1. Runninghead:VALUE-DRIVEN CAPTURE:AROUSALOR 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

2. 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: _________________________

3. 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.

4. 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

5. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? iv
Stimuli_______________________________________________________17
Visual search task_______________________________________17
Evaluative priming task___________________________________18
Design______________________________________________________18
Visual search task_______________________________________18
Evaluative priming task___________________________________19
Procedure____________________________________________________19
Visual search task_______________________________________19
Evaluative priming task___________________________________20
Awareness_____________________________________________21
Preliminary data analaysis_______________________________________21
Results____________________________________________________________22
Visual search task_____________________________________________22
Response time__________________________________________22
Accuracy______________________________________________23
Awareness_____________________________________________24
Evaluative priming_____________________________________________25
Discussion__________________________________________________________26

6. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? v
Experiment 2_____________________________________________________________28
Arousal and valence_________________________________________________31
Method____________________________________________________________32
Participants___________________________________________________32
Apparatus ____________________________________________________32
Visual search task_____________________________________________33
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
Discussion__________________________________________________________41

7. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? vi
Experiment 3_____________________________________________________________43
Method____________________________________________________________45
Participants___________________________________________________45
Apparatus and stimuli__________________________________________45
Design______________________________________________________45
Visual search task_______________________________________45
Procedure___________________________________________________46
Preliminary data analysis________________________________________47
Results___________________________________________________________48
Visual search task_____________________________________________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
Inconsistent findings between Experiment 3 and Le Pelley et al. (in press)________56

8. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? vii
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

9. 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

10. 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

11. 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.

12. 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.

13. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 2
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,

14. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 3
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

15. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 4
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 distractorcircle 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-

16. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 5
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

17. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 6
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.

18. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 7
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

19. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 8
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 distractorcan 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 distractorcolour 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.

20. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 9
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

21. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 10
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

22. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 11
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

23. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 12
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

24. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 13
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

25. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 14
(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

26. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 15
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).

27. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 16
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

28. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 17
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,

29. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 18
.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

30. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 19
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.
Evaluative priming task
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

31. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 20
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.
Evaluative priming task
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).

32. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 21
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.

33. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 22
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, ηp
2 = .46, and a significant main effect of
block, F(11, 286) = 21.4, p < 0.001, ηp
2 = .45, with RT tending to decrease as training
progressed. The distractor type × block interaction was significant, F(22, 572) = 2.40, p <
0.05, ηp
2 = .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.

34. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 23
Figure 3. Mean response time across 12 training blocks for Experiment 1, for trials with high-loss, low-loss and
distractor-absent.Error bars showstandard 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, ηp
2 = .15, and
also a main effect of block, F(11, 286) = 4.81, p < 0.001, ηp
2 = 0.16, with accuracy generally
increasing across blocks. There was no significant interaction, F(22, 572) = .47, p = .91, ηp
2 =
.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.

35. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 24
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’

36. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 25
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, ηp
2 =
.009, but there was a significant main effect of target type, F(1, 26) = 6.01, p = .021, ηp
2 =
.19, with faster responses to positive targets than negative targets. Crucially, there was no
significant interaction effect, F(1, 26) = .49, p = .49, ηp
2 = .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, ηp
2 = .04; and no significant
main effect of target type, F(1, 26) = .49, p = .49, ηp
2 = .018. Crucially, there was no
significant interaction effect, F(1, 26) = 3.54, p = .07, ηp
2 = .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.

37. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 26
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

38. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 27
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.

39. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 28
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

40. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 29
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.

41. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 30
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

42. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 31
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.

43. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 32
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.

44. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 33
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

45. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 34
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.

46. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 35
Evaluative priming task
Stimuli, design and procedure for the evaluative priming task were exactly the same
as for Experiment 1.
Preliminary data analysis
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

47. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 36
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, ηp
2 = .55. The main effect of block was not significant,
F(9,243) = .31, p = .97, ηp
2 = .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, ηp
2 = .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-distractortrials and neutral-distractor trials.

48. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 37
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, ηp
2 = .19, and a significant
main effect of block, F(2,243) = 2.98, p < .005, ηp
2 = .10, with RTs tending to fall as training
progressed. The distractor x block interaction was not significant, F(2,50) = 1.24, p = .23, ηp
2
= .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.

49. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 38
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.

50. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 39
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-distractorand
neutral distractor trials. All errors bars showSEM.
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

51. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 40
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, ηp
2 = .017, but a significant
main effect of target type, F(1, 27) = 6.07, p = .020, ηp
2 = .18; with faster responses to
positive targets than negative targets. Crucially, there was no significant interaction effect,
F(1, 26) = 2.53, p = .12, ηp
2 = .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, ηp
2 = .07; and no significant
main effect of target type, F(1, 27) = .97, p = .33, ηp
2 = .04. Crucially, there was no
significant interaction effect, F(1, 27) = .129, p = .72, ηp
2 = .005. This suggests that
participants did not differ in accuracy when responding to positive versus negative across
different prime types.

52. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 41
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

53. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 42
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

54. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 43
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-

55. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 44
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

56. VALUE-DRIVEN CAPTURE: AROUSAL OR VALENCE? 45
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