Manfred Ng z3379990 Honours Thesis

Running head: VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE?

Untangling the Influences of Arousal and Valence in Value-Driven Capture: Attentional

and Oculomotor Capture by Loss-Related Stimuli?

Manfred Wing Wui Ng

Supervised by Dr. Mike Le Pelley

Submitted in partial fulfilment of the requirements of the Bachelor of Science (Honours) at the University of New South Wales

October 2014

VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? i

Certificate of originality

‘I hereby declare that this submission is my own work and that, to the best of my knowledge and belief, it contains no material previously published or written by another person nor material which to a substantial extent has been accepted for the award of any other degree or diploma of the university or other institute of higher learning, except where due acknowledgement is made in the text.

I also declare that the intellectual content of this thesis is the product of my own work, even though I may have received assistance from others on style, presentation and language expression.’

Signature: ______________________________

Student’s Name: _________________________

VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? ii

Acknowledgements

I offer my sincerest thank-you to my supervisor, Dr. Mike Le Pelley, for his constant

guidance, patience and support throughout the year. I also offer thanks to everyone in the

Associative Learning Lab for their advice and discussions during meetings. A special thanks

to Daniel Pearson for helping out with the programming and for his useful insights.

To the all my wonderful friends who have made it with me through this 4-year

adventure, making it so enjoyable and memorable; I offer my deepest gratitude, and I wish

you the best of luck for your future endeavors (a special mention to Michael, Joe, Maggie and

Andy, who have been with me from the very beginning; and Carina, Hui and Gunadi, for our

food therapy sessions). Furthermore, a special shout-out to Vik and Jammie, who have

consistently accompanied me throughout the year; those late nights in Matthews will not be

forgotten.

And to a certain someone who entered my life half-way through this year; your love

and support has kept me strong till the very end, and for that I am forever grateful.

VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? iii

Table of Contents

Certificate of Originality____________________________________________________i

Acknowledgments_________________________________________________________ii

Table of contents__________________________________________________________iii

List of Tables and Figures__________________________________________________viii

Abstract__________________________________________________________________x

Introduction_______________________________________________________________1

Learned value________________________________________________________2

Value-driven attentional capture____________________________________2

Is task-relevance necessary? _______________________________________6

Attentional and learning related disorders __________________________________9

Value-driven capture: arousal or valence? _________________________________10

Value-driven attentional capture by stimuli paired with aversive events____13

The present study ____________________________________________________14

Experiment 1_____________________________________________________________15

Method____________________________________________________________17

Participants___________________________________________________17

Apparatus ____________________________________________________17

VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? iv

Stimuli_______________________________________________________17

Visual search task_______________________________________17

Evaluative priming task___________________________________18

Design______________________________________________________18



Procedure____________________________________________________19



Awareness_____________________________________________21

Preliminary data analaysis_______________________________________21

Results____________________________________________________________22

Visual search task_____________________________________________22

Response time__________________________________________22

Accuracy______________________________________________23

Awareness_____________________________________________24

Evaluative priming_____________________________________________25

Discussion__________________________________________________________26

VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? v

Experiment 2_____________________________________________________________28

Arousal and valence_________________________________________________31

Method____________________________________________________________32

Participants___________________________________________________32

Apparatus ____________________________________________________32


Stimuli________________________________________________33

Design________________________________________________33

Procedure_____________________________________________34

Evaluative priming task_________________________________________35

Preliminary data analysis________________________________________35

Results____________________________________________________________35

Omission trials_________________________________________________35

Response time_________________________________________________37

Saccade latencies_______________________________________________38

Attentional dwell time__________________________________________39

Awareness___________________________________________________39

Evaluative priming_____________________________________________40

VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? vi

Discussion__________________________________________________________41

Experiment 3_____________________________________________________________43

Method____________________________________________________________45

Participants___________________________________________________45

Apparatus and stimuli__________________________________________45

Design______________________________________________________45


Procedure___________________________________________________46

Preliminary data analysis________________________________________47

Results___________________________________________________________48


Response time__________________________________________48

Accuracy______________________________________________49

Awareness_____________________________________________49

Discussion_________________________________________________________50

General discussion________________________________________________________51

Value-driven capture by loss-predictive stimuli is caused by learning about response-

value ____________________________________________________________________52

Inconsistent accuracy findings between Experiment 1 and 3___________________54

VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? vii

Inconsistent findings between Experiment 3 and Le Pelley et al. (in press)________56

Limitations and Future Research________________________________________57

Theoretical and Clinical Implications____________________________________60

Arousal versus Valence_________________________________________60

Task-relevance versus task-irrelevance_____________________________61

Value and two modes of attention_________________________________61

Drug-addiction and value-driven capture____________________________61

Value and two modes of attention_______________________________________63

Footnotes_______________________________________________________________64

References_______________________________________________________________65

Appendix________________________________________________________________72

VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? viii

List of Tables and Figures

Figures

Introduction

Figure 1: Sequence of trial events in the training phase of Anderson et al’s (2011b)

experiment_________________________________________________________________3

Figure 2: Figure 2. Sequence of trial events for Experiment 2 of Le Pelley et al (in

press)_____________________________________________________________________4

Experiment 1

Figure 3: Mean response time across 12 training blocks for Experiment 1_______________

Figure 4: Accuracy across 12 training blocks for Experiment 1________________________

Experiment 2

Figure 5: Sequence of trial events for Le Pelley et al (in press, Experiment 3)___________9

Figure 6: the mean proportion of omission trials across training blocks of Experiment 2__15

Figure 7: the mean RTs across 10 training blocks for Experiment 2___________________19

Figure 8: mean saccade latencies for omission and non-omission trials, averaged across

training blocks for Experiment 2_______________________________________________21

Experiment 3

Figure 9: Mean RTs across 12 training blocks for Experiment 3_____________________31-i

Figure 10: Accuracy across 12 training blocks for Experiment 3____________________33-i

VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? ix

Tables

Experiment 1

Table 1: the mean response times (RTs, in milliseconds) as a function of prime valence and

target type for Experiment 1_________________________________________________33-i

Experiment 2

Table 2: the mean response times (RTs, in milliseconds) as a function of prime valence and

target type for Experiment 2_________________________________________________33-i

Experiment 3

Table 3: Reward and loss values for Experiment 3_______________________________33-i

VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? x

Abstract

Three experiments examined the extent to which learning about loss-value on

attention would show similar patterns of value-driven capture to learning about reward-value.

In these experiments, participants were never required to look at loss-associated stimuli. The

design was set up such that looking or attending towards these stimuli would, if anything

hinder performance and reduce overall payoff. In Experiment 1, in a visual search task,

certain stimuli signaled the magnitude of an aversive outcome. One coloured-distractor was

always a consistent signal of large monetary loss, and another was a consistent signal of small

monetary loss. Interestingly, high-loss distractors and low-loss distractors did not differ in the

extent to which they captured attention. Consistent with this finding, Experiment 2 found no

differences in the rate of oculomotor capture between loss-valued stimuli and neutral valued-

stimuli. Lastly, Experiment 3 found no differences in attentional capture between gain-

valued, loss-valued and neutral-valued distractors. The results of experiments strongly

suggest that signals of loss do not influence the extent of attentional capture. The implications

of the present findings are discussed in relation to the influences of arousal and valence on

value-driven capture, and how different types of learning may influence this effect.

VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? xi

VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 1

One of the most fundamental processes in human cognition is attention. It allows us to

selectively choose certain aspects of our sensory input for processing. Research has often

suggested that attentional capture by stimuli in our environment is heavily influenced by their

physical salience or properties. That is, attentional capture can be modulated by stimulus

properties such as intensity, abruptness etc. However, more recently, research has suggested

that this might not be the only case; that previous experience with stimuli or learning about

their relationships with other events may also influence attentional capture (Anderson,

Laurent & Yantis, 2011a, 2011b; Della Libera, & Chellazzi, 2009; Kiss, Driver, & Eimer,

2009; Theeuwes & Belopolsky, 2012; Le Pelley, Mitchell, & Johnson, 2013; Le Pelley,

Pearson, Griffiths, & Beesley; in press). The present research will focus on the reciprocal

relationship between attention and learning and how they influence attentional capture.

In this paper, we will first discuss the two traditional models of attentional control in

cognitive psychology (for review, see Theeuwes, 2010). The first is a voluntary, goal-directed

form of attention, where attention is steered by an individual’s intentions and goals. This

suggests that attentional resources can be deployed in a controlled manner to enhance

processing of certain stimuli. For example, in a lecture, a student would employ this form of

attention to prioritize listening to what the lecturer is saying and ignore the people chatting

behind him/her. In contrast, attention can also be involuntarily captured in a stimulus-driven

mode by virtue of a stimulus’s physical salience. That is, in the example before, the student’s

attention would be captured involuntarily by another student’s mobile phone ringing, simply

because it was loud and abrupt. Combined, the implications of this framework suggest that

attention can be used to selectively choose stimuli for processing either directly by an

individual’s goals, or can be automatically captured by physically salient stimuli.


Recent research has shown that our attention can also be influenced by learning about

the value of rewards predicted by stimuli (e.g. Anderson et al., 2011a, 2011b). The research

demonstrated that a stimulus that is consistently paired with large reward is more likely to

capture attention in the future than an equally-salient stimulus paired with low reward (this

will be discussed in detail later). So how do these findings fit into the traditional model of

attentional control? For example, a person might learn that a certain ringtone is always paired

with receiving a sweet and loving text message from their partner. In this case, the person has

learned the value of the specific ringtone as a consistent signal of a rewarding outcome. That

is, the sound of a ringing phone would involuntarily capture the person’s attention. But this

raises some questions. For example, would an equally loud, equally salient ringtone capture

attention be as likely to capture attention as the ringtone that was paired with the loving texts?

If not, then to what extent was this capture due to the physical salience of the ringing phone

(i.e. loudness, abruptness) and to what extent was it due to the learned value imbued?

Similarly, if another equally salient ringtone was consistently paired with a negative text from

an ex-partner, would it have captured attention in the same manner? These questions will be

further discussed in the upcoming sections.

Learned value

Recent years have seen a spate of studies demonstrating the influence of reward

learning on attention. That is, these studies have demonstrated that more attention is paid

towards stimuli that predict a large reward (‘high-value’) over those that predict a small

reward (‘low-value’).

Value-driven attentional capture

As mentioned earlier, the cognitive psychology literature has drawn a distinction

between two types of attentional processes (e.g. Yantis, 2000). That is, attentional selection

can proceed voluntarily, in accordance with participants’ context-specific goals or priorities,


or involuntarily, in accordance with the physical salience of a stimulus. However, these might

not be the only influences, and that learnt value might also play a role (e.g. Anderson et al.,

2011a). That is, previous experience of the relationship between stimuli and reward

influences the extent to which those stimuli involuntarily capture attention in future.

Specifically, these studies have shown that stimuli that are associated with high-value reward

become more likely to capture attention than those associated with low-value reward. This

phenomenon has been termed value-driven attentional capture (Anderson et al., 2011a).

Perhaps the best laboratory demonstrations of value-driven attentional capture come

from studies using visual search paradigms. Anderson et al., (2011a, 2011b) employed a two-

part visual search task, where in an initial training phase, participants learnt to associate

specific colours with certain outcomes over the course of 1008 trials. This was

operationalized by having participants respond as rapidly as possible to the orientation of a

line segment (horizontal or vertical) within a target coloured circle (red or green), among a

set of five other coloured circles (which were never red or green; see Figure 1a). Correct

responses within 600ms were rewarded, where the magnitude of the reward was determined

by the colour of the target circle. For example, for a particular participant, correct responses

where the target circle was red may have typically produced high monetary reward (5c),

while correct responses when the target circle was green typically produced low reward (1c).

Hence for this participant, red was the high-value colour, and green was the low-value colour.

For other participants this colour-reward assignment was reversed, in a counterbalanced

fashion.

In a subsequent test phase, participants were required to respond to the orientation of

a line (horizontal or vertical) within a unique target shape (either a diamond among circles [as

in Figure 1b], or a circle among diamonds). All trials in this test phase were unrewarded.

Occasionally, the test phase display contained a “distractor”, which was a non-target shape


rendered in either red or green (all other shapes were black). Note two important things:

firstly, participants were informed that colour was irrelevant to the task and should be

ignored; and secondly, the target was never rendered in red or green. Nonetheless, Anderson

et al. (2011a, 2011b) still found that participants’ response times (RT) were significantly

slower when the display contained a distractor cue rendered in the high-value colour than in

the low-value colour.

Figure 1. Sequence of trial events in the training phase of Anderson et al’s (2011b) experiment. On each trial, participants reported the orientation of the line segment inside the target (vertical or horizontal). (a) During the training phase, targets were defined by colour (red or green). Correct responses were followed by monetary reward feedback. One of the target colours was followed by high reward and the other by low reward. (b) During the test phase, the target was defined by its unique shape. A distractor circle could be presented, rendered in red or green.

This difference in the extent to which distractors interfered with performance must

have been a consequence of the difference in reward value with which they were previously

associated, as the physical salience (colour brightness) of the distractors was matched across

participants by counterbalancing. The implication is that stimuli associated with high-value

involuntarily captured attention more often than stimuli with low-value, hence impairing

visual search performance. This could be interpreted as reward learning changing the

effective salience of the stimuli. The logic runs like this, 1) consistent pairing of a cue with

high reward will lead to it becoming more effectively salient than a cue paired with low

value; 2) the high-value cue is more likely to automatically capture attention than the low-


value cue, because it is more salient, and therefore more distracting. Thus, the authors

concluded that attentional priority towards valuable stimuli must occur at an involuntary level

outside of strategic control, since attending to these coloured stimuli in the test phase was

contrary to participants’ goal (to respond to the unique shape).

Similarly, value-driven capture has also been demonstrated with an “online” measure

that can track attention on a moment-by-moment basis. One of the most notable features of

visual attention is that it coincides with eye gaze (Posner, 1980). Therefore, an effective tool

to assess whether reward learning affects attention would be to use an eye-tracker to monitor

eye movements. Consistent with this suggestion, Theeuwes and Belopolsky (2012, see also

Anderson & Yantis, 2012) demonstrated that oculomotor capture occurred more often for

stimuli previously associated with high reward than low reward. In a conceptually similar

paradigm to Anderson et al., (2011a, 2011b), participants were trained to make rapid

saccades to either a vertical or horizontal rectangular bar over the course of 240 trials. High-

reward was given for making fast saccades towards a particular bar orientation (the high-

value shape) and low reward for making fast saccades towards a different bar orientation (the

low-value shape). In a subsequent unrewarded test phase, participants were more likely to

involuntarily shift their eye gaze towards the high-value shape when it was present as a

distractor than the low-value shape. This suggests that not only does reward learning exert an

influence on the deployment of spatial attention, but it also affects our saccadic system.

Anderson et al. (2011a, 2011b) argued that value-driven attentional capture reflects a

mechanism of selective attention and attentional priority, where involuntary attentional

capture by high-valued stimuli is beyond what is afforded by physical salience alone. In

support of this notion, Anderson et al., (2011a) reported a correlation between trait

impulsivity and vulnerability to value-driven attentional capture. Past research has suggested

that impulsivity is linked with ability to control behaviour (Dickman, & Meyer, 1988). In


other words, attentional capture by valuable stimuli could stem from a general inability to

control attention and resist distraction. For instance, if I was highly impulsive, then I am more

likely to have my attention captured by stimuli that I perceived as more salient. In addition,

neurobiological studies have also shown that high-reward associated stimuli are represented

more robustly in the early stages of visual system than low-reward stimuli (Hickey et al.,

2010; Serences, 2008; Shuler & Bear, 2006). This suggests a possibility that changes in

attentional priority reflect changes in the visual salience or pertinence of stimuli. Salience has

often been defined as a physical property, where a stimulus stands out in a context by virtue

of visual features. But the studies described above suggest that it is almost as if reward

learning induces a fundamental change to our perception of stimuli, that is, an effective

change in salience above and beyond physical salience.

Therefore, the influence of reward learning on attention provides an intriguing

example of how the automatic processing of sensory input is not fixed, but instead is

malleable based on the individual’s past experiences. Specifically, the studies reported here

demonstrated that attention is not only influenced by goals and intentions of an individual, or

by the physical properties of a stimulus, but also by fundamental learning mechanisms.

Is task-relevance necessary?

The underlying mechanism by which value-driven attentional capture operates,

however, is still unclear. One alternative to the account provided by Anderson et al. (2011a,

2011b) is that perhaps the associations of specific targets with value leads to the persistence

of goal-directed behaviours that were previously rewarded. That is, in their study, the reward-

related stimuli that were used to demonstrate value-driven attentional capture were first

established in an initial training phase. In this phase, participants were given large reward

when they correctly responded to the line orientation inside (say) a red shape and a small

reward when they correctly responded to the line orientation inside (say) a green shape.


Therefore, it could be said that these stimuli were all “task-relevant”, since they were targets

that participants had to orient their attention to in order to receive reward. Le Pelley et al. (in

press) argued that it is thus possible that attentional capture in the subsequent test phase

reflects a “hangover” of an automatic attentional capture response, where participants

continue to search for the distractor cues even when they are no longer task relevant. A

similar account can be applied to Theeuwes & Belopolsky’s (2012) study, where (during the

initial training phase) participants received a larger reward for making a saccade to high-

value shape than low-value shape. In both cases, larger reinforcement is given for orienting

attention towards high-value stimuli than low-value stimuli during training, which lasts for

many hundreds of trials. Therefore, it would not be very surprising if participants continued

to search for these stimuli in the subsequent test phase (for at least a short time), even though

they are no longer task-relevant or rewarded.

A recent study by Le Pelley et al. (in press) investigated this possibility. Unlike the

studies by Anderson et al. (2011a, 2011b) and Theeuwes and Belopolsky (2012), this

experiment did not involve a separate training and test phase. Instead on every trial

participants were required to respond to a unique target shape (a diamond among circles; see

Figure 2). More specifically, the design was conducted such that coloured circles would

always signal the magnitude of the outcome. For example, the high-value colour was a signal

of large reward, since large reward could be obtained only when the high-value colour was

present in the stimulus array. Similarly, the low-value colour was a reliable signal of small

reward. However, participants were never required to respond to or look at these cues. That

is, distractor cues here were always “task-irrelevant” to the participant’s goal of achieving

monetary reward. In fact, not only did attending to distractors conflict with the demands of

the task, but it also resulted in reduced reward. This was because the reward received on each

trial was (partially) influenced by participants’ response time; hence any slowing of the


response to the target (as a result of attentional capture by the distractor) would result in

reduction of reward. Thus, participants’ most effective strategy would have been to supress

attention towards the distractors.

Nonetheless, Le Pelley et al (in press) found evidence of value-driven capture by

these task-irrelevant stimuli; specifically, responses to the target were slower when the

display contained a distractor with high-value colour than low-value colour, suggesting that

the high-value distractor was more likely to capture attention. This provides an intriguing

example of how an attentional bias towards high-valued stimuli can develop even when they

were never task-relevant. Furthermore, they demonstrated that this pervasive pattern

remained stable over the course of extensive training (1728 trials, over three days). This

suggests that even with a great deal of experience, participants did not learn to suppress

attention towards high-valued distractors, which would have benefited their payoff.

Figure 2. Sequence of trial events. Participants respond to the line orientation inside the target diamond (horizontal or vertical). The distractor can be rendered in red, blue or distractor-absent. Fast correct responses to the target shape will prevent monetary loss, depending on the distractor cue in the trial. A high-value distractor colour reliably predicted large loss, while a low-value colour reliably predicted small loss. Distractor-absent trials were equally likely to result in small and large loss.


In the experiments reported by Le Pelley et al (in press), the fact that the distractor

stimuli were task-irrelevant throughout training meant that participants were not rewarded for

orienting attention towards these stimuli; indeed, attending to the distractors would actually

result in loss of reward. However, these different distractor colours were reliable signals of

reward magnitude. That is, the high-value distractor reliably signalled the availability of high

reward, and the low-value distractor reliably signalled low reward. These results therefore

suggest that the crucial determinant of value-driven attentional capture is the magnitude of

reward that is signalled by a stimulus, rather than the reward that is achieved by orienting

attention towards that stimulus. In terms of associative learning theory, this suggests that

value-driven attentional capture is a product of Pavlovian conditioning rather than

instrumental conditioning.

Attention and learning related disorders

It is important to understand the underlying mechanisms in which value-driven

attentional capture operates. While value-driven capture may bring adaptive changes in

speeded detection of reward-related stimuli, it could also be maladaptive. For instance, drugs

of abuse often lead to potent neural reward signals (Robinson & Berridge, 2001), and

consequently stimuli that are present when drugs are ingested may become associated with

these reward signals. This is problematic, because in the clinical setting, attentional capture

by drug-related stimuli (i.e. drug paraphernalia) has been well established in the addicted

population (Garvan & Hester, 2007; Robinson & Berridge, 2008), and is predictive of drug

relapse (Marissen et al., 2006; Cox et al., 2002). That is, say for instance, a recovering drug

addict has an intended goal of drug abstinence, but cannot help but attend to drug-related

stimuli. This would lead to relapse of drug addiction. This maladaptive pattern of attention is

especially problematic for clinical treatment. Broadly speaking, this leads to the possibility

that rewarding learning could lead to involuntary attentional capture by valuable, but


inconspicuous stimuli. Therefore, it is important to examine the exact mechanism by which

involuntary attention towards stimuli that have motivationally significant outcomes occurs as

a consequence of reward learning in the healthy population.

In addition, it is also important to examine the underlying mechanism by which

attentional capture occurs with motivationally significant but task-irrelevant stimuli. In the

example above, we have suggested that task-relevant stimuli (i.e. drugs of abuse) may bring

maladaptive changes in our attentional system. However, in reality, our environment is

saturated with stimuli that signal reward, but have no direct instrumental relationship with

achieving it (i.e. task-irrelevant stimuli). For instance, following the drug example mentioned

above; imagine that a drug addict frequently took drugs in a certain room in their house. This

then makes the room a “context” in which reward occurs. However, many aspects of the

room may signal the effects of drug intake, but none of these have a direct instrumental

relationship with achieving that reward. Therefore, it could be argued that with respect to the

addict’s goal of achieving drug consumption, the room is task-irrelevant. For example, sitting

inside the room does not itself elicit reward, and the effects of drugs are still the same if

consumed in another room. Hence, it is also crucial to investigate the underlying mechanisms

of learning about how task-irrelevant stimuli that predict significant outcomes can

nevertheless capture attention.

Value-driven capture: arousal or valence?

There are still many questions regarding how value-driven attentional capture

operates. In particular, it is interesting to examine the exact nature of the information that

captures attention. In previous sections, literature has suggested that cues that predict

rewarding outcomes are sufficient for value-driven attentional capture to occur (Anderson et

al., 2011, 2011b; Le Pelley et al., in press; Theeuwes & Belopolsky, 2012). That is, all of the


studies that have been discussed so far have examined value-driven capture by stimuli that

were paired with appetitive (i.e. pleasant) outcomes. Therefore, it remains unclear whether a

similar relationship between attention and learning would arise if learning was with regards

to an aversive outcome (i.e. loss-value). Specifically, do these two conditions influence the

attentional system in a different way to each other, or is value-driven attentional capture

influenced by a general mechanism that prioritizes value-ridden stimuli regardless of reward

or loss?

Researchers examining the relationship between attention and emotion have argued

for a distinction between two affective dimensions, arousal and valence (Kuhbandner &

Zehetleitner, 2011; Labar, & Cabeza; 2006; Barrett & Russell, 1999). Arousal defines the

degree of evoked emotion ranging from calm to excited, where both appetitive and aversive

cues are arousing. For example, imagine a scene where there is a car crash and both the cars

have been completely destroyed versus a scene with a picture of a face with a subtle smile. In

this case, disregarding whether the scene was positive or negative, the scene with the car

crash is more arousing than the picture of the face, because it evokes a strong emotion of

excitement. On the other hand, valence defines the degree of pleasantness, that is, whether the

stimulus evokes a positive or negative emotion. So in the example above, the car crash would

be regarded as having negative valence because it is more likely to evoke a negative emotion;

whereas the face is more likely to have positive valence.

These two dimensions are of particular interest to value-driven attentional capture. By

employing stimuli with varying levels of arousal or valence in an experiment and pairing

them with neutral stimuli (i.e. coloured circles), we can gain a better understanding of the

properties of outcome events that give rise to value-driven capture. This aligns itself with our

question of interest mentioned earlier, in regards to whether stimuli that predict aversive

outcomes would capture attention in a similar manner to stimuli that predict appetitive


outcomes. So what would be expected? One possibility is that the type of value learnt is in

regards to the magnitude of the value (the arousing properties). That is, attention and learning

interact in such a way that stimuli that predict more arousing outcomes (regardless of

valence) will capture attention more frequently than stimuli that predict less arousing

outcomes. By contrast, it is possible that attention is guided by the valence of the outcome

signaled by stimuli, and hence different patterns of value-driven attentional capture might be

observed when learning about positively-valenced versus negatively-valenced outcomes.

Looked at a different way, Berridge & Robinson (1998) argued that establishing a

neutral stimulus as a signal of reward might cause some of the motivational salience of the

reward to transfer over to the signal. This could account for the fact that such stimuli come to

capture attention (as shown in the previously described studies of value-driven capture).

Similarly, if a neutral stimulus was consistently paired with an aversive outcome, we might

expect some of the motivational salience of punishment to transfer over to the signal.

Whether we should expect that such transfer would also promote attentional capture is

unclear. For example, it is widely believed that the influences of appetitive and aversive

stimuli operate as “opponent” processes (e.g. Solomon & Corbit, 1974). This has been

supported by neurobiological studies that demonstrated activation of different neural

pathways when presented with stimuli that predict appetitive and aversive outcomes

respectively (for review, see Barberini, Morrison, Saez, Lau, & Salzman, 2012), and by

behavioural studies showing approach to rewarding stimuli and avoidance of punishing

events (Koob & Kreek, 2007, Robinson, & Berridge, 2001; Kelley & Berridge, 2002). This

suggestion of a fundamental difference between reward and punishment might be taken to

imply that the patterns of attentional capture provoked by the two would be different.

However, these two domains do not always seem to work in opposition to one another. Often

they share overlapping properties in triggering attentional orientation and cognitive


processing (Armony & Dolan, 2002; Lang & Davis, 2006), leading to the suggestion that

appetitive and aversive neural circuits interact in complex ways, sometimes in opposition and

sometimes in concert, to determine behaviour (see Barberini et al., 2012 ). On this argument

it would seem possible that the impact of positive or negative events on value-driven

attentional capture may be similar.

Value-driven attentional capture by stimuli paired with aversive events

In line with this latter suggestion, two recent studies have suggested that arousal,

rather than valence, is the underlying mechanism that guides value-driven attentional capture

(Wang, Yu & Zhou, 2013; Wentura, Muller, & Rothermund, 2014). They provided evidence

for this claim by employing stimuli that predicted monetary loss in a visual search task. In

Wentura et al’s (2014) study, participants were trained to respond to the orientation of a line

presented inside a coloured frame. The colour of the frame determined the payoff for

responses: one colour was generally followed by a large gain of points (‘high-gain’), a second

colour was generally followed by a large loss of points (‘high-loss’), and a third colour was

associated with small gains or losses (‘neutral’). Crucially, in a subsequent test phase, high-

gain and high-loss stimuli were more likely to slow response time towards a unique shape

than neutral stimuli. That is, participants were more likely to have their attention captured by

high-gain and high-loss stimuli than neutral stimuli; and this value effect was equally large.

Consistent with this account, Wang et al. (2013) also demonstrated value-driven

attentional capture with cues that previously predicted electric shocks. That is, participants

were trained to associate one coloured circle with an electric shock to the hand, and another

was followed by no outcome. They found that participants were more likely to have their

attention captured by stimuli that previously predicted electric shock than neutral stimuli.

Taken together, these two studies strongly suggest that value-driven attentional capture


reflects a general underlying mechanism that is driven by learning about the magnitude of the

outcomes predicted, rather than its valence. The implication is that stimuli that signal larger

(i.e., more arousing) outcomes will capture attention more often than stimuli that signal small

outcomes, regardless of whether these outcomes are negative or positive.

The present study

The broad aim of the current study was to further investigate the underlying properties

that govern value-driven attentional capture. More specifically, the current study seeks to test

if there are any attentional capture differences in aversive and appetitive stimuli that have

never been task-relevant. Previous studies of value-driven attentional capture have mostly

been conducted using stimuli that predict rewarding value (Anderson at el., 2011a, 2011b;

Theeuwes & Belopolsky, 2012); however, recent studies have also demonstrated attentional

capture by stimuli that predict an aversive outcome (Wang et al., 2013; Wentura et al., 2014).

That is, to date studies have concluded no differences in attentional capture by stimuli that

predict appetitive and aversive outcomes that were previously task-relevant. This suggests

that information learnt in value-driven attentional capture is in regards to the magnitude of

the outcome, i.e. its arousing properties, rather than its valence.

Notably, all these studies were conducted with stimuli that were previously predictive

of a rewarding or aversive outcome, i.e. with stimuli that were task-relevant during the initial

training phase. Recently, Le Pelley et al. (in press), demonstrated that value-driven attentional

capture can occur with stimuli that were never task-relevant. This suggests that value-driven

capture operates by having attention captured by the “signal” of the reward predicted by the

stimuli. However, to date, we know of no study that has examined value-driven capture with

task-irrelevant stimuli that predict aversive outcomes. That is, it is possible previous

demonstrations of value-driven capture by stimuli that were previously associated with


negative consequences, could be a reflection of participants continuing to search for these

stimuli, rather than the associated value capturing attention. For example, Wentura et al’s

(2014) task was set up such that participants always had to respond to the stimulus that

signaled loss. Specifically, this stimulus had a Pavlovian relationship with monetary loss.

However, the task was also set up such that if participants do not respond as quickly as

possible, they would lose even more money. Hence, this raises the possibility of an

instrumental relationship between responding to the cue and preventing large punishment. In

the current study, we aim to dissociate between these two competing possibilities, by

examining value-driven capture by task-irrelevant stimuli that predict aversive outcomes.

That is, these stimuli have a Pavlovian relationship with monetary loss, but were never

stimuli that participants had to respond too. Broadly speaking, the current series of

experiments aim to examine the effects of arousal and valence on attentional capture in a

circumstance where value-predictive stimuli are not the focus of participants’ attention.

Experiment 1

In Experiment 1, we aimed to examine whether there were differences in attentional

capture between task-irrelevant stimuli that predict high-loss and stimuli that predict low-

loss. This was done by using a similar design to that of Le Pelley et al. (in press). However,

as opposed to receiving monetary reward on each trial, participants instead started off the

experiment with a set amount of money and lost money on each trial. Specifically, on each

trial participants were required to search for and respond to a diamond-shaped target among

an array of circles. The amount that participants lost on each trial was determined by two

factors. Firstly, monetary loss was a function of response time, such that the longer a

participant took to respond, the more money they lost on the trial. Secondly, on most trials,

one of the non-target circles (the distractor) would be coloured. When a certain colour of

distractor appeared, the amount lost on that trial would be multiplied by a factor of 10 (hence


this was termed the high loss colour); another colour signalled that the amount lost would not

be multiplied by 10 (low loss colour).

Notably, as in Le Pelley et al.’s (in press) previous study, on every trial participants

was required to respond to the target diamond, and not the distractor circle. Hence, task

demands never required participants to direct their responses or attend to the loss-predicting

stimuli. That is, we designed the experiment such that attending to loss-predictive stimuli,

would hinder participants’ performance and (by slowing response time to the target) increase

the amount lost on the trial.

In addition, following Wentura et al. (2014), after training was complete we included

an evaluative priming task to examine if this training produced changes in the valence of the

neutral coloured distractor cues. This was operationalized by using the colour distractor cues

from the previous visual search task as “primes”. On every trial, participants were presented

with a prime followed by an affectively polarized adjectives (e.g., delightful or dreadful). The

task was then to respond as rapidly as possible to whether this adjective was positive or

negative. If a prime is evaluated as being more negative or positive than another prime, then

there will larger differences in how fast participants categorize positive or negative

adjectives. Crucially, this was to evaluate if the motivational properties of the aversive

outcome (monetary loss) translated over to the distractors. That is, this task will examine if

the stimulus that is associated with larger monetary loss will produce larger differences in

responses between positive and negative words than the stimulus that is associated with

smaller monetary loss. This would suggest that the neutral stimuli had acquired motivational

properties similar to their associated outcomes.

If stimuli associated with loss-value produce value-driven capture, then we would

expect that stimuli that predict higher monetary loss will be more likely to capture attention


than stimuli that predict low monetary loss. Specifically, when averaged across trials,

participants will show significantly slower RTs towards the target diamond shape on high-

loss trials compared to low-loss trials. However, if loss-value ridden stimuli do not produce

value-driven capture, then we would expect similar patterns of RTs across trials. That is,

task-irrelevant stimuli that signal high-monetary loss does not capture attention more often

than stimuli associated with low-monetary loss.

Method

Participants

Twenty-seven first year psychology students from the University of New South Wales

(UNSW), 12 males and 15 females, participated in exchange for course credit. The average

age was 18.9 years, with a range of 17 to 23 years. On top of course credit, they also received

a performance related payment (M = $20.5 AUD, SEM = $0.99).

Apparatus

The experiment was conducted using a standard PC with a 24-inch monitor running at

120 Hz, positioned ~60cm away from the participant. The experiment was programmed and

presented via MATLAB using Psychophysics Toolbox extensions. Participants made all

responses using the keyboard.

Stimuli

Visual search task

The current experiment employed the same stimuli as were previously used by Le

Pelley et al. (in press). On each trial participants saw a fixation display followed by a search

display and feedback display (see Figure 2). The fixation display consisted of a white cross

(subtending 0.5 degrees of visual angle, dva) presented in the center of the screen. The search

display consisted of six shapes (five circles and one diamond, each 2.3 x 2.3 dva) positioned


at equal intervals around an imaginary circle with diameter 10.1 dva that was centred on the

fixation cross (as shown in Figure 2). Four of the circles and the diamond were always grey

in color, while one of the circles, labelled as the distractor, was either red, blue or the same

shade of grey as the other shapes (CIE x, y chromaticity coordinates of .595/360 for

red, .160/.116 for blue, .304/.377 for grey). Red and blue coloured circles were similar in

luminance (~42.5 cd/m2) which were higher than grey coloured stimuli (36.5 cd/m2). The

target diamond always contained either a horizontal or vertical white line (length 0.76 dva).

Similarly, non-target shapes contained a white line that was either tilted left or right by 45°.

The positions of all stimuli and their line orientations were randomly determined on each

trial. All stimuli were always presented on a black background.

Evaluative priming task

The same coloured distractor circles (red/blue) as used in the visual search task were

used as primes in the evaluative priming task. The task consisted of eight affectively

polarized adjectives (taken from Le Pelley, Calvini, & Spears, 2013). Four of these target

nouns were positive (delightful, wonderful, appealing, terrific) and four were negative

(dreadful, frightful, disgusting, terrible). The average word length for positive words was 9

letters (SD= 0.82, ranging from 8 to 10) and 8.75 letters for negative words (SD= .96, ranging

from 8 to 10).

Design

Visual search task

The training phase consisted of 12 blocks of 48 trials. Each block contained 20 trials

with a distractor in high-loss colour, 20 trials with a distractor in the low-loss color, and 8

distractor-absent trials on which there was no colour singleton in the search display. Trial

order was all randomly determined. The assignment of red and blue to high-loss and low-loss


colours was counterbalanced across participants. Distractor location, target location and

target line segment were all randomly determined on each trial.

At the start of the visual search task, participants were provided with $65 and on each

trial lost money dependent on their response time (RT). For responses faster than 1000ms, the

loss was calculated (in cents) as follows, RT x 0.002 x bonus_multiplier, rounded off to the

nearest 0.01¢. In trials with a low-loss distractor, the bonus_multiplier was always 1 (so an

RT of 500 ms would result in loss of 1¢). By contrast, on trials with a high-loss distractor, the

bonus_multiplier was always 10 (so an RT of 500 ms would result in loss of 10¢). On

distractor-absent trials, the bonus_multiplier was equally likely to be 1 or 10. Errors or

responses slower than 1000ms incurred a loss of 20¢ regardless of the type of distractor.


This task consisted of 2 blocks of 32 trials with each block containing 16 trials with a

circle rendered in the high-loss colour as the prime and 16 trials with a circle in the low-loss

colour as the prime. Each of the aforementioned target words (four positive and four

negative) was presented four times in each block. Each colour of prime circle (high-loss and

low-loss) was paired with each of the target words twice per block. Trial order was randomly

determined.

Procedure

Visual search task

The experiment was conducted in a single, 75-minute session. Full instructions to

participants are shown in the Appendix A and B. In particular, initial instructions stated that

participants were to press the “C” key on the keyboard if the line inside the diamond on each

trial was horizontal and the “M” key if the line was vertical. Participants were asked to

respond as quickly and accurately as possible.


This was then followed up with a practice phase of 10 trials, with no reward feedback

and a yellow coloured distractor. Each trial started off with a presentation of fixation display

for a random period of 400, 500 or 600 ms. The search display then appeared until a response

was made or the trial timed out (after 2s). If the correct response was made then the word

“Correct” appeared in the center of the next screen for 2000ms. If an incorrect response was

registered, then word “Error” appeared instead for 2500ms. If a response was slower than

1000ms or if the trial timed out, then the words “Too slow, please try to respond faster”

appeared centrally in the next screen. The experimenter was present in the room to assist in

case participants misinterpreted the instructions.

After the practice trials, instructions informed participants that the procedure for

subsequent trials was the same as in the earlier practice trials (see Appendix for instruction

screen). Participants were informed that they would begin with $65 in the bank, and would

lose money on each trial depending on their response time. They were told that they would

lose 0.2¢ for every 100ms of response time on each trial. They were also informed that some

trials would be “x10” multiplier trials, on which this loss amount would be multiplied by 10.

Finally, they were told that errors or slow responses would incur a loss of 20¢.

Feedback and search displays were similar to ones in the practice trials, except on

non-multiplier trials the feedback display also showed the amount lost and the remaining

money, and on multiplier trials this was accompanied by a yellow box labeled “10 x loss

multiplier!” Participants were not explicitly shown a trial was a multiplier trial until after a

response had been made, nor were they explicitly informed of the relationship between the

multiplier trials and distractor colours. Inter-trial interval was 1s and participants took a short

break every two blocks.



Directly after the completion of the visual search task, instructions informed

participants that in the next phase of the experiment, they would have to make quick

judgments about the valence of words. Participants were instructed to press the “C” key if the

adjective presented on each trial had a positive meaning, and the “M” key if it had a negative

meaning, as quickly and accurately as possible (see Appendix C).

This procedure was based off Le Pelley, Calvini, & Spears (2013). All stimuli were

presented in the center of the screen. Every trial started off with a centrally presented fixation

cross for 700ms, which was replaced with a coloured circle prime for 200ms. Primes and

target words were separated by a blank screen for 100ms, hence giving participants a 300ms

Stimulus Onset Asynchrony (SOA). If a correct response was registered then no feedback

was given. If an incorrect response was given then the word “incorrect” appeared in the

center of the screen for 100ms, and the computer made a beeping noise; on trials where no

responses were registered within 3000ms, a timeout occurred and the words “You took too

long” appeared centrally on the screen for 1000ms and once again the computer beeped.

Inter-trial intervals were 2000ms and participants were given a short break every block.

Awareness test

After the evaluative priming task, we assessed participants’ awareness of colour-

outcome contingencies (see Appendix D). Participants were told that the amount that will be

lost on each trial is dependent on the colour of the coloured circle in the search display. They

were then presented with either a red or a blue circle, in random order, and for each were

asked to indicate whether the trial would have been a x10 or a x1 multiplier trial.

Preliminary data analysis

Preliminary analysis of the data was based on Le Pelley et al. (in press). The first two

trials of the visual search task, and the first two trials after each break, were discarded.


Timeouts (0.06% of all trials) and trials with RTs below 150ms (0.1%) were discarded. Data

analysis for response times (RTs) was then restricted to correct responses only.

Similarly, trials of the evaluative priming task with RTs below 150ms (0% of trials)

were discarded. Data analysis for response times in this task was then restricted to correct

responses only.

Results

Visual search task

Response time

Figures 3 show RTs across training. Trials with a coloured distractor showed

significantly slower RTs than distractor-absent trials. However, trials did not differ in RT

when there was a high-loss distractor present in the display, versus when it contained a low-

loss distractor. RTs were analyzed using a 3 (distractor type: high-loss, low-loss and

distractor absent) x 12 (block) analysis of variance (ANOVA). There was a significant main

effect of distractor type, F(2, 52) = 21.9, p < 0.001, ηp2 = .46, and a significant main effect of

block, F(11, 286) = 21.4, p < 0.001, ηp2 = .45, with RT tending to decrease as training

progressed. The distractor type × block interaction was significant, F(22, 572) = 2.40, p <

0.05, ηp2 = .085. This suggests that the differences between distractor types decrease across

training blocks (see Figure 3).

To further analyze the main effect of distractor type, planned pairwise t-tests were

used, averaging across training blocks. Each type of coloured distractor slowed RT relative to

the distractor-absent trials (M= 565 ms): high-loss versus distractor-absent: t(26) = 5.40, p <

0.001, d = 1.04, low-loss versus distractor absent, t(26) = 5.46, p < 0.001, d = 1.05. However,

there were no significant differences on RT between trials with high-loss distractor (M= 582

ms) and trials with low-loss distractor (M= 582 ms), t(26) = .36, p = .72, d =.07.


Figure 3. Mean response time across 12 training blocks for Experiment 1, for trials with high-loss, low-loss and distractor-absent. Error bars show standard error of the mean (SEM). Response times were not significantly slower on trials with high-loss distractor than trials with low-loss distractor.

Accuracy

Figures 4 shows accuracy across training. For accuracy data, the omnibus 3 x 12

ANOVA revealed a main effect of distractor type, F(2,52) = 4.67, p < 0.05, ηp2 = .15, and

also a main effect of block, F(11, 286) = 4.81, p < 0.001, ηp2 = 0.16, with accuracy generally

increasing across blocks. There was no significant interaction, F(22, 572) = .47, p = .91, ηp2

= .02.

To further analyze the main effect of distractor type, planned pairwise t-tests were

used, averaging across training blocks. High-loss trials (M= 90.04%) showed significantly

lower accuracy than distractor-absent trials (M = 91.66%), t(26) = -3.06, p < 0.05, d = .59.

There were no significant differences in accuracy between low-loss trials (M = 91.22%) and

distractor-absent trials, t(26) = -.83, p = .41, d = .16. However, the difference between high-

loss distractor trials and low-loss distractor trials approached significance, t(26) = -2.00, p

= .056, d = .38, with a trend towards lower accuracy on high-loss trials.


Figure 4. Accuracy across 12 training blocks for Experiment 1, for trials with high-loss, low-loss and distractor-absent. Error bars shown SEM. Accuracy was significantly higher on trials with high-loss distractor than trials with low-loss distractors or distractor-absent.

Awareness

In the final awareness test, seventeen participants showed evidence of awareness of

the colour-reward contingencies, by correctly selecting the high-loss colour signaled 10x

multiplier trials, while the low-loss colour did not. Across all trials, these ‘aware’ participants

showed no significant difference in RTs between high-loss (M= 588ms) and low-loss trials

(M= 588ms), t(16) = .08, p = .94, d = .019. For these participants, accuracy on high-loss trials

(M= 90.55%) was significantly lower than on low-loss trials (M= 91.93%), t(16) = 2.19, p

< .05, d= .53. For the remaining ten participants, who incorrectly matched the distractor

colours with multiplier magnitudes, there was no significant difference in RTs between high-

loss (M= 574 ms) and low-loss (M= 572 ms) trials, t(9) = .50, p = .63, d = .16. For these

‘unaware’ participants, there was no significant difference in accuracy between high-loss (M=

89.17%) and low-loss (M= 90.19%) trials, t(9) = .78, p = .45, d = .25. The difference in RT

between high- and low-loss distractor trials did not significantly differ for ‘aware’ and

‘unaware’ participants, t(25) = .33, p = .75, d = .33. Similarly, the differences in accuracy


also did not significantly differ between ‘aware’ and ‘unaware’ participants, t(25) = .20, p

= .84, d = .20.

Evaluative priming

Table 1 shows RTs and accuracy for the evaluative priming task. RTs were analyzed

using a 2 (prime type: high-loss and low-loss) x 2 (target type: positive and negative)

ANOVA. There was no significant main effect of prime type, F(1, 26) = .22, p = .64, ηp2

= .009, but there was a significant main effect of target type, F(1, 26) = 6.01, p = .021, ηp2

= .19, with faster responses to positive targets than negative targets. Crucially, there was no

significant interaction effect, F(1, 26) = .49, p = .49, ηp2 = .018. This suggests that differences

in RTs towards responding to positive versus negative target words did not differ across

prime types.

Analysis of response accuracy in evaluative priming task were done using a 2 (prime

type: high-loss and low-loss) x 2 (target type: positive and negative) ANOVA. There was no

significant main effect of prime type, F(1, 26) = 1.08, p = .31, ηp2 = .04; and no significant

main effect of target type, F(1, 26) = .49, p = .49, ηp2 = .018. Crucially, there was no

significant interaction effect, F(1, 26) = 3.54, p = .07, ηp2 = .12. This suggests that

participants did not differ in accuracy when responding to positive versus negative across

different prime types.

Colour

Target High-loss Low-loss

Positive 549 (86.5%) 546 (90%)

Negative 566 (90.3%) 575 (88.9%)

△ -17 (-3.8%) -29 (1.1%)


Table 1. Shows the mean response times (RTs, in milliseconds) as a function of prime valence and target type. Accuracy percentages are shown in brackets. △ are the differences between RTs for positive targets subtracting the RT for negative targets.

Discussion

Participants showed significantly slower RT on trials where there was a coloured-

singleton distractor compared to distractor-absent trials. This finding replicates well-

established physical-salience driven attentional capture studies (Theeuwes, 1992, 1994).

Specifically, the distractors capture attention and slow down search for the shape-defined

targets. The implication then is that our attention is captured automatically because attending

towards these stimuli conflict with the demands of the task (i.e. search for the diamond).

However, the present experiment did not report significantly different RTs between

high-loss and low-loss trials. That is, even though high-loss stimuli were predictive of high

monetary loss, they did not capture attention more often than low-loss stimuli that were

predictive of low-monetary loss. Crucially, these stimuli were always task-irrelevant; that is,

they were never stimuli that participants were required to respond to. Indeed, responding

towards these stimuli would, if anything hinder performance and reduce the overall payoff.

Hence, an effective strategy would be to suppress attention towards these cues. Consistent

with this suggestion, the present findings showed no differences between high-loss and low-

loss trials, suggesting that attention was only captured by the physical salience of the

distractors. In other words, cues that signal “loss-value” do not modulate the extent of

attentional capture that is independent of its physical salience. The implication, then is that

value-driven capture by stimuli that are associated with negative consequences are a result of

instrumental conditioning (i.e. the value that is produced by responding towards the

distractor), rather than Pavlovian conditioning (i.e. the value that is signaled by the

distractor). This distinction will be further discussed in the General discussion, and compared

to previous studies of value-driven capture with loss-ridden stimuli (i.e. Wentura et al., 2014).


Another interesting finding in Experiment 1 is that high-loss distractor trials caused

higher rates of errors in comparison to low-loss distractor trials. This would suggest that

participants are seeing the need develop some kind of speed-accuracy trade off on these trials.

One explanation is that participants might perceive the need to respond quicker to reduce loss

on these trials- and to the extent where they make more mistakes. A potential contribution

could be that the relative differences between responding inaccurately and accurately on

high-loss trials is smaller than the differences between responding inaccurately and

accurately on low-loss trials. For example, an inaccurate response always constitutes a loss of

20c regardless of distractor type. Consequently, it might be that participants are learning

about these differences, hence, on low-loss trials, are more likely to respond as accurately as

possible, as not doing such would lead to a large loss. By contrast, on high-loss trials, the

perceived losses of responding accurately and inaccurately might not be that different, hence,

participants might be more willing to respond faster, but with more mistakes. This will be

further discussed in the General discussion.

Awareness of colour-outcome contingencies did not appear to drive the current

patterns of attentional capture. That is, even though participants were fully aware of

relationship between the coloured-distractors and outcome magnitude, these stimuli did not

capture attention as a function of the value predicted. Similarly, evaluative conditioning data

suggests that participants did not change their likes or dislikes towards the different

distractors. Taken together, this provides further evidence that task-relevance is required to

for loss-predicting stimuli to capture attention.

The broader implication of Experiment 1 is that valence might play a more crucial

role than arousal in determining value-driven attentional capture. That is, the influence of

learning on attention is dependent on the valence of the outcome associated. In Experiment 2,


we aimed to further examine these effects in a design with an eye-tracker; a more direct and

diagnostic tool for examining value-driven capture.

Experiment 2

Experiment 1 examined the extent to which learning associations between task-

irrelevant distractors and monetary loss influenced attentional capture. However, the

methodology used provided an indirect measure of attention. That is, Experiment 1 showed

that it takes more time to find the target diamond shape when a colour-singleton distractor

was present, regardless of whether that distractor signals small or large loss. The implication

is that attentional capture may not be influenced by the exact magnitude of loss that is

predicted by a stimulus. However, it is possible that learning colour-outcome contingencies

may influence other types of attentional processes (Theeuwes & Belopolsky, 2012; Le Pelley

et al., in press). One alternative is that learned associations between a stimulus and monetary

outcome increase attentional dwell time. That is, after attention has been captured, the time

required to disengage from the cue may be affected (Theeuwes, 2010). As a consequence, it

is possible that the results observed in Experiment 1 reflect a mixture of effects on attentional

capture and attentional dwell time. For example, stimuli that predict large monetary loss

might be less likely to capture attention, but might be harder to disengage from when they do

capture attention, than stimuli that predict small monetary loss. This may result in similar

mean RT to the target for both types of distractor when averaged across all trials. Therefore, a

more diagnostic and direct technique is needed to assess the relationship between learning

and attention in regards to aversive stimuli. Experiment 2 will address these issues.

It is well-established that one of the most notable features of visual attention is that it

is tightly coupled with eye movements: this is referred to as overt attention (Posner, 1980).


While it is possible to make covert shifts of attention without initiating eye gaze, it is thought

to be impossible to shift the eyes without first shifting attention (Godijn & Theeuwes, 2004).

That is, measuring saccadic eye movements provides an ideal index for measuring the effects

of learning on attention. One way to study this is to measure participants’ overt attention

directly by using an eye tracker.

Involuntary capture of eye movements by salient stimuli is referred to as oculomotor

capture (Theeuwes & Belopolsky, 2012). As noted in the Introduction, past studies have

demonstrated that stimuli that were previously associated with large monetary reward are

more likely to elicit oculomotor capture than stimuli that were previously associated with

small reward (Theeuwes & Belopolsky, 2012, see also Anderson & Yantis, 2012). In

addition, Le Pelley et al. (in press) have recently used an eye-tracker to examine value-driven

oculomotor capture by stimuli that were never task-relevant. In their Experiment 3, Le Pelley

et al. used a gaze-contingent paradigm in which, on each trial, participants were required to

move their eyes to a target diamond as quickly and accurately as possible (see Figure 5).

Reward magnitude was determined by the colour of the distractors: a fast and accurate eye

movement to the diamond always led to large reward (10c) when a high-value coloured

distractor was present, and a small reward (1c) when a low-value coloured distractor was

present. However, if at any point participants’ gaze fell on or near the distractor, the reward

that they would have received on the trial was cancelled – hence these were termed omission

trials. Therefore, participants were never rewarded for looking at or near the distractor, and

so attending to these distractors was directly counterproductive in this task.


Figure 5. Sequence of trial events for Le Pelley et al (in press, Experiment 3). Participants respond to the target diamond by moving their eyes to it. The distractor could be rendered in red or blue, or on distractor-absent trials there was no colour singleton distractor in the display. Dotted lines (not visible to participants) indicate the region of interest (ROI) around the target and distractor within which eye gaze was defined as falling on the corresponding stimulus. Fast, correct responses received monetary reward, depending on the distractor colour. A high-value distractor colour reliably predicted large reward; a low-value colour reliably predicted small reward; on distractor-absent trials, large and small reward were equally likely. If any gaze fell within the distractor ROI (or, on distractor-absent trials, an equivalent ROI positioned around a randomly-chosen circle), the trial was deemed an omission trial and no reward was delivered.

Nevertheless, even under such circumstances, the experiment demonstrated that high

value distractors produced significantly more omission trials than low-value distractors. In

other words, participants were more likely to have their overt attention captured by distractors

that predicted large reward than small reward, even if doing so was directly

counterproductive to the demands of the task (since it resulted in loss of reward that would

otherwise have been received). A similar pattern of results was observed in RTs: participants

generally took longer to move their eyes to the diamond shape on trials with the high-value

distractor than the low-value. Hence, the findings in this experiment provide strong evidence

that differences in RTs towards unique target shapes are due to differences in attentional

capture, rather than differences in attentional dwell time. That is, if attentional dwell time was

the process driving the differences in RTs in the presence of a high-reward distractor versus a

low-reward distractor, then we would not have observed differences in the number of


omission trials. Therefore, this is a good example of how an eye-tracker can be used to

examine value-driven attentional capture.

Arousal and valence

However, these studies do not distinguish between effects of arousal and valence.

That is, studies of oculomotor capture to date have used only reward-related stimuli

(Theeuwes, & Belopolsky, 2012; Anderson & Yantis, 2012; Le Pelley et al., in press): no

studies have examined oculomotor capture by loss-related stimuli. Hence, it is possible that

previous demonstrations of value-driven capture by loss-valued stimuli with RT could reflect

differences in attentional dwell time rather than attentional capture (Wang et al. 2013;

Wentura et al., 2014). For example, Wentura et al.’s (2014) study found significantly slower

RTs on trials with stimuli that were previously associated with loss as compared to trials with

neutral stimuli. While it is possible that these differences could be due to differences in the

likelihood with which these stimuli produced attentional capture, it is also possibly due to

differences in the length of attentional dwell time, i.e. consistently pairing a stimulus with

loss may increases the difficulty of disengaging attention from that cue. Indeed, studies have

shown that stimuli of different valences can differ in the lengths of time taken to disengage

attention (Calvo & Avero, 2005; Tamir & Robinson, 2007). This warrants the need to

examine value-driven capture by loss-associated stimuli more directly, using an eye-tracker.

In Experiment 2, we used a procedure based on that of Le Pelley et al. (in press,

Experiment 3) to investigate whether stimuli associated with loss elicited oculomotor eye

capture more often than neutral stimuli (i.e., stimuli associated with neither loss nor gain).

Notably, the current study employed neutral-valued stimuli as opposed to stimuli associated

with low-loss (as in Experiment 1) in order to further clarify the influence of value on

attention. Specifically, it is unclear in Experiment 1 whether distractors associated with loss-


value modulated the extent of attentional capture that is independent of physical salience.

One possibility is that learning about loss-value does influence attentional capture, but that

any stimulus consistently associated with loss-value is as effective as another in capturing

attention, regardless of the size of that loss; that is, a stimulus consistently paired with a 1¢

loss will be just as likely to capture attention as a stimulus paired with 10¢ loss. An

alternative is that learning about loss-value has no influence on attentional capture. Following

this suggestion, it would be expected that there will be equal oculomotor capture by loss-

valued and neutral-valued stimuli; that is, any effects of oculomotor capture on distractor-

present trials would be driven by the physical salience of the colour distractors. Hence, to

dissociate between these competing accounts, stimuli imbued with neutral value (i.e. no

value) were employed.

Method

Participants

Twenty eight first year psychology students from UNSW, 11 males and 17 females,

participated in exchange for course credit. The average age was 19.3 years, ranging from 17

to 23 years. In addition to course credit, participants also received a performance related

bonus (M = $16.0 AUD, SEM = $1.43).

Apparatus

Experiment 2 used a Tobii TX300 eye-tracker, with 300 Hz temporal and 0.15°

spatial resolution, mounted on a 23-in. monitor running at 60 Hz. Participants’ heads were

positioned in a chinrest 60 cm from the screen. For gaze-contingent calculations, the

experiment script sampled the eye-tracker every 10ms. Current gaze location was defined as

the average gaze location during the preceding 10 ms sample. The eye-tracker was calibrated

using a 5-point procedure prior to the practice phase, prior to the training phase, and after 6

training blocks.


Visual search task

Stimuli

Similar to Experiment 1, on each trial, participants saw a fixation display followed by

a search display and a feedback display. The fixation display was a white cross surrounded by

a white circle (diameter 3.0 dva). Aspects that differed from Experiment 1 in the search

display included: (i) all shapes were filled, (ii) line segments were absent, (iii) there were no

fixation cross, (iv) a darker shade of grey was used (luminance ~32 cd/m2). The feedback

display showed the money lost and the remaining money in the bank. All remaining aspects

were the same as Experiment 1.

Design

For half the participants, the colour red was assigned as the loss-value distractor and

blue was assigned as the neutral-value distractor; the opposite was done for the other half of

the participants. The training phase for this experiment consisted of 10 training blocks of 48

trials each, with blocks structured as in Experiment 1.

A small circular region of interest (ROI) with diameter 3.5 dva was defined around

the diamond target; a larger ROI (diameter 5.1 dva) was defined around the distractor. A

response was registered when participants had accumulated 100 ms of dwell time inside the

target ROI. Responses with RTs slower than a soft-timeout threshold of 600 ms resulted in a

loss of 20¢. Crucially, if any gaze fell inside the distractor ROI prior to a response being

registered, even for a single 10 ms period, the trial was recorded as an omission trial and

participants lost 20¢ for the trial. On distractor-absent trials, one of the grey circles (that was

not adjacent to the target) was chosen at random; gaze falling inside an ROI around the

selected grey circle caused an omission trial in exactly the same way as if the selected circle

had been a distractor.1


If RT was faster than 600ms and no gaze was registered in the distractor ROI, then

participants lost 10¢ if the loss distractor was present, and 0¢ if the neutral distractor was

present; on distractor-absent trials, there was an equal likelihood of losing 10¢ or 0¢. All

other design aspects were the same as in Experiment 1, except with the constraint that the

target shape never appeared adjacent to the target.

Procedure

Procedural details not mentioned in this section were the same as for Experiment 1.

Participants were told that on each trial, they should move their eyes to the diamond shape as

quickly and directly as possible. The session started off with 8 practice trials with a yellow

distractor, and no reward feedback. Subsequent instructions informed participants that on

each trial their task was to move their eyes to the diamond shape, and that they would lose 0¢,

10¢, or 20¢, depending on how fast and accurate they were. They were told that they would

start off this experiment with $60 in the bank and that the faster and more accurate they

responded the less money they would lose, and hence the greater would be their bonus at the

end of the experiment.

Each trial began with the presentation of the fixation display. Participants’ gaze

location was superimposed on this display as a small yellow dot. Once participants had

recorded 700ms dwell time inside the circle surrounding the fixation cross, or if 5s had

passed, the cross and circle turned yellow and the dot marking gaze location disappeared.

After 300ms the screen blanked, and after a random interval of 600, 700 or 800ms the search

display appeared. The trial terminated when a response was registered (see Design), or after

2s (hard timeout). The feedback display then appeared for 1400ms, showing the amount of

money lost for the trial and their remaining total. Inter-trial interval was 700ms.



Stimuli, design and procedure for the evaluative priming task were exactly the same

as for Experiment 1.


Similar to Experiment 1, the first two trials, and the first two trials after each break

were discarded. Preliminary analysis of eye gaze data was exactly the same as the procedures

used in Le Pelley et al. (in press). Hard timeouts (4.14% of all trials) were discarded, as were

all trials on which valid gaze locations was registered in less than 25% of 10-ms samples

between presentations of the search display and registering a response (3.17%). For

remaining trials, averaging across participants, valid gaze location was registered in 92.1%

(SEM = 1.94%) of samples from the eye-tracker, suggesting high fidelity of the gaze data.

Saccade latencies were analyzed using raw data from the eye-tracker (sampled at

300Hz, as opposed to 100Hz in gaze-contingent calculations). All trials where no eye-gaze

was recorded within 5.1 dva (100 pixels) of the fixation point during the first 80 ms after the

onset of the search display were excluded from further analysis. Saccade latencies were then

calculated by identifying the first point at which 5 consecutive gaze samples were 5 dva away

from the fixation point. All saccades faster than 80ms were also excluded from analysis.

These exclusions resulted in an additional loss of 14.1% of trials.

Results

Omission trials

Figure 6 shows the proportion of omission trials across training. Unsurprisingly, trials

with a physically salient coloured distractor led to more omission trials than trials without

distractors. However, we did not find significant differences in the number of omission trials

when the display contained a loss-signalling distractor than when it contained a distractor that


signalled no loss. The data in Figure were analysed using a 3 (distractor type: loss, neutral,

distractor-absent) x 10 (block) ANOVA. There was a significant main effect of distractor

type, F(2,54) = 33.53, p < .001, ηp2 = .55. The main effect of block was not significant,

F(9,243) = .31, p = .97, ηp2 = .01, suggesting that the mean proportion of omission trials did

not change greatly across training. The distractor type x block interaction was not significant,

F(18,486) = 1.19, p = .27, ηp2 = .04.

Planned pairwise t-tests, averaging across training blocks, were used to further

analyze the main effect of distractor type. Each type of coloured distractor produced more

omission trials than for trials without distractors – Loss versus Distractor-absent: t(27) = 6.98,

p < 0.001, d = 1.32; Neutral versus Distractor-absent: t(27) = 6.89, p < 0.001, d = 1.30. Most

importantly, trials with loss-predicting distractors did not produce more omissions than trials

with neutral distractors, t(27) = -1.02, p = .32, d = .19.

Figure 6 shows the mean proportion of omission trials across training blocks of Experiment 2, for trials with loss, neutral and distractor-absent. Error bars show SEM. There were no differences in the proportion of omission trials between loss-distractor trials and neutral-distractor trials.


Response time

Figure 7 shows RTs across training blocks. For these data, 3 x 10 ANOVA revealed a

significant main effect of distractor type, F(2,54) = 6.19, p < .005, ηp2 = .19, and a significant

main effect of block, F(2,243) = 2.98, p < .005, ηp2 = .10, with RTs tending to fall as training

progressed. The distractor x block interaction was not significant, F(2,50) = 1.24, p = .23, ηp2

= .04. Follow up t-tests, averaging across training blocks, revealed that RTs were fastest on

distractor-absent trials- Loss (M= 500 ms) versus Distractor-absent (M= 485 ms): t(27) =

2.49, p < 0.05, d = 0.47; Neutral (M= 505 ms) versus distractor-absent: t(27) = 2.88, p <

0.001, d = 0.54. Crucially, RTs for trials with distractors signaling monetary loss were not

significantly different from RTs for trials with neutral distractors, t(27) = 1.05, p = .30, d

=.20.

Figure 7. shows the mean RTs across 10 training blocks for Experiment 2, for loss, neutral and distractor-absent trials. Error bars show SEM. Response times were not slower for trials with loss-distractors than trials with neutral-distractor.


Saccade latencies

Figure 8 shows saccade latencies for omission trials (i.e., trials on which participants

looked at the distractor prior to looking at the target) and non-omission trials (trials on which

participants did not look at the distractor), averaged across training blocks. Saccade latencies

for distractor-absent trials on omission trials were excluded from analysis as there were

generally very few trials that fell in this category; 10 out of 26 participants had no trials in

this category, and hence mean saccade latency for distractor-absent omission trials could not

be calculated for these participants.

Analysis showed that saccade latency was generally faster for non-omission trials

than omission trials: this was true for trials with the loss distractor, t(27) = 11.4, p < .001, d =

2.15, and trials with the neutral distractor, t(27) = 6.48, p < .001, d = 1.22. In non-omission

trials, saccade latencies on trials with coloured distractors were longer than distractor-absent

trials – loss versus distractor-absent, t(27) = 2.52, p < .05, d = .48; neutral versus distractor-

absent, t(27) = 2.20, p < .05, d = .42. Importantly, there were no significant differences

between saccade latency on trials with the loss distractor versus neutral distractor: on non-

omission trials, t(27) = .59, p = .56, d = .11, and on omission trials, t(27) = .04, p = .97, d

= .008.


Figure 8. shows the mean saccade latencies for omission and non-omission trials, averaged across training blocks. Saccade latencies were generally slower for non-omission trials than omission trials. On non-omission trials, latencies were shortest on distractor-absent trials, but did not differ significantly on loss-distractor and neutral distractor trials. All errors bars show SEM.

Attentional dwell time

The duration of attentional dwell time on the distractor on omission trials did not

differ significantly between trials with loss distractors (M= 119 ms, SEM= 6.6 ms) and

neutral distractors (M= 124 ms, SEM= 6.8 ms), t(27)= 1.19, p =.25, d = .22.

Awareness

In the final awareness test, fifteen participants showed awareness of the colour-reward

contingencies, by correctly selecting the loss colour signaled a loss of 10¢, while the neutral

colour signaled no loss (0¢). Across all trials, these ‘aware’ participants showed no

significant differences between loss and neutral trials for proportion of omission trials, t(14)

= -1.56, p = .14, d = .40. Similarly, the difference in RT was also non-significant, loss (M=

489 ms) versus neutral (M= 486 ms), t(14) = .63, p = .16, d= .53. For the remaining thirteen

participants, who selected the loss-colour as predictive of no loss, and the neutral-colour as

predictive of monetary loss, proportion of omission trials were also not significantly different


between loss and neutral, t(12) = .55, p = .60, d = .15. There were also no significant

differences in RT, loss (M= 514 ms) versus neutral (M= 527 ms), t(12) = -1.84, p = .09, d

= .51. The difference in proportion of omission trials between loss and neutral distractor trials

did not significantly differ for ‘aware’ and ‘unaware’ participants, t(26) = -1.54, p = .14, d

= .33. Similarly, the differences in RT also did not significantly differ between aware and

unaware participants, t(26) = 1.94, p = .06, d = .20.

Evaluative priming

Table 2 shows RTs and accuracy for evaluative priming task. RTs were analyzed

using a 2 (prime type: loss and neutral) x 2 (target type: positive and negative). There was no

significant main effect of prime type, F(1, 27) = .48, p = .50, ηp2 = .017, but a significant

main effect of target type, F(1, 27) = 6.07, p = .020, ηp2 = .18; with faster responses to

positive targets than negative targets. Crucially, there was no significant interaction effect,

F(1, 26) = 2.53, p = .12, ηp2 = .09. This suggests that differences in RTs towards responding

to positive versus negative target words did not differ across prime types.

Analysis of response accuracy in evaluative priming task were done using a 2 (prime

type: high-loss and low-loss) x 2 (target type: positive and negative) ANOVA. There was no

significant main effect of prime type, F(1, 27) = 1.90, p = .18, ηp2 = .07; and no significant

main effect of target type, F(1, 27) = .97, p = .33, ηp2 = .04. Crucially, there was no

significant interaction effect, F(1, 27) = .129, p = .72, ηp2 = .005. This suggests that

participants did not differ in accuracy when responding to positive versus negative across

different prime types.


Colour

Target Loss Neutral

Positive 662 (93.9%) 674 (92.2%)

Negative 714 (92.1%) 686 (91.3%)

△ -52 -12

Table 2. Shows the mean response times (RTs, in milliseconds) as a function of prime valence and target type. Accuracy percentages are shown in brackets. △ are the differences between RTs for positive targets subtracting the RT for negative targets.

Discussion

To address the issue of distinguishing between effects of attentional capture and

attentional dwell time, an eye-tracker was employed in Experiment 2. The results

demonstrated that participants did not show significant differences in the proportion of

omission trials between loss distractor trials and neutral distractor trials. This would imply

that there were no differences in the extent these stimuli elicited oculomotor capture.

Furthermore, the data suggests attentional dwell time did not differ between distractor types

on omission trials. That is, after having eye gaze captured, the length of time taken to

disengage attention did not differ. Broadly speaking, the findings of Experiment 2 replicated

a well-established pattern of salience-driven oculomotor capture (e.g. Ludwig & Gilchrist,

2012, 2013), where physically salient stimuli in a search display capture eye movements. The

implication of the current findings, then, is that task-irrelevant stimuli associated with

negative value are no more likely to capture attention than physically salient stimuli; nor does

it change the extent to which it takes to disengage attention away from these cues.

Taken together with the findings of Experiment 1, the findings of Experiment 2

strongly suggest that task-relevance is necessary for value-driven capture by loss-provoking

stimuli to occur. That is, contrasting Wentura et al’s (2014) study, Experiment 1 and 2


showed that the extent of attentional capture is not influenced by the magnitude of loss

signaled by the task-irrelevant distractors. More specifically, Experiment 2 demonstrated that

this rate of attentional capture by loss-provoking stimuli was at a similar rate to attention

capture to stimuli imbued with neutral value. This suggests that patterns of attentional and

oculomotor capture observed in the two experiments were produced by the physical salience

of distractor cues. Interestingly, this pattern of data was still observed in participants who

were aware of the colour-outcome contingencies, suggesting that even awareness did not

cause value-driven capture by task-irrelevant stimuli associated with loss. This awareness

finding was also found in Experiment 1. Evaluative priming data demonstrated no significant

differences between the magnitudes of RT’s across prime types. This suggests that priming

with distractor cues did not influence the speed of participants’ responses to positive or

negative adjectives.

Further evidence that attention was captured by the physical salience of distractors

comes from mean saccade latency data in Experiment 2. It was found that latencies were

generally shorter on omission trials (i.e. trials where participants looked at the distractor

before looking at the target) than on non-omission trials (i.e. trials where participants did not

look at the distractor before looking at the target). This data could be interpreted as physically

salient stimuli had a tendency to elicit rapid oculomotor capture in a stimulus-driven manner.

However, this process could sometimes be avoided by using a time-consuming inhibitory

process. Thus, the longer saccade latencies observed in non-omission trails could be due to

the use of this inhibitory process. Furthermore, saccade latencies on distractor-absent trials

were significantly shorter than distractor-present displays. This suggests that participants only

suppress stimulus driven saccades on distractor-present trials.

More interestingly, saccade latencies did not differ on non-omission trials featuring

loss-distractors and neutral-distractors. This suggests that the same amount of cognitive effort


was required to suppress saccades towards the distractors. In Le Pelley et al’s (in press)

experiment, saccade latencies on non-omission trials featuring high-gain distractors were

significantly longer than non-omission trials featuring low-gain distractors. That is, the higher

the value predicted by a stimulus, the more cognitive effort required to suppress capture. The

finding that saccade latencies did not show significant differences, implies that the underlying

“value” between loss-distractors and neutral-distractors were similar. This provides further

support that oculomotor capture by loss-distractors operated at the level of physical salience.

The saccade latency did not differ between loss-distractor trials and neutral-distractor

trials on omission trials. This implies that while there are no significant differences in the rate

of oculomotor capture between distractor types, the distractors also captured eye gaze with

the same ‘force’.

To reiterate, Experiment 1 and 2 examined the extent to which task-irrelevant stimuli

that signaled monetary loss influenced attentional and oculomotor capture. Le Pelley et al. (in

press) ran experiments using similar designs to examine attentional and oculomotor capture

by task-irrelevant, reward-predicting stimuli. However, the two series of experiments

demonstrate different patterns of attentional capture. The stronger implication is that valence

plays a crucial role in determining value-driven capture.

Experiment 3

Experiments 1 and 2 examined the effects of arousal and valence on value-driven

capture by employing stimuli associated with loss or neutral value that were never task-

relevant. That is, to date, all studies examining arousal and valence in value-driven capture by

task-irrelevant stimuli have drawn inferences by comparing cues associated with gains and

losses across different experiments. In Experiment 3, we sought to examine the effects of

arousal and valence on value-driven capture by including both loss-provoking and gain-


provoking stimuli in a within-subjects design. In a similar design to Experiment 1,

participants had to respond as fast and accurately as possible to the line orientation inside a

target diamond shape. However, a task-irrelevant coloured distractor signalled the magnitude

of reward or loss that could be obtained on each trial. We aimed to examine whether there

were attentional capture differences between the distractor types.

Notably, Experiment 3 differed from Experiment 1 in several ways. Firstly, rather

than using money, Experiment 3 involved gains or losses of points (which participants were

told would determine their monetary payoff at the end of the experiment). Wentura et al.

(2014) demonstrated that stimuli associated with point gain and loss captured attention in

similar ways to cues associated with monetary outcomes. This suggests the possibility that

points can also be used to examine value-driven capture in Le Pelley et al’s (in press)

experimental design. Crucially, the use of a point system would allow for the manipulation of

outcomes with greater magnitude. That is, as opposed to consistently pairing a stimulus with

a “relatively” small monetary outcome (i.e.10c); using point systems, a stimulus can be

consistently paired with a relatively larger (and more aversive) outcome of, say, 1000 points.

Consequently, this would provide a better measure of differences of attentional capture

between loss-associated stimuli and neutral stimuli. Secondly, three types of coloured-

singleton distractors were used, these being a gain-value stimulus, a loss-value stimulus and a

neutral-value stimulus. Thirdly, all trials contained a coloured distractor; that is, there were

no distractor-absent trials. Fourth, each trial had a response time latency limit. On each trial,

the outcome was based on the whether the response given was faster or slower than the limit;

and also the colour of the distractor. For example, if the high-gain distractor was present and

a response faster than the limit was given; the outcome would be +1000 points (see Design).

By contrast, if a response slower than the limit was given on this trial, then the outcome

would be 0 points. This was done to correct for differences between inaccurate and accurate


responses for different distractor types in Experiment 1. For example, in Experiment 1,

participants always lost 20c if they made an error or if they responded slowly regardless of

distractor type. However, an accurate response on a low-loss trial would lead to a small

monetary loss, whereas, an accurate response on a high-loss trials leads to a large one. That

is, the differences between responding accurately and inaccurately on a low-loss trial are

larger than on a high-loss trial. Consequently, this may have driven the accuracy data seen in

Experiment 1, where high-loss distractor trials lead to lower accuracy. Therefore, to provide a

more effective measure of attentional capture, the differences between responding accurately

and inaccurately for gain- and loss- distractor trials were always 1000 points (see Design).

Lastly, the evaluative priming task was excluded, as previous experiments suggest that

participants do not change their evaluations of the distractors.

Method

Participants

Twenty-four first year psychology students from UNSW, 8 males and 16 females,

participated in exchange for course credit. The average age was 19.1, with a range of 17 to 22

years. On top of course credit, they also received a performance related payment (M = $10.6

AUD, SEM = $0.06). The money given to participants in this experiment was a randomly

calculated value between 10 and 11.

Apparatus and stimuli

Apparatus and stimuli were the same as Experiment 1, except Experiment 3 contained

a green stimulus (CIE x, y chromatically coordinates of .300/.611).

Design

Visual search task


The colours red, blue and green were assigned to the roles of gain-value, loss-value

and neutral-value distractors in a counterbalanced fashion across participants. The training

phase of Experiment 3 consisted of 12 blocks of 48 trials, with each containing 16 trials with

the distractor with the gain-value distractor, 16 trials with the loss-value distractor, and 16

trials with the neutral-value distractor.

On each trial participants either gained or lost points dependent on their accuracy,

response time (RT), and the type of distractor in the display (see Table 3). Correct responses

to the target that were faster than the participant’s latency limit (see Procedure) were

followed by feedback indicating reward. If that trial had a gain-value distractor, reward was

large (+1000 points); if it had a loss-value distractor, reward outcome was zero (+0 points);

and if it had a neutral-value distractor, reward was small (+10 points). Correct responses

slower than the latency limit received large loss (-1000 points) if the trial had a loss-value

distractor present, zero (0 points) if it had a gain-value distractor, and small loss (-10 points)

if it had a neutral-value distractor. Errors resulted in the same outcome as a response slower

than the latency limit (i.e. if an error was made on a loss-value distractor trial, outcome would

be a loss of 1000 points). All other aspects of design were the same for Experiment 1.

Correct,fast response

Correct,slow response

Incorrect response Timeout

Gain-value distractor + 1000 points 0 points 0 points 0 points

Loss-value distractor 0 points – 1000 points – 1000 points – 1000 points

Neutral-value distractor + 10 points – 10 points – 10 points – 10 points

Table 3. Reward and loss values for Experiment 3, which depended on accuracy, response time, and the nature of the distractor. Fast and slow responses refer to responses that are faster or slower than the latency limit (see Procedure), respectively.

Procedure


Procedural details not mentioned in this section were the same as those of Experiment

1. Instructions informed participants that if they responded faster than a time limit, then they

would win either 0 points, 10 points or 1000 points. However, if they responded slower than

the time limit or made an error, they would lose either 0 points, 10 points or 1000 points.

Participants were also told that the time limit would not be the same on every trial, but that

the task may become harder as they progressed. Instructions stated that they would begin the

experiment with 100,000 points and should try to maintain as many points as possible.

The latency limit used to distinguish between ‘fast’ and ‘slow’ responses was set at

1000ms for the first two trials of the task. On subsequent trials, the latency limit was

calculated as the median RT for the previous six correct responses (or, if participants had not

yet made six correct responses, then the median for all correct responses so far) plus 15 ms.

This ensured that participants would register similar numbers of ‘fast’ and ‘slow’ responses,

and hence would be exposed to the outcome contingencies built into the design (see Table 3).

After the visual search task, participants completed an awareness test. They were

shown each coloured distractor twice and asked select what outcome they believed this

distractor signaled if they responded either faster than the limit or slower. For example,

participants were shown a high-gain distractor and were asked which response out of three

options (+0, +10, +1000) would occur if they responded faster than the latency limit.

Participants were asked to give a confidence rating ranging from 1 to 5 on each of their

answers.


The first two trials of the visual search task, and the first two trials after each break,

were discarded. Timeouts (0% of all trials) and trials with RTs below 150ms (0.85%) were


discarded. Data analysis for response times (RTs) was then restricted to correct responses

only.

Results

Response time

Figure 9 shows RTs across training blocks. A 3 (distractor type: Gain-value, loss-

value, neutral) x 12 (block) ANOVA was conducted. There was a significant main effect of

block, F(11, 253) = 53.4, p < 0.001, ηp2 = .70, with RTs generally decreasing as training

progressed. However, the main effect of distractor type was not significant, F(2, 46) = .612, p

= .55, ηp2 = .03, suggesting no differences in RTs between distractor types. The interaction

between distractor type and block was not significant, F(22, 506) = 1.44, p = .09, ηp2 = .06.

Figure 9. Mean RTs across 12 training blocks for Experiment 3, for trials with high-gain, high-loss and neutral distractors. Error bars show SEM. Response times were not significantly different between trials with high-gain distractor, high-loss distractor and neutral distractor


Figure 10. Accuracy across 12 training blocks for Experiment 3, for trials with high-gain, high-loss and neutral distractors. Error bars show SEM. Accuracy scores were not significantly different on trials with high-gain distractor than trials with high-loss distractors or neutral distractors.

Accuracy

Figure 10 shows accuracy across training blocks. For accuracy data, the omnibus 3 x

12 ANOVA revealed no significant differences: main effect of distractor type, F(2, 46)

= .458, p = .635, ηp2 = .02; main effect of block, F(11, 253) = 1.36, p = .19, ηp

2 = .06; and

interaction between distractor and block, F(22, 506) = 1.04, p = .41, ηp2 = .04.

Awareness

Only one out of twenty-four participants correctly paired all coloured distractors with

outcome magnitude for both responses that were faster and slower than the latency limit. On

average, participants got roughly 2 correct responses out of 6, and were showed an average

confidence rating of roughly 2 out of 5. Overall, the data suggests that participants generally

had little awareness of the colour-outcome contingencies in this final test.


Discussion

The findings in Experiment 3 were consistent with the findings of Experiment 1 and

2: there were no significant differences in RTs between distractors that signaled loss and

neutral outcomes. Once again this suggests that task-irrelevant stimuli that signaled loss did

not influence the extent of attentional capture that would have differed from capture by

physically salient stimuli. More specifically, it implies that task-relevance is necessary for

loss-associated stimuli to capture attention.

Crucially, distractor types did not significantly differ in terms of accuracy.

Inconsistent with the findings of Experiment 1, this finding suggests that loss-predicting

stimuli do not significantly cause participants to engage in the development of a speed-

accuracy trade-off strategy. That is, participants appear to respond equally fast and equally

accurate across all distractor trials. However, this could partially be driven by methodological

differences between Experiment 1 and 3. This will be further discussed in the General

Discussion.

More interestingly, Experiment 3 did not show significant differences between gain-

valued distractor trials and neutral-valued distractor trials. This finding is inconsistent with

previous findings of Le Pelley et al. (in press), which showed that attention was captured as a

function of the reward magnitude signaled. One potential account for this inconsistency is

due to the complexity of Experiment 3. That is, participants may have found the task

confusing. In support of this notion, only one out of twenty-four participants were fully aware

of all the colour-outcome contingencies. This demonstrates the difficultly or complexity of

the task. This will be further discussed in the General Discussion.

Taken together, the findings of Experiment 3 suggest that task-irrelevant stimuli that

signal different magnitudes of rewards and losses do not significantly differ in the rate that

they capture attention. Interestingly, the time taken to find a unique target shape on valued-


distractor trials were the same as neutral-valued distractors, implying that attentional capture

observed in the current experiment was due to physical salience. Then broadly speaking, the

current findings did not replicate patterns of value-driven capture by either negative or

positive valued stimuli.

General Discussion

The aim of the present study was to examine how arousal and valence influence the

effects of learning on attention. Specifically, the three experiments reported here investigated

whether assigning negative value to task irrelevant stimuli influences reaction time or eye

gaze in a visual search task, by influencing attentional or oculomotor capture.

Experiment 1 compared the extent of value driven capture between distractors that

signaled high and low monetary loss. Interestingly, the findings suggested no differences in

attentional capture between the two distractor types. In Experiment 2, we used a procedure

based on that of Le Pelley et al. (in press, Experiment 3) to investigate whether stimuli

associated with loss elicited oculomotor eye capture more often than neutral stimuli. Notably,

Experiment 2 employed neutral-valued stimuli as opposed to stimuli associated with low-loss

(as in Experiment 1) in order to further clarify the influence of value on attention.

Nevertheless, as demonstrated in Experiment 1, the results of Experiment 2 showed no

differences in oculomotor capture between loss- and neutral-valued stimuli. Lastly, in

Experiment 3, gain- and loss-valued distractors were examined in the same study. This was to

further examine if there were differences between loss-predictive and gain-predictive stimuli

in the same experiment. However, the RT data of Experiment 3 suggested that there were no

differences in attentional capture across gain, loss, and neutral distractor types.

Collectively, these results showed no evidence that learning about loss influences

attentional capture. In the following sections, an account will be provided to explain the

present findings, as well as any inconsistencies between present and past experiments.


Value-driven capture by loss-predictive stimuli is caused by learning about response-value

Across all three Experiments, the results replicated a well-established physical-

salience driven pattern of attentional and oculomotor capture (Theeuwes, 1992; Ludwig &

Gilchrist, 2012, 2013). That is, response times (RTs) were slower on distractor-present trials

than on distractor-absent trials. In addition, Experiment 2 demonstrated that the proportion of

omission trials were significantly higher on distractor-present trials than distractor-absent,

suggesting that the distractors captured eye-gaze. Across all three experiments, the extent to

which RTs are slowed (or proportion of omission trials for Experiment 2) was not a function

of the magnitude of loss predicted. This suggests that learning about loss-value had no

influence on attentional capture.

One interpretation of these findings is that the influences of learning about loss on

attentional capture can only be established if the stimulus capturing attention was previously

task-relevant. Indeed, past studies that have demonstrated value-driven capture by stimuli

associated with loss, have done so by establishing value in an initial training phase (Wentura

et al., 2014; Wang et al., 2013). For example, in Wentura et al’s (2014) study, participants’

were initially given extensive training to respond rapidly towards (say) a blue circle in order

to avoid a large loss of points. Hence, negative reinforcement might play a causal role in

eliciting an automatic orienting response towards these blue stimuli (i.e. response-value).

These blue stimuli may have been more distracting than neutral stimuli in the subsequent test

phase because 1) they were previously task-relevant and 2) participants developed a learned

automatic response in orienting attention towards the blue distractor.

In contrast, the current studies were set up such that stimuli associated with loss were

never task-relevant and hence were never the focus of attention. Consequently, such a

manipulation would insure any patterns of value-driven capture seen here cannot be a

hangover from participants’ previous experiences of directing attention towards the critical


stimuli to avoid loss; but instead, may be due to differences in the signals of the distractors.

Nevertheless, equally slowed RTs by loss-valued stimuli (regardless of loss magnitude) in

Experiment 1, suggest that learning about the signal-value of loss had no influence on

attentional capture. That is, since participants were never required to orient attention towards

loss-predictive stimuli, they could not learn the response-value that would have otherwise

captured attention. That is, attentional capture by loss-predictive stimuli could not develop as

participants were never required to orient attention towards them. Hence, they could not learn

the response-value, which would have otherwise captured attention. This suggests that value-

driven capture by stimuli that are associated with negative consequences are a result of

Instrumental conditioning (i.e. the value that is produced by responding towards the

distractor), rather than Pavlovian conditioning (i.e. the value that is signaled by the

distractor).

Consistent with this interpretation, Experiment 2 and 3 provided further evidence that

the learning about loss in a task-irrelevant context had no effect on attention, by showing that

there were no differences in attentional and oculomotor capture between loss-predictive

stimuli and neutral-valued stimuli. The findings here once again suggest that task-relevance is

necessary. However, an interesting point to take away from Experiment 2 and 3 is that any

attentional or oculomotor capture observed in these studies could be attributed to the physical

salience of the distractors. More specifically, since neutral-valued stimuli were never

consistently paired with any ‘valued’ outcomes, they could be considered as a standard

coloured-distractor. This could suggest that across all studies all loss-predictive distractors

were capturing attention because of their physical salience properties (i.e. colour).

Interestingly, awareness of the colour-outcome contingencies was not a crucial

determinant of the absence of value driven capture. That is, awareness data from Experiment

1 and 2 implied that even though participants had learned the signals of outcome magnitude


and were fully aware of the contingencies, it did not influence the extent of attentional

capture. Evaluative priming data also provided interesting insights. In a previous study,

Wentura et al. (2014) suggested changes in how neutral stimuli were evaluated as a

consequence of having participants respond towards that stimulus and receive an aversive

outcome in a visual search task. By contrast, the present study reported no changes in how

loss-associated stimuli were evaluated. One potential explanation is that there were no

evaluative changes because the distractors were always task-irrelevant. That is, evaluative

changes occur only if the stimulus was associated with loss-value in a direct context. The

implication, then, is that the issue of task-relevance not only extends to attention, but it also

influences the way stimuli are evaluated. Combined, the two findings here suggest that

regardless of whether participants had learned the colour-outcome contingencies, learning

about signals of loss will not capture attention.

The broader implication of the findings across the three experiments is that valence is

the crucial determinant of value-driven capture. That is, the nature of learning for gain-

predictive stimuli is different from loss-predictive stimuli. In other words, following the

suggestion that instrumental conditioning is crucial for value driven capture by loss-

predictive stimuli; the current studies consistently found value-driven capture as participants

never learned the response-value of the distractors that signaled loss.

Inconsistent accuracy findings between Experiment 1 and 3

In Experiment 1, the results demonstrated that across training trials, participants were

less accurate on high-loss distractor trials than on low-loss distractor trials. One interpretation

of this finding is that the extent to which a distractor signals a loss outcome does not

influence attention, but rather affects other cognitive processes that might be related to

decision making. Specifically, a signal of high monetary loss increases the likelihood of

making an error, than a signal of low monetary reward. This is inconsistent with Wentura et


al (2014) and Wang et al.’s (2013) studies, which showed no differences in accuracy between

high-loss and neutral stimuli in their test phase. Furthermore, Experiment 3 did not

demonstrate any differences in accuracy between loss-predictive stimuli and neutral ones.

One alternative explanation for these findings could be that participants were exerting

strategic control over their responses. The participant’s goals are clear in this task: respond as

quickly and accurately as possible to reduce the loss of money. However, over the course of

the task, they may learn that on high-loss distractors trials, the difference between a slow but

accurate response is not that different from an inaccurate response. That is, consistent with

this suggestion, a better strategy to reduce large losses is to respond as fast as possible at the

expense of accuracy on high-loss trials. For example, let’s say that on trials where

participants’ attention have been captured by a coloured distractor that the fastest response

they can make is 750ms. If the distractor signalled high-loss, then corresponding loss amount

would be 15c, whereas a low-loss distractor corresponds with a loss of 1.5c. Consequently,

participants may be inclined to make a “guess” on a high-loss trial as the penalties for doing

such are not that different from taking more time to make a correct response. By contrast,

participants may not be as inclined to use such a strategy on low-loss trials, as the amount lost

is much smaller for an accurate response than an inaccurate one.

If the accuracy findings in Experiment 1 are driven by the relative differences in

outcome between accurate and inaccurate responses, then controlling these differences might

reduce the effect. Indeed, in Experiment 3, the differences between responding accurately

(and fast) and inaccurately were always 1000 points for loss- and reward-predictive stimuli.

Hence, the finding that there were no differences in accuracy between the loss-distractor trials

and other distractor trials suggests that by keeping a constant difference in outcomes between

accurate and inaccurate responses will remove any accuracy effects. This could potentially

explain the discrepancy in findings between Experiment 1 and 3.


Inconsistent findings between Experiment 3 and Le Pelley et al. (in press)

Experiment 3 did not replicate previous demonstrations of value-driven capture by

stimuli that predicted reward (Le Pelley et al., in press). Specifically, the data showed no

significant differences in RTs between high-gain trials and neutral distractor trials, suggesting

the absence of differences in attentional capture between these distractors. However, this

finding could be explained by the complexity of Experiment 3 and possible effects of noise

(i.e. sample size, soft timeout limits).

Experiment 3 had several methodological differences in comparison to past studies.

Firstly, three types of coloured-distractors were employed; with each signalling rewarding or

aversive outcomes with varying magnitude. By contrast, Le Pelley et al’s (in press) study

only used two types of distractors that signalled either high or low reward. As a consequence,

the use of three distractors may increase cognitive load, slowing the influence of learning on

attention. Secondly, the coloured distractors in Experiment 3 signalled probabilistic

outcomes, as high-gain stimuli signalled large rewards on trials where responses were faster

than latency limit. However, the same distractor also signalled an absence of reward for

responses slower than the latency limit. These findings suggest that participants were only

exposed to contingencies between high-gain distractors and high reward on roughly half of

the high-gain distractor trials. On the contrary, in all of Le Pelley et al.,’s (in press)

experiments, coloured distractors were fully predictive of outcome magnitudes. That is, a

high-gain distractor consistently signalled high monetary reward (or a large bonus

multiplier); while a low-gain distractor consistently signalled low monetary reward (or a

small bonus multiplier). Thus, the design used in Experiment 3 was arguably more

“complex” in nature, since they involved learning more colour-outcome in fewer exposures.

A potential explanation for the failure in replicating previous studies could hence be

attributed to slowed or reduced effects of learning. In other words, participants took longer to


learn about the signals of rewards and hence did not influence attention. Indeed, this claim is

supported by awareness data, which showed that only one out of twenty four participants was

fully aware of all the colour-outcome contingencies. This strongly suggests that participants

had not learned the value associated with the distractors.

A second contribution to the discrepancy could be due to effects of random noise, or

‘uninteresting’ differences in parameters between Experiment 3 and previous studies. Indeed,

a recent study by Pearson, Donkin, Tran, Most & Le Pelley (in preparation) gives evidence

that random noise may play a role in differences in attentional capture. In their study,

superficial differences between the results of two experiments were found in spite of identical

procedures. Their findings suggested different patterns of value-driven capture. In another

Experiment, Pearson et al. (in preparation) also reported that the effects of learning on

attention did not show until half way across training, exhibiting no differences in oculomotor

capture between high-loss and low-loss distractor trials for the first 5 blocks; but significant

differences in the latter 5 blocks of training. This suggests that the exact way in which value-

driven capture emerges across training blocks is subject to variability.

Taking these two factors into account, it may not be surprising that Experiment 3

failed to replicate effects of value-driven capture between by reward-related stimuli. Hence,

to reconcile the issues suggested here, future studies should employ extensive training

periods as well as use a larger sample size, to study Experiment 3 in greater depth.

Limitations and Future research

A major limitation of the current study was that extensive training periods were not

used. The current studies used relatively short training periods (576 trials in Experiment 1

and 3; 480 trials in Experiment 2). Hence, it is possible that the test phases were not sensitive

to the changes in attentional capture. That is, the effects of learning on attention may vary in

development due to effects of random noise (in regards to participant samples, soft timeout


limits etc; Pearson et al., in preparation). To correct for these potential issues, extensive

training periods should be used in future studies to examine whether the effects of learning on

attention are consistent throughout the experiment or whether effects emerge at a later stage

of training. Consequently, such studies would clarify the findings in Experiment 3.

Specifically, it is unclear whether the findings in Experiment 3 were due to a genuine effect,

where there were no differences in the rate of attentional capture between task-irrelevant

stimuli that signal different outcomes; or whether training periods were too short for value-

driven capture effects to occur. Hence, interpretations regarding the findings of Experiment 3

should be approached with caution. Future studies may also reexamine Experiment 2 with

extensive training periods. That is, the interpretations regarding saccade latencies and

attentional dwell time data on omission trials in that study were speculative in nature and

both based on null results. In particular, the frequency of omission trials for each participant

were relatively low, hence the current experiment may not have had enough power to detect

differences on these measures. Therefore, future studies should use more extensive training to

generate more omission trials to further examine these findings in more depth.

Another potential limitation in Experiment 1 and 2 was that participants were always

punished for orienting attention towards the target. That is, the demands of the task required

participants to respond to the target to reduce loss. Nonetheless, consistently receiving

monetary loss for responding towards a target could possibly lead to the development of

attentional avoidance away from the target (i.e. positive punishment), which may conflict

with the demands of the task. That is, if participants avoid responding towards the target and

make more slow responses, then this may have interfered with the development of value-

driven capture. Interestingly, in Experiment 3, on loss-distractor trials, participants always

responded to the target to avoid loss. That is, by responding faster than the latency limit, an

aversive outcome can be avoided. Indeed, this would provide motivation for participants to


respond as quickly as possible towards the target, which would align well with the demands

of the task. Hence, future studies could reexamine Experiment 1 and 2 using a similar

manipulation. For example, a high-loss stimulus could be a consistent signal of large loss if a

response that is slower than the latency limit is given. Similarly, a low-loss stimulus could

consistently signal a small loss if a slow response is given. However, in both cases, if a

response is faster than the latency limit is given, then loss can be avoided. Consequently, this

would provide a better measure of value-driven capture by task-irrelevant stimuli associated

with loss.

Future studies examining the influences of learning about loss-value on attention can

follow similar advances in studies of value-driven capture by reward-predictive stimuli. For

example, recent studies have suggested that learning about reward modulates the extent of

attentional capture by task-irrelevant stimuli even when those stimuli were not physically

salient (Anderson et al., 2011b; Failing & Theeuwes, submitted). That is, in a visual search

task, the outlines of each shape are defined by a unique, but equally salient colour as the

distractor that is associated with reward. Since distractors associated with value are no longer

defined as coloured-singletons, it would be expected that these they would not capture

attention based on their physical salience. Nevertheless, it was demonstrated that high-gain

distractors trials caused slower responses to the target compared to low-gain distractor trials,

suggesting that high-gain distractors captured attention more than low-gain distractors. This

finding was demonstrated with stimuli that had previously been task-relevant, but capture

was shown in a subsequent test phase in which these stimuli no longer predicted reward

(Anderson et al., 2011b); and also with stimuli that were never task-relevant (Failing &

Theeuwes, submitted). Crucially, these studies suggest that reward-learning is sufficient to

produce attentional capture even by non-salient stimuli. Future studies should also examine

these possibilities with stimuli consistently paired with loss.


Theoretical and Clinical implications

Arousal versus Valence

The results of the current study suggest that valence may be a crucial determinant of

value-driven attentional capture. Taking this into consideration, the current findings along

with previous studies (i.e. Le Pelley et al., in press) imply that the nature of learning that

modulates value-driven capture is dependent on the valence of the outcome that is used in

learning. Attentional capture by reward-predictive stimuli is a product of Pavlovian

conditioning, where learning occurs in relation to the extent a stimulus signals reward, as a

signal of large reward is more likely to capture attention than a signal of small reward.

Conversely, attentional capture by stimuli associated with negative consequences is a product

of Instrumental conditioning, where learning is about the relationship between a response

given and the negative outcome avoided. In other words, the larger the magnitude of a

negative outcome that is avoided by orienting attention towards a stimulus, the more likely an

automatic attentional response will be elicited towards the same stimulus.

As noted earlier, appetitive and aversive stimuli often operate as an opponent process

(e.g. Solomon & Corbit, 1974). This is distinguished by neurobiological studies that have

demonstrated the activation of different neural pathways when presented with stimuli that

predict appetitive and aversive outcomes (Barberini et al., 2012). In line with this suggestion,

the fundamental differences between reward and punishment may have transferred over to the

neutral stimuli through learning; hence provoking different patterns of attentional capture.

The broader implication, then, is that value-driven capture is not only dependent on the way

in which value is established, but also with respect to the valence of the outcome that is

associated with the stimulus.


Task-relevance versus task-irrelevance

In previous demonstrations of value-driven capture with positive-valued stimuli,

studies have demonstrated a complementary relationship between studies that employed

distractors that were previously task-relevant (e.g. Anderson et al., 2011a, 2011b) and studies

with distractors that were never task-relevant (e.g. Le Pelley et al., in press). That is, in both

cases, the findings suggest that extent of attentional capture is a function of the reward that

signaled by a stimulus. The broader implication, then, would be that the context of learning

about rewarding stimuli is trivial, and that the effects of value-learning on attentional capture

may be comparatively general.

Such a general relationship does not appear to fit in with value-driven capture studies

examining loss-value on attention. The current findings show inconsistent findings with

previous demonstrations of value-driven capture by stimuli that were previously associated

with aversive outcomes (Wentura et al., 2014; Wang et al., 2013). The implication then is that

task-relevance is necessary for value-driven capture by aversive stimuli to occur.

Consequently, differences in attentional capture by previously task-relevant and task-

irrelevance stimuli that signal loss provides further support that valence is the crucial

determinant of value-driven capture.

Value and two modes of attention

The findings in the current study as well as the others (e.g. Anderson et al., 2011a,

2011b) call for a re-evaluation of the traditional frameworks of attention (Theeuwes, 2010).

Specifically, traditional models of attentional control may not explain the influences of

learned value on attention. To reiterate, cognitive psychology models of attention have often

distinguished between two forms of attention: goal-directed accounts of attention and

stimulus-driven modes of attention. Goal-directed attention refers to a voluntary mode of

attention that is guided by an individual’s intentions and goals. On the contrary, attention can


also be captured involuntarily by the physically salient aspects of a stimulus (i.e. its

brightness, colour, etc). This is referred to as stimulus-driven forms of attention.

How does the interaction between attention and learning fit into this model? The

studies reported before have demonstrated attentional biases that conflict with the intentions

or goals of an individual, as well the demands of the tasks they are currently doing. This

suggests that value-driven capture cannot be goal-directed. Conversely, it also cannot be

stimulus-driven. That is, consistently pairing a red circle with a valued outcome will not

change its sensory properties; it remains equally red, circular, bright etc. This strongly

suggests that attentional biases towards stimuli associated with value are a result of events

occurring inside the participant (i.e. learning) rather than being a property of the physical

world. The findings of the present study suggest that the language of attentional research

should be updated to introduce two categories: one for signal-value driven attention and

another for response-value driven attention.

Drug addiction and value-driven capture

The study of value driven capture also has implications for the development and

maintenance for many clinical disorders. In particular, the current findings may have specific

implications in the realm of drug addiction. For instance, individuals initially take drugs

because of the rewarding effects, but repeated intake will dampen these effects and increase

the length of an aversive withdrawal period (Kelley & Berridge, 2002). The current findings

suggest that the development of maladaptive attention towards drug-related stimuli may

initially be guided by learning about the signals of drug reward. Over time, patients start

taking drugs to avoid withdrawal, attention might be guided by response-value. That is,

addicts learn to orient attention towards drug-related stimuli to avoid negative outcomes (i.e.

withdrawal). If the maladaptive attention towards drug-related stimuli by drug addicts is


maintained by response-value, then clinical interventions could develop techniques to

extinguish learned responses.

Conclusions

Attention and learning are two of the most fundamental cognitive processes. Attention

describes the collection of cognitive mechanisms that selectively chooses certain stimuli for

processing and action (Anderson, 2005). On the other hand, learning is an adaptive process

that assists an organism in responding in an effective manner that maximizes reward and

survival (Schacter, Gilbert, & Wegner, 2009). The findings of the present study suggest a

reciprocal relationship between learning about value and attention. That is, while the present

findings did not demonstrate any direct influences of learning on attention; it suggested that

previous findings about the influence of learning about loss on attention could be interpreted

from a different perspective. It may be proposed that the influence of learning on attention

differs depending on the valence of the outcome that is learned. Thus, the paradigms used in

the present study provide a starting point to further our understanding of how value-driven

capture operates. With broader implications in understanding the etiology and maintenance of

clinical disorders including as drug abuse, this research provides valuable contributions to

understanding learning attention that may capture the attention of researchers for years to

come.


FOOTNOTES

1 Allowing for omissions on distractor-absent trials is useful, because it permits a valid test of

influence of stimulus salience on oculomotor capture, by comparing the rate of omissions on

trials featuring a salient distractor with the rate on distractor-absent trials. This comparison

controls for causes of omissions trials that are not related to distractor salience (e.g.

inaccuracy in the recording of gaze location, random eye movements by the participant, etc),

since these will be equal on trials with a salient distractor and distractor-absent trial. Taken

from Le Pelley et al. (in press)


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Appendix A

Visual search task

Practice trials instruction screens

Experiment 1, and 3 Practice trials Instruction screens



Appendix B

Training Phase Instruction Screens

Experiment 1 and 3




Appendix C

Evaluative Priming Instruction Screens

Experiment 1 and 2



Appendix D

Awareness Test Instructions


Appendix E

Experiment 1 Analyses- Visual search task

ANOVA for RTs KEY: Grey= Distractor-absent

Descriptive statistics for RTs


Follow-up t-tests for RTs KEY: Grey= Distractor-absent

ANOVA for accuracy


Descriptive statistic for Accuracy KEY: Grey= Distractor-absent


Follow-up t-tests for Accuracy

Descriptive statistics for Aware RT

Aware RT t-tests

Descriptive statistics for Aware Accuracy KEY: Grey= Distractor-absent


Aware Accuracy t-tests

Descriptive statistics for unaware RT and Accuracy

Unaware RT and Accuracy t-tests

Aware-Unaware differences t-tests KEY: Grey= Distractor-absent

Experiment 1 Analyses- Evaluative priming task


ANOVA for priming RT

ANOVA for priming accuracy KEY: Grey= Distractor-absent


Appendix F



ANOVA for Omissions KEY: Grey= Distractor-absent

Descriptive statistics for Omissions

Follow-up t-tests for Omissions KEY: Grey= Distractor-absent


ANOVA for RT

Descriptive statistics for RT

Follow up t-tests for RT KEY: Grey= Distractor-absent


Descriptive statistics for aware and unaware

T-tests for aware and unaware for omissions and RTs

Aware and unaware differences omissions and RTs t-tests

Descriptive statistics for saccade latencies KEY: Grey= Distractor-absent


t-tests for saccade latencies

Descriptive statistics for attentional dwell time

t-test for attentional dwell time KEY: Grey= Distractor-absent


Experiment 2 Analyses- Evaluative priming task

ANOVA for RT

ANOVA for Accuracy KEY: Grey= Distractor-absent


Appendix G



ANOVA RT KEY: Grey= Distractor-absent

ANOVA accuracy KEY: Grey= Distractor-absent


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