CALIFORNIA STATE UNIVERSITY NORTHRIDGE
The Influence of Threat on Feature-Based Evaluations
A thesis submitted in partial fulfillment of the requirements
For the degree of Master of Arts in Psychology
General-Experimental
By
Aerielle M. Allen
May 2015
The thesis of Aerielle M. Allen is approved:
_______________________________________
Que-Lam Huynh, Ph.D.
____________
Date
_______________________________________
Bradley McAuliff, Ph.D.
____________
Date
_______________________________________
Debbie S. Ma, Ph.D., Chair
____________
Date
California State University, Northridge
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ACKNOWLEDGEMENTS
I would like to thank my committee members Dr. Que-Lam Huynh, Dr. Bradley
McAuliff, and Dr. Debbie Ma for serving on my committee and for their continued
support and insightful comments throughout this process. Specifically, I would like to
thank Que-Lam for providing me with thoughtful comments and recommendations
towards helping me improve my introduction and methods. In addition, I would like to
thank Bradley for meeting with me to discuss real world implications that feature-based
stereotyping has on the legal system and how to decompose main effects and interactions.
I am beyond thankful to Debbie for being my mentor. Taking me on as a graduate student
and being willing to work with me has immensely changed my life for the better. I am
thankful for your uplifting comments and advice, as well as for all the time you spent
helping me with writing, statistics, and furthering my research skills. I am eternally
grateful and will never forget you and the impact that you have had on me. I look forward
to collaborating with you again in the future.
I would also like to thank my first mentor Dr. Jenessa Shapiro for introducing me
to the field of social psychology; exploring stereotypes and prejudice with you paved the
way for my career and research goals. I am also thankful to Dr. Maria-Elena Zavala and
CSUN’s MBRS program for their words of encouragement and funding towards my
thesis. Additionally, I am thankful to my parents, both near and far, my good friend
Emerald, and my partner Alex for supporting me and encouraging me to work harder
than I did the day before. In particular I am thankful to my mother, Michelle Parker, who
has been my rock throughout this process and the most supportive person in my life.
Because of all of your sacrifices I will be the first in my family to graduate with a
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Masters and go on to a doctoral program. I am grateful to all of you for what you have
brought and continue to bring to my life both inside and outside the realm of academia.
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DEDICATION
For my loving grandmother and angel Loretta Funderburg. You taught me patience and
strength, I will treasure your love and your spirit forever.
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Table of Contents
Signature Page
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Acknowledgement
iii
Dedication
vi
Abstract
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Introduction:
1
Stereotyping: Judging and Evaluating Others
1
Feature-Based Stereotyping
2
When Features Influence Judgment
7
The Importance of Threat
8
Threat and Attention
13
The Present Study
14
Method:
17
Participants and Design
17
Materials
17
Procedure
20
Results :
21
Discussion:
25
Limitations
27
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Future Directions
29
References:
32
Appendix A:
39
Appendix B:
41
Appendix C:
42
Appendix D:
45
Appendix E:
48
Appendix F:
51
Appendix G:
55
Appendix H:
56
Appendix I:
57
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Abstract
The Influence of Threat on Feature-Based Evaluations
By
Aerielle M. Allen
Master of Arts in Psychology, General-Experimental
Research on racial categories and threat-relevant associations suggests that there are
attentional biases associated with judgments of Blacks compared to Whites (Donders,
Correll, & Wittenbrink, 2008). Researchers have found that Black-danger associations
significantly predicted the extent to which Black faces captured attention faster and held
attention longer compared to White faces (Donders et al., 2008). Feature-based
stereotyping examines the use of individuals’ physical features to understand stereotyping
above and beyond the effects of category membership alone (Blair, Judd, & Fallman,
2004). What we find is that in these studies people are responding to feautral differences
in Whites but not in Blacks (Ma & Correll, 2010; Ronquillo et al., 2007). Using a
modified dot-probe task (Donders et al., 2008; Koster, Crombez, Verschuere, &
DeHouwer, 2004), the current study examines the context that seems to drive people to
evaluate featural differences in White targets but not in Blacks. More specifically, we
examine whether or not threat moderates people’s attention to within-category variations.
We conclude with a discussion of our findings and how they provide further insight on
the role situational and motivational factors play in attention allocation towards different
social categories.
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INTRODUCTION
In 1994, two magazines published stories regarding the O.J. Simpson trial.
Whereas Newsweekly used the original mug shot of O.J. Simpson, Matt Mahurin, a
photo-illustrator for Time magazine, manipulated Simpson’s skin tone, making him look
darker, unshaved, and slightly blurred the picture. When the photographer for Time was
interviewed regarding the photo, he stated that he sought to “make it [the image] more
artful, more compelling” (Meltzer, 1997). Speculatively, it seems that Mahurin was
attempting to construct a more dangerous and threatening image of Simpson that others
would take note of (Dixon & Maddox, 2005). This real-world example illustrates how
physical features, such as skin tone, can potentially influence our judgments and
evaluations of others.
Stereotyping: Judging and Evaluating Others
Categories help individuals make sense of a complex social world (Allport, 1954).
By using these categories we tend to overgeneralize the individuals within these groups
with stereotypes, which are a natural outcome of categorizations and a result of wanting
to make quick evaluations about our surroundings. Stereotypes are the thoughts and
beliefs that we have about individuals or specific groups (Allport, 1954; Devine, 1989).
For instance, Blacks are stereotyped as more aggressive than Whites and Whites as more
intelligent than Blacks (Devine & Elliot, 1995; Wittenbrink, Judd, & Park, 1997).
Although applying stereotypes can occur automatically and without conscious knowledge
(Devine, 1989), there are contextual and situational factors that influence the activation
and application of stereotypes (Wittenbrink, Judd, & Park, 2001; Maddux, Barden,
Brewer, & Petty, 2004).
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Prior to the last decade, research focused almost exclusively on category-based
stereotypes, which is stereotyping based on social category membership (Gaertner &
McLaughlin, 1983). Category-based approaches to stereotyping compare the differences
between social groups (e.g. Blacks and Whites). Consider, for example, taking the
average evaluative responses to a group of White targets and comparing them to the
average evaluative responses to a group of Black targets. When conducting a statistical
analysis, the researcher collapses across the within-category differences and these
differences are not factored into the overall comparisons between the two social groups.
For example, during evaluations of Black and White individuals, research has shown
when comparing the associative strength of two items, Black primes facilitate Blackrelated stereotypes whereas White primes facilitate White-related stereotypes (Gaertner
& McLaughlin, 1983). Instead of considering how a Black target with a lighter skin
complexion may be evaluated differently compared to a Black target with a darker skin
complexion, the analytic strategy of averaging targets together assumes that these withincategory differences are relatively inconsequential. In these implicit tasks where targetlevel differences are not being accounted for, researchers have assumed that the facial
stimuli used in these tasks serve as average representations of their racial categories and
therefore any evaluations made about these facial stimuli are based on how the racial
category is usually viewed (Livingston & Brewer, 2002). Although these Black faces
surely represent the Black category, a category-based approach does not systematically
account for how these differences in features can influence the degree to which an
individual person is stereotyped.
Feature-Based Stereotyping
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Recently, featural differences have been systematically analyzed, such that social
psychologists have begun exploring how social category membership and differences
within these social categories uniquely contribute to stereotyping (Blair, Judd, Sadler, &
Jenkins, 2002). Considering the example mentioned above, where researchers compare
individuals based on their racial category membership as Blacks or Whites, researchers
who have adopted a feature-based approach also systematically account for featural
variations in the analysis of evaluations, such that within-category differences (e.g., eye
shape, skin tone, lip fullness) are considered when assessing the differences between
racial categories.
Feature-based stereotyping is a methodological approach that examines the use of
individuals’ physical features to understand stereotyping above and beyond the effects of
category membership alone (Blair, Judd, & Fallman, 2004). That is, researchers consider
both social category membership (e.g. whether the target is Black or White) and targetlevel differences within those social categories (e.g., the targets’ skin tones). Although
category-based approaches have not systematically investigated the influence of features
(i.e., treated features as an independent variable), feature-based approaches have allowed
features to vary as independent variables and in the statistical analysis they are treated as
a factor. Evaluations are considered based on a target’s social category membership as
well as differences in a target’s prototypicality – that is, the extent to which an individual
possesses features that are stereotypic of his/her racial group. For example, prototypic
features for Blacks include darker skin tone, fuller lips, wider nose, dark eye color, and
kinkier hair texture (Blair et al., 2002). These within-group variations influence the
degree to which a person is stereotyped. Blacks are stereotyped as more aggressive than
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Whites, but this is especially the case for those Blacks who possess more prototypically
Black features (Blair et al., 2004). Practically, the difference between category-based
stereotypes and feature-based stereotypes is revealed in the researcher’s approach to the
statistical analysis. Researchers are either examining responses to categorical dependent
variables (e.g. race) or the degree to which a target is viewed as a particular race,
continuous and categorical dependent variables.
In similar evaluative studies where feature-based prejudice is investigated, White
individuals judged the valence of a word to be “good” or “bad”. Participants completed a
sequential priming task where they were primed with a dark-skinned/ more prototypical
Black face, a dark-skinned/ less prototypical Black face, a light-skinned/ more
prototypical Black face, a light-skinned/ less prototypical Black face, and a White male
face as a control. Then, they judged whether a presented word such as, beauty, joy,
cockroach, or vomit were good or bad as quickly and accurately as possible. Results
suggest that for White individuals, darker skinned Black primes facilitate the recognition
of negative valence words but lighter skinned Black primes facilitate the recognition of
positive valence words compared to the darker skinned primes (Hagiwara, Kashy, &
Cesario, 2011). Also, in a study conducted by Maddox and Gray (2002) participants
listed the characteristics associated with light-skinned and dark-skinned Blacks.
Participants listed more stereotypic traits for dark-skinned Blacks compared to lightskinned Blacks. Additionally, it is important to note that participants seemed to list more
negatively valenced traits for dark-skinned compared to light-skinned Blacks. From a
prejudice perspective, the results demonstrate that light-skinned Blacks were viewed
more positively than dark-skinned Blacks, similar to the results demonstrated by
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Hagiwara et al. (2011). Studies conducted by (Blair et al., 2002; Blair et al., 2004) have
found that individuals – both Blacks and Whites who possess more Afrocentric traits are
associated with more Black stereotypes compared to individuals with fewer Afrocentric
traits, while those who possess more Eurocentric traits are associated with more White
stereotypes.
Similarly, in another study participants indicated how likely it was that a target
photo belonged to a description that included stereotypes that were either positive or
negative and stereotypic or counter-stereotypic (Blair et al., 2002). In the Black target
condition, the more prototypic Black target photos were, the more participants associated
them with a negative description compared to a positive or positive counter-stereotypic
description. This was the same for White target photos, such that the more Afrocentric
features they had (i.e. the less prototypic White they appeared) the more likely the photo
was believed to belong to a negative description compared to positive or positive counterstereotypic description. Again, demonstrating the idea that stereotypes and evaluations
vary as a function of prototypicality, such that the more prototypic an individual is of his
or her racial category, the more race-related stereotypes that become attributed to him/her,
and the less prototypic an individual is of his/her racial category, the fewer race-related
stereotypes that become attributed to him/her. Although some descriptions were
negatively valenced, there was no element of threat that potentially competed with the
assessment of featural difference in both the Black and White targets or biased their
attention to these features.
In addition to the study mentioned above, prototypicality also appears to influence
implicit judgments. In a first-person shooter task employed by Ma and Correll (2010),
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participants were shown images of Black and White targets that varied in degree of
prototypicality and were either armed with a gun or unarmed. The participants are
instructed to shoot armed targets and avoid shooting unarmed targets as quickly and
accurately as possible. Overall, first-person shooter task studies have shown that there is
not just a main effect of race (i.e. that Black targets are seen as armed more than White
targets); the critical thing is that the response to the object depends on the race of the
target (Correll, Park, Judd, & Wittenbrink, 2007) and how prototypic targets are of their
race (Ma & Correll, 2010). When targets were armed, participants failed to shoot Whites
more then Blacks, but when targets were unarmed there was no evidence of racial bias,
such that there was not a significant difference between Blacks and Whites (Ma & Correll,
2010). Because Blacks are stereotypically regarded as dangerous (Devine & Elliot, 1995;
Dixon & Maddox, 2005), people may assume that Black individuals are armed and
therefore shoot them. Ma and Correll (2010) demonstrated that within-category variations
in target prototypicality predicted errors on the first-person shooter task above and
beyond racial category, but White, not Black targets, drove this effect. For Black targets,
prototypicality did not moderate the difference between errors in gun versus non-gun
trials. For White targets, prototypicality significantly moderated the amount of errors
made in gun versus non-gun trials. Increasing White targets’ prototypicality constrained
participants’ shooting of armed Whites while also facilitating don’t shoot responses to
unarmed Whites. Interestingly to note, as prototypicality decreases there is a reverse
effect of racial bias; low prototypic Whites were marginally evaluated in ways similar to
Blacks. We are interested in examining the context that prompts evaluating features in
White targets, but not in Black targets. In the current study, prototypicality will be used to
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distinguish between individuals who vary with respect to the degree in which a target
possesses features that are stereotypical of his/her group. High prototypic targets will
possess more stereotypic features of his/her racial group whereas low prototypic targets
will posses fewer stereotypic features of his/her racial group.
We are interested in examining the context that prompts evaluating features in
White targets, but not in Black targets. In the current study, prototypicality will be used to
distinguish between individuals who vary with respect to the degree in which a target
possesses features that are stereotypical of his/her group. Highly prototypic targets
possess more stereotypic features of his/her racial group whereas low prototypic targets
possess fewer stereotypic features of his/her racial group.
When Features Influence Judgment
As the studies we reviewed suggest, research generally demonstrates that features
influence judgments over and above the effects of category membership; however, the
nature of this influence appears to vary as a function of social category and context. One
of the key findings in first-person shooter tasks is that participants make more mistakes
on the non-gun Black trials and the gun White trials (Correll et al., 2007). In the study by
Ma and Correll (2010), the bias was moderated by target prototypicality, but the effect of
prototypicality was significant for Whites but not for Blacks. For both high prototypic
and low prototypic Black targets, more errors were made when targets were unarmed
compared to armed, such that participants were more likely to shoot Black targets
regardless of prototypicality. For White targets, participants made more errors when the
White target was low prototypic compared to high prototypic; low prototypic Whites
were shot more than high prototypic Whites. Studies such as Blair et al. (2004)
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demonstrate that prototypicality influences the stereotypic inferences that people
regarding both Black and White targets. Conversely, Ma and Correll (2010) show that
although features matter on average, this effect is driven by sensitivity to featural
variations among White targets and show no measurable effect for Blacks.
Similar results have been found when evaluating feature-based prejudice while
looking at the effects of prototypicality on amygdala activity. The amygdala is a brain
structure that has been associated with threat and emotional processing (Phelps et al.,
2000). In fMRI studies, research has shown that exposure to less prototypic White males
(e.g., White males who have darker skin) leads to greater amygdala activity compared to
light skinned or more prototypic White males (Ronquillo et al., 2007). Here, participants
showed changes in amygdala activity to light versus dark skinned Whites but did not
show the same variations in amygdala activity for Black faces varying in skin tone.
Racial bias depended on how prototypic targets were, but only for Whites.
The Importance of Threat
Overall, the research supports the notion that features matter in stereotyping.
However, there seems to be a discrepancy regarding when features affect judgments and
for which targets. When looking at the feature-based stereotyping studies just reviewed
and the differences in whether features are being considered in the stereotypes associated
with White and Blacks, the discrepancies seem to be due to the differences in the tasks
that the participants were asked to complete. Social judgments are motivated and context
driven (Schaller, Park, & Faulkner, 2003; Maner et al., 2012), and these factors can lead
to differences in how we stereotype others. In some of the feature-based tasks,
participants are being asked to assign cultural beliefs or stereotypes to racial groups
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(Maddox & Gray 2002: Blair et al., 2002; Blair et al., 2004) or they are being asked to
make a decision to shoot an armed versus an unarmed target based on race and
prototypicality in the first-person shooter task (Correll et al., 2007; Ma & Correll, 2010).
We predict that the difference in these tasks is threat, such that some tasks made threat
salient (i.e. the first-person shooter task) but other tasks do not.
Threat is evolutionarily significant in our evaluation of others and is necessary for
survival. Fear and threat lead people to process social information in ways that help them
to avoid perceived harm (Cottrell & Neuburg, 2005; Maner, Miller, Moss, Leo & Plant,
2012). In the Maner et al. (2012) study, White participants viewed clips that were
intended to evoke self-protective motives and then participated in a task similar to a
memory game where they were instructed to pair similar cards. The cards had a
photograph of either a White or Black target person. Participants who had more selfprotective motives tended to categorize people more along the lines of race as opposed to
featural differences. Thus, threatening situations increased White individuals’ sensitivity
to what differentiates ingroup versus outgroup members and enhanced the tendency to
categorize people based on race (Maner et al., 2012). Making self-protection needs
salient enhanced a bias towards outgroup categorization. Because Blacks are considered
threatening, they cause people to just categorize and not attune to features. When Whites
were primed with a self-protective motive they tended to categorize racially ambiguous
others as Black. Overall, self-protective motives increase the likelihood of categorizing
others as outgroup members.
Outgroup categorization leads to outgroup homogeneity, a phenomenon in which
individuals are less capable of perceiving differences within outgroup members (Judd &
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Park, 1988). Because White individuals were the majority of the participants in these
studies, you have Whites being unable to differentiate featural differences in outgroup
members but attuning to featural differences in Whites. While outgroup-homogeneity
may be the cause of greater sensitivity for prototypicality in Whites, because there are
primarily Whites making these evaluations, some researchers have demonstrated similar
results using both Black and White participants (Ma & Correll, 2010). Ronquillo et al
(2007) found that both Black and Whites participants demonstrated greater amygdala
activity for Black targets compared to White targets. For this reason, there is evidence to
suggest that outgroup homogeneity may not explain everything because when there is
threat we see that the same effect happens with both White and Black participants;
however, this can not be said confidently because this was not examined specifically as a
research question. We propose that what drives individuals to either attune or not to
featural differences is context dependent.
In situations involving threat, people try to make evaluations as quickly as
possible and heightened attention is given to natural threat cues (Öhman, Flykt, &
Esteves, 2001). In tasks where individuals look at an array of stimuli, threatening stimuli
such as snakes and spiders capture attention faster than images of neutral stimuli such as
flowers. If certain races are stereotypically regarded as threatening (Donders et al., 2008),
then in situations where threat is salient, the racial category should receive more attention
than how prototypical a person is of that race because the threshold to categorize people
as outgroup members is amplified when people are threatened. Research has shown that a
large subset of the stereotypes associated with Blacks relate specifically to danger and
threat (Donders et al., 2008). Greater amygdala activity is a result of exposure to Black
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compared to White faces (Cunningham et al., 2004), individuals interpret ambiguous
behaviors as more hostile when performed by Blacks compared to Whites (Duncan 1976;
Sagar & Schofield, 1980), and individuals have a tendency to interpret ambiguous
behaviors of others to be more hostile when primed with Black-related stereotypes
compared to neutral unrelated stereotype words (Devine, 1989). We predict that when
threat is salient, people will not attune to featural variations for Blacks, but will for
Whites. When threat is not salient, we predict that people will attune to feature variations
for both Blacks and Whites.
Conversely, a study by Ackerman et al. (2006, but see Gwinn, 2014)
demonstrated the opposite effect; such that angry faces, which can be perceived as
threatening, resulted in outgroup heterogeneity bias. Participants showed greater
recognition accuracy for Black angry faces than White angry faces when shown angry
Black faces, angry White faces, neutral Black faces, and neutral White faces and later
asked to recall whether they had previously seen the faces. The researchers argue that
although self-protection may initially result in the stereotypical presumption that all
outgroup members are dangerous, there may be an additional need to differentiate
between outgroup members to determine who actually poses the greatest threat. In the
Ackerman et al. (2006) study, facial expressions were manipulated to be threatening
(angry expression) or not (neutral expression), and it is possible that the change in facial
expressions produced more stringent evaluations of features in outgroup members
compared to what an external threatening source may cause when evaluating only neutral
faces. The need for self-protection increases people’s sensitivity to racial boundaries
lowering the threshold for outgroup classification and minimizes sensitivity to features
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(Maner et al., 2012). Blacks have cultural stereotypes that associate them with threat, for
this reason it is possible that threat trumps evaluating features even though some argue
that feature-based evaluations are an automatic process. This may explain why previous
research has demonstrated null results for how threatening Black targets are evaluated.
However, for Whites who do not have cultural stereotypes associating them with threat,
sensitivity to features should not be minimized. If threat is particularly salient, we predict
that for Blacks, the racial category should receive more attention while simultaneously
minimizing attention to within-category differences, more specifically when the threat is
not overtly expressed on the individual but rather externally cued. However for Whites,
attention to featural differences should not be minimized.
Along with the attentional effects that are a result of exposure to emotional
stimuli, research suggests that there are also differences in the way people react to threat
and other emotional stimuli. Processing emotional stimuli may have adverse effects on
task performance because the brain is using resources to process that information rather
than using resources to execute a specific task (Tipples & Sharma, 2000). Again, from an
evolutionary perspective, individuals have a means of identifying threat so that they are
able to effectively respond, choosing to flee or fight. Fernandes et al. (2013) proposed
that response times vary as a function of the direction of threat (i.e., whether threat is
directed towards or away from the observer). In their study, participants viewed neutral
stimuli, photographs of a person directing a gun toward the observer (direct threat), or
photographs of a person holding a gun away from the observer (indirect threat), and then
completed a task where they indicated the orientation of peripheral bars. Responses were
recorded based on how quickly and accurately individuals responded to these bars.
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Results suggested that direction of threat modulated response times such that threat
directed towards the observer decreased response times (individuals responded faster to
the task) and threat directed away from the observer increased response times
(individuals responded slower). One explanation of these findings was that threat directed
towards the observer was judged as being more intense and inescapable, requiring a quick
response compared to threat directed away from the observer. In the current study, threat
is manipulated based on exposure to crime related articles, which can be judged as more
escapable and less intense then a firearm directed at the participant. For this reason it is
possible that response times in our task may be slower on average given the direction and
intensity of the perceived threat. However, with threat being made salient we still predict
that participants will fail to attune to featural variations in Black targets but will for
Whites.
Threat and Attention
Threatening stimuli bias our attention, such that individuals are better able to
detect threatening stimuli when surrounded by unthreatening stimuli (Öhman et al., 2001).
When participants are shown pictures of threatening versus nonthreatening stimuli,
results demonstrate a typical “pop out effect,” such that participants are quicker at
detecting fear-relevant pictures (i.e. spiders or snakes) compared to fear irrelevant
pictures (i.e. flowers or mushrooms). Though we would expect that White participants
would avoid angry Black faces that are viewed as threatening, it is possible that these
angry Black faces capture and hold individuals’ attention because attention capture and
attention holding are associated with threat.
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Attentional bias associated with threatening stimuli can be divided into ‘capture’
versus ‘holding’. Threatening stimuli may capture attention faster (i.e., immediately grab
a person’s attention) or hold (i.e., maintain a person’s attention) attention longer than
nonthreatening stimuli (Donders et al., 2008; Koster, Crombez, Verschuere, & De
Houwer, 2004). Dot probe tasks can be used to examine both attention capture and
attention holding (Posner, 1980). Recently, these attentional cueing tasks have been used
to investigate selective attention to threat. Results from these studies indicate that the
threat cue captures attention faster and holds attention longer compared to the nonthreating cue. Black faces were predicted to capture attention faster and hold attention
longer than White faces due to the prevalent amount of threat-related stereotypes
associated with Blacks (Donders et al., 2008). When making associations between Blacks
and danger, results indicate that Black faces capture attention faster and hold attention
longer than White faces.
The Present Study
The current study contributes to prior research involving differential treatment of
individuals based on their racial category and the effect of within-group variations or
prototypicality by testing the boundary condition in which prototypicality will and will
not affect judgments. We first seek to replicate previous findings that attention allocation
is racially biased, such that Black targets will grab attention faster and hold attention
longer compared to White targets as demonstrated by Donders et al. (2008). Next, we
seek to examine if prototypicality moderates racially biased attention allocation, such that
high prototypic targets exacerbate this effect while low prototypic targets lessen the effect
of racial bias on attention allocation. Lastly, we seek to examine if threat further
14
moderates the influence of prototypicality on racially biased attention allocation. More
specifically, this study seeks to investigate if threat moderates people’s attention to
within-category variation.
Our primary hypotheses include (1) when threat is made salient (e.g., when
people are feeling threatened), people should not pay attention to featural variations for
Black targets but should for White targets, as observed in the Ma and Correll (2010) firstperson shooter task and (2) when threat is not made salient, people should pay attention
to featural variations for both Black and White targets, as observed in studies such as
Blair et al., (2004). More specifically, when participants read a crime article relative to
the control article, response times for Black targets should not vary as a function of
prototypicality but should for White targets. When participants read a control article,
response times for both Black and White targets should vary as a function of
prototypicality. The reason for this being the cultural stereotypes associated with Blacks
and threat, and making threat salient might trump evaluating features within Blacks.
Because there are no cultural associations between Whites and threat, threat will have no
influence on how features are evaluated in Whites targets. However, when there is not a
threat signal, people will attend to feature variations and they will influence judgments
for both Black and White targets because nothing will be biasing participants’ attention
away from the features. Additionally we hypothesize that attention allocation is racially
biased, such that Black targets will capture attention faster and hold attention longer
compared to White targets. We also predict that prototypicality will moderate racially
biased attention allocation, such that when targets are highly prototypic of their racial
category racially biased attention allocation will be exacerbated and will be lessened
15
when the targets are low prototypic. Participants will complete a modified dot probe task
(Koster et al., 2003) which is used to measure selective attention.
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METHOD
Participants and Design
A total of 88 undergraduates (61 female, 27 males; 65 Whites, 5 Hispanics, 1
Asian, 1 Black, and 16 who identified as White and another ethnicity; Mage = 19.79)
from California State University, Northridge participated for course credit. Participation
was completely voluntary, and participants were informed that all of their responses
would be completely confidential. One participant was excluded from the analysis due to
identifying as Black. Thus, the final sample consisted of 87 undergraduates. We also ran
the same analyses using only participants who identified as White and found effects
similar to what we discuss in the results section – for that reason, we have elected to keep
all of the participants in the final analysis.
Participants were randomly assigned to read one of three fictitious newspapers
articles involving a White criminal, Black criminal or a traffic article (control) before the
attentional cueing task. Thus, the experiment involved a 3 (Threat: White-criminal article,
Black-criminal article, traffic article) × 2 (Target Race: Black, White) × 2
(Prototypicality: High, Low) × 2 (Trial Type: Valid vs. Invalid) mixed-model design,
with repeated measures on the last three factors.
Materials
Attentional Cueing Task
We measured attentional bias between Black and White faces that varied in
prototypicality (See Appendix A) using an attentional cueing task modeled after Koster et
al. (2004) and Donders et al. (2008) (See Appendix B). The task involved the following
sequence of events: first, a fixation cross appeared in the center of the computer screen
17
for 500 ms. Next, a face appeared either on the left or right side of the screen for 40 ms.
The face was a high prototypic or low prototypic Black or White male. The face then
disappeared and was masked with black and white scrambled pixels for 150 ms. A cue
then appeared on either the left or right side of the screen for 1500 ms. This cue was a
black square dot. Participants responded to the cue’s location by pressing the “a” key if
the cue appeared on the left side of the screen, or the “l” key if the cue appeared on the
right side of the screen. Correct responses were followed by “Correct!” on the middle of
the screen. If an incorrect response occurred, then “Incorrect!” appeared on the middle of
the screen accompanied by a buzzing a sound. A timeout (response latency greater than
5000 ms) was followed by “Go Faster!” on the middle of the screen, also accompanied by
a buzzing sound. For incorrect and time-outs participants did not have to fix their
previously wrong or slow response, but continued on to the next trial.
The task included two primary trial types: valid and invalid. Valid trials occurred
when the dot cue appeared on the same side of the screen as the face. If Black faces,
regardless of prototypicality, capture attention faster than White faces, participants should
respond to the dot cue especially quick on valid trials following a Black face rather than a
White face. If low White prototypic faces capture attention faster than high White
prototypic faces, participant should also respond to the dot cue especially quick on valid
trials following a low prototypic White face compared to a high prototypic White face. If
Black faces, regardless of prototypicality, hold attention longer than White faces,
participants should respond more slowly on invalid trials following a Black face rather
than a White face. If low prototypic White faces hold attention longer than high
18
prototypic White faces, participants should also respond more slowly on invalid trials
following a low prototypic White face compared to a high prototypic White face.
To ensure that participants were following instructions to the task and were
paying attention to the location of the dot cue, following Donders et al. (2008), so-called
catch trials and digit trials were included in the task. On catch trials, a face appeared, but
no dot cue followed. These trials were included to verify that participants responded to
the location of the dot cue and not the location of the face. On digit trials, following the
fixation cross, a single-digit number was presented for 100ms, after which no face or dot
cue appeared. Participants typed in the digit that they saw. These trials were included to
verify that participants maintained engagement with the task and were attending to the
fixation cross at the beginning of each trial as instructed.
The task consisted of two blocks, with the first block composed of 16 practice
trials. Block 2 was the experimental block and consisted of 200 trials. Participants saw 40
right valid, 40 right invalid, 40 left valid, 40 left invalid, 20 catch, and 20 digit trials. In
all blocks, the race of the face, the prototypicality, and the dot cue’s location were
counterbalanced across trial type. Within each block, trials appeared in random order.
Newspaper Articles
To manipulate threat, following Correll et al. (2007) and Sim, Correll, and Sadler
(2008), articles describing violent armed robberies were included in the experimental task.
In the Black-criminal condition, the article asserted that a pair of Black males between
the ages of 30 and 35 committed the robberies (See Appendix C). In the White-criminal
condition, the content of the article was identical except the suspects were described as
White males (See Appendix D). The traffic article described traffic congestion and
19
researchers attempts to reduce traffic related issues (See Appendix E). This article served
as the control.
Procedure
Participants were greeted by a White male experimenter, who was blind to the
purpose of the experiment, and sat at one of six cubicles equipped with a desktop
computer and a keyboard. The study was described as a memory task where participants
would be asked to view words and/or images and make classifications on the computer.
Prior to beginning the experiment participants received consent form to read and verbal
consent was obtained.
All participants were randomly assigned to a White-criminal, Black-criminal, or
traffic article condition and were given the appropriate article and 5 minutes to the study
the material in the article. Participants were told that there would be a memory test later
concerning the information described in the article. After reading the article participants
completed the practice trials of the dot probe task and were given the opportunity to ask
the experimenter any questions they might have, then completed the test phase of the dot
probe task. Following the test phase, participants accessed a Qualtrics.com survey (See
Appendix F) and were given approximately 5 minutes to recall as many details as
possible about the article they previously reviewed. Lastly, participants completed a
demographic questionnaire. Participants were then debriefed, thanked for their time, and
given 4 course credits for their participation.
20
RESULTS
We excluded data from trials where participants responded either faster than
150ms (1%) or slower than 1000ms (1%) (Donders et al., 2008) or that was inaccurate
(1%) (Richeson & Trawalter, 2008). We also excluded catch and digit trials from the
analysis. Latencies from the correct responses were subjected to a natural log
transformation. Using all remaining data we first sought to test examine if attention
allocation is racially biased. We ran a repeated measures analysis of variance to test
within subject effects of race, trial type, and the race × trial type interaction. There was
no evidence for a main effect of race such that participants responses times did not
significantly vary between being exposed to Black (M = 410.43 ms, SD = 114.11) versus
White (M = 411.05 ms, SD = 118.77) target faces, F(1,87) = 0.25 , p = 0.62; d < 0.001.
There was no evidence for a main effect of trial type such that response times did not
significantly vary between valid (M = 404.93 ms, SD = 117.56) versus invalid trial types
(M = 416.60 ms, SD = 115.05), F (1,87) = 0.64, p = 0.43; d = 0.004.
Additionally, we examined a race × trial type interaction. Although Donders and
his collegaues (2008) found a non-significant race × trial type interaction, before examing
this interaction with danger-related stereotypes, we hypothesized that this interaction
would be significant such that the Black targets would capture attention faster and hold
attention longer compared to White targets. Our analysis revelaed no evidence for a race
× trial type interaction (See Appendix G, Figure 1), similar to the Donders el al. (2008)
study. Participants’ responses time did not vary as a function of the target race and the
trial type, F (1,87) = 1.07, p = 0.30; d < 0.001, for Black targets valid trial (M = 405.74
ms, SD = 111.31), Black targets invalid trial (M = 405.74 ms, SD = 116.64), White
21
targets valid (M = 404.12 ms, SD = 118.50), and White targets invalid trial (M = 418.02
ms, SD = 118.66).
Does prototypicality moderate racially biased attentional allocation?
The race × trial type × prototypicality interaction tested our hypothesis that the
magnitude of racially biased attentional allocation depends on target prototypicality. To
test this, we conducted a multilevel model in which we computed the mean slope of
response time representing this interaction, as well as race, trial type, prototypicality, race
× trial type, and the race × prototypicality interaction, and compared the means to zero
using a one-sample t-test. The within-participant regression coefficients were examined
across participants using a one-sample t-test. By doing this we were able to determine
whether or not the coefficients, on average, differed significantly from zero. For example,
in this study, did participants respond faster to high prototypic Black valid trials
compared to low prototypic Black valid trials. By comparing the average of these slopes
to zero, we are able to evaluate the reliability of an effect in our sample. For the race ×
trial type × prototypicality interaction a positive slope would indicate that racially biased
attention allocation was greater among more prototypic targets.
The analysis yielded a nonsignificant race × trial × prototypicality three-way
interaction, t(88) = -0.35, p = 0.73; d < 0.001 (See Appendix H, Figure 2). There was no
evidence that prototypicality moderated differences in response times to valid or invalid
trials for Black or White targets. The analysis did yield a significant main effect of trial
type, t(88) = 5.54, p < .001; d = 0.004. Participants responded slower to invalid trials (M
= 403.43, SD = 1.15) compared to valid trials (M = 391.51, SD = 1.14) collapsing across
race and prototypicality. The main effects of race and prototypicality, as well as the race
22
× prototypicality, trial type × prototypicality, and the race × trial type interactions were
not statistically significant, ts(88) ≤ 1.32, ps ≥ .99; ds ≥ 0.001.
Does article type further moderate the influence of prototypicality on racially biased
attention allocation?
Finally, the race × trial × prototypicality × threat interaction tested the primary
hypothesis of the current study that threat moderates people’s attention to within-category
variation. Furthermore, when there is a threat signal, people should not pay attention to
featural variations for Black targets but should for White targets, and when there is not a
threat signal, people should pay attention to featural variations for both Black and White
targets. To test this hypothesis, the slopes obtained from the multilevel model above were
compared across the three article types using contrast codes. Our first set of contrast
codes compared participants who read the control article to those who read a crime article
(control = -2, Black crime = +1, White crime = +1). The second set of codes compared
participants in the two crime article conditions (control = 0, Black crime = -1, White
crime = +1). We then regressed each of the mean slopes from the above analysis on
these two sets of contrast codes.
Overall, this analysis did not yielded a statistically significant race × trial type ×
prototypicality × threat four-way interaction, F(2,85) = 0.13, p = 0.88; d < 0.001 (See
Appendix I, Figure 3). This suggests that when participants read a crime related article
relative to a control article, there was no evidence for significant differences in response
times, F(1,85) = 0.14, p = 0.71; d = 0.002. Additionally, when participants read a Black
criminal article compared to a White criminal article there was no evidence for
significant differences in response times, F(1,85) = 0.11, p = 0.74; d = 0.001. When
23
threat was salient prototypicality did not influence response times for Black or White
targets for either valid or invalid trial types. The main effects of race, trial type, and
prototypicality, as well as the race × prototypicality, trial type × prototypicality, and the
race × trial type interactions were not statistically significant, Fs(2,85) ≤ 1.17, ps ≥ 0.72;
ds ≥ 0.035.
24
DISCUSSION
Researchers have suggested that Black faces are evaluated in similar ways to
threat-relevant stimuli (Levin, 2000; Öhman et al., 2001). Donders et al. (2008) and
Richeson and Trawalter (2008) provided evidence that Black faces bias attention due to
the danger stereotypes with which they are associated. These stereotypes predicted
increases in attentional capture for Black targets compared to White targets and increases
in attentional holding for Black targets compared to White targets. We sought to examine
the effect of racially biased attention allocation. Participants completed a dot probe task
responding to the location of a dot probe after being exposed to a Black or White target
prime. Similar to Donders et al. (2008) we failed to replicate a basic race ✕ trial type
interaction effect. Participants did not demonstrate increases in attentional capture or
attentional holding for Black targets compared to White targets. While some researchers
have demonstrated ingroup bias in attention such that White participants spend more time
looking at White faces compared to Black faces (Eberhardt, Goff, Purdie, & Davies,
2004), we did not find racially biased attention allocation. Similar to the Donders and
colleagues (2008) study we exposed participants to the target faces for an extremely brief
period of time (40 ms) while Eberhardt and colleagues (2004) presented faces for a much
longer time (450 ms). While our purpose for having short exposure time to the faces was
to control for participants’ ability to detect the purpose of the task and to demonstrate that
participants are able to engage in quick threat detection, this may have inhibited
participants’ ability to truly process and differentiate between faces.
In addition to examining if attention allocation is racially biased, our study aimed
to examine the influence of prototypicality on racially biased attention allocation.
25
Previous research has shown prototypicality effects such that individuals list more
stereotypic traits to targets that look more prototypic of their racial category compared to
targets that are less prototypic (Blair et al., 2002; Maddox & Gray, 2002). We
hypothesized that prototypicality moderated racially biased attention allocation. High
prototypic targets should exacerbate racially biased attention allocation and low
prototypic target should lessen the effect of racially biased attention allocation. More
specifically, participants should demonstrate increases in attentional capture and
attentional holding for high prototypic Blacks compared to low prototypic Blacks, and
increases in attentional capture and attentional holding for low prototypic Whites
compared to high prototypic WhitesWe found that prototypicality did not moderate
participants’ response times to Black or White targets for valid or invalid trials. Again,
we believe that the short exposure time may have inhibited participants’ ability to process
the faces in a more pronounced way. In first-person shooter tasks where participants are
making shoot/don’t shoot decision, racial bias depended on how prototypic targets were
(Ma & Correll, 2011). In their study there was a significant effect of prototypicality on
racial bias, unlike what we found in the current study. Similar to what we examine with
the Eberhardt and colleagues (2004) study, in the Ma and Correll (2011) study, the
amount of time participants saw the targets was much greater than 40 ms. Again,
suggesting that the amount of time we exposed participants to target faces did not allow
for deep processing.
Previous research has also examined feature-based stereotyping within Black and
White targets in relation to threat (Ma & Correll, 2010; Ronquillo et al., 2007). What we
find is that in these studies people are responding to featural differences in Whites but not
26
in Blacks. We sought to contribute to a body of research aimed at understanding featurebased stereotyping by examining the contexts that are causing people to evaluate features
in Whites but not in Blacks, the context being threat. Participants were assigned to a
crime article condition (Black criminal vs. White criminal) or a control condition (traffic
article). The crime articles were used to induce a sense of threat within the participants
(Sim et al., 2013). Threatening situations increase outgroup categorization, which leads to
outgroup homogeneity (Judd & Park, 1988). When this happens, you have participants
failing to look at the features of the outgroup. Also, Blacks have cultural stereotypes that
are associated with threat. Given these phenomenon, since certain races are
stereotypically regarded as threatening, we hypothesized that in situations where threat is
salient for Blacks, the racial category should receive more attention than how prototypic a
person is of that race. Additionally, for Whites, prototypicality should always matter.
Threat did not further moderate the influence of prototypicality on racially biased
attention allocation in the present study. Correll and colleagues (2007) study suggested
that reinforcing and undermining racial stereotypes linking Blacks to danger and crime
influence racial bias. In first-person shooter tasks reading a Black criminal article relative
to a White criminal article lead to an increase in decisions to shoot Black compared to
White targets. There was no evidence of racial bias in decisions to shoot when reading
the White criminal article. While the crime articles may have made threat salient, this
effect may have been trumped by participants’ inability to process and individuate the
faces, resulting in no evidence for significant differences in response times.
Limitations
27
As mentioned previously, a large part of our null findings may be due to
participants’ brief exposure to target faces (40 ms). While humans are experts at face
recognition and are able to process faces quickly (Bradshaw & Wallace, 1971; Farah,
Wilson, Drain, Tanaka, 1998) it is possible that 40 ms did not allow for deep processing
of featural variations. Researchers have found racially biased attention allocation when
faces have been displayed for only 30 ms (Richeson & Trawalter, 2008), however these
findings also take into account individual differences that can dramatically affect
judgments and behavior. Additionally, our images were gray-scaled and this may have
caused people to rely more on skin complexion allowing participants to differentiate
among the race of the targets, but not the degree of prototypicality in terms of other
features (e.g., lip fullness, nose width, etc.). Because skin tone is a key indicator of race
and prototypicality, de-saturating these images may have limited our ability to find
effects.
Another possible explanation involves the demographics of our sample. Our
sample consisted of 65 Whites, 5 Hispanics, 1 Asian, and 16 participants who identified
as White and one or more other ethnicities. Previous research has primarily used only
White samples; however, we choose to include other ethnicities in order to have a greater
sample size. The analysis excluding non-White participants also yielded non-significant
results. It is possible that racial differences as well as other individual differences may
contribute to how participants responded to targets and the task.
Donders and his colleagues (2008) examined individual differences in danger
stereotypes and their relation to attention. They found that individuals who possessed
higher accessibility to danger stereotypes on the Extrinsic Affective Simon Task (EAST)
28
exhibited faster attentional capture to Black targets compared to White targets.
Additionally, Black faces held attention marginally longer compared to White faces, for
participants with highly accessible danger stereotypes. Being external motivated to
respond without prejudice towards Blacks is also associated with biased patterns of
attention allocation that suggest a threat response to Black targets (Richeson & Trawalter,
2008). Using a dot probe attentional bias paradigm, results demonstrated that participants
who were highly externally motivated to respond without prejudice revealed an
attentional bias towards neutral Black faces presented for 30 ms, and attentional bias
away from neutral Black faces presented for 450 ms. Again, individual differences such
as danger stereotype associations and motivation to respond without prejudice affects
attention allocation. In the current study, we did not account for individual differences on
either of these two factors that may have regulated, controlled, or exacerbated racial bias
in attention allocation.
Additionally, our sample consisted of individuals who have high interracial
contact, which has the potential to make replicating racially biased effects challenging.
The Intergroup Contact Theory posses that prejudice and stereotypes can be reduced
through interpersonal contact between ingroup and outgroup members (Allport, 1954;
Dasgupta & Rivera, 2008; Pettigrew & Troop, 2000). California State University is a
fairly diverse campus and the high interracial contact amongst students may eliminate
some of the danger stereotypes that are associated with Blacks. If our participants have
low levels of danger stereotype accessibility this could explain why we found no
evidence of racially biased attentional capture or holding (Donders et al., 2008).
Future Directions
29
Although we failed to replicate previous findings demonstrating no evidence for
racially biased attention allocation and found no evidence that prototypicality moderated
racially biased attention allocation nor evidence that threat further moderated this effect,
it is important to investigate what factors moderate the importance of features when
evaluating others. Although research generally finds that features influence judgments
over and above the effects of category membership this influence is contingent upon
social category and context.
A promising solution includes increasing the amount of time participants are
exposed to target faces. Increasing exposure may allow for deeper processing and
individuating of target faces. Additionally, it is important to consider individual
differences such as danger stereotype accessibility and motivation to respond without
prejudice. These differences may moderate the influence of prototypicality on racially
biased attention in contexts where threat is or is not made salient. Likewise, it would be
informative to examine if Black individuals respond in ways similar to their White
counterparts. While there is some research that suggests they do (Ma & Correll, 2011;
Ronquillo et al., 2007), examining this directly can address whether feature-based
stereotyping effects are driven by Whites because of outgroup homogeneity or something
else.
What situations influence social evaluations and whether or not people attune to
featural variations has significant implications for the field of social psychology and the
real world. With regards to the law, feature-based stereotyping has effects on court
decisions such as whether or not an individual is likely to receive the death penalty
(Eberhardt et al., 2006) and can influence the accuracy of eyewitness identifications
30
(innocenceproject.org). If people fail to attune to featural variations in Blacks when
threat is made salient, then in situations where people are having to identify a suspect
they may rely more heavily on categorical information rather than specific feautral
information and in turn be less accurate in their identifications. Knowing the contexts that
drive people to attune to featural variations in Whites and not in Black has the potential to
improve this bias by making people aware that their attention allocation is racially biased.
Gaining a better understanding of feature-based stereotyping, when the effects manifest,
and for whom can provide us with further insight into how people come to make
judgments and evaluations of others.
31
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Sim, J. J., Correll, J., & Sadler, M. S. (2013). Understanding Police and Expert
Performance When Training Attenuates (vs. Exacerbates) Stereotypic Bias in the
Decision to Shoot. Personality and Social Psychology bulletin, 39, 291-304. doi:
10.1177/0146167212473157
Tipples, J., & Sharma, D. (2000). Orienting to exogenous cues and attentional bias to
affective pictures reflect separate processes. British Journal of Psychology, 91,
87-97. doi: 10.1348/000712600161691
Wittenbrink, B., Judd, C., & Park, B. (1997). Evidence for racial prejudice at the implicit
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Wittenbrink, B., Judd, C., & Park, B. (2001). Spontaneous Prejudice In Context:
Variability In Automatically Activated Attitudes. Journal of Personality and
Social Psychology, 815-827. doi: 10.1037/0022-3514.81.5.815
38
Appendix A
39
40
Appendix B
Representation of the average trial type in the attentional cueing task (Donders et al., 2008)
41
Appendix C
Instructions
PNUM.:
Please read the following article and remember as much about it as possible.
Later on in the study, your task will be to recall the events or ideas that were mentioned,
when and where they occurred, and who was involved. You should also try to remember
any pictures that are presented in the article.
You may read the article more than once, but you will only have five minutes to study it.
Please be reminded that you will need to keep the details of the article in mind for the
next hour.
If you have any questions, please call the experimenter now. Otherwise, you may begin.
42
Police Search For Gunmen In Armed Robbery Spree
Robbers Strike 3 Parking Lots, Shoot 2 Shoppers
Police are looking for two men who committed three violent armed robberies in Orange
County. Two shoppers were shot and wounded, WESH NewsChannel 2 reported.
The robberies happened in parking lots of different stores. The first happened around 6
p.m. Sunday outside an ABC liquor store at Edgewater and Lee Road. The men robbed a
man at gunpoint as he came out of the store, but he was not hurt.
Thirty minutes later at the Best Buy in Ocoee, the robbers snatched Lauren Thompson’s
purse and shot her in the right thigh.
Thompson said it happened as she approached her vehicle. As soon as the man grabbed
her, she said she immediately complied with his demands, but before she could get the
purse off her arm, he shot her anyway.
Thompson said she is thankful to be alive.
The laid-off electrician said this is the second time she's been shot. The first time, she
was accidentally shot in the shoulder by a Hilti gun -- an electrician's tool. She said that
time, the wound was a hairline from hitting the main artery to her heart.
Thompson advised other shoppers to be more careful. She said when she came out of the
Best Buy, she was not paying attention to her surroundings and said that's why she was
caught off guard.
"I feel if you don't catch them, they're going to continue, and somebody is going to get
killed," Thompson told NewsChannel 2. "When this guy talked to me, there was
something in his voice that was so horrid and cold. And I know that he's going to do it
again."
43
The third incident occurred on Monday at a Super Wal-Mart on South Kirkman Road in
Orlando.
According to police, Jonathon Miles was loading his car when two men approached him,
one from either side. They demanded his wallet and, when he hesitated, shot him in the
hip. One of the men then took Miles’ wallet from his pocket.
Miles was taken to Princeton Hospital in Orlando where he is stable condition.
Lt. Adam Frisk of the Orlando Police Department said the suspects should be considered
armed and dangerous.
“These men are just walking up to people and robbing them,” said Frisk. “What’s more,
they seem only too willing to hurt their victims. They really had no reason to shoot.”
Frisk advised shoppers not to resist armed robbery.
Police are looking for a red Chevrolet Cavalier with damage to the right side. The
department released composite sketches of the suspects.
The first suspect was described as a black male, 30 to 35 years old, 5 feet 11 inches tall
and weighing about 180 pounds. He wore a dark blue shirt and had a goatee.
The second suspect was described as a black male, 30 to 35 years old, 5 feet 10 inches
tall and weighing about 190 pounds. He wore a black sweat shirt and dark baseball cap.
If you have any information, call 1-800-423-TIPS (8477).
44
Appendix D
PNUM.:
Instructions
Please read the following article and remember as much about it as possible.
Later on in the study, your task will be to recall the events or ideas that were mentioned,
when and where they occurred, and who was involved. You should also try to remember
any pictures that are presented in the article.
You may read the article more than once, but you will only have five minutes to study it.
Please be reminded that you will need to keep the details of the article in mind for the
next hour.
If you have any questions, please call the experimenter now. Otherwise, you may begin.
45
Police Search For Gunmen In Armed Robbery Spree
Robbers Strike 3 Parking Lots, Shoot 2 Shoppers
Police are looking for two men who committed three violent armed robberies in Orange
County. Two shoppers were shot and wounded, WESH NewsChannel 2 reported.
The robberies happened in parking lots of different stores. The first happened around 6
p.m. Sunday outside an ABC liquor store at Edgewater and Lee Road. The men robbed a
man at gunpoint as he came out of the store, but he was not hurt.
Thirty minutes later at the Best Buy in Ocoee, the robbers snatched Lauren Thompson’s
purse and shot her in the right thigh.
Thompson said it happened as she approached her vehicle. As soon as the man grabbed
her, she said she immediately complied with his demands, but before she could get the
purse off her arm, he shot her anyway.
Thompson said she is thankful to be alive.
The laid-off electrician said this is the second time she's been shot. The first time, she
was accidentally shot in the shoulder by a Hilti gun -- an electrician's tool. She said that
time, the wound was a hairline from hitting the main artery to her heart.
Thompson advised other shoppers to be more careful. She said when she came out of the
Best Buy, she was not paying attention to her surroundings and said that's why she was
caught off guard.
"I feel if you don't catch them, they're going to continue, and somebody is going to get
killed," Thompson told NewsChannel 2. "When this guy talked to me, there was
something in his voice that was so horrid and cold. And I know that he's going to do it
again."
46
The third incident occurred on Monday at a Super Wal-Mart on South Kirkman Road in
Orlando.
According to police, Jonathon Miles was loading his car when two men approached him,
one from either side. They demanded his wallet and, when he hesitated, shot him in the
hip. One of the men then took Miles’ wallet from his pocket.
Miles was taken to Princeton Hospital in Orlando where he is stable condition.
Lt. Adam Frisk of the Orlando Police Department said the suspects should be considered
armed and dangerous.
“These men are just walking up to people and robbing them,” said Frisk. “What’s more,
they seem only too willing to hurt their victims. They really had no reason to shoot.”
Frisk advised shoppers not to resist armed robbery.
Police are looking for a red Chevrolet Cavalier with damage to the right side. The
department released composite sketches of the suspects.
The first suspect was described as a white male, 30 to 35 years old, 5 feet 11 inches tall
and weighing about 180 pounds. He wore a dark blue shirt and had a goatee.
The second suspect was described as a white male, 30 to 35 years old, 5 feet 10 inches
tall and weighing about 190 pounds. He wore a black sweat shirt and dark baseball cap.
If you have any information, call 1-800-423-TIPS (8477).
47
Appendix E
Instructions
PNUM.:
Please read the following article and remember as much about it as possible.
Later on in the study, your task will be to recall the events or ideas that were mentioned,
when and where they occurred, and who was involved. You should also try to remember
any pictures that are presented in the article.
You may read the article more than once, but you will only have five minutes to study it.
Please be reminded that you will need to keep the details of the article in mind for the
next hour.
If you have any questions, please call the experimenter now. Otherwise, you may begin.
48
Researchers Search For Solutions In Traffic Congestion
Traffic Strikes, Congestion Increases With Delays
Researchers are looking for ways to reduce traffic congestion in Orange County. Many
commuters are continually affected by the traffic, WESH NewsChannel 2 reported.
Traffic congestion often occurs on many freeways primarily during two times of the day.
The first wave of congestion happens around 7 in the morning. The second wave happens
towards 4 at night. If you live in a large city or any area where there are a lot of
commuters on the road, then you are likely to experience this congestion.
Thirty minutes is usually the amount of time it takes to travel a distance of 5 miles when
traveling during the traffic waves.
Lauren Thompson, a frequent commuter, said that this happens almost daily. As soon as
she enters her car, she said she immediately hopes for a lack of delay in reaching her
destination, but before researching the freeway on ramp, there is traffic.
Thompson said she is thankful there are usually no accidents.
The unemployed electrician said this is the second time she's lost a job due to traffic
congestion. The first time, she was stuck in gridlock traffic for almost 4 hours –
becoming 3 hours late for work. She said that time, the traffic almost gave her an anxiety
attack.
Thompson advised other commuters to be more careful. She said when she lost her job
the first time, she was not paying attention to traffic reports and said that's why she was
caught off guard.
"I feel if you don’t pose a solution, traffic congestion is going to continure, and
somebody is going to become unemployed," Thompson told NewsChannel 2. "When I
49
am on the freeway looking at other commuters, there is something in their faces that is
horrid and overwhelmed. And I know that they are at risk of losing a job too."
The third incident occurred on Monday for a Super Wal-Mart employee on South
Kirkman Road in Orlando.
According to coworkers, Jonathon Miles was running to clock in for his shift when the
managers asked him to come to the back. They demanded he leave immediately, when he
tried to explain that he was late due to traffic, escorted him out by the shoulders. One of
the managers had even heard about the traffic congestion on the freeway.
Miles has since found a job closer to his home where he does not have to face the traffic
congestion.
CHP Adam Frisk of the Orlando California Highway Patrol said that commuters should
consider carpooling of purchasing a FastPass for certain driving lanes.
“The degree of traffic congestion is really impacting peoples’ lives,” said Frisk. “What’s
more, traffic congestion seems to only be getting worse. There really needs to be a
solution to traffic congestion.” Frisk advised commuters to listen to traffic broadcast
before going on the freeway.
Researchers are looking for possible solutions to eliminate traffic congestion. They have
released temporary solutions that will help to decrease traffic congestion for now.
The first solution was described as using alternate modes of transportation. These include
subways and buses.
The second solution was described as using the right lanes for slower traveling speeds
and the left lanes for faster traveling speeds. It is still important to maintain area speed
limits.
If you have any suggestions, call 1-800-423-TIPS (8477).
50
Appendix F
Memory Test and Demographics Spring 2015
MT Please list as many details as possible about the article you read.
51
Thank you for participating in this study. Please take a moment to answer a few questions
about yourself.
Q1 What is your participant number? (not your student ID number)
Q2 What is your age? Please respond with the actual number.
Q7 Please indicate the gender you identify with.
m Male (1)
m Female (2)
m Other (3) ____________________
Q4 Please indicate your race or ethnicity (select all that apply).
q
q
q
q
q
q
q
White (1)
Hispanic/ Latino(a) (2)
Black/ African American (3)
Native American/ American Indian (4)
Asian (5)
Pacific Islander (6)
Other (7) ____________________
52
Debriefing
Thank you for your participation in this study. The purpose of the present research is to
examine the situations in which people make cue-based versus category-based decisions
when categorizing Black and White targets in threat inducing versus non-threat inducing
situations. Studies such as Blair et al., (2002) found that targets who were more
prototypical of Blacks were judged to have more stereotypical traits of African
Americans than individuals that were less prototypical – here showing that evaluations
were cue-based. Other studies such as Lierberman et al., (2005) used fMRIs to show that
there is a greater amygdala response to African American faces than to Caucasian
American faces demonstrating a more category-based versus cue-based evaluation
approach. Limited research has been done to explore the why in certain situations racial
prototypicality matters while in others it does not. This study aims to test whether the
differences in evaluations of African Americans based on their physical features or racial
category alone is influenced by the contexts in which these individuals are being
evaluated, such as those involving threat versus those that do not.
In this study, all participants were asked to complete computer tasks aimed to measure
selection attention to threat for Blacks rated either high in prototypicality, average, or low
in prototypicality and Whites rated high in prototypicality, average, or low in
prototypicality. The computer task was modified off of the Koster et al., (2003) study.
Participants completed a dot probe task. Paticipants were shown a screen with either a
“High Prototypic Black,” “ Average Black,” “Low Prototypic Black,” “High Prototypic
White,” “ Average White,” or “Low Prototypic White,” face, a masked screen, and then a
screen with a dot. Trails were either be congruent/valid or incongruent/invalid. This task
measures selection attention to threat based on racial categories. It is hypothesized that it
for congruent trails, individuals will be faster responding to the dot probe task then in
incongruent trails. However, they will be even faster for “High Prototypic Black” faces
then any other face. For incongruent trails, it is hypothesized that in general it should take
participants longer to disengage their attention from where the target photo appeared then
in the congruent trial. However, the disengagement will take even longer for “High
Prototypic Black” faces then any other face.
Again, we would like to thank you for your participation in this project. If you have any
questions or would like to know the results of the study when it is completed, you can
email the student researcher, Aerielle Allen, at [email protected] or the
principle investigator, Debbie Ma, at [email protected]. If you have felt any
discomfort in response to participating in this study, please feel free to contact the CSUN
University Counseling Services for help. They can be reached at 818-677-1200.
53
Finally, we ask that you do not discuss this experiment with other people. Prior
knowledge of the intent of this study might affect how individuals respond and perform.
Your help in this regard is incredibly important.
54
Appendix G
Figure 1. Representation of the race × trial type interaction.
Average Slope of Response Times Race x Trial Type
Interaction
6.4 6.3 6.2 6.1 6 5.9 5.8 5.7 5.6 5.5 5.4 Black White Valid Invalid Trial Type
55
Appendix H
Average Slope of Response Times
Figure 2. Representation of the race × trial type × prototypicality interaction for valid
trial type and invalid trial type.
Race x Valid Trial x Prototypicality
Interaction
6 5.99 5.98 Black 5.97 White 5.96 5.95 Low High Average Slope of Response Times
Prototypicality
Race x Invalid Trial x Prototypicality
Interaction
6 5.99 5.98 Black 5.97 White 5.96 5.95 Low High Prototypicality
56
Appendix I
Figure 3. Representation of the race × trial type × prototypicality × threat interaction for
Average Slope of Response Times
valid trial type and invalid trial type.
Race x Valid Trial x Prototypicality x Threat
Interaction
6.04 6.02 6 5.98 5.96 5.94 5.92 5.9 5.88 White Criminal Black Criminal TrafFic (Control) Low Black Low White High Black High White Average Slope of Response Times
Race and Prototypicality
Race x Invalid Trial x Prototypicality x Threat
Interaction
6.08 6.06 6.04 6.02 6 5.98 5.96 5.94 5.92 5.9 White Criminal Black Criminal TrafFic (Control) Low Black Low White High Black High White Race and Prototypicality
Figure 4. Representation of the race × trial type × prototypicality × threat interaction for
valid trial type and invalid trial type.
57