Emotional States and Goal-Direted Behaviour

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Emotional States and Goal-Directed Behaviour: Does anxiety affect control of goal-
directed behaviour?
By Charlotte Springett, 1233124
Submitted in partial fulfilment
of the requirements for the degree of
Bachelor of Science
Final Year Project
School of Psychology
University of Birmingham
Word Count (excluding abstract, figures and appendices) - 4000

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Abstract
Emotional stimuli cause interference on goal-directed behaviour due to activation and
inhibition of neural networks in the anterior cingulate cortex. This appears to be
exaggerated by anxiety, but the valence of emotion has been shown to have different
effects depending on the type of emotional stimuli used to induce task-interference. It is not
clear how those with higher anxiety levels differ from those with lower anxiety when
presented with different emotional stimuli. To test this, anxiety levels were measured with
the Generalised Anxiety Disorder 7 item scale. Schematic faces (either happy, angry, neutral
or scrambled) were used as primes, and participants were required to locate a target
amongst an array of distractors. Results show that for all participants, there was an effect of
emotional stimuli on goal-directed behaviour, albeit a complicated one. Those with higher
levels of anxiety were slower in all conditions compared to those with lower anxiety levels.
The data also suggests that neutral conditions were processed as a negative stimuli, rather
than a control stimulus. The results show that the effect that emotional stimuli have on
goal-directed behaviour in those with higher anxiety levels is extremely complex, and
potentially dependent upon by stimuli types.

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“Emotional States and Goal-Directed Behaviour: Does anxiety affect control of goal-directed
behaviour?”
Our brains are constantly battling to ignore irrelevant and distracting emotional
stimuli in order to focus behaviour to the current task. Emotional stimuli’s distracting effect
on goal-directed behaviour is robust, and has been frequently demonstrated (e.g. Aquino &
Arnell, 2007; Bannerman, Milders, & Satiraie, 2009; Dolcos & McCarthy, 2006; Gupta &
Raymond, 2012; MacNamara & Proudfit, 2014; Most, Chun, Widders, & Zald, 2005;
Srinivasan & Gupta, 2010; Verbruggen & DeHouwer, 2007). Goal-directed behaviour is any
action (covert or overt) focused towards completing a particular task (Plessow, Kiesel &
Kirschbaum, 2012). Interference occurring as a result of emotional stimuli is thought to be
due to disruption within the neurological structures that integrate emotional and
attentional information (Bush, Luu, & Posner., 2000). This interference can be increased
depending on a person’s emotional state (e.g. Checko et al., 2013; Plessow et al., 2012;
Solomon, O’Toole, Hong, and Dennis, 2014).
Dichotomous models of cognitive control argue that there are oppositional
constraints between cognitive mechanisms which are emotionally responsive, and non-
emotional mechanisms, causing emotional stimuli to interfere with cognitive processes. A
recent study by Gupta and Raymond (2012) provides evidence from two experiments to
suggest that when people are primed with emotional faces, they are slower at identifying a
target in a visual-search task. The first experiment involved participants being primed with
either an emotional (happy, angry, sad or fearful) or neutral face, and participants were to
locate a target letter located either to the left or right of a central fixation point amongst an
array of distractor letters. The second experiment was a control condition, and included the

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same visual-search task. It used both upright and upside down neutral faces, a scrambled
face and an empty oval as primes. Gupta and Raymond found that when targets were
presented in the left visual field, participants were slower at identifying it than if the target
were presented in the right visual field. Goal-directed behaviour in these trials was more
affected by emotional primes, which suggests that the right hemisphere (processing the left
visual field) is more at risk of interference by emotional stimuli compared to the left
hemisphere.
Neurobiological research reveals that emotional and attentional information are
processed by subdivisions within the Anterior Cingulate Cortex (ACC), and when one is
activated, the other is inhibited (Bush et al., 2000; Yamasaki, LaBar, & McCarthy, 2002).
Bush and colleagues (2002) describe two subdivisions within the ACC, the dorsal cognitive
division (ACcd) which processes cognitive information, and the rostral-ventral affective
division (ACad) which processes emotional information. During cognitive tasks, the ACcd
activates, and simultaneously inhibits the ACad (Drevets & Raichle, 1998). Similarly, during
tasks requiring the processing of emotional information, the ACad is activated, which
subsequently inhibits the ACcd (Mayberg, 1997). This suggests that when people are primed
with emotional stimuli such as in Gupta and Raymond (2012), their ACad is activated,
subsequently inhibiting their ACcd. This inhibition causes participants responses to slow as
they have to work significantly harder to answer correctly. It should be noted that it is only
reaction times that are affected (processing efficiency), and not accuracy. This seesaw
relationship between the divisions appears to be exaggerated in people experiencing certain
emotional states.

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Anxiety is an aversive emotional state arising during circumstances perceived to be
threatening (Eysenck, Deraksham, Santos & Calvo, 2007). During anxious states, an
individual feels unable to change an event, object, or interpretation that they perceive to be
threatening their current goal (Power and Dagleish, 1997). Moreover, anxious individuals
perceive facial expressions as an evaluation of themselves and their current situations
(Phillipot & Douilliez, 2005). Emotional states are evolutionary adaptations that are crucially
involved in the regulation of behaviour during complicated situations (Damiso, 1999). The
detrimental effect anxiety has on cognitive performance has been widely demonstrated,
with highly anxious individuals showing greater impaired attentional control and inefficiency
in processing (Eyesenck & Deraksham, 2011). However, evidence from previous studies
regarding how the valence of emotions affect those with high levels of anxiety is
confounded.
While some have found that those with increased anxiety only display increased
distractibility in the presence of threatening stimuli (e.g. Moser et al., 2008; Mueller et al.,
2012; Wieser, McTeague, & Keil., 2012; Yoon et al., 2007), others have found that emotional
stimuli cause interference in cognitive tasks in a highly anxious population regardless of
valence (e.g. Dressler, Mériau, Heekeren, & van der Meer, 2009). The variances in results
here may be explained due to differences in the stimuli used. While Mueller et al. (2012)
and Wieser et al. (2012) used photos of emotional faces, Dressler et al., (2009) used
emotional nouns. This suggests that those with anxiety attribute more emotion to facial
expressions compared to words. However, one consistent aspect of the literature is that
those with increased anxiety levels are more affected by emotional stimuli compared to
those with low anxiety levels (Berggren & Derakshan, 2013; MacNamara & Proudfit, 2014;
Moriya & Tanno, 2010)

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While anxious individuals’ accuracy during cognitive tasks with emotional distractors
is spared, their reaction times significantly increase, suggesting they employ greater effort
to achieve accurate performance (Evans et al., 2005; Moser et al., 2008; Qi, Ding, & Li., 2014;
Yoon et al., 2007). Eysenck et al. (2007) proposed two theories which may explain why
anxious individuals need to employ more effort during cognitive tasks with distracting
stimuli.
Eysenck and colleagues (2007) first propose the Processing Efficiency theory,
suggesting that an anxious individual’s worrisome thoughts consume attentional resources,
consequently leaving fewer resources for goal-directed behaviour. Anxious individuals must
therefore exert more effort during cognitively demanding tasks (e.g. Murry & Janelle. 2007;
Owens, Stevenson, Norgate, & Hadwin. 2008), and as a result, should be more at risk from
emotional interference during cognitive tasks (Moriya & Tanno., 2011). The second theory
posited is the Attentional Control theory, which argues that the systems controlling
attention are impaired in anxious individuals by the enhanced processing of the stimulus-
driven system which is more reactive to threatening and emotional stimuli. This should
result in slower responses when primed with threatening stimuli during cognitive tasks (e.g.
Ansari & Derakshan. 2011; Judah, Grant, Mills, & Lechner. 2013). However, as detailed
previously, while some research suggests that threatening stimuli cause more interference
to anxious individuals (consistent with the Attentional Control theory), others suggest that
all task-irrelevant stimuli, representing both threat and safety, will result in slower
processing (consistent with the Processing Efficiency theory).
In order to explore the inconsistencies regarding the effects of emotional valence,
schematic faces will be used as emotional primes in this study. Evans, Wright, Wedig, Pollak,

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and Rauch., (2005) and Straube Mentzel, and Miltner. (2005) used schematic faces as
primes, and found no differences in valence on attentional interference. Schematic faces are
diagrams of faces that have all of the structural components of a face, without the risk of
inducing gender biases. An example of these gender biases has been demonstrated by Duval,
Lovelace, Aarant, and Filion (2013), who previously found that female faces are perceived as
more positive over all valences compared to male faces. Therefore, by using schematic faces,
this study aims to rid any gender bias related to emotion perception.
To address the question of how anxiety interacts with the robust effect emotional
stimuli have on goal-directed behaviour, Gupta & Raymond’s visual-search task was adapted,
and participant’s anxiety levels were measured. This study therefore aimed to investigate
how these factors affect participant’s cognitive performance. The lateralisation aspect was
excluded, rather the general effect of emotion on cognitive performance was assessed. The
study measured anxiety levels using the Generalised Anxiety Disorder 7-item as developed
by Spitzer, Kroenke, Williams and Lowe (2006). The visual-search task required participants
to locate a target number within a visual array containing multiple distractor letters and to
indicate whether the number was “odd” or “even” after having been primed with a control
or emotional schematic face.
As in Gupta and Raymond’s (2012) study, all participants’ response times (but not
accuracy) should decrease when primed with emotional schematic faces. Previous research
makes it difficult to predict how our stimuli should affect participants with higher anxiety
levels. While Eysenck’s Processing Efficiency theory and Attentional Control theory differ in
how valence of faces should affect a participant, we cannot be entirely sure how our
participants will respond. However in this instance, as we used schematic faces (similar to

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those used in Evans et al., 2005) we predict that highly anxious participant’s reaction times
should be higher in all trials with happy and angry primes compared to those with lower
anxiety levels.

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Methods
Participants
Twenty-four right-handed, non-dyslexic students (1:1 male:female ratio, mean age
µ=19.79, SD=0.66) who all reported normal, or corrected-to-normal vision voluntarily
participated in exchange for course credit and gave informed consent.
Questionnaires
Anxiety was measured using the Generalised Anxiety Disorder 7-item (GAD-7) scale
which uses a four-point Likert scale (see Appendix A for full questionnaire). The scale has
excellent internal consistency (Cronbach 𝛼=.92). Participants are considered moderately
anxious with a score of 10 and above.
Apparatus
The questionnaires and stimuli were displayed on a flat screen colour monitor with a
16-in viewable screen (75Hz, resolution, 1280 x 960 pixels). Participant viewing distance was
approximately 65cm. E-prime software (version 2.0) operating on a Stone computer with a
3.30 GHz processor generated the stimuli and recorded accuracy, and response times (RTs)
in milliseconds (ms). Responses were recorded using the ‘up’ and ‘down’ keys of a standard
QWERTY keyboard.
Stimuli
Four upright schematic black and white faces (70mm x 90mm) were used as primes.
Each was either happy, angry, or neutral in expression, or scrambled (see Figure. 1). The
scrambled prime had the same components as the happy face, randomly configured within
the oval. The faces were taken from Kreegipuu et al. (2013) and adapted from Öhman,

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Lundqvist, and Esteves, (2001). The happy and angry faces made up the experimental
condition, while the neutral and scrambled primes acted as controls. The visual-search array
consisted of a grey background, two large distractor letters (either black or white, Arial font;
15mm, letters used: W, T, K, A, M, H), one smaller distractor letter and the target number
(ranging from 1-6, Arial font; 7.5mm), both displayed on top of the large letters in the
contrasting colour (i.e. if the large letter was black, the small letter and number would be
white). The target and distractors appeared 35mm to the left or right of the central fixation
point (CFP).
Experimental Design and Procedure
The experiment was a within-participants design (i.e. all participants completed the
same tasks). Participants first completed GAD-7 at their own pace. This was followed by the
visual-search task. Each trial began with the presentation of a 450ms – 550ms CFP. The time
the CFP was presented for was varied to prevent strategic behaviour as to when the prime
was expected. An 85ms prime was subsequently presented, followed by a 15ms CFP, then a
200ms target display. A blank screen was presented until the participant had indicated
whether the target was “odd” or “even” (see Figure. 2 for an example trial). Participants
were asked to maintain fixation to the CFP, to ignore the irrelevant distractors while
searching for the target, and to indicate whether the target was “odd” or “even” as fast and
as accurately as possible. Participants indicated responses using the index and middle
fingers of their right hands with the up and down keys. The tasks were counterbalanced so
that up or down corresponded to odd or even equally, and the order of the primes were
randomised for the participants. The task began with a practice block of 16 trials, with
feedback following each trial. The main task consisted of four blocks of 80 trials with no

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feedback. Participants completed a value-learning task and questionnaires measuring
autistic traits and impulsivity before completing the study. This data was used for another
study and is not presented here.
Data Analysis
Data from one participant was excluded as their accuracy scores were below 27%.
Trials that were incorrect or had response times (RTs) shorter than 200ms or longer then
3000ms were excluded. The mean RT was then taken for each participant in each trial type
(e.g. all trials with happy primes), and any trial with a RT more than three standard
deviations away from that mean was excluded. Participants were not from a clinical
population, therefore a median split was performed on the questionnaire data to divide
participants in to low and high anxiety groups. Repeated measures analyses of variance
(ANOVAs) were conducted for accuracy and RT (with each prime type as a level), and to
compare the anxiety levels to prime types. Final planned post-hoc comparison t-tests were
performed. The alpha level was set to .05.

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Figure 1. The schematic faces used as primes in the visual search
task. A) Happy; B) Angry; C) Neutral; D) Scrambled
Figure 2. An example trial – participants were presented with a fixation point for 450 – 550ms, followed the prime, a
schematic face (either expressive [happy or angry], neutral, or scrambled), which lasted for 85ms. Another fixation point
was very briefly presented (15ms) and was then followed by the visual-search array (200ms) in which three distractor letters
were presented at both sides of a central fixation point, and the target number was presented either to the left or right of
the central fixation point. A blank screen was then presented until the participant had indicated their response (“odd” or
“even”). The next trial would then begin immediatelyafter the response was indicated.

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Results
Preliminary Analysis
After inspecting the raw data, concerns were raised regarding the scrambled prime.
The RTs from the neutral and scrambled face conditions (the two control conditions) were
compared with a one-way paired sample t-test. Unexpectedly, the scrambled face caused
significantly increased RTs compared to the neutral face (t(22)=-1.995, p<.03). The effect of
the scrambled face is interesting (it obviously caused interference), but it had not acted as a
control prime, and thus the data was excluded from further analysis.
Main Analysis
This research sought to investigate whether emotional primes interfere with decision
making in the visual-search task, resulting in slower RTs. This study also aimed to investigate
whether anxiety levels would affect performance, and if there were variances between how
different valences would be perceived in participants with different anxiety levels.
A one-way ANOVA was performed on the accuracy data. As expected, we found a
non-significant effect of emotional prime and accuracy (F(2,44)=.071, p=.932). However,
there was a significant effect of prime on RT (F(2,44)=4.238, p=.021) (see Figure. 3). Post-hoc
t-tests were carried out to locate the main effect. The largest effect was between the happy
and neutral primes (p=.024), with participants performing slower (µ=14ms) when primed
with the happy face. Similarly, participants performed significantly slower (µ=12ms) when
primed with a happy face compared to an angry face (p=.046). There was no interaction
between the angry and neutral primes (p=.567), with participants performing only 2ms
slower on trials with angry primes.

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A mixed design ANOVA was conducted on RT data to identify whether anxiety levels
exaggerated interference caused by emotional primes on participants’ RTs. The following
analysis does not look at accuracy data as there was a non-significant effect of this in the
initial analysis. The ANOVA found a marginal main effect of group (F(2,42)=2.957, p=.063),
suggesting that those with higher anxiety levels were slower in the visual-search task
compared to those with lower anxiety levels (Figure 4). T-tests for each prime type were
conducted, and showed a significant effect of groups for each prime time (Happy,
t(22)=26.754, p<.001; Angry, t(22)=29.626, p<.001; Neutral, t(22)=31.028, p<.001). This
indicates that those with higher anxiety levels were slower overall compared to those with
low anxiety levels. The largest difference was in the happy condition, with highly anxious
participants performing slower than those with low anxiety levels (µ=74ms).
570 558 556
525
535
545
555
565
575
585
595
Happy Angry Neutral
RT(ms)
Prime type
Figure 3. Group mean RT and prime type with error bars indicating ± the standard error

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An ANOVA was conducted to highlight how those with higher anxiety levels were
affected by different valences, which found that there was a significant main effect of facial
expression in those with higher anxiety levels (F(2,18)=4.739, p=.022). Further planned t-
tests were conducted, showing that happy faces were significantly more interfering than
neutral faces (p=.042) with highly anxious participants performing, on average, 26ms slower.
Happy faces were also significantly more interfering than angry faces (p=.046, µ=24ms), with
slower RTs when primed with happy faces. However, angry faces were just as interfering on
attention compared to neutral faces (p=.740), with only 2ms between them. Figure 5
isolates the RTs of the participants with higher anxiety levels. The final ANOVA was
conducted to find any main effect that valence has on RTs in participants with low anxiety
levels. Those with low anxiety levels showed no difference in RT when primed with different
emotional stimuli (F(2,24)=.480, p=.625) with only a 5ms range of average RTs (see figure 6).
538 535 533612 588 586
480
500
520
540
560
580
600
620
640
Happy Angry Neutral
RT(MS)
PRIME TYPE
Low Anxiety Levels High Anxiety Levels
Figure 4. Comparison ofgroup meanRT andPrime type against high and low levelsof anxietywith error
bars indicating± the standarderror

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612 588 586
500
520
540
560
580
600
620
640
660
Happy Angry Neutral
RT(ms)
Prime type
Figure 5. Graph showing Mean Reaction Times of Participants with High Anxiety Levels, with error bars indicating ± the
standarderror
538 535 533
480
490
500
510
520
530
540
550
560
Happy Angry Neutral
RT(ms)
Prime Type
Figure 6. Graph showing Mean Reaction Times of Participants with Low Anxiety Levels, with error bars indicating ± the
standard error

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Discussion
This study sought to investigate whether emotional stimuli cause interference on
goal-directed behaviour and whether this was mediated by anxiety levels. Participants were
primed with emotional schematic faces and then required to locate a target number within
a visual array, indicating whether it was “odd” or “even”. If participants took longer to
respond after being primed with an emotional stimulus compared to a control stimulus, this
would show that task-irrelevant emotional stimuli cause more interference on goal-directed
behaviour compared to neutral task-irrelevant stimuli. Furthermore, participants should
experience more interference if they reported higher anxiety levels, compared to those who
reported low anxiety levels.
At first, our statistical analysis appears to indicate that all participants were slowed
when responding after encountering a happy prime, but their RTs when primed with angry
and neutral faces were of practically equal value. However, this changes when anxiety levels
are taken in to account. Participants with higher anxiety levels were significantly slower in
all trials compared to those with lower anxiety levels, and participants with higher anxiety
levels showed similar levels of interference between angry and neutral stimuli. It could be
interpreted that the angry condition was processed similarly to the neutral control condition,
leaving only the positive prime being processed as an emotional stimulus. However, it is far
more likely that the neutral condition was processed as a negative valence.
Duval et al. (2013) and Yoon and Zinbarg (2008) have showed that those with high
anxiety levels perceive neutral expressions as threatening, and our data replicates this.
Those with high anxiety levels were slower in the visual-search task when primed with
either the angry or neutral face compared to those with lower anxiety levels. Therefore the

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differences in participant RTs (without taking anxiety in to account) may have been skewed
by the anxiety levels. Moreover, it appears that those with lower anxiety levels showed
practically no difference in RTs over all conditions, suggesting that the primes were
processed in similar neural networks. All this would suggest that there was no control
condition, rather three emotional faces.
A review by Barrett, Mesquita, and Gendron (2011) offers an alternative explanation
for the way the neutral stimulus was processed. They found that without context, facial
expression recognition can be inaccurate. The schematic stimuli here were presented on a
blank screen without any indication as to what could have caused the stimuli’s expression.
Therefore the neutral expression may have been incorrectly perceived as negative,
consequently explaining why angry and neutral faces resulted in similar RTs. Combined with
the knowledge that those with higher anxiety levels process neutral stimuli as negative, this
may also be the case for those with lower anxiety levels when there is a lack of situational
context.
Furthermore, our data showed that participants with high anxiety levels were slower
in the visual-search task after being primed with a happy face, compared to the angry and
neutral faces. If it is assumed that the neutral face was processed as a negative valence,
then our data contradicts existing literature, where negative emotions are more interfering
than positive emotions on cognitive processes (e.g. Mueller et al., 2012; Weiser, McTeague,
& Keil., 2012) or a general interference of emotion on attention regardless of valence (e.g.
Dressler, et al., 2009). It appears that this is the first study to find that positive stimuli cause
more interference on goal-directed behaviour compared to negative faces.

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A previous study by Srinivasan and Gupta (2011) found that happy faces are
processed globally, as opposed to negative faces which are processed locally. Being primed
with a happy face facilitates global processing, which in turn requires participants to shift
their attentional processes to a local processing mechanism (which was required for the
visual-search task), thus slowing RTs. This shift in attentional processes was not needed
when participants were primed with negative faces as their local processing mechanism
would already be activated. This effect appears to be mediated by a person’s emotional
state. When individuals are consumed by a negative emotional state, such as anxiety, their
local processing mechanisms are facilitated, and those in positive emotional states were
biased to process globally (Frederickson & Branigan, 2005; Gasper & Clore, 2002). As a result,
anxious participants should be more distracted by positive faces as they have to first shift to
a global mechanism to process the happy face, and then shift their attention back to local in
order to complete the visual-search task. It cannot be assumed that those with low anxiety
levels were in a positive emotional state, and so no conclusions can be made regarding how
they may have been affected by the primes in terms of global and local processing.
Our data is consistent with Eysenck et al., (2007) Processing Efficiency theory to
some extent. The theory posits that those with anxiety should experience greater
interference on goal-directed behaviour as a result of emotional stimuli compared to those
without anxiety. Our data contradicts Eysenck and colleague’s secondary theory (the
Attentional Control theory) which argues that threatening stimuli should cause more
interference for those with anxiety compared to stimuli representing safety (positive
stimuli). Participants in this study experienced greater interference as a result of the positive
stimulus, compared to the negative stimuli, however this may be accounted for by the
global to local attentional shifts participants made.

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Our study was accompanied by some limitations. Firstly, it could be argued that the
participant cohort was relatively small compared to other studies such as Gupta and
Raymond (2012) who had 40 participants, (nearly double the cohort reported here). A
power analysis revealed that our sample size may not be large enough to generalise our
findings to a general population. Secondly, it has been argued previously that self-report
measures are not useful by themselves for calculating a person’s current emotional state
due to lack of accurate self-knowledge (Ganellen, 2007). It must also be noted that the
researcher sat in on the testing session, which may have affected participants’
questionnaire responses as they may have felt conscious about their responses not being
anonymous. These factors may have resulted in an inaccurate measure of anxiety. Lastly, it
has been reported that students completing studies for compulsory course credit results in
minimal effort for the study, which ultimately reduces the validity of the data collected
(DeRight & Jorgensen, 2014).
Our data highlights emotional stimuli’s complex effect upon goal-directed behaviour,
especially in those with higher anxiety levels. The exact result different emotional valences
have upon cognitive processes, and the differences in how those with anxiety are affected
by different valences is still unclear. The differences may be due to high anxiety levels
causing alterations in the ACad and ACcd. In order to disentangle these complexities, future
research should look to neuroimaging techniques, which incorporate a clinical sample and
different emotional stimuli. This would help resolve how different types of stimuli (e.g.
words and faces) are processed in those with anxiety compared to a healthy population, and
how they cause different levels of interference, while controlling for the global to local
attentional shifts participants make. It should also be noted that this study excluded the

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scrambled data. The effect scrambled stimuli have on cognitive processes should also be
explored further.
From our data, we can conclude that participants with higher anxiety levels were
more at risk of interference from task-irrelevant emotional stimuli compared to participants
with low anxiety levels. Our data appears to be some of the first to observe greater
interferences as a result of positive valences. The most likely explanation for this would be
participants’ shifts from global to local attention for different aspects of the visual-search
task. Our data also shows that participants with higher anxiety levels were apparently
processing neutral stimuli as negative. The data from this study further suggests that
anxious individuals encounter daily struggles when attempting to complete mundane
cognitive tasks. As the nature of distracting emotional stimuli becomes clearer, therapies
may incorporate this in order to aid those with anxiety to negotiate the difficulties caused
by their anxious state.

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25
Appendix A
Anxiety Scale
GAD 7 ItemScale:

26
Appendix B
SPSS Outputs
Anova 1 – Maineffectof Prime type
and Accuracy
Tests of Within-Subjects Effects
Measure: Accuracy
Source
Type III Sum
of Squares df
Mean
Square F Sig.
Partial Eta
Squared
Emotion Sphericity
Assumed
.000 2 8.261E-5 .071 .932 .003
Greenhouse-
Geisser
.000 1.898 8.703E-5 .071 .924 .003
Huynh-Feldt .000 2.000 8.261E-5 .071 .932 .003
Lower-bound .000 1.000 .000 .071 .793 .003
Error(Emotio
n)
Sphericity
Assumed
.052 44 .001
Greenhouse-
Geisser
.052 41.765 .001
Huynh-Feldt .052 44.000 .001
Lower-bound .052 22.000 .002
Descriptive Statistics
Mean Std. Deviation N
Accuracy_Happy .8939 .06051 23
Accuracy_Neutral .8974 .05065 23
Accuracy_Angry .8943 .06104 23
Mauchly's Test of Sphericitya
Measure: Accuracy
Within Subjects
Effect
Mauchly's
W
Approx. Chi-
Square df Sig.
Epsilonb
Greenhouse-
Geisser
Huynh-
Feldt
Lower-
bound
Emotion .946 1.155 2 .561 .949 1.000 .500
Tests the null hypothesis thatthe error covariance matrix of the orthonormalized transformed dependent
variables is proportional to an identity matrix.
a. Design:Intercept
Within Subjects Design:Emotion
b. May be used to adjustthe degrees offreedom for the averaged tests of significance.Corrected tests are
displayed in the Tests of Within-Subjects Effects table.

27
ANOVA2 - main effectof prime type and RT
RT_Happy 569.8898 102.08438 23
RT_Neutral 555.5954 85.81036 23
RT_Angry 557.9437 90.23552 23
Measure: RT
Within Subjects
Effect
Mauchly's
W
Approx. Chi-
Square df Sig.
Epsilonb
Greenhouse-
Geisser
Huynh-
Feldt
Lower-
bound
Emotion .826 4.018 2 .134 .852 .916 .500
Tests the null hypothesis thatthe error covariance matrix of the orthonormalized transformed dependent
variables is proportional to an identity matrix.
a. Design:Intercept
b. May be used to adjustthe degrees offreedom for the averaged tests of significance.Corrected tests are
displayed in the Tests of Within-Subjects Effects table.
Measure: RT
Source
Type III Sum
of Squares df
Mean
Square F Sig.
Partial Eta
Squared
Emotion Sphericity Assumed 2702.896 2 1351.448 4.238 .021 .162
Greenhouse-
Geisser
2702.896 1.703 1586.788 4.238 .027 .162
Huynh-Feldt 2702.896 1.832 1475.609 4.238 .024 .162
Lower-bound 2702.896 1.000 2702.896 4.238 .052 .162
Error(Emotion
)
Sphericity Assumed 14030.571 44 318.877
Greenhouse-
Geisser
14030.571 37.474 374.405
Huynh-Feldt 14030.571 40.298 348.173
Lower-bound 14030.571 22.000 637.753

29
Pairwise comparison t-tests
Within-Subjects Factors
Measure: RT
Prime_Type
Dependent
Variable
1 RT_Happy
2 RT_Neutral
3 RT_Angry
Pairwise Comparisons
Measure: RT
(I) Prime_Type (J) Prime_Type
Mean Difference
(I-J) Std. Error Sig.b
95% Confidence Interval for
Differenceb
Lower Bound Upper Bound
1 2 14.294* 5.915 .024 2.027 26.561
3 11.946* 5.648 .046 .232 23.660
2 1 -14.294*
5.915 .024 -26.561 -2.027
3 -2.348 4.037 .567 -10.720 6.023
3 1 -11.946*
5.648 .046 -23.660 -.232
2 2.348 4.037 .567 -6.023 10.720
Based on estimated marginal means
*. The mean difference is significantatthe .05 level.
b. Adjustmentfor multiple comparisons:LeastSignificantDifference (equivalentto no adjustments).

30
Anova 3 – Anxiety(2) compared with Prime type RTs (3)
Measure: RT
Emotion
Dependent
Variable
1 RT_Happy
2 RT_Neutral
3 RT_Angry
Percentile Group of Anxiety Mean Std. Deviation N
RT_Happy 1 537.6319 71.36038 13
2 611.8250 123.39916 10
Total 569.8898 102.08438 23
RT_Neutral 1 532.5496 60.20119 13
2 585.5550 106.78370 10
Total 555.5954 85.81036 23
RT_Angry 1 535.1119 69.15138 13
2 587.6250 108.60958 10
Total 557.9437 90.23552 23
Multivariate Testsa
Effect Value F Hypothesis df Error df Sig.
Partial Eta
Squared
Emotion Pillai's Trace .282 3.934b
2.000 20.000 .036 .282
Wilks'Lambda .718 3.934b
2.000 20.000 .036 .282
Hotelling's Trace .393 3.934b
2.000 20.000 .036 .282
Roy's LargestRoot .393 3.934b
2.000 20.000 .036 .282
Emotion * NAnxiety Pillai's Trace .177 2.144b
2.000 20.000 .143 .177
Wilks'Lambda .823 2.144b 2.000 20.000 .143 .177
Hotelling's Trace .214 2.144b
2.000 20.000 .143 .177
Roy's LargestRoot .214 2.144b
2.000 20.000 .143 .177
a. Design:Intercept+ NAnxiety
b. Exact statistic

31
Measure: RT
Source
Type III Sum
of Squares df
Mean
Square F Sig.
Partial Eta
Squared
Emotion Sphericity
Assumed
3237.535 2 1618.767 5.528 .007 .208
Greenhouse-
Geisser
3237.535 1.794 1804.788 5.528 .010 .208
Huynh-Feldt 3237.535 2.000 1618.767 5.528 .007 .208
Lower-bound 3237.535 1.000 3237.535 5.528 .029 .208
Emotion *
NAnxiety
Sphericity
Assumed
1731.794 2 865.897 2.957 .063 .123
Greenhouse-
Geisser
1731.794 1.794 965.402 2.957 .069 .123
Huynh-Feldt 1731.794 2.000 865.897 2.957 .063 .123
Lower-bound 1731.794 1.000 1731.794 2.957 .100 .123
Error(Emotion) Sphericity
Assumed
12298.776 42 292.828
Greenhouse-
Geisser
12298.776 37.671 326.478
Huynh-Feldt 12298.776 42.000 292.828
Lower-bound 12298.776 21.000 585.656
Tests of Between-Subjects Effects
Measure: RT
Transformed Variable: Average
Source
Type III Sum
of Squares df
Mean
Square F Sig.
Partial Eta
Squared
Noncent.
Parameter
Observed
Powera
Intercep
t
10620809.5
00
1
10620809.5
00
283.026 .000 .969 283.026 1.000
Error
337732.883 9 37525.876
a. Computed using alpha = .05

32
T-Test Happy Condition
Paired Samples Statistics
Mean N
Std.
Deviation
Std.
Error
Mean
Pair 1 RT_Happy
569.8898 23 102.08438 21.28606
Percentile
Group of
Anxiety
1.43 23 .507 .106
Paired Samples Test
Paired Differences
t df
Sig. (2-
tailed)Mean
Std.
Deviation
Std. Error
Mean
95% Confidence
Interval of the
Difference
Lower Upper
Pair
1
RT_Happy
-
Percentile
Group of
Anxiety
568.45500 101.89875 21.24736 524.39068 612.51932 26.754 22 .000
T-Test – Angry Condition
Mean N
Std.
Deviation
Std.
Error
Mean
Pair 1 RT_Angry 557.9437 23 90.23552 18.81541
Percentile
Group of
Anxiety
1.43 23 .507 .106
Paired Samples Test
Paired Differences
t df
Sig. (2-
tailed)Mean
Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
Lower Upper
Pair
1
RT_Angry
-
Percentile
Group of
Anxiety
556.50891 90.08731 18.78450 517.55224 595.46559 29.626 22 .000

33
T-test– Neutral condition
Mean N
Std.
Deviation
Std.
Error
Mean
Pair 1 RT_Neutral
555.5954 23 85.81036 17.89270
Percentile
Group of
Anxiety
1.43 23 .507 .106
Paired Samples Test
Paired Differences
t df
Sig. (2-
tailed)Mean
Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
Lower Upper
Pair
1
RT_Neutral
- Percentile
Group of
Anxiety
554.16065 85.65302 17.85989 517.12151 591.19979 31.028 22 .000

34
Anova 4 – High anxietycompared withValence
Measure: RT
Prime_type Dependent Variable
1 High_Anx_H
2 High_Anx_N
3 High_Anx_A
Measure: RT
Within Subjects
Effect Mauchly's W
Approx. Chi-
Square df Sig.
Epsilonb
Greenhouse-
Geisser Huynh-Feldt Lower-bound
Prime_type .622 3.802 2 .149 .726 .829 .500
Tests the null hypothesis thatthe error covariance matrix of the orthonormalized transformed dependentvariables is
proportional to an identity matrix.
a. Design:Intercept
Within Subjects Design:Prime_type
b. May be used to adjustthe degrees offreedom for the averaged tests of significance.Corrected tests are displayed in the
Tests of Within-Subjects Effects table.
Measure: RT
Source
Type III
Sum of
Squares df
Mean
Square F Sig.
Partial Eta
Squared
Noncent.
Parameter
Observed
Powera
Prime_type Sphericity
Assumed
4266.793 2 2133.396 4.739 .022 .345 9.479 .717
Greenhouse-
Geisser
4266.793 1.451 2940.446 4.739 .037 .345 6.877 .608
Huynh-Feldt 4266.793 1.657 2574.574 4.739 .031 .345 7.855 .653
Lower-bound 4266.793 1.000 4266.793 4.739 .057 .345 4.739 .494
Error(Prime_
type)
Sphericity
Assumed
8102.467 18 450.137
Greenhouse-
Geisser
8102.467
13.06
0
620.421
Huynh-Feldt
8102.467
14.91
6
543.224
Lower-bound 8102.467 9.000 900.274
High_Anx_H 611.8250 123.39916 10
High_Anx_N 585.5550 106.78370 10
High_Anx_A 587.6250 108.60958 10

35
Measure: RT
(I) Prime_type
Mean
Difference
(I-J) Std. Error Sig.b
95% Confidence
Interval for Differenceb
Lower
Bound
Upper
Bound
Happy Neutral
26.270*
11.382 .046 .522 52.018
Angry
24.200*
10.190 .042 1.148 47.252
Neutral Happy
-26.270*
11.382 .046 -52.018 -.522
Angry
-2.070 6.057 .740 -15.771 11.631
Angry Happy
-24.200*
10.190 .042 -47.252 -1.148
Neutral
2.070 6.057 .740 -11.631 15.771
Based on estimated marginal means
*. The mean difference is significantatthe .05 level.
b. Adjustmentfor multiple comparisons:LeastSignificantDifference (equivalentto
no adjustments).

36
ANOVA 5 Low anxiety compared with Valence
Measure: RT
Prime_type
Dependent
Variable
1 Low_Anx_H
2 Low_Anx_N
3 Low_Anx_A
Low_Anx_H 537.6319 71.36038 13
Low_Anx_N 532.5496 60.20119 13
Low_Anx_A 535.1119 69.15138 13
Measure: RT
Within Subjects
Effect
Mauchly's
W
Approx. Chi-
Square df Sig.
Epsilonb
Greenhouse-
Geisser
Huynh-
Feldt Lower-bound
Prime_type .961 .440 2 .802 .962 1.000 .500
Tests the null hypothesis thatthe error covariance matrix of the orthonormalized transformed dependentvariables is
proportional to an identity matrix.
a. Design:Intercept
Within Subjects Design:Prime_type
b. May be used to adjustthe degrees offreedom for the averaged tests of significance.Corrected tests are displayed
in the Tests of Within-Subjects Effects table.
Measure: RT
Source
Type III
Sum of
Squares df
Mean
Square F Sig.
Partial Eta
Squared
Noncent.
Paramete
r
Observed
Powera
Prime_type Sphericity
Assumed
167.898 2 83.949 .480 .625 .038 .960 .119
Greenhouse-
Geisser
167.898 1.924 87.243 .480 .618 .038 .924 .118
Huynh-Feldt 167.898 2.000 83.949 .480 .625 .038 .960 .119
Lower-bound 167.898 1.000 167.898 .480 .502 .038 .480 .098
Error(Prime_
type)
Sphericity
Assumed
4196.309 24 174.846
Greenhouse-
Geisser
4196.309
23.09
4
181.706
Huynh-Feldt
4196.309
24.00
0
174.846
Lower-bound
4196.309
12.00
0
349.692

37
Appendix C
Project outline
Charlotte Springett
1233124
University of Birmingham, School of Psychology
Third Year Project Outline: How do motivational and emotional states affect control over
goal-directed visual cognition?
Project Tutor – Jane Raymond
Word Count – 1500 (excluding cover page and references)

38
Our brains are constantly battling to keep our behaviour focused towards our
current goals, whilst ignoring distracting stimuli. Our expectations and knowledge allow us
to focus on particular details of a visual scene that we may have otherwise overlooked.
Goal-directed behaviour depends on executive processes, including Working Memory,
which allows us to maintain and manipulate information that is relevant to tasks over short
periods of time (Baddeley, 1986). Wentura, Müller, and Rothermund (2013) argue that our
attentional systemis tuned to prioritise goal-relevant stimuli.
Vision is aided by cognition, which allows the brain to create, maintain and
manipulate representations of what’s important while we continue to survey the visual
scene, thus directing attention and behaviour (Corbetta & Schulman, 2002). Visual attention
is controlled by cognitive (top-down) factors, (which include knowledge, expectations and
existing goals), and bottom-up factors which mirror sensory stimulation (Della Libera &
Chellazzi, 2009). Visual Selective Attention (VSA) mediates goal-directed behaviour to
ensure efficiency by regulating the development of cognitive mechanisms towards objects
that are behaviourally relevant. VSA inhibits irrelevant items coming to attention and
prevents potentially harmful distraction (Serences & Yantis, 2006).
Identifying emotions from facial expressions requires the use of diverse
psychological processes to be implemented in a large range of neural structures (Fusar-Poli,
et al., 2009). There is a strong interference effect of emotional stimuli on visual attention
(Eimer & Holmes, 2007) which is robust and persistent (Houwer & Tibboel, 2010). Emotional
Stimuli draw attention away from tasks with ease (Srinivasen & Gupta, 2010). When
irrelevant emotional stimuli are presented simultaneously with task information,
performance is reduced. This is thought to show interference of emotionally responsive

39
neural systems on those that are required for non-emotional tasks (Gupta & Raymond,
2012). Emotional faces have a robust effect on cognition, and attract more attention as
opposed to neutral faces (Eimer et al., 2003).
Emotional stimuli command attentional resources (Fox, Russo, Bowles, and Dutton,
2001). A recent study by Gupta and Raymond (2012) demonstrated the robust effect of
lateralisation in attention and emotional interference. Studies with participants from typical
samples have shown more accurate and faster performance when faces were presented in
the left visual field (LVF) as opposed to the right visual field (RVF) (Sergent and Bindra, 1981).
A model formulated by Yovel, Levy, Grabowecky, and Paller (2003) describes hemispheric
interaction and utilisation of information at the early stages of face perception, suggesting
that LVF superiority is due to the greater facial processing mechanisms in the right
hemisphere which code faces directly, yielding more accurate facial depiction, but only at
the later stages of visual perception when the depiction of the face is formulated. However,
the activity is enhanced in the brain regions responsible for emotional processing. We will
be replicating the effects of Gupta and Raymond (2012) using schematic faces, and
extending the findings by incorporating personality characteristics and investigating their
effects.
We appear to perceive the emotions of others by extracting the majority of
information from their faces (Suzuki & Naiton, 2003). Bruce and Young (1986) posited a
model of face perception describing separate routes for emotional processing and facial
identity, with some prosopagnosic patients unable to recognise the identity of a face, able
to identify the emotional expression (e.g. Tranel, Damiso & Damiso, 1988). Threatening
stimuli may capture attention by default due to control settings as a result of evolution (Folk,

40
Remington & Johnston, 1992). Therefore, we may expect that threat stimuli such as angry
faces will be more likely to automatically capture attention (Öhman & Mineka, 2001; Öhman,
Flykt & Esteves, 2001).
A hallmark of Autism Spectrum disorders (ASD) is impaired social communication,
and it is thought that people with ASD are diminished in distinguishing basic emotions from
facial expressions (Kennedy & Adolphs, 2012). The evidence surrounding this is, however,
confounded with contradictory results. While studies by Mazefsky and Oswald (2007), and
Macdonald et al., (1989) have found that those with autism perform significantly worse in
emotional perception tasks compared to typical adults, Adolphs, Seers and Piven (2002)
found that overall performance is similar to typical populations. This study will investigate
how emotional schematic faces interfere with cognitive tasks in individuals high in autistic
traits, and will be measured using the Autism-Spectrum Quotient (AQ) (Baron-Cohen,
Wheelwright, Skinner, Martin and Clubley, 2001), a 50 item measure, shown to have
excellent validity by Armstrong and Iarocci (2013). From this, we may add to, and help
clarify the contradictions in the current literature.
The effect of anxiety on cognitive performance has been widely demonstrated, with
highly anxious individuals showing greater impaired attentional control and inefficiency in
processing (Eyesenck & Deraksham, 2011). Those who are anxious worry often about
threats to their current goals and therefore try and develop effective strategies to reduce
their anxiety and achieve their goals (Eysenck, Deraksham, Santos & Calvo, 2007). Power
and Dalgleish (1997) define anxiety as a state in which an individual feels unable to change
an event, object, or interpretation which is perceived as threatening to a current goal.
Anxiety is an aversive emotional and motivational state arising during circumstances

41
perceived to be threatening (Eysenck et al., 2007). Emotional states are an evolutionary
adaptation that are crucially involved in the regulation of survival mechanisms and
behaviour in complicated situations (Damiso, 1999). Eysenck et al. (2007) proposed The
Attentional Control Theory of processing biases in anxiety, arguing that the systems
controlling attention are impaired in anxious individuals by the enhanced processing of the
stimulus-driven system which is more reactive to threatening stimuli. This study will
examine the effect of anxiety on performance when distractor faces are presented in a goal-
directed cognitive task. We will measure individual’s anxiety levels using the Generalised
Anxiety Disorder (GAD) 7-item scale (Spitzer, Kroenke, Williams and Löwe, 2006), a reliable
and valid self-report measure for anxiety in the general population (Löwe et al., 2008). This
will further our understanding as to how the everyday lives of anxious individuals are
affected.
In line with current research, this study anticipates to show that when emotional
faces are presented before having to attend to a visual scene, performance worsens. This
would replicate the findings of Gupta and Raymond (2012), but we intend to extend the
research further, to examine potential links with personality traits. Due to the conflicting
nature of the literature surrounding the effect that autism has on emotional perception, it’s
difficult to make assumptions as to how autistic traits may affect performance in the
experiments. However, bearing in mind that autism is partially defined by social
communication impairment, it’s predicted that performance from those who are high in
autistic traits should be significantly worse than individuals low in autistic traits. High levels
of anxiety are also predicted to significantly impair performance compared to individuals
with low anxiety levels.

42
Methods
This study intends to recruit 24 participants through a voluntary research
participation scheme. Due to hemispheric lateralisation effects, only right-handed persons
will be asked to volunteer. Similarly, due to the nature of the cognitive task, individuals with
dyslexia will be asked not to volunteer in order to eliminate possible confounding variables.
The scales will be presented for all participants to complete on a computer, and the
cognitive task will then be presented to participants using Psychopy, a custom designed
computer software which presents stimuli and records responses. Psychopy will also be
counterbalanced, in order to account for any order effects that may occur.
The attention task assesses how emotional faces affect cognitive performance. Four
types of schematic faces will be used; happy, angry, neutral and scrambled. The participants
will be told to look for a number in a briefly presented visual scene and indicate if the
number is odd or even by using the up and down keyboard buttons. However, before each
scene is presented, a schematic face will be flashed on the screen and performance should
be hindered by the angry and happy faces as shown in Gupta and Raymond (2012).
The response times and percentage of trials answered correctly will be the data that
is collected, alongside the scores for the GAD 7-item scale and AQ. An average of the
reaction time for each participant will be taken along with the average percentage error rate,
and then both will be compared against the distractor type (emotional face) and the scores
acquired from the GAD 7-item scale and AQ.
We expect that reaction time and error rate will significantly worsen during a visual
perception cognitive task when distractor schematic emotional faces are briefly presented

43
before the visual scene. This effect should be more pronounced in individuals who have high
anxiety levels and high autism trait levels as measured by the GAD 7-item scale and AQ
respectively. The effect should be seen for the angry and happy faces, and not the neutral
and scrambled faces. This study intends to (a) replicate findings by Gupta and Raymond
(2012) and extend them to explore links with personality variables, (b) to help clarify current
confounding research surrounding Autism and emotional perception, and (c) contribute to
the literature regarding anxiety’s interference with attention.

44
References
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faces in autism. Journal of Cognitive Neuroscience. 13, 232 – 240
Baddeley, A. D. (1986). Working Memory. Oxford: Oxford UP.
Armstrong, K., and Iarocci, G. (2013). Brief Report: The Autism Spectrum Quotient has
Convergent Validity with the Social Responsiveness scale in a high-functioning
sample. Journal of Autism and Developmental Disorders. 43, 2228 - 2232
Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., and Clubley, E. (2001). The Autism-
Spectrum Quotient (AQ): Evidence from Aspergers Syndrome/Higher-functioning
Autism, Males, Females, Scientists and Mathematicians. Journal of Autism and
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