University of Southern California Dissertations and Theses

Emotional arousal amplifies the selectivity of visual selective attention

(USC Thesis Other)

Emotional arousal amplifies the selectivity of visual selective attention

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content  
 
EMOTIONAL AROUSAL AMPLIFIES THE SELECTIVITY OF VISUAL SELECTIVE 
ATTENTION 
 
by 
 
Matthew Ryan Sutherland 
 
 
 
 
 
 
A Thesis Presented to the
FACULTY OF THE USC GRADUATE SCHOOL 
UNIVERSITY OF SOUTHERN CALIFORNIA 
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF ARTS 
(PSYCHOLOGY) 
 
December 2010 
 
Copyright 2010 Matthew Ryan Sutherland 
ii 
Table of Contents 
 
 
List of Tables iii 
List of Figures iv 
Abstract v 
Introduction 1 
Experiment 1 6 
Experiment 2 14 
Experiment 3 21 
General Discussion 28 
References 33 
 
 
 
 
 
 
 
 
 
 
iii 
List of Tables 
 
Table 1: Descriptive Statistics for Letter Identification Scores  10 
 and Covariates (Experiment 1) 
 
Table 2: Results of Multi‐level Analysis with all Covariates  12 
 Included (Experiment 1) 
 
Table 3: Descriptive Statistics for Experiment 2 Condition 1 15 
 
Table 4: Descriptive Statistics for Experiment 2 Condition 2  16 
Table 5: Experiment 2 (Condition 1) Results of Multi‐level  18 
 Analysis with all Covariates Included 
 
Table 6: Experiment 2 (Condition 2) Results of Multi‐level  19 
 Analysis with all Covariates Included 
 
Table 7: Descriptive Statistics for Letter identification Scores  22 
 (percent correct) and Covariates (Experiment 3) 
 
Table 8: Significance Test for Main Factors and Interaction 23 
Table 9: Results of Multi‐level Analysis with all Covariates  23 
 Included 
iv 
List of Figures 
 
Figure 1: Timeline diagram of experimental procedure. Each letter 6 
 array consisted of 3 high contrast letters and 5 low contrast
 letters. 
 
Figure 2: Letter Identification rates (percent correct) for low and  11 
 high contrast letters on neutral and arousing trials
 (Experiment 1). 
 
Figure 3: Letter Identification rates (percent correct) for low and  17 
 high contrast letters on neutral and arousing trials
 (Experiment 2, Condition 1). 
 
Figure 4: Letter identification rates (percent correct) for low and  26 
 high contrast letters on neutral and arousing trials collapsed
 across Experiment 1, Experiment 2 Condition 1 and
 Experiment 3. 
v 
Abstract 
 
In 3 experiments 111 young adults (ages 18‐30) performed a letter identification 
task to test the hypothesis that emotional arousal increases attention to high 
priority stimuli and inhibits attention to low priority stimuli. A circular array of 
letters was briefly presented to each subject following exposure to negative 
arousing or neutral sounds. Contrast level of the letters was manipulated to make 
some letters higher in priority. The results support the arousal‐biased competition 
hypothesis, as exposure to emotionally arousing sounds increased identification 
rates for high priority letters and decreased identification rates for low priority 
letters. Yet this effect was limited to inter‐stimulus intervals (ISI) from 750 to 3000 
milliseconds (ms).
1 
Introduction 
 
Attention controls what information enters awareness. It also shapes what 
we remember and how we behave. Similarly exposure to emotionally arousing 
stimuli also influences what enters awareness, what we remember, and guides 
subsequent behavior. Studies examining interactions between emotion and selective 
attention indicate that emotionally arousing stimuli occupy a privileged position in 
the competition for attentional resources, often at the expense of competing non‐
emotional stimuli (Alpers & Pauli, 2006; Alpers, Ruhleder, Walz, Muhlberger, & 
Pauli, 2005; Anderson & Phelps, 2001; Vuilleumier, Armony, Driver, & Dolan, 2001). 
But more recently it has been shown that these perceptual enhancements may 
persist after an emotional stimulus disappears, resulting in enhanced perception of 
subsequent neutral stimuli (Bocanegra & Zeelenberg, 2009; Phelps, Ling, & 
Carrasco, 2006; Zeelenberg & Bocanegra, 2010). Yet little is known about how 
exposure to an emotionally arousing stimulus influences perceptual processing of 
multiple non‐emotional stimuli competing for the same attentional resources. In 
three experiments we investigate the effects of emotional arousal on biases in 
selective attention for competing non‐emotional visual stimuli. We provide 
preliminary evidence that emotional arousal influences selective attention by 
amplifying biased‐competition processes. 
From a behavioral perspective, objects rather than locations is the level at 
which selective attention operates (for a review see Scholl, 2001). When two objects 
2 
occupy the same location, and attention is directed to that location, perceptual 
enhancements for both objects are not observed (O`Craven, Downing, & Kanwisher, 
1999; Vecera & Farah, 1994). Instead they compete with one another for the same 
attentional resources. Moreover, single cell studies of primates (Chelazzi, Duncan, 
Miller, & Desimone, 1998; Chelazzi, Miller, Duncan, & Desimone, 2001) and fMRI 
studies with humans (Kastner, De Weerd, Desimone, & Ungerleider, 1998) suggest a 
similar object‐based view of attention. These models posit that neural 
representations of objects come at the cost of weaker representations of other 
objects, as neural responses to single objects are inhibited when additional objects 
are placed into one’s field of view (Desimone, 1998; Desimone & Duncan, 1995; 
Duncan, 2006; Kastner & Ungerleider, 2001). Yet this inhibition is lifted when 
attention is directed to a single object, allowing it to dominate the competition for 
neural resources. For this reason selective attention is conceived of as a biasing 
mechanism that manipulates contests among perceived objects vying for neural and 
mental representation.
Yet understanding the forces that influence selective attention is as 
important as understanding how it operates. A wide range of findings suggest that 
interactions between the perceptual saliency of a stimulus, its affective properties, 
and the current goals of the perceiver determine what objects are given high 
priority with respect to perceptual processing. For example, increasing the contrast 
level of a visual stimulus enhances neural responses to that stimulus (Proulx & 
Egeth, 2008). Similarly, when directed to covertly attend to a visual stimulus, 
3 
increases in neural responses to the attended stimulus are also observed (Chelazzi, 
et al., 1998). Thus, the perceptual saliency of a stimulus and the task‐demands 
placed on the perceiver together control what objects are given priority (Fecteau & 
Munoz, 2006). However, the affective properties of a visual stimulus can also bias 
the distribution of neural and mental resources to sensory input. Stimuli capable of 
eliciting an emotional response dominate competitions for neural resources (Lang, 
et al., 1998; Vuilleumier, Richardson, Armony, Driver, & Dolan, 2004) and more 
easily enter awareness (Alpers & Pauli, 2006; Alpers, et al., 2005; Anderson & 
Phelps, 2001; De Martino, Kalisch, Rees, & Dolan, 2009). Because emotions mobilize 
our bodies and thoughts to deal with environmental challenges—such as an 
approaching rattlesnake in the grass or a near collision on the highway—it seems 
quite plausible that perception becomes more selective under states of emotional 
arousal. Many studies corroborate this assumption, yet the observed increases in 
selectivity are usually confined to the emotion‐eliciting stimulus, for in these studies 
the emotional cue both serves as an emotion elicitor and as a target for attention 
(Alpers & Pauli, 2006; Alpers, et al., 2005; Anderson, 2005; Anderson & Phelps, 
2001; Keil & Ihssen, 2004; Knight, et al., 2007; LaBar, Mesulam, Gitelman, & 
Weintraub, 2000; for a review see Mather & Sutherland, under review; 
Nummenmaa, Hyona, & Calvo, 2006).
However, more recent investigations indicate that brief exposure to an 
emotionally arousing stimulus can increase perceptual sensitivity to non‐emotional 
targets. For example, brief exposure to faces expressing fear can enhance one’s 
4 
ability to identify tilt directions of Gabor patches presented at low contrast levels 
(Phelps, et al., 2006). Moreover, briefly viewing an emotionally arousing word can 
enhance perception of non‐emotional words presented next (Bocanegra & 
Zeelenberg, 2009). And similar enhancements can occur across different sensory 
modalities. When participants hear rather than view emotionally arousing words, 
this can enhance their ability to visually identify briefly presented non‐emotional 
words (Zeelenberg & Bocanegra, 2010). These findings suggest that once emotional 
arousal is elicited, and the arousal‐eliciting stimulus is removed, perception of non‐
emotional task‐relevant stimuli is enhanced. However, no studies have examined 
how emotional arousal influences visual perception for multiple non‐emotional 
targets that are in direct competition for attention.
In regard to visual processing, priority is controlled by interactions between 
the perceptual saliency of a stimulus and the goals/task‐demands of the perceiver. If 
a stimulus is perceptually salient or goal‐relevant, it will receive a higher degree of 
priority, and will thus dominate the competition for attention. Therefore, an 
investigation into the effects emotional arousal has on attention should begin by 
examining how emotions impact perceptual processing of high and low priority 
stimuli. Thus far studies demonstrating perceptual enhancements for non‐
emotional stimuli have used paradigms where only a single target stimulus is 
presented (Bocanegra & Zeelenberg, 2009; Zeelenberg & Bocanegra, 2010), or 
multiple targets that are equally salient (Phelps, et al., 2006). These results suggest 
that visual processing of high priority stimuli is enhanced by emotional arousal. 
5 
Moreover, biased competition models of attention suggest that focusing on one 
object inhibits the processing of other objects (Desimone, 1998; Desimone & 
Duncan, 1995; Duncan, 2006; Kastner & Ungerleider, 2001). Therefore, if emotional 
arousal enhances perceptual processing of high priority stimuli, it should also 
increase inhibition of low priority stimuli.
 
6 
Experiment 1 
To test the hypothesis that exposure to emotionally arousing stimuli 
enhances perception of high priority stimuli, at the cost of processing low priority 
stimuli, we briefly presented participants with an array of letters that differed in 
contrast level, and preceded each presentation with a negative arousing or neutral 
auditory stimulus (see Figure 1). Each letter‐stimulus in the array was equal with 
respect to goal‐relevance. However, we assumed high contrast letters would receive 
higher priority than the low contrast letters due to greater perceptual saliency. For 
this reason we predicted that exposing participants to emotionally arousing sounds 
would increase identification rates for high contrast letters and decrease 
identification rates for low contrast letters.
 
 
Figure 1: Timeline diagram of experimental procedure. Each letter array consisted 
of 3 high contrast letters and 5 low contrast letters.
 
7 
Stimuli 
Emotional arousal was manipulated using auditory stimuli from the 
International Affective Digital Sound system (Bradley & Lang, 2007). Each audio clip 
lasted 6 seconds, and consisted of naturally occurring sounds such as screams, 
vomiting, car accidents and bomb explosions. The audio was presented via 
headphones. Each participant was exposed to 40 sound clips, 20 of which were high 
in arousal and of negative valence, while the remaining were low in arousal and of 
neutral valence. 
The visual stimuli used in the letter detection task consisted of letters that 
appeared in uppercase Arial font, with each subtending 1.12°. All letters in the 
alphabet were used with the exception of ‘I’, as it too closely resembled lowercase 
‘L’. The high contrast letters had RGB values of #666666, while the low contrast 
letters had RGB values of #CCCCCC. Stimuli were presented on an iMac monitor with 
media white point values of X: 0.9505 Y: 1.0 Z: 1.0891.
Prior to the experiment, participants completed the Positive Affect Negative 
Affect Schedule (PANAS) as a measure of their current positive and negative 
affective state (Watson, Clark, & Tellegen, 1988). Participants also completed the 
Center for Epidemiological Studies Depression scale (CES‐D) as a measure of 
depressive symptoms experienced during the past week (Radloff, 1977). 
Participants 
Thirty‐two young adults (22 female) participated in Experiment 1. Subjects 
consisted of USC students and individuals from the surrounding community. All 
8 
subjects either received course credit or a monetary compensation for their time. 
Ages ranged from 18 to 28 (M = 19.9). Fifteen participants identified themselves as 
Caucasian, 13 as Asian, 1 as African American, and 1 as bi‐racial. The remaining two 
participants did not identify with any of the racial categories listed.
Method 
Informed consent was obtained upon arrival. Participants then completed a 
demographic questionnaire followed by the CES‐D and the PANAS measures. Each 
trial began with a fixation cross that appeared at the center of the screen and 
remained on the screen for most of the trial. After 4 seconds, participants were 
exposed to an audio clip that lasted 6 seconds. Once the audio clip ended a circular 
array of eight letters were presented around the fixation cross. Each array consisted 
of three high contrast letters and five low contrast letters. A short inter‐stimulus 
interval (ISI) between the end of the audio clip and the presentation of the letter 
array was used, ranging from 750 to 3000 ms. The letters remained on the screen 
for 200 milliseconds (ms). After the letters were removed, the fixation cross was 
replaced by the words “recall now” (Figure 1). Participants were directed to focus 
on the fixation cross at all times. Each letter was an equal distance from the fixation 
cross. Moreover, participants were directed to ignore differences in contrast level 
among the letters. They were specifically informed not to purposely attempt to 
detect only the low or high contrast letters on any given trial. They were instead 
directed to simply try to identify as many of the letters in the array as possible. 
Participants reported what letters they saw via key press. It was also emphasized 
9 
that they should be less concerned with making errors and more concerned with 
identifying letters.
When all 40 trials were complete, each sound was rated. After each sound 
clip participants provided valence and arousal ratings on a scale from 1 to 9 via key 
press. The ratings were followed by a post test questionnaire that included inquiries 
about how challenging they found certain parts of the task, and what strategies they 
may have developed in identifying the letters. Participants were then debriefed and 
excused from the study. 
Data Analysis 
Letter identification rates were analyzed in a 2 × 2 factorial with both factors 
representing within‐subject variables. Each subject had four separate scores, which 
included identification rates for low and high contrast letters on either arousing or 
neutral trials. Moreover, we had three covariates: PANAS scores for both positive 
and negative affect, and CES‐D depression scores. Letter identification rates were 
calculated as percent correct. All three covariates represented continuous variables 
and were calculated as total raw scores. The data were first subjected to a 2 ×2 
repeated measures ANOVA. We then subjected the data to a multi‐level model 
analysis using each subject as the classification variable (see Singer, 1998).  
Results and Discussion 
The descriptive statistics can be observed in Table 1. The repeated measures 
ANOVA yielded a significant main effect of contrast level, indicating that as expected 
high contrast letters were more often detected than low contrast letters, F(1,31) = 
10 
41.48 p < 0.001. No main effect of arousal was observed F(1,31) = 0.39, p = 0.54, 
suggesting that overall letter detection rates did not differ across arousing and 
neutral trials. However, a disordinal interaction was observed between contrast and 
arousal, F(1,31) = 6.25, p = 0.018, indicating that high contrast letters were more 
often identified on arousing trials (M = 0.67) compared to neutral trials (M = 0.64), 
while low contrast letters were more often identified on neutral trials (M = 0.39) 
compared to arousing trials (M = 0.37) (Figure 2). 
 
Table 1 
 
Descriptive Statistics for Letter Identification Scores and Covariates (Experiment 1) 
Variable N Min Max Mean SD 
Neutral 32 0.325 0.731 0.481 0.091 
Arousing 32 0.350 0.713 0.480 0.091 
Low Contrast 32 0.005 0.630 0.379 0.145 
High Contrast 32 0.383 0.983 0.650 0.144 
PANAS 
Positive 
32 13 45 27.2 7.397 
PANAS 
Negative 
32 10 20 12.7 2.635 
CES‐D 32 3 21 10.8 5.331 
 
 
Next we performed a mixed model analysis. We first entered both factors 
(arousal and contrast level) into the model without covariates. The contrast term 
was significant, t(93) = 5.66, p < 0.001 indicating that high contrast letters were 
more often identified compared to low contrast letters. The arousal term was not 
11 
significant, t(93) = ‐1.43, p = 0.1559, however the interaction term did reach 
significance, t(93) = 2.62, p = 0.0103, again suggesting the existence of a disordinal 
interaction in the predicted direction. Finally, a paired t‐test revealed that there was 
no difference in errors made across emotional (M = 16.81) and neutral (M = 16.56) 
trials, t(31) = ‐0.37, p = 0.714. These data support our hypothesis, as performance 
increased for high contrast letters and decreased for low contrast letters on 
arousing trials, compared with neutral trials. 
Figure 2. Letter Identification rates (percent correct) for low and high contrast 
letters on neutral and arousing trials (Experiment 1). 
 
 
 
In addition, when all three covariates were entered into the mixed model, the 
arousal, contrast and interaction terms remained virtually unchanged (see Table 2). 
However, the PANAS positive affect term appeared as a marginally significant 
predictor, t(28) = 1.96, p = 0.06 indicating that those experiencing higher levels of 
12 
positive affect performed better on the letter detection task after controlling for all 
other variables in the model.
 
Table 2 
 
Results of Multi‐level Analysis with all Covariates Included (Experiment 1) 
Effect b SE df t Sig 
Intercept 0.410 0.080 28 5.10 p < 0.0001 
Arousal ‐0.020 0.014 93 ‐1.43 p = 0.1559 
Contrast 0.245 0.043 93 5.66 p < 0.0001 
PANAS pos 0.004 0.002 28 1.92 p = 0.0647 
PANAS neg ‐0.010 0.006 28 ‐1.62 p = 0.1165
CES‐D ‐0.001 0.003 28 ‐0.22 p = 0.8288
Arousal × 
Contrast 
0.052 0.020 93 2.62 p < 0.05 
 
 
 
These results suggest that exposure to emotionally arousing sounds 
amplifies biases in attention towards high priority stimuli, which comes at the cost 
of attending to low priority stimuli. The main effect of contrast level confirmed that 
high contrast letters were given high priority, as they were more often identified 
compared to low contrast letters. However, no main effect of arousal was observed, 
suggesting that overall identification rates did not differ across emotionally 
arousing and neutral trials. Moreover, these results were corroborated by a mixed 
model analysis, which also suggests that these observed effects were present after 
controlling for differences in depressive symptoms experienced over the past week, 
13 
and differences in positive and negative affective state. In addition, positive affect 
appeared as a marginally significant predictor suggesting that those experiencing 
more positive affect identified more letters. Taken together, these findings indicate 
that emotional arousal interacts with biased competition processes in attention, and 
does so by amplifying differences in selection, allowing high priority stimuli to 
dominate attention to an even greater extent, while low priority stimuli are 
inhibited to an even greater degree.
 
14 
Experiment 2 
To corroborate the results of Experiment 1 and to further explore the length 
of time that arousal impacts selective attention, we added a second condition that 
included longer intervals between the audio stimulus and the presentation of the 
letter array.
Participants 
Thirty‐two young adults (24 females) ranging from 18 to 22 (M = 19.9) 
participated in condition 1. The same number of young adults (29 females) ranging 
in age from 18 to 26 (M = 20.9) participated in condition 2. Subjects were randomly 
assigned to one of the two conditions. Participants were USC students and 
individuals from the surrounding community. They received either 1‐hour of course 
credit or monetary compensation for their participation. Of those who completed 
Condition 1, 8 identified themselves as Caucasian, 11 as Asian, 3 as Hispanic, 3 as 
African American, 5 as biracial, and the remaining either selected ‘other’ or declined 
to state their race/ethnicity. Of those who completed Condition 2, 12 identified as 
Caucasian, 11 as Asian, 3 as African American, 3 as Hispanic, 4 as biracial, and the 
remaining identified with no racial categories listed. 
Method 
The procedure for Condition 1 was identical to that of Experiment 1. The 
procedure for Condition 2 was similar to Experiment 1, except the duration of time 
between the audio stimulus and the letter array presentations varied from 4000 to 
6000 ms, rather than 750 to 3000 ms.
15 
Results and Discussion 
The descriptive results for Conditions 1 and 2 can be observed in Tables 3 
and 4, respectively. For Condition 1, a repeated measures ANOVA performed on 
letter identification rates revealed a significant main effect of contrast level, F(1,31) 
= 29.21, p < 0.001, again indicating that high contrast letters were more often 
identified compared to low contrast letters. The main effect of arousal was not 
significant, F(1,31) = 0.73, p = 0.40. A disordinal interaction in the expected 
direction was only marginally significant, F(1,31) = 2.914, p = 0.098 (Figure 3). 
Paired t‐tests revealed no difference in incorrect responses between emotional (M = 
16.3) and neutral trials (M = 15.19), t(31) = ‐1.454, p = 0.156.
 
Table 3 
 
Descriptive Statistics for Experiment 2 Condition 1
Variable N Min Max Mean SD 
Neutral 32 0.338 0.675 0.502 0.080 
Arousing 32 0.344 0.656 0.493 0.070 
Low Contrast 32 0.245 0.665 0.434 0.100 
High Contrast 32 0.317 0.883 0.605 0.133 
PANAS 
Positive 
32 12 42 25.9 7.938 
PANAS 
Negative 
32 10 41 13.58 5.97 
CES‐D 32 2 48 8.84 8.837 
 
16 
Table 4 
 
Descriptive Statistics for Experiment 2 Condition 2
 
Variable N Min Max Mean SD 
Neutral 32 0.363 0.619 0.492 0.080 
Arousing 32 0.325 0.669 0.489 0.088 
Low Contrast 32 0.13 0.605 0.434 0.105 
High Contrast 32 0.358 0.792 0.584 0.106 
PANAS 
Positive 
32 14 45 27.7 7.44 
PANAS 
Negative 
32 10 19 13 3.27 
CES‐D 32 1 32 10.19 7.4 
 
 
As in Experiment 1, the results of Condition 1 were subjected to a mixed 
model analysis using subject as the grouping variable. The contrast term was 
significant, t(93) = 4.67, p < 0.001, indicating that high contrast letters were more 
often detected compared to low contrast letters. In contrast to the results of 
Experiment 1, the arousal term was marginally significant, t(93) = ‐1.91, p = 0.0597, 
indicating better performance on neutral versus arousing trials when controlling for 
all other variables in the model. Moreover, as in Experiment 1 the interaction term 
was significant, t(93) = 2.11, p = 0.0378 indicating that on arousing trials 
performance increased for high contrast letters and decreased for low contrast 
letters. The results of the mixed model analysis remained virtually unchanged when 
all three covariates were entered into the model (Table 5). However, contrary to 
17 
Experiment 1 PANAS positive affect scores failed to reach significance, t(28) = ‐0.67, 
p = 0.51.
 
 
Figure 3. Letter Identification rates (percent correct) for low and high contrast 
letters on neutral and arousing trials (Experiment 2, Condition 1). 
 
 
 
18 
Table 5
 
Experiment 2 (Condition 1) Results of Multi‐level Analysis with all Covariates 
Included 
 
Effect b SE df t Sig 
Intercept 0.4237 0.05246 28 8.08 p < 0.0001 
Arousal ‐0.02219 0.01164 93 ‐1.91 p = 0.0597 
Contrast 0.1543 0.03306 93 4.67 p < 0.0001 
PANAS pos ‐0.00105 0.001563 28 ‐0.67 p = 0.5079 
PANAS neg 0.004408 0.003833 28 1.15 p = 0.2599
CES‐D ‐0.00101 0.002595 28 ‐0.39 p = 0.6988
Arousal × 
Contrast 
0.03469 0.01646 93 2.11 p = 0.0378 
 
 
All analyses performed on Condition 1 were performed on data from 
Condition 2. The repeated measures ANOVA revealed a significant main effect of 
contrast level, F(1,31) = 34.15, p < 0.001, indicating that high contrast letters were 
more often identified compared to low contrast letters (see Table 4). But neither the 
arousal main effect, F(1,31) = 0.457, p = 0.504 nor the interaction term reached 
significance, F(1,31) = 0.918, p = 0.345. Moreover, apart from the contrast term, 
t(93) = 5.86, p < 0.001 demonstrating that high contrast letters compared to low 
contrast letters were more often identified, the mixed model analysis revealed no 
significant results. And these findings remained unchanged when all 3 covariates 
were entered into the model (see Table 6). Finally, no differences in errors were 
observed across emotionally arousing and neutral trials, t(31) = ‐0.536, p = 0.596. 
19 
Table 6
 
Experiment 2 (Condition 2) Results of Multi‐level Analysis with all Covariates 
Included 
 
 
Effect b SE df t Sig 
Intercept 0.3908 0.08153 28 4.79 p < 0.0001 
Arousal 0.005625 0.01377 93 0.41 p = 0.6839 
Contrast 0.1601 0.02734 93 5.86 p < 0.0001 
PANAS pos 0.003473 0.002046 28 1.70 p = 0.1006 
PANAS neg ‐0.00471 0.004117 28 ‐1.14 p = 0.2622
CES‐D 0.000508 0.002100 28 0.24 p = 0.8105 
Arousal × 
Contrast 
‐0.02177 0.01947 93 ‐1.12 p = 0.2665 
 
 
We then analyzed data from both conditions simultaneously using a 2 × 2 × 2 
repeated measures ANOVA with contrast and arousal as within‐subject variables, 
and condition type as a between‐subject variable. A main effect of contrast was 
observed, F(1, 62) = 62, p < 0.001, indicating that high contrast letters (M = 0.60) 
were more often identified compared to low contrast letters (M = 0.43). In addition, 
the interaction between arousal, contrast and condition type was marginally 
significant, F(1, 62) = 3.463, p = 0.067, consistent with the finding that the observed 
interaction between arousal and contrast occurs only at shorter ISI. No other effects 
reached significance.
These results both corroborate and extend the findings of Experiment 1, 
suggesting that emotionally arousing stimuli amplify biased competition processes, 
20 
but only at shorter ISI. In Experiment 1 and Condition 1 of Experiment 2, the letter 
array appeared between 750 and 3000 ms after the auditory stimulus was removed. 
This duration of time was extended in Condition 2, with the letter array appearing 
between 3000 and 5000 ms after removal of the auditory stimulus. Thus it appears 
that exposure to emotionally arousing auditory stimuli enhances perception for high 
priority stimuli and decreases perception for low priority stimuli, but only for a 
short period of time after the exposure.
21 
Experiment 3 
 
To further corroborate the results of Experiments 1 and 2, we performed a 
third experiment and included skin conductance measures as a marker of 
sympathetic arousal responses to the auditory stimuli. We predicted that SCR 
magnitude would positively and negatively correlate with letter identification rates 
for high and low contrast letters, respectively. 
Participants 
Fifteen young adults (10 female) consisting of USC students and individuals 
recruited from the surrounding community participated for a monetary 
compensation. Ages ranged from 19 to 29 (M = 23.2). Five participants identified 
themselves as Caucasian, 5 Asian, 3 African American, 1 Hispanic and 1 as bi‐racial.
Method 
Electrodermal activity (EDA) was recorded using a Biopac Systems MP 150 
and a GSR 100c amplifier. Disposable electrodes were placed on the index and 
middle fingers of the non‐dominant hand. Skin conductance responses (SCR) were 
sampled at a rate of 62.5 samples per second with a threshold of 0.001 μmho. Event‐
related electrodermal activity in response to the sounds was defined as any 
response occurring 1000 ms to 7000 ms after auditory stimulus onset. Apart from 
the physiological recording devices, Experiment 3 was identical to Experiment 1 and 
Condition 1 of Experiment 2.
22 
Results and Discussion 
The descriptive results for Experiment 3 can be observed in Table 7. The 
repeated measures ANOVA revealed a significant main effect of contrast as in the 
previous 2 experiments, F(1,14) = 21.69, p < 0.001, while the main effect of arousal 
did not reach significance, F(1,14) = 1.13, p = 0.305. However, in contrast with the 
previous 2 experiments the interaction did not reach significance, F(1,14) = 1.68, p = 
0.216. Yet this test most likely failed to reach significance due to low statistical 
power (n = 15), as mean scores were in the expected direction.
Table 7 
 
Descriptive Statistics for Letter identification Scores (percent correct) and 
Covariates (Experiment 3) 
 
Variable N Min Max Mean SD 
Neutral 15 0.431 0.825 0.520 0.097 
Arousing 15 0.450 0.806 0.564 0.092 
Low Contrast 15 0.210 0.565 0.386 0.010 
High Contrast 15 0.358 0.950 0.648 0.168 
PANAS 
Positive 
15 16 38 29.73 7.38 
PANAS 
Negative 
15 10 18 12.4 2.13 
CES‐D 15 3 23 12.47 5.71 
 
 
The mixed model analysis revealed similar results, as only the contrast term 
reached significance (see Table 8). Moreover, these results remained virtually 
unchanged when all 3 covariates were entered into the model (see Table 9). 
23 
However, the PANAS positive affect term did reach significance, t(10) = 2.71, p < 
0.05, indicating that those experiencing greater affect prior to the experiment 
performed better on the letter detection task when controlling for all other 
variables in the model.
Table 8 
 
Significance Test for Main Factors and Interaction 
 
Effect b SE df t Sig 
Intercept 0.3857 0.02889 13 13.35 p < 0.0001 
Arousal ‐0.01000 0.01701 39 ‐0.59 p = 0.5601 
Contrast 0.2738 0.05528 39 4.95 p < 0.0001 
Arousal × 
Contrast 
‐0.02786 0.02406 39 1.16 p =0.2600 
 
Table 9 
 
Results of Multi‐level Analysis with all Covariates Included 
 
Effect b SE df t Sig 
Intercept 0.1813 0.1465 10 1.24 p = 0.2441 
Arousal ‐0.01000 0.01701 39 ‐0.59 p = 0.5601 
Contrast 0.2738 0.05527 39 4.95 p < 0.0001 
PANAS pos 0.007034 0.002600 10 2.71 p < 0.05 
PANAS neg ‐0.00068 0.008748 10 ‐0.08 p = 0.9399 
CES‐D 0.000342 0.003391 10 0.10 p = 0.9217 
Arousal × 
Contrast 
‐0.02786 0.02406 39 1.16 p = 0.2540 
 
 
24 
One subject was excluded from SCR correlation analyses, as zero responses 
to the audio stimuli were detected. The mean number of SCR’s detected across the 
40 trials was 12, and the number of responses detected for arousing (M = 6.47) and 
neutral trials (M = 5.47) did not significantly differ, t(14) = ‐1.45, p = 0.169. 
Correlations between mean SCR (magnitude) collapsed across all trials did not 
correlate with overall letter identification rates for high, r(12) = 0.221, p = 0.448, 
and low contrast letters, r(12) = 0.09, p = 0.769. Next we calculated Pearson r 
correlation coefficients between mean SCR and low contrast letters, and between 
mean SCR and high contrast letters for each individual subject, resulting in two 
correlation coefficients for each subject. The means of these correlation coefficients 
for high and low contrast letters and SCR were compared via paired t‐test. However, 
no differences in mean correlation coefficients between SCR magnitude and 
accuracy for low contrast letters and SCR magnitude and accuracy for high contrast 
letters were observed, t(13) = 0.428, p = 0.676.
Cross Experiment Analyses 
Given that Experiment 1, Experiment 3, and Condition 1 of Experiment 2 
were identical, we combined these data to increase statistical power and performed 
repeated measures ANOVA on letter identification rates. In addition we also 
performed item analyses by testing correlations between mean subjective valence 
and arousal ratings for each auditory stimulus and letter identification rates for high 
and low contrast letters. The results of the repeated measures ANOVA support our 
initial hypothesis, as a main effect of contrast was observed, F(1,78) = 89.86, p < 
25 
0.001, η
p
2
 = 0.535, no main effect of arousal was observed, F(1,78) = 0.18, p = 0.675, 
η
p
2
 = 0.002, while a disordinal interaction in the predicted direction was observed, 
F(1,78) = 10.74, p = 0.002, η
p
2
 = 0.121 (Figure 4). We then directly compared 
identification rates for high and low contrast letters on arousing and neutral trials 
using paired t‐tests. These analyses revealed that low contrast identification rates 
were significantly higher, t(78) = 2.86, p = 0.005, on neutral trials (M = 0.41) 
compared to arousing trials (M = 0.39); and that identification rates for high 
contrast letters were significantly higher, t(78) = ‐2.58, p = 0.012, on arousing trials 
(M = 0.64) compared to neutral trials (M = 0.62). These results concur with the 
results observed individually in each experiment, indicating that letter identification 
rates for low and high contrast letters are influenced differently by exposure to 
emotionally arousing auditory stimuli. Moreover, no differences in errors were 
observed across emotionally arousing (M = 15.91) and neutral (M = 15.58) trials, 
t(78) = ‐0.662, p = 0.536. 
26 
Figure 4. Letter identification rates (percent correct) for low and high contrast 
letters on neutral and arousing trials collapsed across Experiment 1, Experiment 2 
Condition 1 and Experiment 3. 
 
 
 
For the item analyses, we expected mean valence ratings for each auditory 
stimulus to negatively and positively correlate with mean letter identification rates 
for high and low contrast letters, respectively. In addition, we also expected mean 
arousal ratings to negatively correlate with low contrast identification rates, and to 
positively correlate with high contrast identification rates. The correlation test 
between arousal ratings and high contrast identification rates for the 40 auditory 
stimuli did reach marginal significance, r(38) = 0.265, p = 0.099, however the 
correlation between arousal ratings and low contrast identification rates failed to 
reach significance, r(38) = ‐0.139, p = 0.394. With respect to valence ratings, the 
correlation test with high contrast identification rates did not reach significance, 
r(38) = ‐0.206, p = 0.202, yet the correlation with low contrast identification rates 
27 
did, r(38) = 0.340, p = 0.032, suggesting that auditory sounds that were rated more 
positively were associated with better performance for low contrast letters across 
all trials.
 
 
 
 
 
 
 
 
 
 
 
 
28 
General Discussion 
 
The goal of this study was to test the hypothesis that, following exposure to 
emotionally arousing sounds, high priority visual stimuli would dominate attention 
to an even greater extent, while the inhibition of low priority stimuli would further 
increase. Previous studies have demonstrated that exposure to emotional stimuli 
enhances visual perception for non‐emotional stimuli that are the focus of attention. 
However, no previous studies have examined whether this enhancement is 
observed in the presence of other stimuli vying for the same limited pool of 
resources. Furthermore, enhancements in perception for non‐emotional stimuli 
have only been observed at low ISI—no more than 1000 ms. Finally, no studies have 
used physiological markers of arousal to predict perceptual enhancements for non‐
emotional stimuli. Our study addressed each of these issues, and our results indicate 
that arousal enhances perception for non‐emotional visual stimuli that are high 
priority due to stronger perceptual contrast. But such enhancements come at the 
cost of processing competing stimuli that are low priority due to their low 
perceptual contrast.
According to arousal‐biased competition theory, emotional arousal enhances 
perception for high priority stimuli and minimizes perception for low priority 
stimuli (Mather & Sutherland, under review). When multiple stimuli are perceived, 
each stimulus is given a degree of priority based on its perceptual saliency and its 
relevance to current goals or task demands. The higher the priority, the more 
29 
attention the object receives. We manipulated the contrast level of multiple non‐
emotional stimuli to make some higher in priority, and thus more likely to be 
selected by attention. We reasoned that high priority stimuli would more easily 
capture attention and thus enjoy the perceptual benefits afforded by exposure to 
emotionally arousing stimuli. However, we assumed this would come at the cost of 
attending to competing stimuli that were lower in priority, and thus less likely to 
capture attention.
At the neural level, selective attention mechanisms are biased towards high 
contrast (Proulx & Egeth, 2008) and goal‐relevant stimuli (Chelazzi, et al., 1998). 
Given that every letter in the stimulus array was equated in terms of goal‐relevance, 
we argue that our findings show that exposure to emotionally arousing sounds 
amplifies existing selection biases in attention that are driven by perceptual 
contrast. Previous findings indicate that exposure to an emotionally arousing 
stimulus can enhance perception for single task‐relevant targets (Bocanegra & 
Zeelenberg, 2009; Phelps, et al., 2006; Zeelenberg & Bocanegra, 2010), hinting at the 
possibility that emotional arousal enhances visual perception for stimuli that are the 
focus of attention. Our results confirm this possibility, as high contrast letters 
dominated the competition for attention in the presence of competing low contrast 
letters—an effect that was enhanced following exposure to emotionally arousing 
sounds.
Enhancements in perception for visual stimuli following exposure to 
emotionally arousing stimuli has only been observed at relatively short inter‐
30 
stimulus intervals (ISI), ranging from 0 to 1000 ms (Bocanegra & Zeelenberg, 2009; 
Phelps, et al., 2006; Zeelenberg & Bocanegra, 2010). Results from experiments one 
and two add to these findings, and suggest that arousal‐induced perceptual 
enhancements for high priority stimuli can occur at 750 to 3000 ms ISI. To further 
examine the duration of this effect, experiment two included a second condition 
where ISI ranged from 4000 to 6000 ms. Arousal did not enhance identification 
rates for the high contrast letters in this condition, nor did it reduce identification 
rates for low contrast letters. Thus it appears that exposure to emotionally arousing 
sounds does not enhance visual perception beyond 3000 ms.
Contrary to expectation, EDA responses to the audio stimuli were limited, 
and were not significantly greater for arousing than for neutral sounds. The 
expected correlations between mean EDA responses and performance on 
identification rates for low and high contrast letters were not observed. Yet one 
must consider that the letter identification task involved central fixation, which may 
have elicited orienting responses throughout the experiment. This may have 
masked EDA responses that are known to occur in response to the emotional stimuli 
used in our experiment (Bradley & Lang, 2000).
Depression scores were included in our analyses as a covariate based on 
evidence that depression is associated with reduced electrophysiological responses 
to visual perceptual contrast (Bubl, Kern, Ebert, Bach, & van Elst, 2010) and slower 
rates of information processing (Tsourtos, Thompson, & Stough, 2002). However, 
controlling for depression scores had no effect on other variables included in the 
31 
mixed model analyses, and did not appear as a significant predictor. Measures of 
affective state were included in the mixed model, as there is evidence that positive 
mood increases one’s scope of visual attention (Fredrickson & Branigan, 2005; 
Rowe, Hirsh, & Anderson, 2007). In Experiment 3, PANAS positive affect cores 
appeared as a significant predictor when entered into the mixed model analysis as a 
covariate, and in Experiment 1 these scores also appeared as a marginally 
significant predictor. This suggests that higher levels of positive affect predicted 
better overall performance on the letter identification task—a finding that is 
consistent with the Broaden and Build Theory of Positive Emotions (Fredrickson, 
1998). According to this theory, positive affect increases an individual’s scope of 
attention, enhancing one’s ability to focus on global rather than local features of 
visual stimuli (Fredrickson & Branigan, 2005; Gasper & Clore, 2002). And other 
studies have shown that individuals experiencing more positive affect have greater 
difficulty ignoring distracting information, as in the Eriksen flanker tasks, where 
flankers divert attention away from task‐relevant stimuli (Rowe, et al., 2007). In 
other words positive affect allows one to focus on a broader array of information, 
which in the Eriksen flanker paradigm includes distracter stimuli that interfere with 
rapid responses to task‐relevant stimuli.
Taken together, our findings indicate that when in direct competition for 
attentional resources, exposure to emotionally arousing stimuli increases the 
margin by which high priority stimuli dominate competitions for perceptual 
processing. These results provide evidence for the arousal‐biased competition 
32 
hypothesis (Mather & Sutherland, under review), which predicts that experiencing 
emotional arousal amplifies biased competition processes by strengthening 
representations of high priority stimuli, and weakening representations of low 
priority stimuli. These results also lend support to the idea that experiencing 
positive affect increases one’s scope of attention, a prediction made by the Broaden 
and Build Theory of Positive Emotions (Fredrickson, 1998).
 
 
 
 
 
33 
References 
 
Alpers, G. W., & Pauli, P. (2006). Emotional pictures predominate in binocular 
rivalry. Cognition & Emotion, 20(5), 596‐607. doi: 
10.1080/02699930500282249 
Alpers, G. W., Ruhleder, M., Walz, N., Muhlberger, A., & Pauli, P. (2005). Binocular 
rivalry between emotional and neutral stimuli: A validation using fear 
conditioning and EEG. International Journal of Psychophysiology, 57(1), 25‐
32. doi: 10.1016/j.ijpsycho.2005.01.008 
Anderson, A. K. (2005). Affective influences on the attentional dynamics supporting 
awareness. Journal of Experimental Psychology: General, 134(2), 258‐281. doi: 
10.1037/0096‐3445.134.2.258 
Anderson, A. K., & Phelps, E. A. (2001). Lesions of the human amygdala impair 
enhanced perception of emotionally salient events. Nature, 411(6835), 305‐
309. doi: 10.1038/35077083 
Bocanegra, B. R., & Zeelenberg, R. (2009). Dissociating Emotion‐Induced Blindness 
and Hypervision. Emotion, 9(6), 865‐873. doi: 10.1037/a0017749 
Bradley, M. M., & Lang, P. J. (2000). Affective reactions to acoustic stimuli. 
Psychophysiology, 37(2), 204‐215. doi: 10.1111/1469‐8986.3720204 
Bradley, M. M., & Lang, P. J. (2007). The international affective digitized sounds (2nd 
Edition; IADS‐2): Affective ratings of sounds and instruction manual. 
Technical report B‐3. University of Florida, Gainesville, FL. 
Bubl, E., Kern, E., Ebert, D., Bach, M., & van Elst, L. T. (2010). Seeing Gray When 
Feeling Blue? Depression Can Be Measured in the Eye of the Diseased. 
Biological Psychiatry, 68(2), 205‐208. doi: 10.1016/j.biopsych.2010.02.009 
Chelazzi, L., Duncan, J., Miller, E. K., & Desimone, R. (1998). Responses of neurons in 
inferior temporal cortex during memory‐guided visual search. Journal of 
Neurophysiology, 80(6), 2918‐2940.
Chelazzi, L., Miller, E. K., Duncan, J., & Desimone, R. (2001). Responses of neurons in 
macaque area V4 during memory‐guided visual search. Cerebral Cortex, 
11(8), 761‐772. doi: 10.1093/cercor/11.8.761 
34 
De Martino, B., Kalisch, R., Rees, G., & Dolan, R. J. (2009). Enhanced Processing of 
Threat Stimuli under Limited Attentional Resources. Cerebral Cortex, 19(1), 
127‐133. doi: 10.1093/cercor/bhn062 
Desimone, R. (1998). Visual attention mediated by biased competition in 
extrastriate visual cortex. Philosophical Transactions of the Royal Society of 
London Series BBiological Sciences, 353(1373), 1245‐1255. doi: 
10.1098/rstb.1998.0280
Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual‐attention. 
Annual Review of Neuroscience, 18, 193‐222.
Duncan, J. (2006). EPS mid‐career award 2004 ‐ Brain mechanisms of attention.
Quarterly Journal of Experimental Psychology, 59(1), 2‐27. doi: 
10.1080/17470210500260674 
Fecteau, J. H., & Munoz, D. P. (2006). Salience, relevance, and firing: a priority map 
for target selection. Trends in Cognitive Sciences, 10(8), 382‐390. doi: 
10.1016/j.tics.2006.06.011 
Fredrickson, B. L. (1998). What good are positive emotions? Review of General 
Psychology, 2(3), 300‐319. doi: 10.1037/1089‐2680.2.3.300 
Fredrickson, B. L., & Branigan, C. (2005). Positive emotions broaden the scope of 
attention and thought‐action repertoires. Cognition & Emotion, 19(3), 313‐
332. doi: 10.1080/02699930441000238 
Gasper, K., & Clore, G. L. (2002). Attending to the big picture: Mood and global 
versus local processing of visual information. Psychological Science, 13(1), 
34‐40. doi: 10.1111/1467‐9280.00406
Kastner, S., De Weerd, P., Desimone, R., & Ungerleider, L. C. (1998). Mechanisms of 
directed attention in the human extrastriate cortex as revealed by functional 
MRI. Science, 282(5386), 108‐111. doi: 10.1126/science.282.5386.108 
Kastner, S., & Ungerleider, L. G. (2001). The neural basis of biased competition in 
human visual cortex. Neuropsychologia, 39(12), 1263‐1276. doi: 
10.1016/S0028‐3932(01)00116‐6
Keil, A., & Ihssen, N. (2004). Identification facilitation for emotionally arousing verbs 
during the attentional blink. Emotion, 4(1), 23‐35. doi: 10.1037/1528‐
3542.4.1.23 
35 
Knight, M., Seymour, T. L., Gaunt, J. T., Baker, C., Nesmith, K., & Mather, M. (2007). 
Aging and goal‐directed emotional attention: Distraction reverses emotional 
biases. Emotion, 7(4), 705‐714. doi: 10.1037/1528‐3542.7.4.705 
LaBar, K. S., Mesulam, M. M., Gitelman, D. R., & Weintraub, S. (2000). Emotional 
curiosity: modulation of visuospatial attention by arousal is preserved in 
aging and early‐stage Alzheimer's disease. Neuropsychologia, 38(13), 1734‐
1740. doi: 10.1016/S0028‐3932(00)00077‐4
Lang, P. J., Bradley, M. M., Fitzsimmons, J. R., Cuthbert, B. N., Scott, J. D., Moulder, B., 
et al. (1998). Emotional arousal and activation of the visual cortex: An fMRI 
analysis. Psychophysiology, 35(2), 199‐210. doi: 
10.1017/S0048577298001991 
Mather, M., & Sutherland, M. R. (under review). Arousal‐biased competition in 
attention and memory.
Nummenmaa, L., Hyona, J., & Calvo, M. G. (2006). Eye movement assessment of 
selective attentional capture by emotional pictures. Emotion, 6(2), 257‐268. 
doi: 10.1037/1528‐3542.6.2.257 
O`Craven, K. M., Downing, P. E., & Kanwisher, N. (1999). fMRI evidence for objects as 
the units of attentional selection. Nature, 401(6753), 584‐587. doi: 
10.1038/44134 
Phelps, E. A., Ling, S., & Carrasco, M. (2006). Emotion facilitates perception and 
potentiates the perceptual benefits of attention. Psychological Science, 17(4), 
292‐299. doi: 10.1111/j.1467‐9280.2006.01701.x 
Proulx, M. J., & Egeth, H. E. (2008). Biased competition and visual search: the role of 
luminance and size contrast. Psychological ResearchPsychologische 
Forschung, 72(1), 106‐113. doi: 10.1007/s00426‐006‐0077‐z 
Radloff, L. S. (1977). The CES‐D scale: A self‐report depression scale for research in 
the general population. Applied Psychological Measurement, 1(3), 385‐401. 
doi: 10.1177/014662167700100306
Rowe, G., Hirsh, J. B., & Anderson, A. K. (2007). Positive affect increases the breadth 
of attentional selection. Proceedings of the National Academy of Sciences of the 
United States of America, 104(1), 383‐388. doi: 10.1073/pnas.0605198104 
Scholl, B. J. (2001). Objects and attention: the state of the art. Cognition, 80(1‐2), 1‐
46. doi: 10.1016/S0010‐0277(00)00152‐9 
36 
Singer, J. D. (1998). Using SAS PROC MIXED to fit multilevel models, hierarchical 
models, and individual growth models. Journal of Educational and Behavioral 
Statistics, 23(4), 323‐355. doi: 10.3102/10769986023004323
Tsourtos, G., Thompson, J. C., & Stough, C. (2002). Evidence of an early information 
processing speed deficit in unipolar major depression. Psychological 
Medicine, 32(2), 259‐265. doi: 10.1017/s0033291701005001 
Vecera, S. P., & Farah, M. J. (1994). Does visual‐attention select objects or locations. 
Journal of Experimental PsychologyGeneral, 123(2), 146‐160. doi: 
10.1037/0096‐3445.123.2.146 
Vuilleumier, P., Armony, J. L., Driver, J., & Dolan, R. J. (2001). Effects of attention and 
emotion on face processing in the human brain: An event‐related fMRI study. 
Neuron, 30(3), 829‐841. doi: 10.1016/S0896‐6273(01)00328‐2
Vuilleumier, P., Richardson, M. P., Armony, J. L., Driver, J., & Dolan, R. J. (2004). 
Distant influences of amygdala lesion on visual cortical activation during 
emotional face processing. Nature Neuroscience, 7(11), 1271‐1278. doi: 
10.1038/nn1341 
Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief 
measures of positive and negative affect ‐ The PANAS scales. Journal of 
Personality and Social Psychology, 54(6), 1063‐1070.
Zeelenberg, R., & Bocanegra, B. R. (2010). Auditory emotional cues enhance visual 
perception. Cognition, 115(1), 202‐206. doi: 10.1016/j.cognition.2009.12.004

Abstract (if available)

Abstract In 3 experiments 111 young adults (ages 18-30) performed a letter identification task to test the hypothesis that emotional arousal increases attention to high priority stimuli and inhibits attention to low priority stimuli. A circular array of letters was briefly presented to each subject following exposure to negative arousing or neutral sounds. Contrast level of the letters was manipulated to make some letters higher in priority. The results support the arousal-biased competition hypothesis, as exposure to emotionally arousing sounds increased identification rates for high priority letters and decreased identification rates for low priority letters. Yet this effect was limited to inter-stimulus intervals (ISI) from 750 to 3000 milliseconds (ms).

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Emotion, attention and cognitive aging: the effects of emotional arousal on subsequent visual processing

PDF

Emotional reactivity and memory biases in older and younger adults

PDF

Age differences in the effects of anxiety on selective attention: a test of the vigilance-avoidance hypothesis

PDF

Noradrenergic mechanisms of arousal-enhanced memory selectivity

PDF

Aging and emotion regulation in the judgment of facial emotion

PDF

Influence of age and anxiety on recognition of facial expressions of emotion: exploring the role of attentional processes

PDF

How age-related changes in the locus coeruleus affect selective attention and memory

PDF

The development of electrodermal fear conditioning from ages 3 to 8 years and relationships with antisocial behavior at age 8 years

Asset Metadata

Creator Sutherland, Matthew Ryan (author)

Core Title Emotional arousal amplifies the selectivity of visual selective attention

School College of Letters, Arts and Sciences

Degree Master of Arts

Degree Program Psychology

Publication Date 12/17/2010

Defense Date 10/20/2010

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag emotional arousal,OAI-PMH Harvest,selective attention,stimulus priority

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Mather, Mara (committee chair), Baker, Laura A. (committee member), Dawson, Michael E. (committee member)

Creator Email mattryansutherland@gmail.com,mrsuther@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m3607

Unique identifier UC1501121

Identifier etd-Sutherland-4200 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-429378 (legacy record id),usctheses-m3607 (legacy record id)

Legacy Identifier etd-Sutherland-4200.pdf

Dmrecord 429378

Document Type Thesis

Rights Sutherland, Matthew Ryan

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu