to when the attribute

is of low importance to the decision-maker. In order to interpret the direction of

effects for bottom-up processes, we plotted the predicted fixation likelihood for all

six attributes across the three presentation formats. The plots revealed that for the

most part the slopes were fanning in, indicating that the fixation likelihoods became

more similar across presentation formats over time.

In the fourth step of the analysis we tested the modulatory strength of top-down

and bottom-up processes over time. In order to do so, we estimated the effects

sizes of importance and presentation format factors for each trial separately. The

analysis revealed an increase in the effect size of importance over time, suggesting

that top-down modulation increases over time, thus confirming H1. The results for

bottom-up modulation were less clear, as the effect size of presentation format factors

did decrease over time, although the slope was not statistically significant. Future

experiments with more observations are required to further test changes in effect

sizes in order to adequately confirm or reject H2.

Alternative interpretations of our data

So far, we have mainly focused on one interpretation of our data in accordance with

the information reduction hypothesis (Haider & Frensch, 1999) and the reward based

model of gaze allocation (Hayhoe & Rothkopf, 2011; Tatler, et al., 2011). However, it

is worth considering at least a few alternative interpretations of the data. While the

information reduction hypothesis considers developments in perceptual efficiency it

would be worthwhile to also consider whether developments in cognitive efficiency

could explain the observations or part hereof. If we think of cognitive efficiency in

terms of cognitive skill acquisition there are at least four possible interpretations (Lee

& Anderson, 2001): The decrements in fixation proportions and fixation likelihood

could have been driven by cognitive skill acquisition through a) transforming or

collapsing the individual components of the procedure, b) strengthening the compo-

nents of the procedure, or c) changing the procedure altogether. Finally we can also

conceive of cognitive skill acquisition as a process of becoming familiar with the task

requirements thereby reducing initial task confusion. The first view of cognitive skill


acquisition is to see it as a result of transforming or collapsing a multistep procedure

into one or more macro procedures (Newell & Rosenbloom, 1981). One could, for

instance, hypothesize that the decrease in proportion of fixations would stem from

participants collapsing smaller process steps such as scanning alternatives for an

overview, comparing alternatives or attributes, checking chosen alternative and so

forth into larger process steps. One possibility could, for instance, be to collapse

two binary comparison steps into one trinary comparison (on binary and trinary

comparisons see (Russo & Leclerc, 1994)) thereby decreasing the time and number

of fixations needed to complete the decision task. Based on our class of analysis we

cannot exclude the possibility that participants were collapsing process steps and

thereby decreasing the number of fixations and decision times. Future studies might

address this question using different classes of scanpath analyses (Holmqvist et al.,

2011). It would, for instance, be interesting to examine if there is a shift in binary to

trinary comparisons over time or whether other steps in the decision process are

being transformed over time.

A second view of cognitive skill acquisition is to see it as resulting from increased

efficiency in performing the individual task components (Anderson, 1982). Although

this particular view is strongly associated with the notion of "strengthening" in Ander-

son’s ACT theory one could also think of efficiency in terms of automaticity. It seems

plausible that individual process steps will require more deliberation in the early trials

while the later trials might be characterized by a more automatic execution. Another

possibility is that participants will rely increasingly on memory retrieval, for instance,

when searching for acceptable attribute levels. Because of the D-optimal design all

attribute levels were present in every choice set which would allow participants to

retrieve the last attribute level from memory after having fixated the first three levels.

Such a mixed visual search and memory retrieval approach would lead to a decrease

in the number of fixations needed to complete the decision task and perhaps also a

reduction in the decision time. Based on our analyses we cannot exclude the possibil-

ity that participants became more efficient in terms of automaticity nor can we do

so by simply inspecting individual fixation durations over time as fixation durations

might not be closely related to deliberate versus automatic decisions (Horstmann,

Ahlgrimm, & Glöckner, 2009). Future studies might wish to examine such processes

by analyzing changes to the scanpath over time or by examining multiple process

measures (Glöckner, 2009).

A third view of cognitive skill acquisition is to see it as resulting from selecting a

faster method or strategy (Crossman, 1959). In a decision task this could mean that

participants change from one decision strategy to another that is faster such as going

from a weighted additive strategy to a lexicographic or satisficing strategy (see the

introduction). If we qualitatively compare individual participants? scanpaths from

the first and the last trials it does, in fact, seem like the participants are changing their

decision strategy dramatically. While the first few trials are characterized by partici-

1.4. DISCUSSION 23

pants fixating many or most of the attributes the last trials are often characterized

by the participants fixating only one or two rows of attributes. Such a simple inspec-

tion suggests that participants over time go from a slower strategy involving more

fixations to a faster strategy involving fewer fixations. The only problem with this

claim is that the change in strategy would have to occur gradually which is in conflict

with the way decision strategies are currently specified. If we adhere to the view that

decision strategies are qualitatively different and discrete processes then the gradual

change in fixation patterns can suggest two things: a) either participants use the

same decision strategy throughout the experiment but the way it is implemented in a

search process changes (concerning instability of search patterns see (Costa-Gomes,

et al., 2001; Svenson, 1979)) or b) participants simply do not use decision strategies

in the form or to the extent they have been specified in the decision literature (for a

related discussion see (Orquin & Mueller Loose, 2013)). In the case of participants

maintaining a stable decision strategy but changing their fixation pattern over time,

it is unlikely that we can either confirm or reject the hypothesis based on the choice

and fixation data alone, but future studies might look into this question using other

techniques. Regarding the second implication, one can either reach the conclusion

that decision-makers do not rely on decision strategies or that decision strategies

are not discrete entities. Future studies might wish to relax the assumption about

discrete and deterministic decision strategies and look into probabilistic or stochastic

specifications instead (Bergert & Nosofsky, 2007). Given a relaxed assumption about

discrete decision strategies we cannot exclude that participants actually changed

their strategy during the experiment and that this strategy change explains the devel-

opment in proportion of fixations and fixation likelihood. However, we can exclude

that participants made any discrete changes in their decision strategies since our

data reveals a gradual change in fixation patterns.

Another possible view of our data is to see the decline in fixation proportions and

fixation likelihood as a consequence of task-level familiarization, or in other words a

reduction in task-confusion. This hypothesis suggests that participants during the

first few trials are uncertain about the task requirements which would increase the

number of fixations because additional time and effort is spent on becoming familiar

with the task. If participants are confused about the task requirements during the first

few trials we should also expect them to be less consistent in their choices. Inspecting

the development in choice consistency over time indicates that participants are,

in fact, less consistent in the beginning of the experiment. However, two aspects

speak against participants being confused: First of all, choice consistency increases

gradually throughout the experiment albeit with the strongest increase in consistency

in the beginning of the experiment (See Appendix 2). Second, even in the first trial

participants are highly consistent with more that 70 % choosing the highest utility

alternative. The high degree of consistency suggests that participants cannot be

confused about the task requirements otherwise choices would be random. Although


it is plausible that participants experienced a certain degree of confusion in the first

couple of trials it is difficult to say whether this could explain the development in

proportion of fixations or in top-down and bottom-up control. Future studies could

look into this process by more directly manipulating task confusion and test whether

it amplifies, for instance, bottom-up control of attention.

1.5 Implications and future research

The study has several implications for research on decision-making, as well as re-

search on eye movements and attention. Firstly, we replicate findings from prior

studies on top-down and bottom-up effects in decision-making (Orquin & Mueller

Loose, 2013), and extend these findings by showing that the modulatory processes

change over time. The study lends support to the reward-based model of gaze alloca-

tion (Hayhoe & Rothkopf, 2011; Tatler, et al., 2011) by showing that learning increases

top-down modulation of attention. Furthermore, we present evidence suggesting

that top-down and bottom-up processes compete for influence over eye movements,

and that increases in top-down modulation could diminish bottom-up modulation.

However, based on this experiment alone, we cannot say whether the likely reduction

in bottom-up modulation is caused by the increase in top-down modulation. Another

possibility could be that bottom-up modulation decreases over time even when top-

down modulation remains equal. On the other hand, there are theoretical reasons

(Desimone & Duncan, 1995), as well as empirical findings (Kowler, 2011), suggest-

ing that the two processes influence each other in what could be a competition for

control over eye movements. Future studies should address this issue and examine

the more precise circumstances under which top-down and bottom-up processes

are in competition, and when they interact to amplify attention capture (e.g. Folk,

Remington, & Johnston, 1992).

Furthermore, due to the experimental approach, several bottom-up factors were

manipulated simultaneously across the three presentation formats. Although it was

possible to demonstrate separate effects of top-down and bottom-up processes

using this approach, it still leaves open the question of what particular bottom-up

processes were acting on attention capture. Future studies could take advantage of a

more structured approach in which bottom-up factors, such as visual saliency, size,

and position, are manipulated separately.

The study also lends support to the information reduction hypothesis (Haider &

Frensch, 1999), by showing that participants over time learn to fixate important infor-

mation and ignore less important information. The results imply that participants

in choice experiments become more efficient at a perceptual level over time, which

could explain how decision times decrease and accuracy increases over the course

of repeated measures experiments. We do not wish to say that no gains occur at a

cognitive skill level, which most likely is the case, however our findings suggest that

1.5. IMPLICATIONS AND FUTURE RESEARCH 25

an important reason for the reduction in decision time and increment in accuracy is

due to perceptual efficiency. An interesting question for future research is, therefore,

to examine the degree to which the reduction in decision time and gain in accuracy

can be explained by gains in perceptual efficiency and cognitive skill level.

Another implication from our findings relates to process tracing studies, partic-

ularly studies measuring eye movements. The fact that attention processes change

over time, as a consequence of learning, should matter to most experimenters, since

the vast majority of eye tracking studies in judgment and decision-making are based

on repeated measures experiments. Because of the pervasive use of repeated choice

experiments, we must conclude that these studies lead to learning effects as demon-

strated in this paper. Our data show that learning occurs rapidly right from the begin-

ning of the experiment. Particularly the effect of bottom-up modulation is subject to

a steep decline in the first 3-4 trials (see Figure 6). It could potentially be problematic

to aggregate process measures over time particularly if one is interested in improv-

ing choice models based on process measures (Balcombe, Fraser, & McSorley, 2011;

Hensher, Rose, & Greene, 2005; Meißner, Musalem, & Huber, 2012; Scarpa, Zanoli,

Bruschi, & Naspetti, 2013). Assuming that temporal changes in top-down and bottom-

up processes is problematic for the estimation of choice models one could possibly

counteract or at least diminish this change by randomizing the position of attributes

within-subjects. It has, for instance, been shown that randomizing the position of

information elements from trial to trial diminishes information reduction (Haider

& Frensch, 1999). However, randomizing the attribute position within-subjects may

lead to other effects besides diminishing information reduction and more studies

would be needed to examine the effects of such a manipulation on top-down and

bottom-up processes.

Last but not least, throughout the study we have argued that temporal dynamics

constitute a theoretical challenge to the strategy selection view of decision-making.

In the introduction we demonstrated that there is no ordering of decision strategies

that will lead to a decrease in decision time and increase in accuracy. In the previous

section we discussed different possibilities for relaxing the assumptions about deci-

sion strategies and whether this could explain our observations. Although relaxing

the assumptions about strategy selection and what defines a decision strategy would

fit the theory better to the data we suggest that a more promising approach would be

to reconsider the role of visual processes in decision-making.


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1.7 Appendix

Appendix 1

Fixation likelihood as a function of trial for high importance and low importance.

The upper and lower quartiles were used as measures of high and low importance

respectively.

Figure 1.7. Fixation likelihood for the six attributes across presentation formats.

Product Verbal Visual

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1.7. APPENDIX 33

Appendix 2

Figure 1.8. Decision time a) and hit rate b) as function of trial. The hit rate is the percentageof participants who choose the alternative with the highest utility according to their ownpreferences. The hit rate indicates the degree of choice consistency and is conceptually similarto the measure of choice accuracy in Payne et al. 1988.

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2EFFICIENCY GAINS IN REPEATED BINARY CHOICE

ADAPTATION TO THE TASK ENVIRONMENT1

SUBMITTED TO ORGANIZATIONAL BEHAVIOUR AND HUMAN DECISION PROCESSES

Martin Petri Bagger

Aarhus University

Abstract

It is a well-established result in judgment and decision-making research that

decision-makers become faster and more accurate through experience with a

task. However, if the structure of the task environment varies, different types of ef-

ficiency gains may occur, and under some conditions there may even be no gains

at all. Four multiple-cue probability learning experiments were conducted in

which the task environment was varied. In addition to the values of 16 attributes,

we varied their visual salience and (across experiments) the validity of visual

salience as a cue to the values of the attributes. Participants became faster under

all conditions but they only became more accurate when visual salience had high

cue validity. The results suggest that decision-makers learn to utilise relevant

information and discard irrelevant information quite rapidly and that changes in

task performance are controlled by changes in cue reactivity and other attention

control processes that depend on the structure of the task environment.

1Acknowledgments: I would like to thank Hans Jørn Juhl, Anne Odile Peschel, Jacob Lund Orquin,Joachim Scholderer and participants in the Markets, Organization and Behaviour Seminar in February2016 at Aarhus University for valuable comments on this research project.

35

36 CHAPTER 2. EFFICIENCY GAINS IN REPEATED BINARY CHOICE

2.1 Introduction

Procedural invariance is a central assumption in models of rational decision-making

(Tversky, Slovic & Kahneman, 1990): equivalent ways of representing a choice task

should not change the order in which a rational decision-maker prefers the alterna-

tives in a choice set. Empirically, however, violations of this assumption appear to be

the norm rather than the exception (e.g., Slovic, 1995). Classical examples include

preference reversals (Slovic & Lichtenstein, 1968; Tversky et al., 1990) and framing

effects (Tversky & Kahneman, 1981).

Unlike classical decision analysis, the Brunswikian research tradition has always

held it as a central notion that performance on a decision task simultaneously de-

pends on the characteristics of the task environment and the utilisation of the avail-

able information by the decision-maker (e.g., Brunswik, 1952, 1955; Gigerenzer &

Goldstein, 1996; Hammond, 1966; Hammond & Stewart, 2001; Tucker, 1964). From

this perspective, a surface attribute that characterises the way in which a choice task

is represented is not necessarily an unwanted confounder. Three prototypical cases

can be distinguished:

• Case 1: Noise. If the surface attribute is unrelated to the objective function of

the decision task, it might weaken the performance of a decision-maker but in

an unsystematic manner.

• Case 2: Non-zero cue validity but insufficient cue utilisation. If the surface

attribute is correlated with the objective function but its diagnostic value is not

utilised by the decision-maker, it should leave the performance of the decision-

maker unaffected or, if its cue validity is particularly weak, add noise to the

task.

• Case 3: Non-zero cue validity and sufficient cue utilisation. If the surface at-

tribute is correlated with the objective function and the decision-maker utilises

its diagnostic value, the performance of the decision-maker may even increase

if the surface attribute can substitute for other attributes that are more difficult

to process.

The aim of the research presented here is to investigate how decision-makers learn

to cope with the presence of such a surface attribute and how the efficiency of their

choice processes is moderated by the type and strength of the relationship between

the surface attribute and the objective function of the task. In the four experiments

presented later on, we will utilise a multiple-cue probability learning task. The surface

attribute we are interested in is visual salience, perhaps the most obvious but maybe

also the most important of all surface attributes.

2.1. INTRODUCTION 37

Multiple-cue probability learning

Multiple-cue probability learning (MCPL) is a paradigm for studying how people

learn probabilistic relationships between cues and outcomes (Holzworth, 2001).

MCPL studies have been conducted in numerous contexts, including social judgment,

weather forecasting, medical diagnoses, and decision-making. In MCPL studies,

participants are faced with a series of trials over which they have to judge the value of

a criterion based on a number of cues with varying degrees of validity. The cues can

have positive and negative predictive value for the criterion or can be irrelevant. After

each trial, participants are given feedback about their performance.

One stream of MCPL research has been concerned with the nature of the feedback

that is required for learning. Feedback about the overall performance in a trial ("out-

come feedback"; e.g., correct versus incorrect) mimics learning in many repetitive

real-life decision tasks. The effectiveness of this type of feedback has been debated

extensively and research on the matter has not been fully conclusive (e.g. Klayman,

1988; Newell, Weston, Tunney & Shanks, 2009). Some studies do provide evidence

that learning can be achieved from outcome feedback alone (e.g. Castellan, 1974;

Pachur & Olsson, 2012), and in Karelaia and Hogarth’s meta-analysis of lens model

studies, 2008, the authors conclude that subjects are indeed able to learn from out-

come feedback, provided that the number of cues in the task is relatively low and

the cues are not too correlated. Klayman (1988) finds that subjects can discover new

cues in rather complex and uncertain environments if they are able to structure their

own learning environment, and Nestler, Egloff, Küfner & Back (2012) speculates that

feedback might not lead to major shifts in utilisation but may lead to discovery of

new valid cues.

Another, smaller stream of MCPL research has focused on the effect of irrelevant

information on performance. The general finding is that irrelevant information has a

negative effect on performance, i.e., irrelevant information decreases the utilization of

relevant information. Castellan (1973) found that the effect of irrelevant information

is dependent on the validity of the relevant information and if cue-validity of the

relevant information is high, the effect of irrelevant information is low. Subjects were

also able to improve their performance and ignore irrelevant information, but they

were not able to completely ignore irrelevant information even after many trials.

Edgell, Castellan Jr, Roe, Barnes, Ng, Bright & Ford (1996) found that the effect of

irrelevant information is not due to the irrelevant nature of the information, but

rather due to information overload.

Visual salience as a surface attribute in decision tasks

Although it was Egon Brunswik who coined the term "ecological validity", Brunswikian

researchers rarely considered that the cue values and cue validities in many real-life

decision tasks are systematically related to certain surface attributes (notably, their vi-


sual salience), very much in the manner of a Stroop task (Stroop, 1935) and that many

people can look back on a long history of learning to utilise these relationships when

they form preferences and make choices. And not just that: in today’s competitive

markets, we see a proliferation of entire functional specialisations that try to exploit

such learned relationships in a systematic manner. Marketing communications is

a good example: marketers try to make attributes on which a product is superior to

its competitors particularly salient on the packages of a consumer product, in the

ads and commercials that try to convince consumers of the product’s virtues. Recent

studies indicate that this is to a certain degree successful: the more the visual salience

of a given attribute is increased over its baseline, the more important the attribute

tends to become in consumer’s product choices (e.g. see Bialkova & van Trijp, 2011;

Milosavljevic, Navalpakkam, Koch & Rangel, 2012; Orquin et al., 2013).

A similar neglect of physical stimulus properties has prevailed in research on

multi-attribute decision-making. Often tasks are presented using numeric represen-

tation of attributes in a matrix layout (e.g. Payne, Bettman & Johnson, 1988). However,

in many real-life situations, information is not presented numerically but visually.

Already in 1971 Hammond found evidence that graphical representations of cues

produce better judgments (Hammond, 1971). His result has largely been overlooked,

and subsequent studies have yielded somewhat less conclusive results regarding

this effect (see Bayindir, Bolger & Say, 2015). Recent work does, however, support

that graphic representation supports learning (Bayindir et al., 2015), and Lurie &

Mason (2007) showed that visualisations may change the way decision-makers col-

lect information. In extreme cases, this can lead to myopic tendencies such that

decision-makers focus on a small and possibly highly biased set of attributes.

Visual salience can be defined as the ability of a visual stimulus to attract the

attention of the viewer. Although it is based on the physical characteristics of the

stimulus object (including its size, colour, brightness and movement), salience is

a perceptual property and therefore subjective. Visual salience captures attention

by making objects "pop-out" in a bottom-up fashion (Theeuwes, 2004; Theeuwes

& Burg, 2011), but it has been shown to interact with task instructions and goals

(Desimone & Duncan, 1995; Itti & Koch, 2001), and it has also been shown that such

top-down factors can completely override the bottom-up effects of visual salience

(Kowler, 2011).

One aspect of visual salience that is often neglected is that the visual salience of

an object can only be defined by the visual properties of the object relative to the

rest of the visual environment. Changing the physical properties of one object in the

visual environment therefore alters the entire saliency distribution and not just the

salience of the object. When visual salience is controlled by one binary feature (e.g.,

high versus low opacity), the properties of each attribute can be defined in terms of

this feature if all other features of the attribute (e.g. colour and shape) can be argued

to be approximately equally salient. In the experiments presented in this article


we argue that the shape-colour combinations used as attributes are approximately

equally salient, and that the only additional feature inducing variation in their visual

salience is the variation in the opacity levels of the shape-colour combinations.

Similar manipulations have been applied in previous research by Bialkova & van Trijp

(2011) and Milosavljevic et al. (2012).

With the present research we seek to contribute to the existing research on MCPL

and multi-attribute decision-making by investigating how different levels of inter-

correlation between the task-irrelevant stimulus characteristics and the relevance of

a cue affects performance. We seek to investigate the effect on initial performance as

well as changes that might occur through repeated exposure to a given task environ-

ment.

The task

Studies that take stimulus characteristics into account to explain decision-making

often use naturalistic stimuli (e.g. Bialkova & van Trijp, 2011; Milosavljevic et al., 2012;

Orquin et al., 2013). This makes the task more realistic and readily applicable, for

example in contexts such as package design. However, such an approach does not

control for the fact that participants may have pre-existing and very well-learned

mental models of the task structure: a strong source of endogeneity.

One aim of the present work was to construct a task in which the relationship

between cue validity and perceptual salience was under full experimental control and

at the same time to construct an inferential choice task that simulated a preferential

choice task. In our approach, participants first had to learn the relationship between

the stimulus objects encoding the attributes and their associated value. The attributes

were represented by colour-shape combinations. Their values were binary (either

zero or one).

Participants first completed a series of learning trials to learn the value of each

attribute. After learning the values of attributes, participants completed a repeated

binary decision task. Each of the two alternatives consisted of six attributes each, and

one alternative was dominant in terms of total attribute value. The visual salience

of the attributes was varied within and between the choice sets, and the correlation

between visual salience and attribute value was varied between treatments (for a

thorough description of task and procedure, see section ”Overview of experimental

procedure” below). The salience-value correlation could be either zero, low and

positive, high and positive, or high and negative.

In many daily decision tasks (e.g., a consumer choice between products on a

product shelf) decision makers strive to be fast while choosing the preferred alter-

native. The definition of efficiency use in the present work is therefore one which

comprises speed and accuracy. We instructed participants to make accurate choices

as fast as possible and they were compensated according to their performance on


both parameters2.

Predictions

Initial biasing effects of visual salience

Only few studies have been published until now in which the biasing effects of

visual salience on choice behaviour are investigated (Bialkova & van Trijp, 2011;

Milosavljevic et al., 2012; Towal, Mormann & Koch, 2013). They all found that salience

leads to a particular bias in decisions: the more salient the visual representation of

a particular alternative, the higher the likelihood that it will be chosen. However,

salience was manipulated at the alternative level in these studies, and all trials were

pooled in the analysis, neglecting potential learning effects that might have occurred

over the various trials in the respective experiments.

Studies that used multiple-cue probability learning paradigms (MCPL) also found

evidence of salience biases: increasing the salience of valid cues appears to increases

the utilisation of these cues (Edgell & Morrissey, 1992; Edgell, Neace, Harbison &

Brian, 2015; Kruschke & Johansen, 1999) whereas increasing the salience of invalid

cues decreases the utilisation of valid cues (Kruschke & Johansen, 1999). It should

be noted though that other authors could not reproduce the latter finding (Edgell &

Morrissey, 1992) and suggested that the original finding may actually have reflected

an increase in task complexity (Castellan, 1973; Edgell et al., 1996). Based on the

findings in the decision-making and MCPL literature, we expect that visually salient

attributes will bias decisions in such a way that the probability of making the correct

choice in initial trials strongly depends on the correlation between the salience and

the value of attributes. The following hypothesis can be derived:

H1.1: The higher the correlation of salience and value, the higher the choice accuracy.

Even though previous research on the effect of salient invalid cues on performance

has been inconclusive (Edgell & Morrissey, 1992; Kruschke & Johansen, 1999) we

expect that accuracy will be negatively affected in situations where invalid cues are

more salient than valid cues. Assuming that a visually salient object has a higher

likelihood of capturing attention regardless of its task relevance (Theeuwes, 2004;

Theeuwes & Burg, 2011), salience will have a negative biasing effect on attention to

objects which are not salient, i.e., the likelihood of attending a valid cue will be low if

valid cues has low salience. The following hypothesis is therefore derived:

H1.2: When correlation is negative, the effect is reverse, i.e., negative correlation leads

to lower choice accuracy.

2Participants were payed a fixed sum (approximately 4e) for their participation in the experimentand on top of that they could earn an extra 0.1e per choice trial if the choice was correct and faster than2.5 sec. They received feedback on their performance after each trial


Research has so far mainly been interested in the effects of salience on choice

accuracy. However, when the goal is not only accuracy but efficiency, measures of

decision speed (or reaction time) are of equal importance. We expect that reaction

time will be affected by the correlation between salience and value in a similar manner

as choice accuracy:

H2.1: The higher the correlation of salience and value, the lower reaction time, i.e.,

high correlation leads to faster decisions.

H2.2: When correlation is negative, the effect is reverse, i.e., negative correlation leads

to slower decisions.

A subject’s overt visual attention can be seen as the mediating process that may

lead to biased choice behaviour. Previous research on visual attention in decision-

making has found that participants fixate more on visually salient information

(Bialkova & van Trijp, 2011; Milosavljevic et al., 2012; Towal et al., 2013), consistent

with most cognitive models of visual attention that posit that attention is (partially)

guided by the visual features of the stimuli, for example feature integration theory

(Treisman & Sato, 1990) and Guided Search (Wolfe, Cave & Franzel, 1989; Wolfe, 1994).

We therefore expect to observe the following:

H3: More fixations initially on visually salient attributes regardless of the value of

attributes.

Learning effects and debiasing

Most research on the effects of visual salience on performance takes (often indirectly)

a static or stable view. We are not aware of any studies in which the stability of the

effect was actually analysed over the repetitions of a repeated choice task. There

is good reason to believe that the effect of salience will actually change over such

repetitions.

In studies that exposed decison-makers to many repetitions of the same type of

decision task, reaction time tends to decrease (Meißner & Decker, 2010; Meißner

et al., 2016), decision accuracy tends to increase (Meißner et al., 2016; Orquin et al.,

2013; Payne et al., 1988), and decision-makers tend to use decreasing amounts of

information to make their decisions (Bialkova & van Trijp, 2011; Payne et al., 1988).

The simultaneous increase in accuracy and decrease in reaction time and information

use show that decision-makers increase their efficiency through repeated exposure

to a task, and this efficiency can be explained by changes in the attention process

(Orquin et al., 2013). It is, however, still unclear how the attention process is altered

through learning and under which conditions this efficiency increase can occur.

There is an ongoing debate about early filtering and late inhibition models of

attention which has been articulated for decades. Early filtering models propose

that available information is filtered pre-perceptually and only relevant information


is selected for further processing, whereas late inhibition models assume that all

information is perceptually selected and selection is performed at a later stage before

entering short-term memory. In this sense early filtering is a rapid process whereas

late inhibition is slow. Broadbent (1958) advocated the view of early filtering whereas

Deutsch & Deutsch (1963) and Norman (1968) argued for late inhibition (for a review

see Friedenberg & Silverman, 2011). Advocates of other models, for example the

multimode model of attention (Johnston & Heinz, 1978) and the perceptual load

theory (Lavie & Tsal, 1994), have made an attempt to merge the two views by arguing

that selection can be both early and late depending on the needs of the subject or the

perceptual load inflicted by the task.

Efficiency gains can be explained by one of the two modes and each mode would

lead to different predictions regarding how salience interferes. If participants are able

to become more accurate on the basis of change by early filtering of the stimuli, the

effects would only be present if salience-value correlation is non-zero (and possibly

also high for the effect to be detectable), however, if changes should be based on the

notion of late inhibition of information, there will not be a difference between the

different salience-value correlations. As visual salience is operationalised as a low

level feature, it is likely that selection will occur pre-perceptually through a change

in the weighting features as proposed by Guided Search, for instance. If changes

in choice accuracy are an effect caused by changes in early filtering, the following

hypothesis should be true:

H4: Accuracy will increase if visual salience carries task-relevant information, that

is, in experiments with a non-zero salience-value-correlation.

Reaction time could decrease on the basis of both early and late selection, though.

In the case of late selection there should not be a difference between different salience-

value correlations, whereas in the case of early selection the decrease should depend

on the salience-value correlation: the larger the salience-value correlation, the larger

the decrease.

H5: Participants will become faster especially in the high correlation experiments.

Haider & Frensch (1999) argue that efficiency gains can be explained by percep-

tual filtering of redundant information. They had participants evaluate alphabetic

strings under different conditions and found that participants learned to attend

task-relevant information and to ignore task-redundant information at the percep-

tual level. Research on expertise supports this idea: experts, as compared to novices,

have shorter times-to-first-fixation on relevant information, more fixations on task-

relevant information and fewer fixations on task-irrelevant information (for a review

see Gegenfurtner, Lehtinen & Säljö, 2011). It is still uncertain how this change occurs,

that is, how people become better at directing their attention to task-relevant infor-

mation. One explanation could be that people change the weighting of specific visual

2.2. OVERVIEW OF EXPERIMENTAL PROCEDURE 43

features after learning the relevance of these features. That would have the impli-

cation that efficiency should only increase if a task-relevant visual feature actually

exists. We therefore expect that choice efficiency will only increase in experiments

with non-zero salience-value correlation:

H6: Assuming that H3 holds, the effect of salience on attention should not decrease

as long as the salience-value correlation remains positive. However, in the

case of a zero or negative correlation, the effect of salience on attention will

decrease.

2.2 Overview of experimental procedure

In the following section the experimental procedure and the statistical modelling

approach will be described. Afterwards, each experimental treatment will first be

analysed separately and then integrated with the others in a cross-treatment analysis.

Stimuli

The experimental stimuli consisted of 16 attributes that were used to generate choice

alternatives. The attributes were constructed as combinations of four polygon shapes

(triangle, square, rhombus and pentagon) and four colours (red, blue, yellow and

green). Each of these sixteen attributes was associated with a specific value (one or

zero). Six were randomly assigned the value one and ten were assigned the value

zero, with the restriction that the value had to be uncorrelated with colour and shape

in order to make attention deployment based on either single feature impossible.

The total number of attributes was chosen based on a pilot study that showed that

participants were able to learn the value of sixteen attributes within the time frame

of this experiment. Visual salience was manipulated by varying the opacity level of

the attributes (0.2 or 1), that is, an attribute could either have high or low salience.

The statistical design was varied between the experimental treatments by ma-

nipulating the correlation between value and salience, thereby changing the task-

relevance of visual salience. Table 2.1 shows the statistical design of the different treat-

ments and the associated salience-value correlations. In Treatment 1 the correlation

between salience and value was 0. Here, salience did not provide any task-relevant

information to the participants. The salience-value correlation in Treatment 2 was

.258. Salience provided task-relevant information but had low diagnostic value. In

Treatment 3, the correlation was .775. Here, salience provided task-relevant infor-

mation and also had high diagnostic value. In Treatment 4, this was reversed: the

correlation had the same absolute value as in Treatment 3 but was negative (-.775).


Table 2.1. Distribution of salience-value combinations in the treatments

SalienceValue High Low Correlation

Treatment 1High 3 3

0Low 5 5

Treatment 2High 4 2

.258Low 4 6

Treatment 3High 6 0

.775Low 2 8

Treatment 4High 0 6 −.775Low 8 2

Apparatus

Stimuli were presented on a 22-in. monitor (1920∗1080 pixel resolution, 60 Hz refresh

rate) controlled by a Fujitsu Celsius W520 Long Lifecycle running Windows 7. Python

2.7 and PsychoPy (Peirce, 2009, 2007) were used to create and control the experiment.

Eye-tracking

Eye movements were recorded on a desktop-mounted eye-tracker (EyeLink 1000)

using monocular sampling at a sample rate of 500 Hz. Using a head-supported setup,

the accuracy is .25◦− .5◦ and precision .01◦. At a viewing distance of 60 cm, the screen

subtended a visual angle of 44.18◦ horizontally and 25.72◦ vertically. Because the data

were sampled at high speed, saccade detection was performed using an algorithm

developed by SR Research, which is based on velocity and acceleration (SR Research,

2008; Holmqvist et al., 2011). The velocity, acceleration, and motion thresholds were

30◦/sec, 8,000◦/sec2, and 0.15◦, respectively. The stimulus objects representing the

attributes in the decision task subtended a visual angle of approximately 1.4◦. Their

centers were aligned at distances of 7.4◦ horizontally and vertically. For each choice

set, areas of interest (AOIs) were defined around each object. The margins of the AOIs

were approximately 3◦ on all sides in order to minimise the number of false negative

fixation detections. For a discussions of AOI margins, see Orquin, Ashby & Clarke

(2016) and Holmqvist et al. (2011).

Design and procedure

An experimental paradigm was developed in which participants first learned the value

of the sixteen attributes. After learning the attribute-value combinations, participants


completed a repeated binary forced-choice task. Figure 2.1 shows a visualisation

of the experimental flow. Participants were asked to carefully read and follow the

instructions on the screen (instructions are not included in Figure 2.1).

Learning trials

Figure 2.1(A-C) shows the flow of the learning task. Participants were told that the

first part of the experiment would be a learning session with a total of 96 trials, and

that the subsequent choice task would depend on them learning the attribute-value

conjunctions. The 96 trials were divided into six blocks of 16 trials. A screen with

an overview of all attributes and their associated values was displayed before each

trial block. A positive smiley was used to signal the attribute value one and a neutral

smiley signalled the attribute value zero. Each trial consisted of a grey rectangle and

a rating scale ranging from zero to six. The participants were instructed to think of

the rectangle as a product on a supermarket shelf. The rectangle carried between

one and six attributes. The participants were asked to calculate the total value of the

attributes and indicate their answer using the rating scale. The participants were

instructed to think of the attributes as attributes of consumer goods and to treat their

values as hedonic (as opposed to monetary). Task difficulty was gradually increased.

The number of attributes in the learning trials increased from one to six: in Block 1,

only one attribute was present in each trial. In Block 2, two attributes were present. In

Block 6, six attributes were present (see Appendix A for an example from each block).

After each answer, participants received feedback in the form of a correct/incorrect

message plus the correct answer if the provided answer had been incorrect. The

feedback message was displayed for three seconds on the screen.

Choice trials

After completing the learning task, participants completed a repeated choice task.

Figure 2.1(D-F) shows the flow of the task. A fixation cross was displayed for two

seconds, followed by the actual choice trial where participants had to choose between

two alternatives. Both alternatives were of same shape and colour as the "product"

in the learning task. Each carried six attributes, selected by a random draw without

replacement from the total set of sixteen attributes available. The alternatives were

separately constructed and could therefore carry the same attributes but with the

restrictions that (a) one alternative could not be exactly the same as the other and

(b) one alternative had to be dominant in terms of total attribute value. The pairwise

value difference between the alternatives varied from one to six. The participants

were instructed to choose the alternative with the highest value and to do so as

quickly as possible, using the left and right arrow keys on the keyboard. After each

trial, performance feedback about speed and accuracy was displayed in the center of

the screen for a duration of two seconds. All participants completed 60 choice trials.


Figure 2.1. The figure show the experimental flow excluding all text parts of the experiment.(A) shows an example of attribute overview displayed before each trial block. (B) is an exampleof a learning trial from the last block (including six attributes - example of the other blockscan be found in Appendix A) and (C) shows feedback on (wrong) response in the precedinglearning trial. (D) the fixation cross is displayed before each choice trial, (E) shows an exampleof a choice set consisting of two alternatives and (F) is an example of feedback on the speedand accuracy in the preceding choice trial.

Lear

nin

gta

skD

ecis

ion

task

16 trials

6 blocks

60 trials

(until response)

(until response)

(3 sec)

(2 sec)

(until response)

(2 sec)

(A)

(B)

(C)

(D)

(E)

(F)


The opacity level of the attributes was varied as a manipulation of salience, with

opacity levels of 20% and 100% for low-salience and high-salience attributes, respec-

tively. The four treatments varied in terms of the correlation between the salience

and the value of the attributes (no correlation, positive correlation, highly positive

correlation and highly negative correlation). The statistical designs and correlations

are shown in Table 2.1.

Statistical modelling approach

The data from the four experimental treatments was analysed both separately and

combined to account for complexity and non-linearity which would not be captured

in a cross-treatment analysis alone. Three relevant measures were used to test the

hypothesis of the experiment (see Predictions): choice accuracy, reaction time, and vi-

sual attention. Choice accuracy and reaction time were both measured on alternative

level (i.e. the alternatives being the lowest level of analysis) whereas visual attention

was measured on attribute level (i.e. attributes being the lowest level of analysis).

The statistical computing environment R (R Core Team, 2015) was used for pro-

cessing and analysing the data. All models were fitted using the "lme4" package

(Bates, Mächler, Bolker & Walker, 2015). Tables were generated using the "texreg"

package (Leifeld, 2013)and graphical visualisations were created using the "ggplot2"

package (Wickham, 2009).

Choice accuracy

The probability that a participant correctly chose the dominant alternative was mod-

elled by means of a generalised linear mixed model (GLMM) with a binomial response

distribution and a logit link function. The dependent variable (accuracy) took the

value one if the chosen alternative was the dominant one, and zero if not. Two inde-

pendent variables were included in the model: the value difference between the two

alternatives involved in the choice set in question (ranging from one to six) and trial

order (ranging from one to sixty). Value difference was not of direct interest but it was

included as a covariate to account for possible confounding effects. Trial order was

transformed using a power function to account for non-linearities in the learning

patterns.

The fixed-effects model included both main effects and all interactions of the

independent variables, i.e., the full model. A "step-up" procedure was applied to find

the best-fitting specification for the random part of the model. In the baseline model,

only a random intercept for participant was included. After that, random effects were

added. AIC and BIC were used as selection criteria. In situations where there was not

agreement between the two, the principle of complexity reduction was applied and

the most conservative criterion (BIC) was used for selection.


Choice accuracy models were estimated separately for each treatment and the

results of these models were subsequently used in a cross-treatment analysis to test

Hypothesis 1.1, 1.2, and 4.

Reaction time

Reaction time was measured as the time from stimulus onset until the participant

pressed the key that indicated the choice. Reaction time was modelled using a linear

mixed model (LMM) with the same independent variables as in the analysis of choice

accuracy (value difference and trial order). Again, the fixed part of the model included

main effects as well as all interactions of the independent variables, and the best

specification for the random part was chosen based on the "step-up" procedure

outlined above.

Reaction time models were estimated separately for each treatment and the

results of these models were subsequently used in a cross-treatment analysis to test

Hypothesis 2.1, 2.2, and 5.

Attention

Attention can be either overt or covert. Overt attention occurs when the stimulus

is caught by the fovea where the sensory neurons of the eye have a high density.

The high density serves to enhance visual processing. In contrast, covert attention

occurs when the object of attention is not in fovea (i.e. you look at one object while

attending to another). A typical assumption when dealing with visual attention and

eye-tracking is the eye-mind assumption. This assumption suggests that fixation on

stimulus is directly related to the processing of the stimulus (Just & Carpenter, 1980),

that is, assuming that attention is overt. The eye-mind assumption has been shown

to hold (Hoffman & Subramaniam, 1995; Posner, 1980) and covert attention normally

only occurs prior to a saccade, when attention shifts to a new location (Shepherd,

Findlay & Hockey, 1986). The use of eye-tracking in decision-making provides a

great measure of information processing by inducing very few constraints on the

participant compared to other process tracing tools (e.g., think aloud, information

search displays). Eye-tracking as process tracing tool has been strongly advocated

(Bialkova & van Trijp, 2011; Glaholt & Reingold, 2011).

The likelihood of attending an attribute was modelled by means of a GLMM with

a binomial response distribution and a logit link function. The dependent variable

(selection) was constructed such that, if an object was fixated within the time-course

of a trial, the value was one and zero otherwise. Three independent variables were

included in the model: the salience of the object representing the attribute in question

(experimentally controlled opacity level of 0.2 or 1), the value of the attribute (zero or

one) and trial order (from one to sixty). Trial order was transformed using a power

function to account for non-linearities in the learning patterns. The fixed part of the

2.3. TREATMENT 1 49

model included both main effects and all interactions of the independent variables.

The best specification for the random part of the model was chosen based on the

"step-up" procedure. Models of attention likelihood were estimated separately for

each treatment to test Hypothesis 3 and 6.

The number of fixations and proportion of fixated attributes was modelled in

a cross-treatment analysis. The models were modelled by means of LMM with the

total number of fixations in a trial and proportion of fixated attributes in a trial as the

dependent variables. Trial order and dummy variables for treatment number were

treated as independent variables. The models were estimated following the same

modelling procedure as in the previous analyses and trial order was transformed

using a power function.

2.3 Treatment 1

Method

In Treatment 1, the correlation between salience and value was zero. In this case,

salience did not provide any task-relevant information; its variation can be regarded

as noise. 74 participants were recruited from the laboratory’s subject pool and com-

pleted the experiment (Female = 64.3%, Mag e = 24.5). All had normal or corrected-

to-normal vision. Participants were compensated according to their performance,

measured in terms of decision speed and choice accuracy. The compensation ranged

from a minimum ofe4 to a maximum ofe10.

21 of the 74 participants were subjected to eye-tracking while they completed

the tasks (Female = 65%, Mag e = 23.35). At the beginning of the session, participants

were asked to sit down, place their chin and forehead in the headrest in front of

the screen and find a comfortable sitting position because they would have to keep

their head in the headrest throughout the experiment. Once participants were seated

comfortably, a nine-point calibration procedure was carried out manually by the

experimenter. Upon successful calibration, i.e., offset of < 1.0◦, the actual experiment

began. Stimulus material, design and procedure of the eye tracking experiments were

identical to those without eye tracking.

Results

Choice accuracy analysis

A model that included only a random intercept in addition to the fixed effects was

found to be the best-fitting model of choice accuracy. Parameter estimates and

standard errors are shown in Table 2.2. The optimal transformation of trial order was

raising it to a power of −0.8. The effect of trial order on choice accuracy was negative

and declining but not significant. The effect of value difference on choice accuracy

increased with the size of the difference: the smaller the value difference, the lower


Table

2.2.GLM

Ms

ofch

oice

accuracy

for

eachtreatm

ent

Treatmen

t1Treatm

ent2

Treatmen

t3Treatm

ent4

(Intercep

t).66

(.12) ∗∗∗.18

(.65).63

(.21) ∗∗−

1.59(.86)

Trialord

er.42

(.44).48

(.87).02

(.01) ∗1.54

(.63) ∗V

alue

differen

ce2

.46(.13) ∗∗∗

.97(1.03)

.58(.27) ∗

−.16

(1.07)V

alue

differen

ce3

1.50(.15) ∗∗∗

1.50(1.17)

.90(.33) ∗∗

−.08

(1.21)V

alue

differen

ce4

1.63(.18) ∗∗∗

4.30(1.31) ∗∗∗

1.97(.44) ∗∗∗

−1.19

(1.34)V

alue

differen

ce5

1.51(.27) ∗∗∗

1.52(1.47)

1.38(.47) ∗∗

−3.35

(1.51) ∗V

alue

differen

ce6

1.91(.39) ∗∗∗

6.30(1.98) ∗∗

1.60(.73) ∗

−4.34

(2.21) ∗Trialo

rder

:Valu

ed

ifference

21.01

(.80)−

.35(1.39)

−.00

(.01).28

(.78)Trialo

rder

:Valu

ed

ifference

3−

1.77(.73) ∗

−.52

(1.59).02

(.02).56

(.88)Trialo

rder

:Valu

ed

ifference

4−

.26(.91)

−3.81

(1.76) ∗−

.02(.02)

1.43(.97)

Trialord

er:V

alue

differen

ce5

2.12(2.32)

−.04

(1.98).03

(.02)3.16

(1.10) ∗∗Trialo

rder

:Valu

ed

ifference

62.15

(3.02)−

6.24(2.62) ∗

.01(.03)

4.02(1.62) ∗

Nu

m.o

bs.

45594558

28184947

Nu

m.gro

up

s:Sub

ject74

7546

83A

IC4179.10

4576.392349.11

5597.65B

IC4262.63

4659.912438.27

5695.25Lo

gLikelih

oo

d−

2076.55−

2275.19−

1159.56−

2783.83V

ar:Intercep

t.57

.51.49

16.53V

ar:Trialord

er.00

9.31C

ov:Intercep

t:Trialord

er−

.01−

12.16

No

te ∗∗∗p<

0.001, ∗∗p<

0.01, ∗p<

0.05.

2.3. TREATMENT 1 51

the accuracy, and vice versa. To make the effect of trial order on choice accuracy more

intuitively interpretable, the logit predictions from the model were transformed into

likelihoods using the formula:

l i kel i hood = exp(l og i t )

1+exp(l og i t )

The logit estimate was the marginal (ceteres paribus) effect of trial order, fixing the

value difference to one. The transformed model predictions are plotted in Figure 2.2.

Reaction time analysis

The best-fitting model included a random intercept for participant and a random

effect of trial order, with an unstructured random-effect covariance matrix. The

optimal transformation of trial order was raising it to a power of −1.2. The effect of

trial order on reaction time was negative and declining but only marginally significant

(p = .72). The marginal effect of trial order on reaction time is visualised in Figure 2.2.

The effect of value difference on reaction time was negative: the smaller the value

difference, the longer the reaction time. Parameter estimates and standard errors are

shown in Table 2.3.

Figure 2.2. Choice accuracy and reaction time as a function of trial order (Treatment 1)

50%

60%

70%

80%

1 20 40 60

Trial order

Cho

ice

Acc

urac

y

Choice Accuracy Reaction Time

2

3

4

5

Rea

ctio

n T

ime

Fixation likelihood analysis

Participants made on average 10.31 fixations per trial and fixated 52.28% of the

attributes in each choice set. The average number of fixations and the proportion of

fixated attributes decreased over the course of the experiment (from 15.16 and 65.99%


Table

2.3.LMM

so

freaction

time

for

eachtreatm

ent

Treatmen

t1Treatm

ent2

Treatmen

t3Treatm

ent4

(Intercep

t)2.79

(.17) ∗∗∗2.60

(.17) ∗∗∗2.38

(.19) ∗∗∗3.02

(.27) ∗∗∗Trialo

rder

.86(.48)

.84(.47)

2.19(.47) ∗∗∗

2.24(.50) ∗∗∗

Valu

ed

ifference

2−

.16(.06) ∗∗

−.16

(.05) ∗∗−

.22(.09) ∗

−.20

(.06) ∗∗∗V

alue

differen

ce3

−.37

(.07) ∗∗∗−

.21(.05) ∗∗∗

−.62

(.09) ∗∗∗−

.23(.06) ∗∗∗

Valu

ed

ifference

4−

.50(.07) ∗∗∗

−.41

(.06) ∗∗∗−

.76(.10) ∗∗∗

−.39

(.07) ∗∗∗V

alue

differen

ce5

−.73

(.09) ∗∗∗−

.54(.07) ∗∗∗

−.82

(.11) ∗∗∗−

.48(.08) ∗∗∗

Valu

ed

ifference

6−

.72(.11) ∗∗∗

−.49

(.09) ∗∗∗−

.83(.17) ∗∗∗

−.61


rder

:Valu

ed

ifference

2.11

(.57).01

(.70)−

.31(.48)

.15(.55)

Trialord

er:V

alue

differen

ce3

.10(.64)

−.69

(.78)1.32

(.47) ∗∗−

.37(.62)

Trialord

er:V

alue

differen

ce4

−.28

(.62)−

.28(.77)

1.14(.55) ∗

1.05(.70)

Trialord

er:V

alue

differen

ce5

.61(.85)

−.37

(.81).61

(.57).81

(.66)Trialo

rder

:Valu

ed

ifference

6−

.59(.86)

−.05

(1.07)−

.65(1.10)

−.94

(1.23)

Nu

m.o

bs.

45594558

28184947

Nu

m.gro

up

s:Sub

ject74

7546

83A

IC16218.31

14883.549066.91

17680.92B

IC16321.11

14986.349162.02

17785.02Lo

gLikelih

oo

d−

8093.15−

7425.77−

4517.46−

8824.46V

ar:Intercep

t2.06

2.071.58

5.76V

ar:Trialord

er6.55

3.336.33

10.90C

ov:Intercep

t:Trialord

er−

.11.06

.12−

1.00V

ar:Resid

ual

1.851.38

1.301.84

No

te ∗∗∗p<

0.001, ∗∗p<

0.01, ∗p<

0.05.

2.3. TREATMENT 1 53

Tab

le2.

4.G

LMM

so

ffixa

tio

nli

keli

ho

od

for

each

trea

tmen

t

Trea

tmen

t1Tr

eatm

ent2

Trea

tmen

t3Tr

eatm

ent4

(In

terc

ept)

−2.2

4(.

61)∗

∗∗−2

.27

(.69

)∗∗∗

.07

(.25

)−.

50(.

52)

Tria

lord

er2.

44(.

77)∗

∗2.

00(.

71)∗

∗−.

50(.

13)∗

∗∗−.

03(.

39)

Salie

nce

−1.3

6(.

60)∗

.02

(.59

).6

2(.

31)∗

2.12

(.47

)∗∗∗

Val

ue

1.50

(.55

)∗∗

.80

(.57

).6

8(.

29)∗

1.00

(.45

)∗Tr

ialo

rder

:Sal

ien

ce2.

78(.

77)∗

∗∗1.

03(.

74)

−.03

(.16

)−1

.19

(.34

)∗∗∗

Tria

lord

er:V

alu

e−1

.40

(.74

)−.

41(.

77)

−.15

(.15

)−.

33(.

33)

Salie

nce

:Val

ue

.08

(.79

).2

9(.

77)

Tria

lord

er:S

alie

nce

:Val

ue

−.05

(1.0

8)−.

47(1

.05)

Nu

m.o

bs.

1440

015

840

2794

842

324

Nu

m.g

rou

ps:

Sub

ject

2022

4061

AIC

1718

3.01

1808

2.64

3350

8.87

4960

8.14

BIC

1728

9.06

1819

0.02

3360

7.73

4971

1.98

Log

Like

lih

oo

d−8

577.

51−9

027.

32−1

6742

.44

−247

92.0

7V

ar:I

nte

rcep

t4.

557.

761.

776.

16V

ar:T

rial

ord

er6.

506.

17.4

33.

71V

ar:S

alie

nce

.70

1.08

.32

.17

Cov

:In

terc

ept:

Tria

lord

er−4

.80

−6.7

1−.

69−4

.31

Cov

:In

terc

ept:

Salie

nce

−.40

−.64

.05

.01

Cov

:Tri

alo

rder

:Sal

ien

ce−.

15.2

8−.

08−.

13

No

te∗∗

∗ p<

0.00

1,∗∗

p<

0.01

,∗p<

0.05

.


at the beginning of the experiment to 8.61 and 47.47% at the end of the experiment,

respectively).

The best-fitting model included a random intercept for participant and additional

random effects of trial order and salience, with an unstructured random-effect co-

variance matrix. The optimal transformation of trial order was raising it to a power

of −0.1. All main effects and the interaction effect of trial order and salience were

significant (see Table 2.4). The model showed a positive effect of value difference

and a negative effect of salience on fixation likelihood. The model also showed that

trial order had a decreasing effect on fixation likelihood, and that this effect was

moderated by salience. Predicted logits were transformed into likelihoods, using

the same transformation as applied in the choice accuracy analysis. The effects are

plotted in Figure 2.3.

Figure 2.3. Fixation likelihood as a function of trial order, attribute salience and attribute value(Treatment 1)

30%

40%

50%

60%

70%

80%

90%

1 20 40 60

Trial order

Fix

atio

n lik

elih

ood

Opacity High Low Value High Low

Discussion

The results of treatment 1 show that trial order had only a marginally significant effect

on reaction time and no effect at all on choice accuracy. Participants did become

slightly faster, but they did not improve the accuracy of their choices over the course of

the experiment. Note that, in this experiment, the salience of the attributes had been

unrelated to the values of the attributes. Apparently, adding this "noise component"

to the decision task prevented participants from increasing their choice efficiency

over the 60 test trials despite the feedback that was provided immediately after each

trial.

The results of the analysis of the eye tracking data clearly show that participants

attended to decreasing amounts of information over the course of the experiment.

2.4. TREATMENT 2 55

This was the case for the total number of fixations on attributes as well as for the

proportion of attributes that were fixated. These results are consistent with previous

results by Bialkova & van Trijp (2011) and Payne et al. (1988) who found that decision-

makers use decreasing amounts of information to form their decisions when they

are repeatedly exposed to the same type of choice task. The results of the experiment

also show that the effect of salience on fixation likelihood was initially positive but

that it decreased over the course of the experiment. This suggests that participants

were indeed initially affected by the visual characteristics of the task environment but

that they gradually learned that the visual salience of the attributes did not actually

provide any task-relevant information. To some degree, participants appear to be able

to inhibit or override the effect of visual salience on their attention. The decrease in

sampled information could be driven by such a change in attention control processes.

2.4 Treatment 2

Method

In treatment 2, the correlation between salience and value was .258. Salience did pro-

vide task-relevant information but, as a cue, had relatively low validity. 75 participants

were recruited from the laboratory’s subject pool and completed the experiment (Fe-

male = 57.7%, Mag e = 24.66). 22 of the 75 participants were subjected to eye-tracking

while they completed the tasks (Female = 59.1 %, Mag e = 25.22). Participants were

instructed as in treatment 1. All had normal or corrected-to-normal vision and were

compensated in the same way as in the previous experiments.

Results


The best-fitting choice accuracy model included only a random intercept for par-

ticipants. The optimal transformation of trial order was raising it to the power −0.1.

The effect of trial order on choice accuracy was negative and declining but not sig-

nificant (see Figure 2.4). The effect of value difference on choice accuracy generally

increased with the size of the difference. However, the interaction effect did not reach

significance either. Parameter estimates and standard errors are shown in Table 2.2.


The best-fitting reaction time model included a random intercept for participants and

a random slope for trial order (see Table 2.3). The optimal transformation of trial was

raising it to a power of −3.3. The effect of trial order on reaction time was negative and

declining, but like in treatment1 the effect was only marginally significant (p = .71).

The marginal effect of trial order on reaction time is plotted in Figure 2.4. The effect


of value difference on reaction time was negative: the smaller the value difference,

the longer the reaction time.


50%

60%

70%

1 20 40 60

Trial order

Cho

ice

Acc

urac

y


2

3

4

Rea

ctio

n T

ime



20%

30%

40%

50%

60%

70%

80%

1 20 40 60

Trial order

Fix

atio

n lik

elih

ood


Participants made on average 9.39 fixations per trial and fixated 46.43% of the

attributes in each choice set. The average number of fixations and the proportion of


2.5. TREATMENT 3 57


respectively).

The best-fitting model included a random intercept and random effects of trial

order and salience. The model showed a decreasing effect of trial order on fixation

likelihood. All other fixed effects were insignificant. The optimal transformation

of trial order was raising it to a power of −0.1. The effect is plotted in Figure 2.5.

Parameter estimates and standard errors are shown in Table 2.4.

Discussion

The results of treatment 2 show the same pattern as the results of treatment 1: par-

ticipants did become faster but they did not improve the accuracy of their decisions

over the course of the experiment. Apparently, the validity of attribute salience as a

cue was still too low (note that the salience-value correlation was only .258) to be

utilisable by participants, despite the performance feedback that was displayed after

each trial.

The results of the analysis of the eye tracking data again show that participants

attended to decreasing amounts of information over the course of the experiment

in terms of total number of fixations as well as the proportion of fixated attributes.

Although this replicates the results of Treatment 1, the descriptive statistics seem to

suggest that the effect is considerably smaller than in Treatment 13. The decreased

effect may suggest that when salience is correlated with value in such a way that it

carries task-relevant information and can operate as a "Brunswikian cue," decision-

makers do not significantly change their perceptual weighting of salience. A cross-

experiment analysis of these measures will be carried out at a later point in this

article.

2.5 Treatment 3

Method

In Treatment 3, the correlation between salience and value was .775. Unlike in Treat-

ment 1 (where it was zero) and Treatment 2 (where it was positive but weak), this

constitutes substantial cue validity. The positive direction of the relationship should

also make it easy for participants to utilise salience as a cue to value. 46 participants

were recruited from the general population and completed the experiment (Female =

48.78%, Mag e = 35.73). All had normal or corrected-to-normal vision. Eye-tracking

measures were collected from all participants. However, six participants had to be

excluded from the eye-tracking analysis due to missing data. Apart from the statistical

3The decrease in number of fixations was 6.55 and 3.93 in Treatment 1 and 2, respectively, and thedecrease in proportion of fixated attributes was 18.52 and 13.66, respectively.


design defining the salience-value combinations, the stimulus materials and the

procedure were the same as in the previous treatments.

Results


The best-fitting choice accuracy model included a random intercept for participants

and random slopes for trial order (with an unstructured random effect covariance

matrix). The effect of value difference was increasing with the size of the difference:

the smaller the value difference, the lower the accuracy. Furthermore, choice accuracy

increased as a function of trial order. The effect of trial order was significant, positive

and close to linear (the exponent of the optimal power transformation was 0.9). The

effect is plotted in Figure 2.6. Parameter estimates and standard errors are shown in

Table 2.2.


The best-fitting reaction time model also included a random intercept for participants

and random slopes for trial order (with an unstructured random effect covariance

matrix). The fixed effect of trial order on reaction time was negative and declining.

The optimal transformation of trial order was raising it to a power of −0.7. The effect

was significant; a plot is shown in Figure 2.6. The effect of value difference on reaction

time was negative, i.e., the smaller the value difference, the longer the reaction time.

Parameter estimates and standard errors are shown in Table 2.3.


50%

60%

70%

80%

1 20 40 60

Trial order

Cho

ice

Acc

urac

y


2

3

4

5

Rea

ctio

n T

ime

2.5. TREATMENT 3 59


Participants made on average 8.42 fixations per trial and fixated 43.9% of the at-

tributes in each choice set. The average number of fixations and the proportion of



respectively).

The best-fitting fixation likelihood model included a random intercept for partici-

pants and random effects of trial order and salience (see Table 2.1). The model did not

include the first-order interaction term Salience : Value and the second-order interac-

tion term Trial order : Salience : Value due to the statistical design of the treatment

(see Table 2.1). The optimal transformation of trial order was raising it to a power of

0.2. The main effects of trial order, salience and value were significant. The interac-

tions included in the model were not significant. Trial order had negative declining

effects whereas salience and value difference had positive effects. The effects are

plotted in Figure 2.7. Parameter estimates and standard errors are shown in Table 2.4.


20%

30%

40%

50%

60%

70%

1 20 40 60

Trial order

Fix

atio

n lik

elih

ood


Discussion

The results of Treatment 3 show that participants became both faster and more

accurate in their choices. Unlike in the previous treatments, where salience had only

been weakly correlated or completely uncorrelated with value, the cue validity in

Treatment 3 appears to have been sufficiently high, enabling participants to utilise

it and increasing their choice efficiency in terms of speed and accuracy. Like in the

previous treatments, the results showed a marked decrease over the course of the


experiment in the amount of sampled information. Furthermore, the results show

that participants were affected by the visual salience of the attributes throughout

the experiment: under conditions of high cue validity and a positive direction of the

salience-value relationship, the effect of salience on attention was sustained.

2.6 Treatment 4

The statistical design of all previous treatments had induced zero or positive salience-

value correlations. In Treatment 4, however, the correlation between salience and

value was −.775. Although the cue validity of salience is technically the same as in

Treatment 3, the reversed direction of the relationship should make it much more

difficult for participants to utilise it as a cue since the bottom-up attention allocation

mechanisms of the visual system are geared towards detecting highly salient objects,

not the other way around.

Method

83 participants were recruited from the general population and completed the exper-

iment (Female = 37.14%, Mag e = 38.1). All had normal or corrected-to-normal vision.

22 participants had to be excluded from the eye-tracking analysis due to missing

data. Apart from the statistical design defining the salience-value combinations, the

stimulus materials and the procedure were the same as in the previous treatments.

Results


The best-fitting model of choice accuracy included a random intercept for partic-

ipants and random slopes of trial order. The effect of value difference on choice

accuracy increased with the size of the difference: the smaller the value difference,

the lower the accuracy. The effect of trial order on choice accuracy was positive and

declining. The optimal transformation of trial order was raising it to the power of

0.1. The effects was significant; it is plotted in Figure 2.8. Parameter estimates and

standard errors for all model parameters are shown in Table 2.2.


The best-fitting reaction time model included a random intercept for participant

and random slopes for trial order. The fixed effect of trial order on reaction time was

negative and declining. The optimal transformation of trial order was raising it to the

power of −1.2. The effect was significant; it is plotted in Figure 2.8. The effect of value

difference on reaction time was negative, corroborating the results of the previous

treatments. Parameter estimates and standard errors are shown in Table 2.3.

2.6. TREATMENT 4 61


40%

50%

60%

70%

1 20 40 60

Trial order

Cho

ice

Acc

urac

y


2

3

4

5

Rea

ctio

n T

ime


Participants made on average 10.63 fixations per trial and fixated 49.27% of the at-

tributes in each choice set. Again, the average number of fixations and the proportion

of fixated attributes decreased over the course of the experiment (from 11.89 and

53.14% at the beginning of the experiment to 9.89 and 46.74% at the end of the

experiment, respectively).

The best-fitting model of fixation likelihood included a random intercept for

participants and random effects of trial order and salience. The model did not include

the first-order interaction term Salience : Value and the second-order interaction

term Trial order : Salience : Value due to the statistical design of the treatment (see

Table 2.1).The fixed-effect part of the model is visualised in Figure 2.9. Trial order

was optimally transformed by raising it to the power of 0.1. The main effects of

salience and value were both positive and statistically significant. The interaction

effect between trial order and salience was negative and also significant. Parameter

estimates and standard errors are shown in Table 2.4.

Discussion

The results of Treatment 4 are consistent with the findings of the preceding treat-

ments, that participants become both faster and more accurate over the course of the

experiment. Especially the increase in accuracy is large in this treatment compared

to the previous treatments. The number of fixations as well as the proportion of

fixated attributes decreased over the course of the experiment. However, the decrease

appears to be descriptively smaller than in the previous treatments (see the next

section "Cross-treatment analysis" for an explicit comparison). The results also show


Figure 2.9. Fixation likelihood as a function of trial order, attribute salience and value (Treat-ment 4)

30%

40%

50%

60%

70%

1 20 40 60

Trial order

Fix

atio

n lik

elih

ood


that fixation likelihood did not change for all attributes when there was a negative

salience-value correlation: the probability of being fixated decreased significantly for

those attributes that were in fact salient, but the effect was not mirrored by a corre-

sponding increase for the attributes that were not salient. It appears that participants

were indeed able to inhibit the biasing effects of the salient attributes (corroborating

the results of Treatment 2), but they were not able to actively direct their attention to

the non-salient attributes that were the carriers of value in this design.

2.7 Cross-treatment analysis

The separate treatment-wise results reported up until now cannot cast light on all of

the predictions we made above. In the following section, the data from the different

treatments will therefore be analysed simultaneously in a cross-treatment analysis.


To compare the shape of the learning curves under the different salience-value cor-

relations, the initial and final cumulative distribution functions (CDFs) of choice

accuracy were estimated for each treatment. Individual estimates were obtained

from the GLMMs of choice accuracy. Based on the respective models, point esti-

mates were calculated for the initial and the final trials. The results are plotted as

between-treatment comparisons in Figure 2.10 and as between-trial comparisons in

Figure 2.11.

From Figure 2.10 it is clear that under conditions of high salience-value correlation

(Treatments 3 and 4), choice accuracy improved whereas under conditions of zero or

2.7. CROSS-TREATMENT ANALYSIS 63

Figure 2.10. Empirical cumulative distribution functions of predicted initial and final levels ofchoice accuracy (between-treatments comparison)

0.00

0.25

0.50

0.75

1.00

0% 25% 50% 75% 100%

Trea

tmen

t 1

0.00

0.25

0.50

0.75

1.00

0% 25% 50% 75% 100%

Trea

tmen

t 2

0.00

0.25

0.50

0.75

1.00

0% 25% 50% 75% 100%

Trea

tmen

t 3

0.00

0.25

0.50

0.75

1.00

0% 25% 50% 75% 100%

Choice Accuracy

Trea

tmen

t 4

Predicted initial trial Predicted final trial


low salience-value correlation (Treatments 1 and 2), choice accuracy remained static.

Descriptively accuracy even deteriorated here; however, the effect was not significant.

Figure 2.11 shows that, initially, participants in the treatments with positive salience-

value correlation performed worse than participants in the treatment with zero

correlation, and those in the negative correlation initially performed worse than the

participants in all other treatments. Over the course of the experiments, however, the

picture changed. In the final trials, participants in the treatment with a high positive

correlation performed better than the participants in all other treatments, and the

participants in the treatment with a negative salience-value correlation performed

on par with the participants in the other treatments.

Figure 2.11. Empirical cumulative distribution functions of predicted initial and final levels ofchoice accuracy (between-trials comparison)

0.00

0.25

0.50

0.75

1.00

0% 25% 50% 75% 100%

Initi

al tr

ial

0.00

0.25

0.50

0.75

1.00

0% 25% 50% 75% 100%

Choice Accuracy

Fin

al tr

ial

1 2 3 4


Similarly to the cross-treatment analysis of choice accuracy, we estimated the ini-

tial and final CDFs of reaction time. Individual estimates were obtained from the

LMMs of reaction time. The results are plotted as between-treatment comparisons in

Figure 2.12 and as between-trial comparisons in Figure 2.13.

The results indicate that, under conditions of high salience-value correlation

(Treatments 3 and 4), participants became significantly faster over the course of the

2.8. GENERAL DISCUSSION 65

experiments, whereas under conditions of zero or low salience-value correlation

(Treatments 1 and 2), the reaction time did not change significantly. The efficiency

gains were generally larger in the treatments with high positive and negative cor-

relation. The reaction time distributions in the treatments with zero and positive

correlations became very similar in the final trials. However, the largest change (and

the only significant change) was observed in the treatment with a high positive

correlation. Participants in the treatment with a high negative correlation also be-

came faster but remained below the levels achieved in the final trials in the other

treatments.

Fixation analysis

The treatment-wise analyses of fixation likelihood already suggested that the effect of

salience on fixation likelihood might be dependent on the salience-value correlation.

To directly compare the decrease in the number of fixations and proportion of fixated

attributes between the four treatments, we estimated two cross-treatment models.

Both models were estimated four times with each treatment dummy as the baseline

for the model. The models were LMMs with the number of fixations and proportion

of fixated attributes as the dependent variables. Trial order and treatment number

were treated as independent variables. The models were estimated following the

same modelling procedure as in the previous analyses. Trial order was optimally

transformed by a power of −1.1 for the total number of fixations and 0.1 for the

proportion of fixated attributes, respectively. The results are plotted in Figure 2.14.

Parameter estimates and standard errors for the models are shown in Table 2.5 and

Table 2.6 for number of fixations and proportion of fixated attributes, respectively.

Trial order had a negative and decreasing effect on the total number of fixations

as well as on the proportion of fixated attributes. The effect was statistically signifi-

cant in both models. In the model prediction total number of fixations, Treatment 3

and 4 were significantly different from each other in the interaction with trial order.

Participants in Treatment 4 had a significantly smaller decrease in total fixations

than in Treatment 3. None of the other independent variables were significant in the

model. In the model predicting the proportion of fixated attributes, Treatments 1,

2 and 3 were significantly different from Treatment 4, and Treatment 2 was signifi-

cantly different from Treatment 1. These differences were as main effects as well as

interaction with trial order. Participants in Treatments 2, 3 and 4 initially fixated a

smaller proportion of the attributes, and the decrease in the proportion was smaller

in Treatment 2 and 4. The decrease was smallest in Treatment 4.

2.8 General discussion

The aim of the research presented in this paper was to identify the processes through

which biasing effects of visual salience operate in repeated decision tasks and to


Figure 2.12. Empirical cumulative distribution functions of predicted initial and final levels ofreaction time (between-treatments comparison)

0.00

0.25

0.50

0.75

1.00

0 5 10 15

Trea

tmen

t 1

0.00

0.25

0.50

0.75

1.00

0 5 10 15

Trea

tmen

t 2

0.00

0.25

0.50

0.75

1.00

0 5 10 15

Trea

tmen

t 3

0.00

0.25

0.50

0.75

1.00

0 5 10 15

Reaction time

Trea

tmen

t 4

Predicted initial trial Predicted final trial


Tab

le2.

5.C

ross

-tre

atm

entL

MM

so

fto

taln

um

ber

offi

xati

on

ses

tim

ated

wit

hea

chtr

eatm

enta

sb

asel

ine.

Trea

tmen

t1Tr

eatm

ent2

Trea

tmen

t3Tr

eatm

ent4

(In

terc

ept)

9.63

(1.4

9)∗∗

∗8.

93(1

.42)

∗∗∗

7.50

(1.0

6)∗∗

∗10

.03

(.86

)∗∗∗

Tria

lord

er10

.23

(2.3

7)∗∗

∗6.

94(2

.26)

∗∗11

.20

(1.6

7)∗∗

∗5.

99(1

.37)

∗∗∗

Trea

tmen

t1.7

0(2

.06)

2.14

(1.8

3)−.

39(1

.73)

Trea

tmen

t2−.

70(2

.06)

1.43

(1.7

7)−1

.09

(1.6

7)Tr

eatm

ent3

−2.1

4(1

.83)

−1.4

3(1

.77)

−2.5

3(1

.36)

Trea

tmen

t4.3

9(1

.73)

1.09

(1.6

7)2.

53(1

.36)

Tria

lord

er:T

reat

men

t13.

28(3

.27)

−.98

(2.9

0)4.

24(2

.73)

Tria

lord

er:T

reat

men

t2−3

.28

(3.2

7)−4

.26

(2.8

1).9

6(2

.64)

Tria

lord

er:T

reat

men

t3.9

8(2

.90)

4.26

(2.8

1)5.

22(2

.16)

∗Tr

ialo

rder

:Tre

atm

ent4

−4.2

4(2

.73)

−.96

(2.6

4)−5

.22

(2.1

6)∗

Nu

m.o

bs.

8353

8353

8353

8353

Nu

m.g

rou

ps:

Sub

ject

142

142

142

142

AIC

5144

6.10

5144

6.10

5144

6.10

5144

6.10

BIC

5153

0.46

5153

0.46

5153

0.46

5153

0.46

Log

Like

lih

oo

d−2

5711

.05

−257

11.0

5−2

5711

.05

−257

11.0

5V

ar:I

nte

rcep

t44

.13

44.1

344

.13

44.1

3V

ar:T

rial

ord

er91

.61

91.6

191

.61

91.6

1C

ov:I

nte

rcep

t:Tr

ialo

rder

1.70

1.70

1.70

1.70

Var

:Res

idu

al24

.87

24.8

724

.87

24.8

7

No

te∗∗

∗ p<

0.00

1,∗∗

p<

0.01

,∗p<

0.05

.


Table

2.6.Cro

ss-treatmen

tLMM

so

fpro

po

rtion

offi

xatedattrib

utes

estimated

with

eachtreatm

entas

baselin

e.

Treatmen

t1Treatm

ent2

Treatmen

t3Treatm

ent4

(Intercep

t)1.03

(.07) ∗∗∗.81

(.07) ∗∗∗.88

(.05) ∗∗∗.66


rder

−.37

(.04) ∗∗∗−

.25(.04) ∗∗∗

−.33

(.03) ∗∗∗−

.13(.02) ∗∗∗

Treatmen

t1.22

(.10) ∗.15

(.09).37

(.08) ∗∗∗Treatm

ent2

−.22

(.10) ∗−

.07(.08)

.15(.08)

Treatmen

t3−

.15(.09)

.07(.08)

.22(.06) ∗∗∗

Treatmen

t4−

.37(.08) ∗∗∗

−.15

(.08)−

.22(.06) ∗∗∗

Trialord

er:Treatm

ent1

−.12

(.05) ∗−

.04(.05)

−.24


rder

:Treatmen

t2.12

(.05) ∗.08

(.05)−

.12(.04) ∗∗

Trialord

er:Treatm

ent3

.04(.05)

−.08

(.05)−

.20(.04) ∗∗∗

Trialord

er:Treatm

ent4

.24(.05) ∗∗∗

.12(.04) ∗∗

.20(.04) ∗∗∗

Nu

m.o

bs.

83538353

83538353

Nu

m.gro

up

s:Sub

ject142

142142

142A

IC−

6388.91−

6388.91−

6388.91−

6388.91B

IC−

6318.61−

6318.61−

6318.61−

6318.61Lo

gLikelih

oo

d3204.45

3204.453204.45

3204.45V

ar:Intercep

t.04

.04.04

.04V

ar:Resid

ual

.03.03

.03.03

No

te ∗∗∗p<

0.001, ∗∗p<

0.01, ∗p<

0.05.


Figure 2.13. Empirical cumulative distribution functions of predicted initial and final levels ofreaction time (between-trials comparison)

0.00

0.25

0.50

0.75

1.00

0 5 10 15

Initi

al tr

ial

0.00

0.25

0.50

0.75

1.00

0 5 10 15

Reaction time

Fin

al tr

ial

1 2 3 4

assess how the resulting biases can be reduced. An experimental paradigm was

developed that allows the separation of the visual salience of attributes from their

task-relevance. Four treatments were conducted in which the paradigm was used.

The treatments differed from each other in terms of the relationship between the

visual salience of the attribute and the values of the attribute. Eight hypotheses were

examined.

The results of the cross-treatment analysis show that the initial effects of visual

salience on choice accuracy differed between the four treatments. However, the

pattern of results was not exactly as we had predicted in Hypothesis 1.1. Contrary

to our expectations (that initial accuracy would linearly depend on the salience-

value correlation), the highest initial accuracy was actually observed in Treatment

1 where attribute salience and attribute value were unrelated. This speaks directly

against the hypothesis. Treatment 1 was the only treatment in which visual salience

had no cue validity and should merely have been acting as ”visual noise” added to

the decision task. The findings also speak against certain results in the literature

(Milosavljevic et al., 2012; Towal et al., 2013). But a recent study (Bagger, Orquin,

Lahm, Tsalis & Grunert, 2016) found that bottom-up factors (such as salience) do

control attention but that choice is controlled by top-down factors (such as goals)

and is merely modulated by attention. This could explain why the initial performance


Figure 2.14. Change in total number of fixations and proportion of fixated attributes for eachtreatment

7.5

10.0

12.5

15.0

17.5

20.0

0 20 40 60

Tota

l Fix

atio

ns

0.4

0.5

0.6

0 20 40 60

Trial Order

Pro

port

ion

Fix

ated

Attr

ibut

es

1 2 3 4

was not better in the two treatments with positive salience-value correlations. This

is also consistent with another result derived from Treatment 1 (zero correlation)

where we saw that the participants attended to a particularly large proportion of

the information available in this treatment (two thirds of the attributes on average).

This may indicate that participants in this treatment also utilised more information

for their decisions, which would in turn have led to higher accuracy. Furthermore,

the high proportion of initially attended information in this treatment suggests that

participants also considered attributes that were low in salience. Although the salient

attributes might have attracted their attention first, the other attributes were not

completely ignored and may have counteracted the expected difference in accuracy.

A negative salience-value correlation, on the other hand, had the expected neg-

ative effect on initial choice accuracy, in line with the predictions of Hypothesis 1.2.

This result is consistent with the findings by (Kruschke & Johansen, 1999) that irrele-

vant but highly salient cues may decrease the utilisation of relevant cues and thereby


lower the performance of a decision-maker.

In Hypothesis 4 we predicted that accuracy would increase over the repetitions of

the choice task, but only under conditions of non-zero salience-value-correlations

(and therefore also positive cue validity) but not under conditions of a zero salience-

value-correlation. The results of our experiments support this hypothesis. There was

a large and significant change in choice accuracy over the course of the experiments

under treatments of a high positive or high negative negative salience-value correla-

tion. The effect was not significant in the treatment with a zero correlation and not in

the treatment with a low salience-value correlation either. Apparently, the cue validity

was too low in the latter condition to be effectively utilised by the participants and

was therefore unable to drive improvements in accuracy. To sum up, the pattern of

the learning effects observed over the many repetitions of the choice task suggests

that, under conditions of sufficiently high positive salience-value correlations, re-

peated choice with feedback can lead to further performance increases and, under

conditions of sufficiently high negative salience-value correlations, it can counteract

the bias that is initially caused by the negative correlation.

Regarding the effects of different salience-value correlations on reaction time

and decision speed, the results point in the same direction as for choice accuracy.

As predicted in Hypothesis 2.2, a negative correlation did have a detrimental effect:

participants in the negative-correlation treatment were considerably slower than

participants in all other treatments. There is no support for Hypothesis 2.1, though.

It appears that participants in the treatments with a zero, a low positive and a high

positive salience-value correlation made their decisions with approximately the same

speed.

As predicted in Hypothesis 5, participants generally became faster over the course

of the experiment, but the largest increase in decision speed was found in the treat-

ments with high salience-value correlations (both negative and positive). Under these

conditions, participants were really able to capitalise on the statistical contingency

between salience and value, learn to utilise salience as a cue, and thereby save time by

directing their attention to the relevant information. It should be noted, however, that

even though the improvement was particularly large in the treatment with a negative

correlation, this might simply have been the case because there was so much more to

gain here: in the final trials of Treatment 4, the average speed of participants was still

below the speed of participants in the other treatments.

We used eye-tracking methodology to investigate how changes in attention pro-

cesses relate to changes in choice accuracy and decision speed. Three of the four

treatments support Hypothesis 3: salient attributes appear to guide initial attention

regardless of their relevance, which is consistent with the results of previous research

(Bialkova & van Trijp, 2011; Milosavljevic et al., 2012; Towal et al., 2013) and provides

a good explanation of the findings regarding accuracy and decision speed in the

treatment with negative salience-value correlation.


Furthermore, the results suggest that attention control processes might be altered

by repeated exposure to the same type of decision task and that this depends on

the correlation between the salience and the value of the attributes. In the zero-

correlation treatment, participants became less affected by visual salience over

the course of the treatment, and the same observation was made in the negative-

correlation treatment. The effect of salience on attention was sustained in the two

positive-correlation treatments. These results support Hypothesis 6 and are consistent

with findings from vision research where the effects of stimulus characteristics have

been shown to interact with task goals (Desimone & Duncan, 1995; Itti & Koch, 2001).

Over the course of the experiments, our participants used decreasing amounts of

information to perform the task, which is in line with the results of previous research

(Gegenfurtner et al., 2011; Haider & Frensch, 1999). However, they also became better

at guiding their attention to relevant cue information. This makes good sense under

the assumption that efficiency increases mainly occur at an early filtering stage. In

the terminology of cognitive models of attention, the many repetitions of the task

may have changed the weighting of specific visual features on the activation map,

as proposed by Wolfe (1994). Wolfe’s notion of an activation map is basically a topo-

logical landscape based on stimulus features. The landscape is shaped by stimulus

features, and the features are weighted by top-down components (such as tasks, goals

and experience) which changes the landscape. Attention prioritisation is controlled

by the peaks in the activation map. Through repeated exposure to a given task and

task environment, the detection of relevant information becomes more efficient.

The learning effects observed in our experiments were achieved through overall

performance feedback alone: participants learned the relationships between salience

and value without ever receiving explicit information about them. This is in contrast

to previous findings on multiple-cue probability learning (see Karelaia & Hogarth,

2008) but our finding shows that people can learn to rapidly detect target patterns

in visual information and to use them in a given task to increase their efficiency,

provided that there are a sufficient number of repetitions and there is performance

feedback.

The present research also suggests that decreasing use of information and in-

creased choice accuracy do not always go together. In all treatments, we observed

certain changes and learning effects, but the nature of these changes and learning

effects depended on the salience-value correlations embedded in the task and to

which participants had to learn to adapt. When that correlation was zero or weakly

positive, participants appeared to become better at noise reduction by learning to

reduce their reactivity to attribute salience. When the correlation was highly positive,

on the other hand, participants appeared to learn to better leverage the diagnosticity

of salience by increasing their reactivity to it. Finally, under conditions of a high nega-

tive correlation, participants even appeared to learn new attention control strategies

that could partly counteract the biasing effects of attribute salience.

2.9. CONCLUSION 73

2.9 Conclusion

People do not make their decisions in the same way every time they are confronted

with the same task. The research presented in this paper has shown that decision-

makers become faster and more accurate through repeated experience with a task.

However, the particular types of efficiency gains that will occur depend on the struc-

ture of the task and the task environment. Simultaneous improvements in speed

and accuracy require visual cues that carry task-relevant information. If such cues

exist, decision-makers can rapidly learn to utilise them with only minimal levels

of feedback. The different types of efficiency gains appear to be associated with

characteristic changes in cue reactivity and other attention control processes.

The results have implications for research in multi-attribute decision-making

and multiple-cue probability learning where the effect of stimulus characteristics

on performance has not received a lot of attention up until now. Researcher need to

consider the visual features of the stimuli and its inter-correlation with task-relevant

attributes as the visual features have a huge effect on both visual search and different

performance measures. The results also have implications for a practitioners (e.g.,

within packaging design) who need to consider the inter-correlation of visual features

and the product characteristics they want to promote or expect to be relevant if they

want people to notice their product.


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2.11. APPENDIX 79

2.11 Appendix

Appendix A provides an example of each of the six learning blocks.

Example of learning task blocks (1-6)

(1)

(2)

(3)

(4)

(5)

(6)

CH

AP

TE

R

3THE PERCEPTUAL PULL IN DECISION MAKING1

SUBMITTED TO ACTA PSYCHOLOGICA

Martin Petri Bagger

Aarhus University

Jacob Lund Orquin, Erik Lahm, George Tsalis, and Klaus G. Grunert

Aarhus University and MAPP

Abstract

Judgement and decision making theories disagree on the relative contri-

bution of top-down and bottom-up control of eye movements. Some theories

assume pure top-down control while others assume a balance between top-down

and bottom-up control. However, without a direct comparison, it is difficult to

know how much weight should be given in decision theories to each process.

To compare the relative importance of the two processes, we conducted an eye

tracking study manipulating bottom-up and top-down processes orthogonally.

We manipulated bottom-up processes by varying the size and salience of a target

attribute as well as the position and number of distractor attributes. To manipu-

late top-down processes we varied the relevance of the target attribute through

three different decision tasks: preferential choice, inferential choice, and prefer-

ential choice with priming. Our results showed that bottom-up processes play a

greater role than top-down processes in determining eye movements, but that

1Acknowledgments: The research described in this paper was commissioned by the Danish Ministryof Food, Agriculture and Fisheries and is a part of the “Contract between Aarhus University and Ministry ofFood, Agriculture and Fisheries for the provision of research-based policy advice, etc., at Aarhus University,DCA – Danish Centre for Food and Agriculture, 2013-2016”.

81

82 CHAPTER 3. THE PERCEPTUAL PULL IN DECISION MAKING

the top-down manipulation influenced the use of information with relevant

and fixated information having a larger impact on decisions. The present study

opens up the question about when bottom-up and top-down processes have the

stronger effect on attention.

3.1 Introduction

Human visual attention is driven by top-down and bottom-up processes, i.e., goal

and stimulus-driven processes (Corbetta & Shulman, 2002). There is an ongoing

debate about the relative contribution of the two processes in different tasks, e.g.,

visual search, natural tasks and scene viewing (Tatler, Hayhoe, Land & Ballard, 2011;

Theeuwes, 2010). In judgment and decision making, most theories assume that visual

search is mostly or entirely top-down driven (see for instance Fiedler & Glöckner, 2012;

Schulte-Mecklenbeck, Sohn, Bellis, Martin & Hertwig, 2013). Toolbox models, for

instance, assume that decision strategies, e.g., take-the-best, elimination-by-aspect

or lexicographic rule, define visual search in a top-down manner (Gigerenzer & Gaiss-

maier, 2011). This assumption is supported by a large number of studies showing

effects of decision-relevant information on visual search (for a review see Orquin

& Mueller Loose, 2013). More recently, models have been developed assuming that

visual search is stochastic (Krajbich, Armel, & Rangel, 2010) or driven by a combina-

tion of bottom-up and top-down processes (Towal, Mormann, & Koch 2013). The

latter seems more realistic given several studies demonstrating bottom-up effects

on visual search in decision making. These studies have, for instance, found effects

of salience (Bialkova & van Trijp, 2011; Milosavljevic, Navalpakkam, Koch, & Rangel,

2012), surface size (Chandon, Hutchinson, Bradlow, & Young, 2009), positioning

(Navalpakkam et al., 2012; Shi, Wedel & Pieters, 2013; Sü tterlin, Brunner & Opwis,

2008), and set size (Horstmann, Ahlgrimm, & Glöckner, 2009; Lohse & Johnson, 1996;

Reutskaja, Nagel, Camerer, & Rangel, 2011; Russo & Dosher, 1983) on visual search. So

far bottom-up and top-down processes have mainly been investigated separately and

we therefore do not know the relative contribution of the two processes in decision

making. Some have even argued that bottom-up processes do not influence visual

search (Tatler et al., 2011), but without a direct comparison it is difficult to say how

much weight decision theories should give to the two processes. In order to directly

compare top-down and bottom-up processes and inform theory development in

judgment and decision making, we conducted a study manipulating top-down and

bottom-up factors orthogonally. We manipulated bottom-up factors by varying the

size and salience of a target attribute as well as the position and number of distractor

attributes in a two-alternative choice task. The target attribute was a food health

label indicating the level of healthiness. We manipulated top-down factors by varying

the relevance of the target attribute to the choice task across three conditions: an

inferential condition in which participants were instructed to choose the healthier al-

ternative, a preferential condition where participants were instructed to choose their

3.2. METHOD 83

preferred alternative, and a priming condition in which participants were exposed to

health information and instructed to choose their preferred alternative.

3.2 Method

Stimulus development

In order to generate target and distractor stimuli, we conducted a stimulus develop-

ment study (N = 225, Mag e = 45.58, SDag e = 15.60, 52 % females) to identify stimuli

relevant for forming health judgments of food products. The study used a within-

subjects design, in which participants assessed whether a product with a given label

was healthier than a product without that particular label. Responses were binary

with zero indicating that the product was considered healthier and one indicating

that the product was not considered healthier with that label. Labels were presented

individually and participants judged 39 labels in total. Mean judgments were cal-

culated by averaging participants’ health judgments with a score of zero indicating

perfect agreement that a label implies healthiness and a score of one that it does not

imply healthiness. Labels with a mean score < .4 were considered targets, and labels

with a mean score > .7 were considered distractors. Two labels with mean scores in the

interval [.4 - .7] were eliminated due to their ambiguity. The stimulus development

study identified four targets and 33 distractor labels. The mean and CIs for the pooled

target and distractor labels are shown in Table 3.1 and Figure 3.1 provide a graphical

illustration of the distribution of the judgments.

Participants

Seventy-six participants were recruited by a market research company (Mag e = 44.84,

SDag e = 13.25, 34 % female). Four participants were excluded due to failing the vision

test, lack of useful eye tracking data, or being intoxicated; the result was a final sample

of 72 participants. Participants had normal or corrected to normal vision. Participants

gave informed consent and received a gift card of DKK 250 for completing the study.

Experimental design

We conducted a two-alternative discrete choice experiment with a full factorial mixed

within-between subjects design manipulating choice conditions (inferential choice,

preferential choice, primed preferential choice) as a between-subjects factor, and

target label surface size (small, large), target label salience (high, low), and set size

(6, 10, 14, 18, 22, 26, 30 labels) as within-subjects factors. The position of target

and distractor labels on each alternative was randomly sampled from 15 possible

locations. The assignment of target labels went as follows: first, the target product

was chosen with an equal probability between left and right side. Second, a target


Figure 3.1. Average judgements of 39 labels rated between 0 and 1

0.00

0.25

0.50

0.75

1.00

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label was drawn from the set of four target labels with the following probabilities P =

(.8, .07, .07, .07). The target label with the highest validity in terms of healthiness was

drawn with a probability of .8. To keep participants from searching for a single target,

there was a .2 probability of the target not discriminating, i.e. being present on both

products. In the event of the target not discriminating, a new target was drawn from

the remaining set of targets. All levels were presented twice resulting in 140 trials.

Materials and measures

To avoid prior preferences influencing the experiment, the stimuli were images of

mock toast bread. Alternatives were presented on the left and right side of the screen.

The salience of target labels was manipulated by controlling the contrast (trans-

parency = 0 % vs. 60 %). The target label size was manipulated by increasing the

surface size from 1.5x1.5 to 3.0x3.0 degrees of visual angle. All distractors were small

(1.5x1.5) and had 0 % transparency. Target and distractors were randomly allocated

to one of 15 positions on each product with an equal probability for each position.

The set size was manipulated by including distractor labels with an even number

of distractors on the left and right product. Distractors were drawn independently

3.2. METHOD 85

Figure 3.2. Example image of a product with the smallest (left) and largest (right) set size

(A) Smallest set size (B) Largest set size

from the complete set of distractors (m = 33) following a uniform distribution (for an

example see Figure 3.2). The experimental stimuli were presented using PsychoPy 2

(Peirce, 2009, 2007). A pre-test ensured that all labels were readable at a distance of 60

cm from the screen. To prevent participants from foveating more than one label at a

time, labels were separated horizontally and vertically by two degrees of visual angle.

Eye movements were recorded using a desk-mounted EyeLink 1000 eye tracker with

a monocular sampling rate of 1000 Hz and a screen resolution of 1920x1200 pixels.

The screen subtended a visual angle of 46.5 degrees horizontally and 30.1 degrees

vertically. Participants used a chinrest at approximately 60 cm viewing distance from

the screen. Fixations were detected using a velocity, acceleration and motion-based

algorithm (SR Research, 2008; Holmqvist et al., 2011) with velocity, acceleration, and

motion thresholds of 30◦/sec , 8,000◦/sec2, and 0.15◦, respectively. An area of interest

(AOI) was drawn around every label. To avoid false positive fixations, the AOIs were

the same size as the object. For a discussion on optimal AOI selection, see Orquin,

Ashby, and Clarke (2015).

Procedure

Upon entering the laboratory, participants were tested for color blindness, visual

acuity and eye dominance. Participants failing either the color blindness or the acuity

test were excluded from the experiment, but received full payment for their inconve-


nience. Participants were randomly assigned to one of the three choice conditions

and received a short questionnaire corresponding to that condition. Participants

in the inferential condition (N = 26) and the primed preferential condition (N = 26)

received the same questionnaire which contained four vignettes concerning the four

target labels and eight questions to ensure that participants had read the vignettes.

Participants in the preferential condition (N = 24) received a control questionnaire

of similar length about shopping habits. After completing the questionnaire, par-

ticipants were calibrated using a 9-point calibration procedure performed at the

beginning of the experiment followed by a 9-point drift validation test. A calibration

offset < 1.0° was considered acceptable. After the calibration, participants in the infer-

ential condition were instructed to choose the healthier product, while participants

in the preferential and primed preferential conditions were instructed to choose the

product they preferred. To control the location of the first fixation, every trial was

preceded by a fixation cross presented at the center of the screen for 2000 ms. Partici-

pants completed 140 trials by indicating their choice pressing the left or right arrow

key. A trial lasted as long as a participant needed to make a choice. To test the validity

of target and distractor labels, participants completed a post study questionnaire

similar to the one in the stimulus development study.

3.3 Results

Manipulation check

To ensure the validity of targets as health cues, participants answered the same

questionnaire as in the stimulus development study. We pooled the judgments for

targets and distractors across conditions. The results (Table 3.1) indicate a high

validity of targets.

Table 3.1. Mean and CIs for the target and distractor judgments across the stimulus develop-ment study, inferential, preferential, and primed preferential choice conditions

Target Distractor

M 95 % CI M 95% CI

Stimulus development study .33 [.29, .38] .89 [.87, .91]

Inferential condition .06 [.00, .12] .85 [.79, .91]

Preferential condition .08 [.02, .14] .80 [.76, .84]

Primed condition .04 [.01, .07] .80 [.74, .86]

3.3. RESULTS 87

Eye movement analysis

We analyzed the likelihood of participants fixating targets using a generalized linear

mixed model (GLMM) with a binomial response distribution and a logit link function.

The model was fitted using the lme4 package (Bates, Mächler, Bolker, & Walker, 2015)

in the statistical software R (R Core Team, 2015). Tables were generated using "texreg"

(Leifeld, 2013), and graphical visualizations were created using "ggplot2" (Wickham,

2009).

The dependent variable (fixation selection) was a binary variable with value one

if a target was fixated and zero otherwise. The set of possible independent variables

for the fixed effects part of the model included size (binary), salience (binary), set size

(metric), distance to center (metric), and condition (categorical). We considered a

set of possible models for which we applied a hold-one-person-out cross validation

procedure for model selection (see Appendix A). For every participant we fitted the

model on the entire sample excluding that participant. The fitted model was then

used to predict the data for the excluded participant. The procedure was repeated for

each model and model selection was based on the Brier score:

Br i er Scor e = 1

N

N∑t=1

(pt −ot )2

where N is the total number of predictions, p is the predicted probability and o is

the true outcome for observation t. The Brier score ranges from zero to one where a

smaller value is better. The final model included main effects of all variables except

condition. The model included a random intercept for each participant. Table 3.2

contains coefficients, standard errors, and variance estimate for the final model.

The model performed reasonably well with 72.01 % accurate predictions. Figure 3.3

illustrates main effects.

Choice analysis

To investigate the effect of target relevance on choice, we analyzed the likelihood of

choosing the target alternative across conditions. We expected that participants in

the inferential condition would be more likely to choose the the alternative that was

dominant in terms of the target attribute than those in the primed preferential condi-

tion. We also expected that participants in the primed preferential condition would

be more likely to choose the alternative that was dominant in terms of the target at-

tribute than participants in the preferential condition. Furthermore we expected that

visually attending to the target attribute would increase the likelihood of choosing

the target alternative. The dependent variable (target choice) was a binary variable

indicating whether a participant chose the target alternative. Fixation selection and

condition were used as independent variables. As with the eye movement analysis,

we applied a hold-one-person-out cross validation procedure for model selection


Figure 3.3. Main effects of fixation likelihood model with error bars representing 95 % confi-dence intervals

0.50

0.54

0.58

0.62

5 10 15 20 25 30

Setsize

Fix

atio

n lik

elih

ood

0.0

0.2

0.4

Low High

Salience

Fix

atio

n lik

elih

ood

●

●

●

●

●

●

●

●

●

●

●

●

●

●

0.25

0.30

0.35

200 300 400

Distance to center (in px)

Fix

atio

n Li

kelih

ood

0.0

0.2

0.4

0.6

Small Large

Size

Fix

atio

n lik

elih

ood

(see Appendix B). The final model included main effects of selection and condition.

The model included a random intercept and slope for trial order grouped by partici-

pant. Table 3.3 contains coefficients, standard errors, and variance estimate for the

final model. The model performed reasonably well with 77.9 % accurate predictions.

Figure 3.4 illustrates main effects.

3.4 Discussion

Decision theories disagree on the relative contribution of top-down and bottom-up

control on visual search in decision making. To directly compare the two processes,

we manipulated top-down and bottom-up factors in an orthogonal design. The most

predictive model of fixation likelihood included in decreasing order of effect: attribute

size, distance from center of screen, set size, and salience. There was no effect of

condition in the final model. This model show that stimulus characteristics attracts

the attention of the decision maker under the design of the present study regardless

of the task condition. The most predictive model of target choice likelihood included

only the effect of task condition. From the manipulation check and the target choice

model, we know that the top-down manipulation was effective. The results of the

manipulation check show that both target and distractor attributes were interpreted

3.4. DISCUSSION 89

Table 3.2. GLMM of fixation likelihood

Fixation likelihood

(Intercept) .046(.195)

Size 1.321∗∗∗(.039)

Salience .347∗∗∗(.039)

Set size −.017∗∗∗(.002)

Distance −.002∗∗∗(.0002)

Num. obs. 14896Num. groups 76AIC 16353.63BIC 16399.28Log Likelihood −8170.82Var: Intercept 2.29

Note ∗∗∗p < 0.001, ∗∗p < 0.01, ∗p < 0.05.

Figure 3.4. Observed and predicted probability of choosing the target alternative with errorbars representing 95 % confidence intervals

●

●

●

●

●

●

0.5

0.6

0.7

0.8

0.9

1.0

Inferential Preferential Primed preferential

Condition

% c

hoic

e of

targ

et a

ltern

ativ

e

Selected ● ●no yes Predicted ●


Table 3.3. GLMM of target choice likelihood

Target Choice

(Intercept) 1.49∗∗∗(.25)

Fixation selection .42∗∗∗(.05)

Preferential condition −1.23∗∗∗(.28)

Primed condition −.84∗∗(.27)

Num. obs. 14896Num. groups 76AIC 13895.20BIC 13948.46Log Likelihood −6940.60Var: Intercept .6767Var: Trial order .0001Corr: Intercept : Trial order .47

Note ∗∗∗p < 0.001, ∗∗p < 0.01, ∗p < 0.05.

correctly, and in the target choice analysis we find that condition influences choice

as expected. Given its influence on choices, it is surprising that condition does not

influence visual search. Our results show that bottom-up factors play a greater role in

visual search than assumed by most contemporary theories of decision making. We

expected target choice to be approximately at chance level if a target attribute was not

fixated. However, target choice was well above chance level for all conditions in both

trials where the target attribute was fixated and trials where it was not. This might be

due to covert (peripheral) attention, i.e., participants fixating a point in proximity of

the target allowing for indirect detection of the target. This does not, however, change

the conclusion that fixation selection is driven by bottom-up processes which at the

perceptual level pull the decision makera attention and therefore also choice.

The present study opens up the question about when bottom-up and top-down

processes have the stronger effect on attention. Under the current experimental

design the effect of bottom-up factors are dominating and driving allocation of visual

attention, however, the effect might be caused by large differences in the variations

of the different manipulations. As such the variation between top-down conditions

might be too small compared to the variations in bottom-up manipulations (i.e. the

different tasks might be to similar). This gives rise to the questions: Which factors

determine whether bottom-up or top-down effects are stronger, how much does

tasks have to differ for it to affect search, and in which situations does bottom-up

3.4. DISCUSSION 91

effects dominate visual search?


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3.6 Appendix

Appendix A

Brier ScoreModel Specification Training Test(1|sub j ect ) 0.282 0.353size + (1|sub j ect ) 0.258 0.332setsize + (1|sub j ect ) 0.281 0.352salience + (1|sub j ect ) 0.281 0.352distance + (1|sub j ect ) 0.281 0.352distance + condition + (1|sub j ect ) 0.281 0.355salience + condition + (1|sub j ect ) 0.281 0.355salience + distance + (1|sub j ect ) 0.279 0.351salience + setsize + (1|sub j ect ) 0.280 0.351setsize + condition + (1|sub j ect ) 0.281 0.356setsize + distance + (1|sub j ect ) 0.280 0.351size + salience + (1|sub j ect ) 0.257 0.331size + condition + (1|sub j ect ) 0.258 0.335size + distance + (1|sub j ect ) 0.257 0.331size + setsize + (1|sub j ect ) 0.257 0.332salience + distance + condition + (1|sub j ect ) 0.279 0.354salience + setsize + condition + (1|sub j ect ) 0.280 0.355size + setsize + condition + (1|sub j ect ) 0.257 0.334setsize + distance + condition + (1|sub j ect ) 0.280 0.354distance + salience + setsize + (1|sub j ect ) 0.278 0.350size + distance + condition + (1|sub j ect ) 0.257 0.334size + distance + setsize + (1|sub j ect ) 0.256 0.330size + salience + setsize + (1|sub j ect ) 0.256 0.331size + salience + condition + (1|sub j ect ) 0.257 0.334size + salience + distance + (1|sub j ect ) 0.256 0.330size + salience + distance + condition + (1|sub j ect ) 0.256 0.333size + distance + setsize + condition + (1|sub j ect ) 0.256 0.333size + salience + setsize + condition + (1|sub j ect ) 0.256 0.333distance + salience + setsize + condition + (1|sub j ect ) 0.278 0.353size + salience + setsize + distance + (1|sub j ect ) 0.255 0.329size + salience + setsize + distance + condition + (1|sub j ect ) 0.255 0.332(size + salience + setsize + distance + condition)2 + (1|sub j ect ) 0.253 0.332(size + salience + setsize + distance + condition)3 + (1|sub j ect ) 0.253 0.332

3.6. APPENDIX 95

Appendix B

Brier ScoreModel Specification Training Testselection*condition + (1+ tr i al |sub j ect ) 0.320 0.280selection*condition + (1|sub j ect ) 0.320 0.332selection + condition + (1+ tr i al |sub j ect ) 0.320 0.277selection + condition + (1|sub j ect ) 0.320 0.330selection + (1|sub j ect ) 0.319 0.345(1|sub j ect ) 0.347 0.377

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