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ADJUSTABLE ALARMS: THE PERSONALITY AND SITUATION VARIABLES
THAT MODERATE THE DETECTION OF SOCIAL EXCLUSION

By

Arfan Qureshi

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
MASTERS OF ARTS
Department of Psychology

2006

ABSTRACT

ADJUSTABLE ALARMS: THE PERSONALITY AND SITUATION VARIABLES
THAT MODERATE THE DETECTION OF SOCIAL EXCLUSION

By

Arfan Qureshi
Like fire, social exclusion is a potentially damaging element that threatens fundamental
human needs. This suggests that, like a smoke alarm, the detection of the signs of social
exclusion is critical in avoiding this potential for destruction. This study examines
whether this “alarm” for social exclusion is adjustable according to personality and/or
situation. The results suggest little, if any, personality or situational moderation in the
detection of social exclusion. Possible theoretical and methodological reasons for these

patterns of results are discussed.

To my mother and father, for their endless love and sacrifices

iii

ACKNOWLEDGEMENTS

I would like to express my sincere appreciation to all the people that assisted in the
formulation, design, and data collection of this study, as well as the preparation of this
manuscript. I would particularly like to thank Professors Cheryl Kaiser and Joel
Aronoff for their helpful suggestions, my fellow graduate colleagues for their moral
support, and the many undergraduate research assistants that worked on various phases

of this project.

A special thanks is due to my advisor, committee chair, and mentor, Norbert L. Kerr.

iv

TABLE OF CONTENTS

LIST OF TABLES ................................................................................. vi
LIST OF FIGURES .............................................................................. viii
INTRODUCTION ................................................................................ l
METHODS ....................................................................................... 22
RESULTS ........................................................................................ 28
DISCUSSION .................................................................................... 42
APPENDICES ................................................................................... 50
REFERENCES .................................................................................. 74

LIST OF TABLES

Table 1. Personality Scale lntercorrelations .................................................. 30
Table 2. Factor loadings for four personality measures ..................................... 30

Table 3. Descriptive Statistics for Overall Accuracy, Reaction Time, and Personality
measures ............................................................................................ 31

Table 4. Descriptive Statistics for Accuracy, Reaction Time, and Personality measures
for participants in the high priming threat condition ......................................... 3]

Table 5. Descriptive Statistics for Accuracy, Reaction Time, and Personality measures

for participants in the low priming threat condition .......................................... 32
Table 6. Regression Analysis on EN Transformed RSQ Accuracy Scores ............... 53
Table 7. Regression Analysis on EN Untransformed RSQ Accuracy Scores ............ 53
Table 8. Regression Analysis on EN Transformed RSQ Reaction Time Scores. . . . . ....53

Table 9. Regression Analysis on EN Untransformed RSQ Reaction Time Scores ...... 54
Table 10. Regression Analysis on EE Transformed RSQ Accuracy Scores .............. 54
Table 11. Regression Analysis on EE Untransformed RSQ Accuracy Scores ............ 54
Table 12. Regression Analysis on EE Transformed RSQ Reaction Time Scores ........ 55
Table 13. Regression Analysis on EE Untransformed RSQ Reaction Time Scores.....55
Table 14. Regression Analysis on EN Transformed NTB Accuracy Scores ............. 55
Table 15. Regression Analysis on EN Untransformed NTB Accuracy Scores .......... 56
Table 16. Regression Analysis on EN Transformed NTB Reaction Time Scores ....... 56
Table I7. Regression Analysis on EN Untransformed NTB Reaction Time Scores.....56
Table I8. Regression Analysis on EE Transformed NTB Accuracy Scores .............. 57
Table 19. Regression Analysis on EE Untransformed NTB Accuracy Scores ........... 57

Table 20. Regression Analysis on EE Transformed NTB Reaction Time Scores ........ 57

vi

Table 21.

Table 22.

Table 23.

Table 24.

Table 25.

Table 26.

Table 27.

Table 28.

Table 29.

Table 30.

Table 3].
Scores. . ..

Table 32.
Scores. . ..

Table 33.
Scores... .

Table 34.

Table 35.

Table 36.
Scores. . ..

Table 37.
Scores. . ..

Regression Analysis on EE Untransformed NTB Reaction Time Scores. . ...58

Regression Analysis on EN Transformed ASQ Accuracy Scores .............. 58
Regression Analysis on EN Untransformed ASQ Accuracy Scores .......... 58
Regression Analysis on EN Transformed ASQ Reaction Time Scores ....... 59

Regression Analysis on EN Untransformed ASQ Reaction Time Scores. ....59

Regression Analysis on EE Transformed ASQ Accuracy Scores .............. 59
Regression Analysis on EE Untransformed ASQ Accuracy Scores ........... 60
Regression Analysis on EE Transformed ASQ Reaction Time Scores ........ 6O
Regression Analysis on EE Untransformed ASQ Reaction Time Scores. ....60

Regression Analysis on EN Transformed Dependency Accuracy Scores. ....61

Regression Analysis on EN Untransformed Dependency Accuracy

........................................................................................... 6]
Regression Analysis on EN Transformed Dependency Reaction Time
........................................................................................... 61
Regression Analysis on EN Untransformed Dependency Reaction Time
........................................................................................... 62

Regression Analysis on EE Transformed Dependency Accuracy Scores... . .62
Regression Analysis on EE Untransformed Dependency Accuracy Scores..62

Regression Analysis on EE Transformed Dependency Reaction Time
........................................................................................... 63

Regression Analysis on EE Untransformed Dependency Reaction Time
........................................................................................... 63

vii

LIST OF FIGURES

Figure 1. Examples of stimulus displays used by Fox et al. (2000) ....................... 18
Figure 2. Schematic facial Stimuli Used ...................................................... 25
Figure 3. Example of 8 —person Happy in Neutral Display ................................. 25
Figure 4. Scree plot for Factor Analysis of 4 Personality measures ....................... 29
Figure 5. Reaction time means for Overall Analysis ........................................ 34
Figure 6. Accuracy Means for Overall Analysis ............................................. 34
Figure 7. Target x RSQ interaction of EN Analysis on Accuracy Scores ................ 39

viii

Adjustable Alarms:
The Personality and Situational Variables that Moderate the Detection of Social

Exclusion

If you wanted to protect your house from fire, then prudence would dictate that you
invest in a smoke alarm. Because fire may be damaging to things that are important to
you (e. g., your life, family, and property), you would want something to detect any sign
of fire (e. g., smoke) and alert you to do something about it (e. g., evacuate yourself and
family from the house, call the fire department, etc.). Although this is not a study of
fire safety, this may very well provide us with a useful metaphor in which we can
organize our thinking with regards to our true focus--detection and coping with social
exclusion. Social exclusion, like fire, is potentially damaging to a wide range of
outcomes and even fundamental human needs, and people have many different ways of
coping with it. However, to cope with a threat, we first need to detect it.

Fulfilling basic human needs is critical to survival. In Maslow’s famed hierarchy of
needs, the most basic physiological needs of food and water are followed by the
psychological needs for safety and belonging. Though few would dispute that
physiological needs or the psychological need to feel physically safe are primary and
fundamental, the fundamental nature of belonging may not be as obvious. Although
Maslow’s assertion that belonging is a fimdamental human need did not come with
empirical data or a review of past research, the accumulation of empirical studies since
then overwhelmingly indicates that this status is properly deserved. Particularly

relevant is Baumeister and Leary’s (1995) seminal paper in which they argue that the

empirical data does indeed indicate that the need to belong is a fundamental need. One
of their criteria was that the lack of need fulfillment should lead to extremely aversive
and deleterious consequences. Indeed, the lack of meeting belongingness needs is
associated with anxiety (Baumeister & Tice, 1990; Leary 1990), loneliness (Perlman &
Peplau, 1984), anger (Williams, Shore, & Grahe, 1998), anti-social and self-defeating
behavior (Twenge et a1, 2001) as well as a host of other negative consequences.

The nature of the regulatory systems governing the detection and management of
resources for maintaining need fulfillment is better understood in the physiological
domain. For our purposes, the crucial need to detect a threat to need fulfillment is
especially relevant. If we never feel hungry, we will starve. If we never feel thirsty,
we would dehydrate. Similarly, if we never felt fear, we may not be motivated to
avoid danger. The nature of the detection of social exclusion, however, is not as well
understood.

In terms of the existing literature, the coping stage has been much more heavily
researched than the detection stage. We know much more about how people deal with
this social “fire”, however we know little of if and how people perceive it early enough
to intervene and avert social conflagration. Investigation of the detection of social
exclusion has begun only recently. For example, one interesting question recently
being grappled with is whether the detection of and/or coping with social exclusion is
moderated by individual or situational factors. That is, can personality or situation lead
people to be differentially sensitive to signs of social exclusion? Two theories are
particularly worthy of note in this regard. Firstly, Williams and Zadro (2004) theorize

that because of the potentially high costs of failing to detect social rejection or

exclusion, this detection process should have evolved to be highly sensitive and
efficient. The maladaptive nature of slow detection for anyone would thus suggest that
people should detect exclusion quickly regardless of personality traits, situational
factors or cultural background. They further theorize that it is only after the detection
of exclusion, in the coping phase, that individual as well as situational differences
manifest themselves.

The studies done by Williams, Zadro, and their colleagues tend to use one of two
paradigms to induce ostracism. One paradigm is a live ball toss manipulation in which
participants are put in a room with two other confederates, and one of the confederates
begins a game of ball toss that is seemingly unrelated to the experiment. Ostracism is
manipulated by either including the participant by tossing them the ball roughly one-
third of the time, or excluding the participant by giving them a few initial tosses
followed by no tosses or eye contact for the next few minutes. This manipulation,
however, is costly and requires trained confederates, so a computer program, called
Cyberball, was developed that would not require confederates. Like the live version,
Cyberball is a ball tossing game in which one is ostracized by not being tossed the ball,
however the ball is tossed to a certain participant by pressing a certain key, and the
other participants’ images are computer-generated.

Using these two ostracism paradigms, Williams and colleagues have a line of
research in support of their ideas concerning detection of coping with exclusion.
Firstly, they have some evidence in support of the hypothesis that coping with social
exclusion is indeed moderated by situational factors and individual differences. For

example, when participating in a group task following an ostracism experience, females

tend to socially compensate while males do not (Williams & Sommer, 1997). Also, if
one way of improving inclusionary status is conformity, people who have been
ostracized by an out-group member tend to conform less than if ostracized by an in-
group member (Williams et al. 2000). People not given control over an aversive noise
blast tend to be more aggressive than those given control (Warburton, Williams, and
Cairns, 2004). And, with regards to a personality, people with social phobia tend to
rebound to normal feelings of inclusion following an ostracism experience slower than
those without it (Borland, Richardson, and Zadro, 2003).

With regards to the hypothesized invariant nature of detection, Williams et al have
found that the immediate response to ostracism is not moderated by a host of
personality variables, such as collectivism/individualism (Smith & Williams, 2003),
self-esteem (Williams et al., 2000), narcissism (Warburton, 2002), and extroversion
(N adasi, 1995). In terms of situational moderators, reactions to ostracism seem to be
similarly negative, regardless of who is doing the ostracizing. lntuitively, one would
think that participants would not care if ostracized by a group that they do want to be a
part of, or even hate. In one such study done in Australia, participants played the
Cyberball game with either in-group members (either fellow Labor or Liberal
supporters), respected out-group members (against Labor of Liberal supporters), or
hated out-group members (the Klu Klux clan) (Gonsalkorale & Williams, 2004). The
results indicated that participants reacted equally negatively in all conditions, even
when ostracized by a hated out-group member (i.e., the Klu Klux Klan) (Gonsalkorale
& Williams, 2004). In a similar test of situational moderation, participants were told

that they would be playing Cyberball against the computer. Although one would think

participants would not mind being ostracized by a computer, they still showed similar
negative reactions (Zadro, Williams, & Richardson, 2005). Another possibility for
situational moderation would be if some reasonable explanation were given for not
being tossed the ball other than being ostracized. For example, participants were told
that the given tosses in a game are scripted before the game begins (Zadro, Williams, &
Richardson, 2005), or that the other participants are unable to toss the ball the to them
(Eisenberg, Liebermann, & Williams, 2003). In the latter experiment, participants
were put into an MRI chamber and were told that their chamber was not yet connected
to the other two participant’s chambers. In both cases, the reactions were similarly
negative, suggesting no moderation by situation.

So, in sum, the work of Williams et al seems to suggest that while coping with
social exclusion is moderated by individual and situational factors, the initial reaction
(and hence, presumably the detection) of social exclusion is not moderated by such
factors, at least in his ostracism paradigm. One possible reason why there have been no
differential reactions according to personality or situation is that the ostracism
paradigm may be too strong or unambiguous a manipulation to allow variation to
emerge. That is, the type of ostracism induced by the live ball toss and Cyberball
paradigms described above may be much stronger and unmistakable than the typical
signs of ostracism or rejection that occur within human interaction. Thus, while the
experimental paradigms may be too strong to allow moderation to emerge, the more
subtle types of ostracism that occur in human interaction may demonstrate this

moderation.

Another line of research by Picket, Gardner, and their colleagues seems to concur
only partly with the Williams et al. model. The empirical as well as theoretical work of
Pickett et al (Picket & Gardner, 2004a; Picket & Gardner, 2004b; Gardner & Pickett,
2004; Gardner, Pickett, & Brewer, 2000) seems to indicate that the need state of a
person, through situational or personality influences, does influence both how one
copes with social exclusion as well as how sensitive one is to detecting it. Specifically,
they theorize that individuals have a Social Monitoring System that regulates their need
to belong by processing external information relevant to their current need state
(Gardner, Pickett, and Brewer, 2000). So, if a person has a particularly strong need to
belong, through either situational or individual difference factors, then their Social
Monitoring System (SMS) should be actively scanning the environment for
opportunities to fiJlfill their belonging needs or avoid risks of fiirther exclusion. This
line of theorizing suggests that the efficiency of detecting social exclusion is indeed
moderated by individual differences and situational factors.

The perceptual bias to negatively-valenced external information is also
demonstrated by work on automatic vigilance. In a series of studies, Pratto and John
(1991) investigated the perceptual bias to automatically evaluate semantically negative
words. The way in which they tested this is to give participants a Stroop task with trait
words that were either desirable (e. g., kind, talented) or undesirable (e. g., sadistic,
hostile). As usual in a Stroop task, participants were asked to identify the color of the
word and ignore its semantic meaning. The line of reasoning is that a hallmark of

automatic processes is that they cause interference with attentive processes despite a

person’s attempt to ignore the interfering stimuli (Shiffi’in, 1988). Thus, in terms of the
Stroop task, higher latencies indicate more interference.

In their study first study, Pratto and John (1991) indeed found higher latencies for
undesirable traits. They also examined the nature of the relationship between word
valence and latencies, and found that it tended to be more categorical than linear. That
is, rather latency linearly increasing with the undesirability of trait words, all
undesirable traits had similarly high latencies, and all desirable traits had similarly low
latencies. This interesting effect suggests that the perceptual mechanism is primarily
oriented to making negative versus positive distinctions, with a bias towards the
negative, and not necessarily gradations of positivity or negativity.

In their second study, the investigators were interested in the precise nature of the
interference. More specifically, they were interested in whether the interference could
be attributed to more attention to the semantic meaning of the undesirable trait words
(the vigilance hypothesis) or the greater cognitive effort required to avoid the semantic
meaning of the undesirable trait words (the perceptual defense hypothesis). This was
assessed by giving participants the same Stroop task and following it with a recall test.
Higher recall of undesirable trait words than desirable trait words would suggest the
vigilance hypothesis, while the opposite (i.e., lower recall of undesirable trait words
than desirable trait words) would indicate the defense hypothesis. The results of study
2 demonstrated higher recall rates for the undesirable words, and thus supported the
vigilance hypothesis.

In their last study, the investigators desired to rule out an alternative explanation that

interference to undesirable trait words are more a function of their low base rate than

valence, per se. That is, it may be that negative information tends to be perceived as
abnormal and exceptional, which would make it more informative than normal, positive
information. To rule out this alternative explanation, valence and base-rate were
unconfounded by identifying positive words that had low base rates and negative words
that had high base rates, and adding these additional words to the color-naming task.
The results indicated that valence, and not base-rate, accounted for interference, and
thus it was indeed the negativity of the stimuli that attracted attentional resources.

The idea that perception may depend on chronic individual differences is, of course,
not a new one. Out of the debates between behaviorism and psychoanalysis over
whether mental processes did nothing or everything, the “new look in perception” of
the 19403 emerged. This “new look” basically suggested that perception was not
simply a positivist registration of external reality, but that perception was instead
selectively processed and affected by internal attributes such as values, memory
accessibility, attitudes, and needs (Bruner, 1992). Some of the early work in this regard
was brought on by the advent of the tachistoscope, which allowed investigators to
precisely measure the threshold of stimulus recognition. What quickly became clear
was that threshold onset was not an all or nothing phenomenon, but was actively
affected by internal attributes such as one’s values (Postman, Bruner, McGinnies,
1948)

In terms of recent research on this question of individual differences, one study by
Pickett, Gardner, and Knowles (2004) examined the relationship between individual
differences in the need to belong and performance on two measures of social sensitivity

— a static emotional identification task and a vocal tone identification task. The facial

expression task asked participants to identify the emotional expression of series of faces
presented for one second. The vocal identification task consisted of a series of words
spoken with a positive or negative tone and asked participants to identify the valence of
the vocal tone. The results of the study indicated that participant’s scores on the need
to belong were positively associated to performance on both measures, suggesting that
individual differences in the need to belong do moderate sensitivity to social
information.

Because the facial expression and vocal identification tasks were particularly
simple, the investigators followed the study up by examining whether the same
relationship will hold if social sensitivity was measured using a more complex
empathic accuracy task. This task required participants to watch a five-minute video of
a female student talking about plans for graduate school and her performance on the
GRE. At four points throughout the video, participants were asked to guess what the
student was feeling at that moment. Accuracy was determined by having blind coders
compare their responses to the actual feelings reported by the student. The
investigators also included a vocal Stroop task in which participants heard various
words said in either a positive or negative tone and were asked to identify the valence
of the semantic meaning of the word. They predicted that participants with a higher
need to belong will have a bigger difference between congruent trials (tone and
semantic meaning match) than incongruent trials (tone and semantic meaning
mismatch), indicating that they attended more to the tone. The results again indicated a
positive association between the need to belong and performance on the empathic

accuracy task and attention to vocal tone.

In an attempt to rule out possible alternative explanations the need to belong is
associated with better performance in general, a third study was conducted assessing
performance on two non-social tasks (a math portion of the GRE and a word formation
task) in a addition to one social task (The Diagnostic Analysis of Nonverbal Behavior
2; Nowicki & Duke, 1994). The DANVA -2 is a social sensitivity measure that
presents 24 male and female faces expressing happiness, sadness, anger, and fear of
high and low intensity. Each face is presented for one second and participants are to
judge the emotional expression conveyed. The social sensitivity measure scores on the
need to belong were uncorrelated with the two non-social measures, yet were positively
correlated with performance on the DANVA-2, even when statistically controlling for
mood, loneliness, and rejection sensitivity.

In a separate, but potentially related, line of perceptual research looking at how
emotion can regulate attention, Ohman, F lykt, and Esteves (2001) demonstrated that
threatening stimuli (e. g., spiders and snakes) are found faster than non-threatening
stimuli, and, more relevant for our purposes, people with a higher fear for snakes and
spiders detected them faster than those with low fear for such stimuli. In these studies,
participants were asked to press one key if there all the stimuli in the display are the
same and another key if there is any discrepancy in the display. Such findings, suggest
that, unlike the theorizing of Williams et al, detection of social exclusion can indeed be
moderated by personality factors. There is also research showing that
psychopathology can also be related to attention. For example, study conducted by
Mogg, Bradley and Williams (1995) showed that clinically depressed people

demonstrate a bias towards depression —related words in implicit and explicit memory

10

tasks. This study also assessed clinically anxious participants, however, and did not
find a bias towards anxiety related words on either implicit or explicit measures.

In terms of moderation by situation, studies by Juth, Lundqvist, Karlsson, and
Ohman (2005) are particularly illuminating. In a series of 5 studies, the investigators
set out to examine the how particular combinations of crowds of faces conveying
varying affect may be detected with varying levels of efficiency. More specifically,
their studies build on the anger superiority effect (Hansen & Hansen, 1988) that will be
explicated in more detail further on in this discussion. Briefly, stated, the anger
superiority effect is the tendency for angry faces in happy crowds to be detected more
quickly and accurately than happy faces in angry crowds.

Juth et al. (2005) attempted to build upon this effect to show that directed faces in a
crowd of averted faces would be detected with more efficiency than averted faces in a
crowd of directed faces, for the same evolutionary reasons that will be discussed below.
They also predicted that target direction will interact with target affect such that the
anger superiority effect will be higher when the target face is directed towards the
observer. They further hypothesized all of these effects would be moderated by trait
and situational social anxiety, such that more anxiety will increase the anger superiority
effect as well as the hypothesized “directed face superiority” effect.

For the first 3 studies, the results were remarkably consistent as well as unexpected.
Instead of demonstrating an anger superiority effect, they found a happy superiority,
such that happy faces in angry crowds were detected faster and more accurately than
angry faces in happy crowds. Also, although they found the “directed face superiority”

effect, the predicted interaction with target affect was not demonstrated. To investigate

ll

the reason for the happy target advantage, they did a study (4A) to examine the ease by
which the facial stimuli were processed. The results indicated that happy faces were
easier to process, and it is these perceptual features that may be explaining these
unanticipated effects. For this reason, experiment 4A was replicated with the exception
that schematic faces were used instead of real pictures. When the happy advantage
disappeared with the use of schematic faces, the investigators returned to their initial
questions by using these stimuli in experiment 5.

In this final experiment, state social anxiety was manipulated using an observer in
the room that is ostensibly evaluating the participant. Trait social anxiety was also
assessed using the Fear of Negative Evaluation questionnaire (Watson & Friend, 1969).
Unlike experiments 1 through 3, the results indicated the expected anger superiority
effect did in fact occur. That is, angry faces were detected more quickly and accurately
than happy faces. Also, there was a 4-way effect such that this anger superiority was
more obvious under high induced and trait social anxiety in the emotional crowds
condition on the accuracy performance data. Thus, with regards to the situational
moderation of the detection of social exclusion, this study showed that when social fear
was experimentally induced, those participants that were more socially anxious
attended to threatening faces more than participants that were less socially anxious.

One way of framing the theoretical issues raised by these two perspectives on the
detection of social exclusion may be to think of these as two different models of smoke
alarms. While the work of Williams et al seems to indicate that the sensitivity of our
social exclusion alarms have sensitive but invariant “manufacturer settings”, the work

of Picket et a1 seems to suggest that the sensitivity and/or the type of “smoke” detected

12

by our alarms are indeed adjustable, depending on whether we have individual
differences in our fear of the social “fire” of exclusion, or whether the situation dictates
that one should be more vigilant of the threat of exclusion.

The answer of which is the better metaphor to describe our detection of exclusion
has a number of interesting theoretical as well as practical implications. One of these is
implications is its relevance to research on interpersonal sensitivity. If our alarms are
indeed adjustable, then one of interesting consequences of this may be the maladaptive
nature of hyper or under vigilance. One example of hyper-vigilance may be rejection
sensitivity (Downey & Feldman, 1996). People high on the individual difference of
rejection sensitivity tend to interpret ambiguous social cues as signs of rejection. This
constant on-guard nature of this vigilance and subsequent “false-hits” may lead to a
number of problematic relationship dynamics (Downey & Feldman, 1996). For
example, much research in the marital attribution literature indicates that people that
attribute a negative intent to their partner’s behavior are more dissatisfied with their
relationships that people who do attribute bad intent (Bradbury & F incham, 1990).
Also, Downey and F eldman (1996) contend that a person’s rejection sensitivity may
have originated in an insecure childhood attachment style. If so, there is ample
evidence in the attachment literature indicating that people with an insecure attachment
style, such as being mistrustful or anxious of not having one’s needs met, have less
satisfying close relationships (Hazan & Shaver, 1987; Kobak & Hazan, 1991; Simpson,
1990). In terms of under-vigilance, people that are more interpersonally sensitive have
more satisfying marriages than those that are less interpersonally sensitive (Noller &

Feeney, 1994). Also, children that are more interpersonally sensitive are liked more by

13

their peers than interpersonally insensitive children (Nowicki & Duke, 1992). Whereas
hyper vigilance may lead more false alarms, under vigilance may lead to a higher
likelihood of being socially “burned.” If, however, detection of social exclusion is not
malleable according to situation or personality, then such problems should be less of a
concern.

An interesting study looking at a physiological reaction to this hyper-vigilance
examined the differential responses of high and low rejection sensitive individuals to a
loud noise while viewing paintings conveying acceptance or rejection (Downey et al.,
2004). The investigators theorized that a rejection-relevant stimulus (in this case,
painting) would activate a defensive motivational cognitive posture for high rejection
sensitive participants (HRS). This activated defensive motivational system (DMS) will
then amplify their eye blink startle response to a loud noise. They further theorized that
viewing an acceptance related painting will not activate their DMS, and thus not
amplify their startle response.

Along these same lines, they predicted that low rejection sensitive participants
(LRS) would not show this amplification in eye blink startle response when viewing a
rejection or acceptance relevant painting. The results of the study did show the
expected pattern. That is, eye blink responses were indeed amplified for HRS
participants when viewing the rejection relevant painting, but not the acceptance related
painting. Also, the amplification did not occur for LRS participants when viewing the
acceptance or rejection painting.

We face a number of problems in trying to reconcile these lines of research to

address these questions. Firstly, there is a dearth of literature directly relevant to this

14

question. Secondly, what research that has been done does not bear directly, but is only
suggestive. Thirdly, one of the possible reasons behind the glaring scarcity of research
directly bearing on this question may be the lack of methodological tools and
paradigms to effectively address such questions. One paradigm that may prove helpful
in such an investigation has been developed to study the so-called “face-in-the-crowd”
effect.

To detect a fire, there must be some manifestations or signs of the fire that are
detectable, such as smoke. In the case of social exclusion, there may be hundreds of
different signals of possible social rejection. These may range from subtle body cues to
clearly rejecting language. Another possible “sign” of social rejection is emotional
facial efference. Much of the accumulated literature on processing emotional facial
stimuli seems to suggest that faces have a special place within the perceptual system.
Some research indicates that facial processing seems to be highly efficient, if not hard-
wired (Zajonc & Markus, 1984). For example, facial features are processed more
efficiently when put in the context of a normal facial configuration than if placed in
context in which facial features are scrambled (Homa, Haver, & Schwartz, 1976).

Even infants have demonstrated the ability to distinguish between emotional facial
gestures as well as devote more attention to threatening faces (Barrera & Maurer, 1981;
Schwartz, lzard, and Ansul, 1985; Serano, lglesias, & Loeches, 1992). This, in turn,
suggests that cognitive mechanisms for facial processing should be highly efficient,
especially for threatening stimuli, and possibly even automatic.

The face in the crowd effect originates from a study by Hansen and Hansen (1988)

in which they investigated two models of processing facial stimuli: automatic versus

15

serial search. Their subjects saw sets of faces (a crowd) and were asked to indicate as
quickly as possible whether there was any face (the target face) with a different
expression than the rest of the crowd. On some trials, the there was no such target—
faces were uniformly neutral, angry, or happy. On critical trials, the target face was
anomalous (e.g., one angry face in a happy crowd). An automatic search process
suggests a parallel search in which the features of an automatically detected stimulus
being looked for (i.e., the angry target face) seems to the perceiver to “pop-out” of the
crowd. A serial search, on the other hand, is done by searching through the array of
stimuli one by one, comparing each stimulus to the desired target. When a stimulus is
found that resembles the target, the search stops. This further suggests a method of
testing search strategy. If search time does not increase with an increase in crowd size,
then that suggests an automatic rather than a serial search strategy. A shallow “search
slope”--calculated as the mean increase in search time for a larger vs. smaller crowd,
divided by the number of additional faces in the larger-crowd display--of 10ms or less
has been suggested as a criterion for demonstrating (near) automatic processing (Fox et
a1, 2000).

Hansen and Hansen investigated the hypothesis that facial processing for threatening
(viz., angry) faces is automatic rather than serial by comparing search times of
threatening as well as non-threatening stimuli across group sizes. They hypothesized,
therefore, that all else being equal, angry faces will be detected faster than happy faces,
and that increase in the number of distracter stimuli should not increase the time to find
the target stimuli. The results did show the hypothesized angry face-in-the-crowd

effect, (i.e., that angry faces were found faster than happy faces) and that search times

16

for the angry face in a happy crowd did not increase with increases in group size, and
thus, automatic processes could be inferred. Search times for happy faces in angry
crowds did increase with larger displays, suggesting the hypothesized serial search
strategy. Although not originally hypothesized, they also found that neutral faces in a
happy crowd were found faster than happy faces in neutral crowds, an effect that will
be discussed shortly.

However, subsequent research on the face in the crowd effect has qualified Hansen
and Hansen’s original conclusions. In particular, lack of convergence among results
(Hampton, Purcell, Bersine, Hansen, and Hansen, 1989), failures to replicate
(Nothdurft, 1993; White, 1995), and possible confounding factors (Purcell et al 1996)
and have led some investigators to question or completely discount the face in the
crowd effect. With regards to inconsistent results, Hampton, Purcell, Bersine, Hanson,
& Hanson (1989) replicated the face in the crowd effect for 9-face displays, but not for
4-face displays. In terms of failures at replication, Nothdrufi (1993) found search
slopes of higher than 10ms for schematic faces, non-faces, or even face targets within
non-face distracters. White (1995), on the other hand, found slopes of less than 10ms
for sad and happy targets. However, they also found these flat search functions even if
the faces were displayed upside down. This is problematic because inverted facial
stimuli has been known to disrupt processing of emotional facial stimuli, and so, if
emotional expression was critical, we should see a different search times for upside
down stimuli than for stimuli displayed right side up (White, 1995).

In of the better-known follow-up studies, Purcell et al. (1996) contended that the

technique Hansen and Hansen used to produce their facial stimuli displays led to an

17

unintended, low-level perceptual artifact. Specifically, through a process called
thresholding, the original gray scale pictures were converted into black and white
pictures and produced large black blotches on the chins and necks of the angry faces.
Using gray scale versions of the original pictures, Purcell et al found no face in the
crowd effect, nor did they find the pop-out effect for the angry faces.

In a study more favorable to the face-in-the-crowd effect, Fox et a1 (2000) chose to
use schematic faces to avoid the problematic and possibly confounding features of real
pictures (see Figure 1). In a series of 5 experiments, the results converged to indicate
that angry faces were indeed found faster than happy faces, that the pattern of data was
not found for inverted faces, and that the search slopes for detecting angry faces were
significantly lower than search slopes for happy faces, but not low enough to indicate a
“pop-out” effect. Thus, while not automatically processed, angry faces were processed

more efficiently.

Figure 1. Examples of stimulus displays used by Fox et al. (2000)

18

 

(D)

Q
6

0

 

 

(C)

0

900
o

 

 

 

We now turn to two fascinating yet unpredicted findings the Hansen and Hansen as

well as Fox et al studies. In addition to the anger-superiority effect, Hansen and

Hansen also found that the neutral targets in happy crowds were also detected faster

than the happy targets in neutral crowds. Furthermore, the neutral targets in happy

crowds were found just as fast as the angry faces in happy crowds. The author’s

referred to this unpredicted finding as “disquieting.” After all, if angry faces were

detected faster because of the threat to safety that it implicated, then why should a

neutral face also be detected faster? Two possible explanations were given. Firstly, it

19

may be that neutral and angry faces are more rare, and therefore their faster detection
time can be attributed to their relative novelty. Although speculation, the novelty
explanation is similar to the Schwartz et al (1985) explanation of why infants find
neutral faces just as attention-grabbing as angry faces (LaBarbera et al., 1976). A
second explanation put forward is that would explain this pattern of findings was that
the angry crowd had more variance in facial expression than the neutral crowd, and the
neutral crowd had more variance in facial expression than the happy crowd. That is,
within each stimulus display, the crowds used in Hansen and Hansen’s (1988) first
experiment consisted pictures of 9 different people. The same crowd of people were
used for the angry, happy, and neutral positions, however the emotional expressions of
the people in the crowd changed accordingly. A discrepant face would be harder to
find in a crowd of more varied faces than a crowd of less varied faces, and therefore
would take longer to search through. Hansen and Hansen go on to disconfirm the
second explanation in a subsequent experiment, but do not explore the first explanation.

A related unpredicted finding was also found in Fox et al’s study. In addition to
using schematic faces instead of real pictures, Fox et al used angry, sad, and happy
faces instead of angry, neutral, and happy faces. Interestingly, they found that the sad
target faces in neutral crowds were also found faster than happy faces in neutral
crowds, which also seems to contradict the theoretical argument that it is threatening
stimuli that are processed faster. That is, if sad faces do not carry a threat value (an
assumption by Fox et al.), why should sad in neutral displays be faster than the happy
in neutral displays? They offer a post-hoe explanation that since the schematic sad

faces only differed from the schematic angry faces by a single feature (viz. the

20

eyebrows), the sad facial stimulus is somewhat ambiguous. Past research (LeDoux,
1996) has shown that when animals perceive ambiguous stimuli, the most threatening
interpretation is automatically activated first, (i.e., the worst is assumed) and then later
corrected if wrong. Fox et a1 contend that this may also occur in humans, and that
participants may initially perceive the ambiguous face as angry, and so perceive it
faster, and later correctly perceive it as a sad face.

So, here we have two unpredicted, but related findings showing that sad and neutral
faces are also detected faster than happy faces and just as fast as angry faces. This was
problematic to both investigators’ theorized notion that stimuli that implicate a threat to
one’s safety should be detected faster, prompting them to put forward post-hoc
explanations.

We contend that there exists another, even more parsimonious explanation that can
account for the seemingly disparate findings. Specifically, that even though sad and
neutral faces may not pose a threat to safety, they may signal a threat to one’s need to
belong. That is, in certain circumstances, a sad or neutral face may convey an
exclusionary threat. For example, if one enters a cocktail party, and everyone turns and
smiles to indicate their pleasure to see you except for one person who has a neutral
expression, that one person may threaten your sense of inclusion or belonging within
the group and may be detected quite quickly. Hence, it may be that a contrast of a less
positive facial expression in a crowd of more positive faces serves as a cue for a threat
of exclusion. In that same vein, little attention has been given to precisely what the
threat is that is conveyed by an angry face. The implicit assumption seems to be that an

angry face may signal aggression or attack. But another possibility is that an angry face

21

signals social rejection. It is noteworthy that, in a study by Bouhuys et al (1995)
looking at ratings of various schematic emotional expressions, they find that one
schematic facial stimulus that was rated as high in anger (100% of participants
perceived anger in the face) were also rated as high in disgust (90%) and rejection
(86.7%).

In this study, I aim to investigate the detection of social exclusion using the classic
paradigm of the face in the crowd effect (Hansen & Hansen, 1988). In particular, I am
interested in using this paradigm to investigate three primary questions relevant to the
detection of social inclusion and exclusion.

My first question is whether chronic individual differences such as rejection
sensitivity (Downey & Feldman, 1996) and the need to belong (Leary et al. 1996)
moderate the efficiency of processing facial stimuli that might convey a threat of social
exclusion. That is, just as Ohman et a1 (2001) found that people more afiaid of snakes
tend to find a snake stimulus faster than people less afraid of snakes, is it the case that
people high on rejection sensitivity or the need to belong would also detect threatening
facial stimuli faster? As mentioned earlier, there are two theoretical perspectives
relevant to this question. While Williams’ (2004) theoretical and empirical arguments
suggest no such moderation of efficient processing, work by Pickett et a1 (2004)
suggests that chronic individual differences will moderate efficiency of processing.

Secondly, in addition to chronic individual differences, do situational influences also
moderate the efficiency of processing threatening facial stimuli? Or more specifically,

would being in a context where a threatening facial expression explicitly (and/or

22

implicitly) conveyed that one might be rejected socially increase the speed at which

people detect such threatening facial stimuli?

23

Method
Overview

To address the individual difference question, four questionnaires were
administered: the Rejection Sensitivity Questionnaire (Downey & Feldman, 1996), the
need to belong scale (Leary et al, 2005), the Adult Attachment Scale (Collins & Read,
1990), and the Attachment Style Questionnaire (Feeney, Noller, & Hanrahan, 1994).
Although the full versions of the attachment scales were administered, only the anxiety
subscales of both measures were analyzed. Copies of these instruments are provided in
Appendix B. The Rejection Sensitivity Questionnaire demonstrates high internal
reliability (or = .83), test-retest reliability (r = .78) as well as reasonable evidence of
validity (Downey & Feldman, 1996). The Adult Attachment Scale also demonstrates
adequate internal reliability (or = .72), test-retest reliability (F52), and validity (Collins
& Read, 1990).

To investigate whether the situational level of threat could moderate efficiency of
processing, 1 included a between subjects priming threat factor. I used a supraliminal
as well as a subliminal priming procedure. For the supraliminal prime, 1 gave
participants a sentence-descrambling task in which they were given 10 sentences to
descramble (Wyer & Srull, 1994; Bargh et al., 2000). For each sentence, participants
were given 5 words, from which they were asked to make a grammatical four-word
sentence. For participants in the high threat condition, one word in each of the
sentences had a rejection—related word (e. g., lonely, excluded). For participants in the
low threat condition, all the words were neutral (see Appendix D). Both tasks were

matched for length and word frequency. Also, a pilot study showed that, on average,

24

both tasks take the same amount of time to complete. Those in the high threat
condition also received a subliminal rejection-related prime immediately before the
stimulus display is shown (e. g., loser, excluded). Participants in the low-threat
condition were given a neutral prime (e. g., pencil). Both types of priming words were
matched for length and fi'equency (see Appendix D).

Previous studies have also included a within-subjects display size factor to address
the question of whether the perceptual process is serial or automatic. That is, if
participants do a serial search to find the discrepant target in the background display,
then we can expect that the reaction times should increase as display size increases. If,
however, the process is not serial but more automatic, then increase in the number of
faces should not increase reaction times. Since even an automatic search process might
be affected somewhat by a large increase in the number of stimuli to be processed, a
search slope of less than 10ms/face has been traditionally recognized as suggesting
automatic processing (Fox et a1, 2000). I included display size factor to address this
question. I also included a within subjects “orientation” factor which controls the
location of the target stimulus on critical trials (either on the vertical-horizontal axes or
on the diagonal axes; details provided below).

Thus, this experiment had a 3 (target stimulus: angry/disgustI vs neutral vs. happy) x
3 (distracter/crowd stimulus: angry/disgust vs. neutral vs. happy) x 2 (threat prime
versus no threat prime) x 2 (display orientation: x vs. +) x 2 (crowd size: 4 face display

vs 8 face display) x 2 (key) mixed factorial design with 4 additional individual

 

I Bouhuys et al (1995) found that a generic schematic angry face (knitted eyebrows, downturned mouth)
was also rated as conveying high levels of disgust and rejection as well (100% of participants perceived

anger in face, 90% perceived disgust, and 86.7% perceived rejection). In the present study, therefore, we
presume that such “angry” faces will likewise convey rejection, disapproval, and disgust (particularly in
the high threat condition).

25

difference measures. Threat and key are both between subjects measures, whereas the
rest of the factors are within-subjects.

In terms of hypotheses, I first of all expected to replicate the finding that participants
will process threatening angry/disgusted faces more efficiently. That is, reaction times
to an angry /rejecting target in a neutral background should be faster than reaction times
to a happy target in a neutral background.

Secondly, I hypothesized that individual differences would moderate the effect of
face-valence on speed of detection such that people with higher scores on rejection
sensitivity and the need to belong would process threatening stimuli faster than people
low on these two measures. I also hypothesized that participants in the threat condition
would experience a pronounced anger superiority effect than those in the no-threat

condition.

Participants
1 utilized 152 volunteer participants fi'om the Department of Psychology subject
pool, which consists of adult University undergraduate students enrolled in psychology

courses. Participants received class credit for their participation.

Apparatus and Stimuli

The stimulus displays and data collection were programmed using MediaLab and
Direct RT and were administered on Windows-based computers. In terms of schematic
faces, there were generic 3 target stimuli (i.e., happy, neutral, or disgust/angry) as well

as 3 background-crowd stimuli (i.e., happy, neutral, or disgust/angry) (see figure 2).

26

Thus, there were 3 types of uniform displays (all happy, all neutral, all angry), and thus

6 different combinations of target in background displays.

Figure 2. Schematic Facial Stimuli Used

O D

Figure 3. Example of 8 —person Happy in Neutral Display

The schematic faces were positioned in a circular display with 8 possible positions
equidistant fi'om the center of the circle (see Figure 3). In clockwise order, the
positions were north, northeast, east, southeast, south, southwest, west, and northwest.

For the 4-face displays, 1 used the north, south, east, and west positions for one set of

27

displays (the “+” orientation) and the northeast, southeast, southwest, and northwest

6

positions for the other set of displays (the ‘ x” orientation). The reason for choosing the
circular pattern was its successful use in the recent face in the crowd literature (e.g.,
Fox et al., 2000, Juth et al., 2005), as well as the ability to control for position of target
such that there is no net confound across all visual displays. Since there are 6 types of
target-in-background displays and 4 possible positions of the target in the 4-face
display (within each of the two orientations), there were 48 different types of 4-face
target-in-background displays controlling for position.

There were also an equal number of homogeneous no-target trials, which means 48
4-face, no-target control displays (16 all angry/disgusted, 16 all neutral, 16 all happy).
Also, there were 3 types of 4-face/all-same displays (all happy, all neutral, all
angry/disgusted) in two orientations; hence I showed each of these displays 8 times to
get 48 4-face/all-same displays. It was necessary to have the same amount of target-
present and target-absent displays so that participants do not develop a response bias to
either type of display. The same considerations mean that there were also 96 8-face
displays: 48 with a target and 48 with no target face. Thus, a total of 192 displays gave
me a completely counterbalanced design in which half were 4-face displays and half

were be 8-face displays, half had a target face whereas half did not have a target face,

and those with a target face displayed it in every possible position.

Procedures

With regards to procedures, the experimenter showed all consenting participants to a

computer and had them sit one foot away from the screen, with the middle of the screen

28

at eye-level. The experimenters then told them to click enter to begin the self-paced
instructions, followed by practice trials, and finally the experimental portion. The self-
paced instructions included the four personality questionnaires as well as the
supraliminal priming task. Within each trial of the experimental portion, participants
were first shown a fixation point in the middle of the screen to focus for 500
milliseconds and were then given a forward mask of a series of X’s. After the forward
mask, they primed with one word for 20 milliseconds (either a rejection-related prime if
in the high threat condition or a neutral prime for the low-threat condition), and were
then given a backward mask of a series of ampersands. After the backward mask, they
were shown a display of schematic faces for 800 milliseconds and were asked to press a
certain key if all the faces are the same, and to press another key if there is at least one
discrepant face in the display. Previous studies have shown 800msec to be ample time
to respond to similar stimulus displays (Fox et al., 2000).

Response keys of ‘z’ or ‘/’ were randomly assigned to participants, such that for half
the participants ‘2’ denoted that all the stimuli are the same and ‘/’ denoted that there is
at least one discrepant stimuli, and for the other half the keys were reversed. After their
response, there was another 2000 millisecond delay before the next fixation point
appears. If there is no key press, then the next trial began after 2000 milliseconds.
There were 15 practice trials and 192 experimental trials. Before each of the trials, the
participants were primed with an affiliation prime, if in the high threat condition, or a

neutral prime, if in the low threat condition.

29

Results

Data Reduction
Performance measures

There were two primary dependent variables-- reaction time and accuracy. Incorrect
responses were not included in the analyses of the reaction time data. I considered any
reaction times that were less than 100 ms, or greater than +3 standard deviations from
an individual’s mean, to be outliers, which resulted in the exclusion of 4% of the
participants. Although the lower exclusionary threshold for a decision-related task is
usually 200ms to 300ms, I decided on using the 100ms threshold used previously in the
face in the crowd paradigm (Fox et al., 2000) to remove the data of participants that
may have been trying to anticipate the display or repeatedly tapping one of the keys to
finish the experiment as soon as possible. I then replaced those values with the
individual’s mean reaction time for that particular stimulus display. The data for each
participant was then aggregated such that they received one mean score for every type
of display they responded to (i.e., for each combination of size, orientation, crowd
affect, and target affect). There were 36 of these combinations (viz., 2 crowd sizes x 3
target affect x 3 crowd affect x 2 orientations) and two dependent variables. Thus each
participant’s performance data was reduced to a total of 72 means. Finally, since
reaction times and accuracy tend to be non-normally distributed, I conducted a log
transformation of the reaction times and an arcsine transformation of the accuracy data,
standard procedure for such analyses (see Juth et al., 2005, for descriptions of similar
procedures).

Personality measures.

30

The personality data was then analyzed to determine whether to combine or treat
scales separately in subsequent analyses. In addition to examining scale
intercorrelations (see Figure 1), I also analyzed the dimensionality of the 4 scales by
conducting an exploratory factor analysis. Firstly, a principal components analysis
indicated two factors with Eigenvalues greater than 1. Inspection of the scree plot (see
Figure 4) also demonstrates this two-factor solution. Based on this two-factor
extraction, two factors were rotated using a Varimax rotation procedure. The rotated
solution indicated that RSQ (rejection sensitivity) and ASQ (attachment style
questionnaire) both loaded heavily on the first factor, while AAS (Adult Attachment
Scale) and NTB (Need to Belong) loaded less heavily on a second factor (see Table 1).

Figure 4. Scree plot for Factor Analysis of 4 Personality measures

 

 

Eigen Value

 

oooo _._.._._.
ON-kODQ-‘N-hamh)
111411;

 

 

 

C1 C2 C3 C4
Component

 

 

 

31

Table 1. Personality Scale lntercorrelations

Correlations

 

 

 

 

 

AAS NTB ! RSQ ASQ _

AAS Pearson Correlation 1 .188’ -.242“ -.465“
Sig. (2-tailed) . .024 .004 .000
N 144 143 144 132

NTB Pearson Correlation .188’ 1 .160 .310W
Sig. (2-tailed) .024 . .055 .000
N 143 145 145 1 33

RSQ Pearson Correlation -,242" .160 1 .516“
Sig. (2-tailed) .004 .055 . .000
N 144 145 146 134
ASQ Pearson Correlation -.465* .310' .516“ 1
Sig. (2-tailed) .000 .000 .000 .
N 132 133 134 134

 

 

 

 

 

 

 

'- Correlation is significant at the 0.05 level (2-tailed).
Correlation is significant at the 0.01 level (2-tailed).

Table 2. Factor loadings for four personality measures

 

 

 

 

 

 

 

Component
1 2
ASQ .897 .060
AAS -.610 .670
NTB .331 .866
RSQ .758 .091

 

Extraction Method: Principal Component

In addition to having a factor loading of .67 on the second factor, the AAS measure
also loaded -.61 on the first factor. After inspecting AAS items, I decided not to
analyze the full AAS in subsequent analyses. Instead, I combined the ASQ Discomfort
scale, the AAS Dependency scale, and the AAS Closeness scale into one measure. I
did this by computing the Z-scores of each of the scales, and then averaging the Z-
scores across scales. 1 will henceforth refer to this combined measure as Dependency.

Even excluding this change, scale intercorrelations as well as the factor analysis
suggested that the 4 measures were clearly not tapping into a single, unitary factor. I

thus decided to examine personality moderation through each of the scales separately.

32

The descriptive statistics for the four scales as well as the overall accuracy and reaction
times are shown in Table 3. Tables 4 and 5 break down the descriptive statistics by
high and low priming threat.

Table 3. Descriptive Statistics for Overall Accuracy, Reaction Time, and Personality
measures.

 

 

Variables M SD Range
Reaction Time 1154.65 218.16 1221.99
Accuracy .90 .09 .59
RSQ 7.60 2.91 16.27
NTB 34.09 6.31 30.00
ASQ 125.27 21.37 111.00
Dep 0 .40 3.03

 

Table 4. Descriptive Statistics for Accuracy, Reaction Time, and Personality measures
for participants in the high priming threat condition.

 

 

Variables M SD Range
Reaction Time 1 170.66 232.66 1170.08
Accuracy .90 .08 .34
RSQ 7.96 2.95 16.27
NTB 33.59 6.55 29.00
ASQ 123.62 21.32 110.00
Dep -.05 .42 2.53

 

Table 5. Descriptive Statistics for Accuracy, Reaction Time, and Personality measures
for participants in the low priming threat condition.

 

Variables M SD Range

 

Reaction Time 1 137.16 201.47 990.44

33

Accuracy .89 .10 .59

 

RSQ 7.23 2.85 12.94
NTB 34.61 6.04 29.00
ASQ 126.97 21.45 90.00
Dep .03 .37 2.22
Crowd effect

 

Before examining any of the target effects, I analyzed accuracy and reaction times
for the all-same displays (i.e., all angry vs. all neutral vs. all happy). Previous studies
have shown that participants tend to take longer and make more mistakes for the all
angry displays than other same affect displays (Fox et al., 2000, White 1995, Hansen
and Hansen, 1988). Fox et al. (2000) speculate that the reaction time pattern may
reflect differential speed to parts of the attentional process. For example, it may be that
the threatening stimulus (i.e., angry face) leads to fast engagement and slow
disengagement, while the non-threatening stimulus (i.e., happy face) leads slow
engagement but fast disengagement. In terms of the higher error rates for the all-angry
displays, it may be that participants experience a sense of frustration that this particular
display is taking much longer than other trials, and thus come to a point of pressing a
key out of a sense of haste to move on with the experiment. To analyze this, I
conducted a one-way within subjects ANOVA on same affect crowds. In terms of
accuracy, participants did differ depending on which all same display they were
reacting to, F (2,302)=51 .330, p<.001, n2 = .254. A post-hoe analysis with a Bonferroni
correction indicated that participants made more errors in the all angry displays

(M=89% correct, SD=.010; unless otherwise noted, all means reported are

34

untransformed) than they did on the all-neutral displays (M=95%, SD=.005, p<.001) or
the all-happy displays (M=95.4%, SD=.006, p<.001). Accuracy on all-neutral and all-
happy displays, however, did not differ. I also found an effect for reaction time,
F(2,302)=1 87.719, p<.001, n2 = .554. A post hoc test with a Bonferroni correction
indicated that participants took longer to react to all-angry displays (M=1344ms,
SD=27.9) than they did all-happy displays (M=1244, SD=25.9, p<.001). The happy
displays, in turn, took longer to react to than the all-neutral displays (M=1134,
SD=23.2, p< .001).

I then analyzed whether this all-same affect main effect was further moderated by
personality or priming threat on accuracy or reaction time. It turned out that this effect
was indeed moderated by priming threat on reaction time, F(2,284) = 4.795, p = .009.
Participants that were primed with a socially threatening word reacted to the all-happy
displays faster (M=1205ms, SD=3 7) than participants that were primed with a neutral
word (M=1276ms, SD=36), p<.001. There were no priming differences for the all-
angry or all-neutral crowds.

Face in the crowd effect

Since our questions of primary interest involve moderation of the face in the crowd
effect, the first step in the analyses was to replicate the actual face in the crowd effect.
First, an overall 3 (target) x 3 (crowd) x 2 (size) x 2 (orientation) x 2 (key) analysis of
variance (with repeated measures on all but the last factor) on the neutral primed
participants revealed a significant target main effect for reaction time, F (2, 1 38) =
68.458, p < .001 , n2 = .498. Post-hoe comparisons, using the Bonferroni adjustment for

multiple comparisons indicated that angry targets (M = l 152ms, SD = 28.83) were

35

 

found faster than happy targets (M=1234, SD=29.34, p<.001). Neutral targets (M =
l 125ms, SD = 27.12) were found quicker than happy targets (1234ms, SD = 29.34, p <

.001), Search times for angry targets, however, did not differ from neutral targets

(p=.155, see Figure 5).

Figure 5. Reaction time means for Overall Analysis

Reaction Time

 

r.__..__ __
1250
1200
1150
1100
1050

Angry Neutral Happy

Target

 

 

In terms of accuracy, we also found a target main effect, F(2,122) = 33.855, p<.001,
n2 = .308. A Bonferroni post-hoe analysis further indicated that participants were more
accurate in detecting angry targets (92% correct, SD=.009) than happy targets
(M=87%, SD=.01 1, p<.001). They were also more accurate in detecting neutral
(M=91%, SD=.010) targets than happy targets (M=87%, SD=.01 1, p<.001). Accuracy
scores between angry and neutral targets, however, did not differ from each other
(p=.172, see Figure 6).

Figure 6. Accuracy Means for Overall Analysis

 

0.94
0.92

.0
<0

0.88

Accuracy

0.86

 

0.84

 

Angry Neutral Happy
Target

 

 

 

36

I also defined more restricted analyses that focused on displays that are most
relevant to our question of interest. These displays are the emotional targets in
emotional crowds (i.e., angry targets in happy crowds or happy targets in angry crowds;
EE) and the emotional targets in neutral crowds (i.e., happy or angry targets in neutral
crowds; EN). For both of these preliminary analyses where the possible moderating
effect of crowd size was not of immediate interest, 1 averaged across the group size
factor. The BB analyses compare participants' performance on angry targets in happy
crowds versus their performance on happy targets in angry crowds. For the EN
analyses, performance on angry targets in neutral fields was compared to happy targets
in neutral fields. In some previous studies’ analyses (e.g., Juth et al., 2005), the crowds
were always completely neutral (as in the present EN analysis); in others (e. g., Hansen
and Hansen, 1988), crowds were non-neutral.

I next looked to replicate the face in the crowd effect for the EN analysis. Parallel
analyses were carried out on transformed reaction time and accuracy data. In terms of
accuracy, participants did indeed make fewer errors when detecting an angry face in a
neutral crowd (M = 93%, SD=.008) than when detecting a happy face in a neutral
crowd (M=79%, SD=.013), F(1,142) = 149.053, p<.001, n2 = .512. Participants also
found angry targets in neutral crowds (M=972, SD=16) faster than happy targets in
neutral crowds (M=1129, SD=19), F(1,142) = 200.302, p<.001, n2 = .585.

I found similar effects for the EB analysis. Specifically, participants made fewer
errors when detecting an angry face in a happy crowd (M=93%, SD=.011) than when

detecting a happy face in a angry crowd (M=86, SD=.009), F(1,142) = 47.679 p<.001,

37

n2 = .251. They also found angry targets in happy crowds (M=1061, SD=20.90)
significantly faster than happy targets in angry crowds (M=1187, SD=23.36), F(1,142)
= 102.458, p<.001, n2 = .419. In summary, the basic face in the crowd effect was
replicated for emotional targets in emotional crowds as well as emotional targets in
neutral crowds.

Is the “face in the crowd” effect a “Pop out” Effect?

Besides the face in the crowd effect, another question of interest was whether these
effects were strongly moderated by the size of the crowd (i.e., the “pop-out” effect).
This question pertains to whether the elements in the display were processed serially or
in parallel. If elements were processed serially, then an increase in display size should
lead to a substantial increase in reaction time. If elements are processed in parallel,
however, then increases in display size should not lead to a substantial increase in
reaction time. Fox et al. (2000) suggest that the criterion for concluding that the angry
faces “pop out” is if the mean increase in overall response time divided by the number
of additional display elements is less than 10ms (i.e., for every additional person in the
crowd, the additional time to make a judgment should be less than 10ms).

This analysis indicated that the size main effect was significant such that
participants did take longer to find targets in larger displays (1271ms) than they did in
smaller displays (1069ms), F (1 , 69) = 388.69, p<.001, n2 = .849. However, there was
no size by target interaction. That is, both angry and happy targets are found slower in
a larger crowd than a smaller crowd. In terms of Fox’s 10ms criterion, this analysis
indicated a search slope of 48ms/crowd member, which suggests non-parallel

processing.

38

Personality grid Priming/Threat Moderation

Of all the personality measures, rejection sensitivity is the most face valid and
directly relevant to my main questions of personality moderation of sensitivity to
socially threatening stimuli. I will thus describe the rejection sensitivity results in
detail here in the main text, and will describe the results for the other three measures in
Appendix A. For all personality analyses, a median split was done to categorize
participants as high or low on a given measure. A regression analysis keeping all
personality measures continuous was also conducted; the results for RSQ are discussed
below and for the remaining personality measures in Appendix A. All initial analyses
will be subsequently described in terms of the median split. I will also begin discussing
personality moderation within the restricted EE/EN analyses, since these permit the
most direct way to test the questions of highest theoretical interest. To further simplify
interpretation of these initial analyses, 1 collapsed the data across group size as well as
other nuisance factors (e.g., key, orientation).

I first looked at whether participants high in rejection sensitivity and/or those primed
with acceptance threats tended to detect angry targets in happy or neutral fields faster,
and with fewer errors, than participants that are low on rejection sensitivity and/or
being neutrally primed (i.e., I checked for main effects for these variables).

For both EN and EE analyses, I conducted a 2 (target affect) x 2 (priming threat) x
2 (rejection sensitivity) analysis of variance with transformed reaction time and
accuracy as the dependent variables. For the EN analysis, which has a constant,
emotionally neutral crowd, there was a main effect for rejection sensitivity such that

participants high in rejection sensitivity were more accurate (M=89%, M=.013) in

39

detecting emotional targets in non-emotional fields than participants low on rejection
sensitivity (M=84%, SD=.013), F(1,l42)=8.101, p=.005, however, they did not differ
in reaction time, F(1,142) = .199, p=.657. In terms of priming, participants that were
primed with a socially threatening word did not differ in accuracy from participants
primed with a neutral word, although there was a trend in this direction, F(1,142) =
2.615, p = .108. There was also no priming difference for reaction time, F( l ,142) =
.423, p = .517.

The BB analysis results indicated that participants high on rejection sensitivity
did not have a significantly higher level of accuracy in detecting emotional targets in
opposite-valenced emotional crowds than participants low on rejection sensitivity,
although there was a trend in this direction, F (1,142)=2.726, p=.101. There were also
no overall differences in reaction time between participants that were high and low on
rejection sensitivity, F(1,123)=.025, p=.874. There was also no main effect for
priming, F( 1,142) = .794, p = .374.

Of primary interest, of course, is whether chronic or situationalIy-induced social
anxiety moderates the usual advantage of threatening (angry) faces; this would be
indicated by the two-way RSQxTarget Affect or Priminngarget Affect interaction
effects, and possibly, the three-way interaction effect. J uth et al. (2004) found a 4 way
target x distracter x state anxiety x induced anxiety interaction such that the face in the
crowd effect for accuracy was more pronounced for emotional distracters and for
participants high in trait and induced social anxiety.

For the EN analysis, I did find a target x RSQ interaction effect for accuracy,

F(1, 142) = 6.292, p = .013, n2 = .042. For participants low on RSQ, accuracy in

40

detection angry faces was 94% (SD=.013), while accuracy for detecting happy faces
was 83% (SD=.015). For participants high on RSQ, accuracy in detecting angry faces
was 92% (SD=.012), while accuracy for detecting happy faces was 75% (SD=.018). I
also did an analysis looking at the simple RSQ effect at each target level. For the angry
targets, the high and low RS participants did not differ in accuracy. However, for the
low RS participants, the accuracy was significantly lower for high RS participants
(M=75%, SD=.018) than for the low RS participants (M=92%, SD=.012), p<.001.
Thus, the RSQ main effect noted above was due entirely to an effect with Happy target
faces (see Figure 7).

Figure 7. Target x RSQ interaction of EN Analysis on Accuracy Scores

Accuracy: Neutral Crowd

9
:9

Proportion Correct.

 

0.7
Angry Target Happy Target
Target Affect

41

There was no comparable Target * RSQ interaction effect for reaction time,
F(l,142) = .134, p = .715. In terms of the target * priming interaction, there was no
effect for accuracy, F(l,142) = .085, p = .772, or reaction time, F(l,142) = .414, p =
.521. For the EB analysis, there was no target x RSQ interaction for accuracy, F (1,142)
= .034, p = .854, or for reaction time, F(l,142)=.268, p = .609. There was also no
target x priming interaction for accuracy, F(l,142)= 1.085,p= .299 , or for reaction
time, F(1 ,142) = .089, p=.766. For both the BE as well as the EN analyses, the 3-way
interactions were not significant.

In addition to analyzing the data by conducting a median split on RSQ, I also did a
regression analysis in which RSQ was kept continuous. To do this, I computed a new
dependent variable that was the difference between performance on displays with an
angry target face and performance on displays with a happy target face. I also centered
the RSQ variable and dummy coded the threat prime as 0 for neutral prime and 1 for
the high threat prime. For the EN analysis on accuracy, I found a significant intercept
(p<.001), which indicates that, overall, participants were more accurate in detecting
angry targets than happy targets. Also, as opposed to the significant target x RSQ
effect on accuracy found when doing the median split, the corresponding RSQ effect on
the target affect accuracy difference score only demonstrated a weak trend (p<.18)
when doing the regression analysis. Finally, the priming and RSQ x priming effects
were not significant (see table 3 and 4). For the EN regression analysis on reaction
time, other than the expected significant intercept, none of the other effects were

significant (see tables 4 and 5). With regards to the EB Regression analyses for both

42

accuracy and reaction time, other than the significant intercept (p<.001), there were no
other significant effects (see tables 6 through 9).

I also conducted the 2 (target) x 2 (priming) x 2 (personality) median split analyses
for the three other personality measures. Of all the tests conducted, there was only one
main effectufor the ASQ measure on accuracy. This effect mirrored that of the RSQ
main effect on accuracy. Namely, that participants scoring low on ASQ (i.e., less
anxiously attached) (M=92%, SD=.013) were more accurate overall than participants
high on ASQ (M=87%, SD=.013) , F(l,130)=9.599, p=.002, n2 = .069. There were no
significant interactions for any of the analyses. The strongest non-significant
interaction also occurred for ASQ. I found a target x ASQ interaction that again had
the same pattern of the target x RSQ interaction discussed earlier. The results, tables of
means, and figures of all three measures are presented in Appendix A. The regression
analyses did not qualify these effects in any substantial way.

In an attempt to extend the analyses done above, I further wondered whether the
target x RSQ interaction found earlier interaction would be further moderated by crowd
size. That is, for example, is it possible that this 2-way interaction would get stronger
for larger crowds, and weaker for smaller crowds. To test this, I conducted a 2 (target)
x 2 (priming) x 2(RSQ) x 2 (size) factorial ANOVA. The results indicate that although
the Target x RSQ interaction effect was still significant (F(l ,142)=6.29, p<.02) the

target X RSQ x size 3-way interaction was not significant, F(l,142) = .195, p=.660.

43

Discussion

The major goal of this study was to ascertain whether the detection of a facial
expression that can communicate a threat of social exclusion was moderated by a
person's chronic state of social anxiety or by situational inclusionary threat. This was
pursued using Hansen and Hansen's (1988) face in the crowd paradigm. Previous
studies (Hansen and Hansen, 1988; Fox et al., 2000; Juth et al., 2004) have shown that
an angry face in a crowd of happy or neutral faces is found faster and with more
accuracy than happy faces in a crowd of angry or neutral faces, respectively. I was
interested in determining whether this affect-of-target effect was further moderated by
personality or priming threat. I was also interested in the "pop-out" effect, and
particularly whether chronic or temporary threat could lead to the use of a more
efficient search strategy (i.e., parallel versus serial processing).

To briefly recap the results, initial factor analyses on the four 4 personality measures
used indicated that there was clearly not a single unitary factor underlying all the
measures. Since all the measures seemed to be plausibly related to a general concern
for inclusionary status, I decided to test personality moderation by analyzing each of
the personality measures in turn. The two measures that were most closely related,
rejection sensitivity and the attachment style questionnaire, also demonstrated the most
similar patterns of effects.

With regards to the search performance data, the first analysis looked at the effect
all-same affective displays (i.e., when there was no target face present). The results of

this analysis indicated that participants made more errors, and took longer, when

44

reacting to all-angry displays than all happy or neutral displays. This finding is similar
to that of other investigators (Fox 2000, White 1995) and suggests that, in general,
participants seem to have a harder time searching through a display of threatening faces
than a display of non-threatening faces. It may be that the attention grabbing
superiority of an angry target that leads to better performance leads to worse
performance when all the stimuli in the display have that attention— grabbing property.
Interestingly, the reaction time effect was further moderated by priming threat. More
specifically, participants that were primed with a socially threatening word reacted to
all-happy displays quicker than participants that were primed with a neutral word. It is
feasible that high situational threat leads participants quicken the ruling out process.
That is, instead of recognizing these stimuli as just happy, participants under high
situational threat may be more interested that they are “not threatening,” and thus are
quicker to process the displays and conclude that there is no threatening face in this
crowd.

I next attempted to replicate the basic face in the crowd effect. An overall analysis
did indicate that participants found angry targets faster, and with fewer errors, than
happy targets. Reaction time and accuracy for angry targets did not differ, however,
from neutral targets. In terms of a "pOp-out" effect, although there is extra efficiency
for the angry target, the speed up in time is not nearly enough to reach the criterion for
parallel processing. Since the group size analysis indicated that participants are
processing stimuli serially, one way in which faster processing can occur is if the
attention grabbing nature of the stimuli leads the observer to start their serial search

closer to the target than otherwise. If the serial search starts closer to the target, then

45

we can expect search times and accuracy to improve. Another possibility is that, if
participants are scanning the stimulus displays multiple times, it is feasible that angry
targets are less likely to be missed during an initial scan whereas a happy target is more
likely to be overlooked.

My main question of interest is whether this face in the crowd target effect is further
moderated by chronic social anxiety or priming threat. In terms of personality
moderation, I found only one moderation effect that was significant at the .05 level.
This effect demonstrated that participants high on rejection sensitivity tended to
mistakenly miss the happy face in a neutral crowd to a greater degree than did
participants low on rejection sensitivity.

This tendency of high rejection sensitive people to confuse the happy and neutral
faces was further demonstrated when I found that the worst performance on accuracy
was when high rejection sensitive participants were searching for neutral targets in
happy crowds. Again, for these findings, it seemed that high rejection sensitive
participants only distinguished between threatening and non-threatening targets,
whereas low rejection sensitive participants can make more nuanced detection of non-
threatening targets (neutral versus happy).

Again, this suggests that, for highly rejection sensitive participants, their cognitive
and perceptual resources are oriented towards, and give priority to, threat detection. In
terms of the original smoke alarm metaphor, while low Rejection Sensitive participants
seem to have alarms that are able to detect the difference between truly threatening
smoke and simply a hazy day, the highly rejection sensitive participants seem to lump

both environmental conditions together into the ”not-smoke" category. Juth et al.’s

46

(2004) experiment 4A attempted to ascertain the ease of recognizing and labeling
happy, neutral, angry, and fearful faces. The results of this experiment indicated that
when errors were made on happy faces, they were most likely to be confused as a
neutral face rather than other emotional faces.

Although the one significant effect I found was interesting, it only appeared when I
analyzed the personality data by conducting a median split on the relevant personality
variables. A subsequent regression analysis in which rejection sensitivity was kept
continuous revealed the same effect to be non-significant (p<.18).

In terms of the priming moderation, priming did not seem to have an effect in any of
the face-in-the-crowd analyses conducted. While I have no conclusive evidence that
the priming manipulation actually worked, the methodology used was a standard
paradigm that has been used in several studies. Quite similar priming manipulations
have been found to increase affiliative tendencies (e. g., Chartrand & Bargh, 1999;
Lakin et al., 2003). To check whether the primes were truly subliminal, I also
conducted a pilot study in which I presented participants with a fixation point, a
forward mask, the 20ms prime, and then a backward mask. 1 then asked the
participants to try to guess the word that was flashed on the screen. None of the
participants were able to report the prime, indicating that 20ms was short enough to
escape conscious detection.

The work by Williams and his colleagues relevant to this question manipulated the
social groups people were being threatened of being ostracized from. One of these
groups was the Klu Klux Klan, a group that most of the participants would not care to

be a part of (Gonsalkorale & Williams, in press). Even so, when threatened with social

47

exclusion, participants felt the same distress of being ostracized whether the relevant
group was the KKK or an admired group. This lack of moderation by the situational
context of social exclusion is consistent with the lack of situational threat priming
effects found in the present study.

The main impetus of this study was to use Hansen and Hansen's (1988) face in the
crowd paradigm to test a theoretical divergence within the social exclusion literature.
As reviewed in the introduction, the Williams et al. position suggests that personality
and situational moderation of the detection of social exclusion would be maladaptive,
given the crucial nature of detection of social exclusion in keeping people in adequate
social standing within the relevant social groups they belong to. The opposing
viewpoint (Picket, Gardner, Downey) would suggest, on the other hand, that the
detection of social exclusion can be moderated in important ways by personality or
situational factors.

In the present study, I conducted a total of separate 48 tests of the
personality/priming threat moderation question, and found only 1 significant effect.
This significant effect, when analyzed using the full continuous range of personality
scores, becomes non-significant. Had I used a Bonferroni correction to adjust for the
total number of tests conducted (across several personality measures), then none of the
effects would have been statistically significant. Furthermore, had I only collected
personality data using the Rejection Sensitivity Questionnaire, I would have only
conducted 12 tests, and the Bonferroni adjusted alpha level would be approximately
.004. Even under those conditions, the one effect that I did observe (without the

correction) would again be non-significant. These results thus suggest that, using these

48

stimuli and methodological paradigm, the Williams et al. position was more strongly
supported. Moreover, it is not plausible that this pattern of null moderation effects can
be plausibly attributed to a lack of statistical power. For example, in the EN analyses,
each simple personality or priming interaction with target affect had a sample size of
142. The procedures recommended by Cohen (1992) indicate that even what he
classifies as a moderate interaction effect of .5 would have been detected with a
probability of approximately 95% with such a sample.

There are some limitations to this study that one must take into account before
prematurely over generalizing these conclusions. One obvious limitation is the highly
artificial nature of the paradigm in which social threat was operationalized. For one,
instead of using real faces to convey social disapproval, I decided to use schematic
faces. The reason for this decision was to avoid perceptual confounds that have
plagued previous studies using the same paradigm. Nevertheless, it is still feasible that
this attempt at methodological control created an overly sterile environment in which a
real threat of social exclusion may not have been reached.

Also relevant in this regard is my decision to operationalize situational threat using
explicit and implicit priming procedures rather than some less subtle means (e.g.,
manipulating the presence of an evaluative audience, as in Juth et al., 2004; putting
participants in a more mundanely real scenario). Preliminary plans of this study did
include a situational threat manipulation that put participants in a scenario that
allegedly threatened their status in a relevant social group. I decided to instead use the
priming procedure to avoid certain problems (e. g., demand characteristics). However,

this again may have resulted in an overly sterile manipulation that did not arouse the

49

situational threat that I originally conceived as being relevant to the detection of social
exclusion.

Another possible limitation is that the experimental procedures may have caused
undue participant fatigue. Participants made a total of 192 discriminations in the main
trial portion of the study. It is conceivable that with repeated trials, the threat value of
the angry face is attenuated. Although discriminations were relatively quick and did not
require much cognitive work, the sheer number of them may have led to fatigue. If
fatigue were truly problematic, however, then I would not have expected any effects
whatsoever. The fact that we did replicate the face in the crowd and get other effects
not related to threat moderation suggests that fatigue is most likely not a substantial
concern. It is also the case that other studies using a similar paradigm had comparable
number of trials and similar results (e. g., Juth et al. 2004).

Finally, it may be the case that this experimental paradigm did not fit the theoretical
questions of interest. For example, it may be that an angry schematic face conveys an
external threat (e. g., the target is angry at someone else) or a non-exclusionary threat
(e. g., the target wants to aggress against me), but not the exclusionary threat of primary
interest here. In future research, it may be wise to explore other paradigms, using other
threat stimuli (e.g., direct exclusion from a discussion group; ostracism in a ball toss
game) or other measures of sensitivity to such stimuli (e.g., Downey's eyelid
conditioning paradigm, Downey et al., 2004), to more effectively manipulate and
measure detection of threat. One of the criticisms of the paradigm used by Williams et
al. may also be relevant in this regard. In finding no personality or situational threat to

moderation, some investigators have suggested that the frequently used cyberball

50

manipulation is too strong and/or unamibiguous to expect any moderation of detection.
In the present study, I used schematic faces that were clearly conveyed negative affect
(i.e., angry), was clearly devoid of affect (i.e., neutral), and clearly conveyed positive
affect (i.e., happy). It may be the case that moderation of detection of social exclusion
may be uncovered using more ambiguous stimuli that did not have such clear affective
meaning. Along these lines, Inzlicht, Kaiser, and Major (2006) recently did a study in
which they had women watch a video of faces of men and women morphing from a
contemptuous expression to a happy expression. The women's task was to indicate
precisely when the expression transitioned from contemptuousness to happiness, and
the hypothesis was that participants high on stigma consciousness would see contempt
last longer on men's faces than on women's. This is exactly what they found, and it is
this type of continuous paradigm that may be more effective than the categorical angry,
neutral, and happy schematic faces used in this study.

In the end, although the results of this study suggests that the social exclusion alarm
does not seem to be adjustable according to personality or situational context, it is
obvious that much more research is needed to fully understand the mechanism that

humans use to avoid being socially burned.

51

Appendix A

The purpose of this appendix is to present the analyses pertaining to the remaining
three personality measures: Need to Belong, Attachment Style Questionnaire, as well as
a measure combining scales of the ASQ and the AAS (Adult Attachment Style). For
each analysis, I will first present the accuracy and reaction time results of the EN
analysis and then that of the EB analysis.

With regards to the main effects for Need to Belong scale, the EN analysis indicated
that participants high on NTB did not differ on overall accuracy from participants low
on NTB, F(l,141) = .340, p=.561. They also did not differ on reaction time,
F(l,141)=.907, p=.343. The results for the BE analysis were similar. There was no
main effect for accuracy, F(l,141)=.001, p=.979, or for reaction time, F(l,141)=.647,
p=.423.

I next analyzed the target x NTB interaction. For the EN analysis, I found no
interaction for accuracy, F(l,141)=1.093, p=.298, or for reaction time, F (1,141)=.476,
p=.492. Similarly, for the BE analysis, I found no target x NTB interaction for
accuracy, F(l,141)=.139, p=.710, or reaction time, F(l,141)=.279, p=.598. Finally,
none of the priming threat effects or target x priming threat x NTB interactions were
significant. 1 also conducted all the above analyses by leaving NTB continuous (on the
difference scores between the angry and happy target, as in the main text for RSQ).
Other than the significant intercepts, none of the EN/EE Accuracy or Reaction time
effects were significant (all regression tables for remaining personality measures will

be shown in Appendix B).

52

I then analyzed the results of the ASQ. For the EN analysis, I did find a main effect
such that participants high on ASQ (M=85%, SD=.OI4) made more errors than
participants low on ASQ (M=90%, SD=.014), F(l,130)=6.921, p = .010, n2 = .051.
There was no ASQ main effect, however, for reaction time, F (l ,130)=2.100, p=.150.

For the EB analysis, the main effect for ASQ on accuracy was again significant.
Participants low on ASQ (M=92%, SD=.013) were more accurate overall than
participants high on ASQ (M=87%, SD=.013) , F(l,130)=9.599, p=.002, n2 = .069. For
reaction time, however, there was no difference, F(l,130)=2.554, p=.112.

In terms of the target x ASQ interaction, the EN analysis revealed a marginal effect
that mirrored that of the target x RSQ interaction discussed in the main text, F(l,130) =
3.308, p=.071, n2 =.025. Namely, for those low on ASQ, accuracy for angry faces were
95% (SD=.018), while accuracy for happy faces were 84% (SD=.019). For participants
high on ASQ, accuracy for angry faces were 92% (SD=.012), while accuracy for happy
targets were 77% (SD=.019). There was no target x NTB interaction for reaction time,
F(l ,130)=.442, p=.507. With regards to the EB analysis, there was no target x NTB
interaction for accuracy, F(l,130)=1.959, p=.164, or reaction time, F(l ,130)=2.275,
p=.134. Also, none of the priming threat effects or the target x NTB x priming 3-way
interactions were significant. In addition to the doing a dichotomous split on ASQ, I
also conducted the above analyses using the continuous measure of ASQ. Other than
the significant intercept (which indicates the face in the crowd effect) there were no
other significant effects.

The last measure that I analyzed was a combination of the ASQ Discomfort, the

AAS Dependency, and the AAS Closeness subscales. This combination was an attempt

53

to capture a chronic personality disposition other than social anxiety that may be
feasibly be relevant to my main question of moderation of the detection of social
exclusion. I combined these scales by computing the Z scores for each subscale and
then computing the average of these Z-scores I will subsequently refer to as
Dependency. For the EN analysis, there was no difference between participants high or
low on Dependency for accuracy, F (l,142)=1 .779, p=.184, or for reaction time,

F (1,142)=.105, p=.747. For the EB analysis, there was a marginal effect such that
participants high on Dependency (M=91%, SD: .012) tended to be more accurate than
participants low on Dependency (M=88%, SD = .012), F(l,142)=3.497, p=.064, n2 =
.024. There was no difference, however, on reaction time performance, F (1 ,142)=.071 ,
p = .790.

In terms of the target x Dependency interaction, the EN analysis revealed no effect
for accuracy, F (1,142)=.001 , p=.972. There was a marginal effect, however, for
reaction time, F(l,142)=3.509, p=.063. For the EB analysis, there was no effect for
accuracy, F(l ,142)=2.277, p=.133, or for reaction time, F(l,142)=.212, p=.646.
Finally, none of priming threat effects or the target x priming x Dependency 3-way
interactions were significant.

In addition to the median split analysis, I also did an analysis that kept the measure

continuous. Other than the significant intercept, there were no other significant effects.

54

Appendix B

Table 6. Regression Analysis on EN Transformed RSQ Accuracy Scores

 

 

 

Coefficients(a)
Unstandardized Standardized
Coefficients Coefficients
Model - B Std. Error Beta t Sto-

1 (Constant) .211 .025 8.316 .000
RSOCent .012 .009 .167 1.372 .172
:gmeDum .003 .035 .007 .088 .930
:gQXPm“ -.002 .012 -.019 -.154 .878

 

 

 

 

 

 

 

Table 7. Regression Analysis on EN Untransformed RSQ Accuracy Scores

 

 

 

Coefficients(a)
Unstandardized Standardized
Coefficients Coefficients
Model _ 8 Std. Error Beta I 519-

1 (Constant) .134 .016 8.316 .000
RSQCent .008 .006 .167 1.372 .172
figmeoum .002 .022 .007 .088 .930
Egoxpfi'“ -.001 .008 -.019 -.154 .878

 

 

 

 

 

 

 

Table 8. Regression Analysis on EN Transformed RSQ Reaction Time Scores

 

 

 

Coefficlents(a)
Unstandardized Standardized
____Coefficients Coefficients
Model 7 8 Std. Error Beta I Sig.
1 (Constant) -.140 .015 9342 .000
RSQCent .001 .005 .014 .1 16 .908
SEND“ -.011 .021 -.045 -.538 .591
:3“an -.007 .007 -.114 -.935 .351

 

 

 

 

 

 

 

Table 9. Regression Analysis on EN Untransformed RSQ Reaction Time Scores

55

 

 

 

Coefficients(a)

 

 

 

 

 

 

 

 

 

 

 

 

 

Unstandardized Standardized
Coefficients Coefficients
MOCICI - B Std. Error Beta I Sig.
1 (Constant) -153.361 16.044 -9.559 .000
RSQCent .148 5.625 .003 .026 .979
figmeoum -5.262 22.375 -.020 -.235 .814
53mm“ -5.820 7.723 -.092 -.754 .452
Table 10. Regression Analysis on EE Transformed RSQ Accuracy Scores
Coefficients(a)
Unstandardized Standardized
_C_oefficients Coefficients
M0d9' , B Std. Error Beta I Sig.
1 (Constant) .092 .022 4.154 .000
RSQCent .005 .008 .086 .702 .484
22mm“ .029 .031 .080 .949 .344
ZSQXPW“ -.004 .011 -.044 -.358 .721

 

 

 

 

 

 

 

Table 1 1. Regression Analysis on EE Untransformed RSQ Accuracy Scores

 

 

 

 

 

 

56

 

 

 

Coefficients(a)
Unstandardized Standardized
Coefficients Coefficients
Model 8 Std. Error Beta I i 559-
(Constant) .058 .014 4.154 .000
RSQCent .003 .005 .086 .702 .484

 

 

 

PrimeDum
my
RSQXPrim
eD

.019 .020 .080

-.002 .007 -.044

.949

-.358

.344

.721

Table 12. Regression Analysis on EE Transformed RSQ Reaction Time Scores

 

 

 

 

 

 

 

 

 

Coefficientsa
Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta t Sig. ‘
1 (Constant) -.098 .015 -6.639 .000

RSQCent .006 .005 .138 1.125 .263

PrimeDummy -.007 .021 -.030 -.360 .720

RSQXPrimeD -.008 .007 -.132 -1 .077 .283

 

 

a. Dependent Variable: DifEEtRT

Table 13. Regression Analysis on EE Untransformed RSQ Reaction Time Scores

 

 

 

 

 

 

 

 

Coefficients“I
Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta 1 Sig.

1 (Constant) -1 16.238 18.079 —6.429 .000
RSQCent 6.841 6.339 .133 1 .079 .282
PrimeDummy -15.989 25.212 -.053 -.634 .527
RSQXPrimeD -8.640 8.702 -.121 -.993 .322

 

 

 

a. Dependent Variable: DifEERT

Table 14. Regression Analysis on EN Transformed NTB Accuracy Scores

57

 

 

Coefficients'

 

 

 

 

 

 

 

 

 

Unstandardized Standardized
Coefficients Coefficients

Model 8 Std. Error Beta 1 Sig.

1 (Constant) .205 .026 8.052 .000
NTBcent .001 .003 .034 .397 .692
PrimeDummy .012 .036 .029 .347 .729
NTBXPrimeD -.007 .061 -.010 -.116 .908

 

a. Dependent Variable: DifENAcT

Table 15. Regression Analysis on EN Untransformed NTB Accuracy Scores

 

 

 

Coefficientsa
Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta t Sig. 1
1 (Constant) .131 .016 8.052 .000

NTBcent .001 .002 .034 .397 .692

PrimeDummy .008 .023 .029 .347 .729

NTBXPrimeD -.004 .039 -.010 -.116 .908

 

 

 

 

 

 

 

a. Dependent Variable: DifENAc

Table 16 Regression Analysis on EN Transformed NTB Reaction Time Scores

 

 

 

Coefficient?
Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta 1 Sig.

1 (Constant) -.140 .015 -9.369 .000
NTBcent ~.001 .002 -.030 -.354 .724
PrimeDummy -.016 .021 -.062 -.739 .461
NTBXPrimeD .008 .036 .020 .239 .812

 

 

 

 

 

 

 

a. Dependent Variable: DifENtRT

58

Table 17. Regression Analysis on EN Untransformed NTB Reaction Time Scores

 

Coefficients’

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta 1 Sig.

1 (Constant) 453.133 15.957 -9.597 .000
NTBcent -.547 1 .786 -.026 -.306 .760
PrimeDummy -9.421 22.422 -.036 -.420 .675
NTBXPrimeD 16.005 37.908 .036 .422 .674

a. Dependent Variable: DifENRT
Table 18. Regression Analysis on EE Transformed NTB Accuracy Scores
Coefficients"
Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta 1 Sig.

1 (Constant) .090 .022 4.058 .000
NTBcent .000 .002 .017 .199 .842
PrimeDummy .032 .031 .088 1.046 .297
NTBXPrimeD -.006 .052 -.010 -.1 19 .905

 

 

 

 

 

 

 

 

a. Dependent Variable: DifEEAcT

Table 19. Regression Analysis on EE Untransformed NTB Accuracy Scores

 

 

 

 

 

 

 

 

 

Coefficientsa
Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta t Sig. ‘
1 (Constant) .057 .014 4.058 .000

NTBcent .000 .002 .017 .199 .842

PrimeDummy .021 .020 .088 1 .046 .297

NTBXPrimeD -.004 .033 -.010 -.119 .905

 

 

a. Dependent Variable: DifEEAc

59

Table 20. Regression Analysis on EE Transformed NTB Reaction Time Scores

 

 

 

 

 

 

 

 

 

Coefficients"
Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta t Sig.

1 (Constant) -.099 .015 -6.752 .000
NTBcent -.001 .002 -.073 -.868 .387
PrimeDummy -.008 .021 -.033 -.396 .692
NTBXPrimeD .006 .035 .015 .177 .860

 

 

a. Dependent Variable: DifEEtRT

Table 2]. Regression Analysis on EE Untransformed NTB Reaction Time Scores

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Coefficient?
Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta t Sig.

1 (Constant) -1 17.762 17.997 -6.544 .000
NTBcent -1.927 2.014 -.081 -.957 .340
PrimeDummy -16.225 25.288 -.054 -.642 .522
NTBXPrimeD 25.187 42.755 .050 .589 .557

a. Dependent Variable: DifEERT
Table 22. Regression Analysis on EN Transformed ASQ Accuracy Scores
Coefficients’
Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta t Sig.

1 (Constant) .194 .025 7.757 .000
ASchnt .001 .001 .150 1.234 .219
PrimeDummy .017 .035 .042 .485 .628
ASQXPrimeD .001 .002 .093 .764 .446

 

 

 

 

 

 

 

 

a. Dependent Variable: DifENAcT

Table 23. Regression Analysis on EN Untransformed ASQ Accuracy Scores

60

Coefficients’

 

 

 

 

 

 

 

 

Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta t Sig.

1 (Constant) .123 .016 7.757 .000
ASchnt .001 .001 .150 1.234 .219
PrimeDummy .01 1 .022 .042 .485 .628
ASQXPrimeD .001 .001 .093 .764 .446

 

 

 

3- Dependent Variable: DifENAc

Table 24. Regression Analysis on EN Transformed ASQ Reaction Time Scores

 

 

 

Coefficientsa
Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta t Sig. ,
1 (Constant) -.144 .016 -9.176 .000

ASchnt .001 .001 .144 1 .168 .245

PrimeDummy -.014 .022 -.057 -.652 .515

ASQXPrimeD -.002 .001 -. 195 -1.579 .1 17

 

 

 

 

 

 

 

 

a. Dependent Variable: DifENtRT

Table 25. Regression Analysis on EN Untransformed ASQ Reaction Time Scores

 

 

 

Coefficients?
Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta t Sig.

1 (Constant) -1 58.607 16.861 -9.407 .000
ASchnt .824 .790 .129 1 .043 .299
PrimeDummy -7.798 23.667 -.029 -.329 .742
ASQXPrimeD -1.464 1.112 -.163 -1.317 .190

 

 

 

 

 

 

 

 

a. Dependent Variable: DifENRT

Table 26. Regression Analysis on EE Transformed ASQ Accuracy Scores

61

CoefficientsI

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Unstandardized Standardized
Coefficients Coefficients
Model B Std. Error Beta 1 Sig. ‘
1 (Constant) .099 .022 4.438 .000
ASchnt .001 .001 .132 1.063 .290
PrimeDummy .023 .031 .064 .727 .468
ASQXPrimeD -.001 .001 -.126 -1.014 .312
a. Dependent Variable: DifEEAcT
Table 27 Regression Analysis on EE Untransformed ASQ Accuracy Scores
Coefficients’
Unstandardized Standardized
Coefficients Coefficients
Model B Std. Error Beta t Sig. ,
1 (Constant) .063 .014 4.438 .000
ASchnt .001 .001 .132 1 .063 .290
PrimeDummy .015 .020 .064 .727 .468
ASQXPrimeD -.001 .001 -.126 -1.014 .312

 

 

 

 

 

 

 

 

a. Dependent Variable: DifEEAc

Table 28. Regression Analysis on EE Transformed ASQ Reaction Time Scores

 

 

 

 

 

 

 

 

 

Coefficientsii
Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta t Si .

1 (Constant) -.097 .015 -6.315 .000
ASchnt .001 .001 .144 1 .162 .247
PrimeDummy -.O12 .022 -.048 -.552 .582
ASQXPrimeD -.001 .001 -.140 -1 .132 .260

 

a. Dependent Variable: DifEEtRT

Table 29. Regression Analysis on EE Untransformed ASQ Reaction Time Scores

62

 

Coefficientsa

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta 1 Si .

1 (Constant) -116.930 18.962 -6. 167 .000
ASchnt 1.031 .888 .144 1.161 .248
PrimeDummy -18.386 26.617 -.060 -.691 .491
ASQXPrimeD -1.500 1.250 -.148 -1.199 .233

a. Dependent Variable: DifEERT
Table 30. Regression Analysis on EN Transformed COMB Accuracy Scores
Coefficientsa
Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta t Sig.

1 (Constant) .210 .025 8.237 .000
COMBcent -.082 .068 -.153 -1 .199 .232
PrimeDummy .007 .035 .018 .209 .835
COMBXPrimeD .074 .091 .103 .813 .418

a. Dependent Variable: DifENAcT
Table 3 l . Regression Analysis on EN Untransformed COMB Accuracy Scores
Coefficientsa
Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta t Sig.

1 (Constant) .133 .016 8.237 .000
COMBcent -.052 .044 -.153 -1.199 .232
PrimeDummy .005 .023 .018 .209 .835
COMBXPrimeD .047 .058 .103 .813 .418

 

 

 

 

 

 

 

 

a. Dependent Variable: DifENAc

Table 32. Regression Analysis on EN Transformed COMB Reaction Time Scores

63

Coefficients“

 

 

 

 

Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta t Sig.

1 Tonstant) -. 140 .015 -9.284 .000
COMBcent -.015 .040 -.047 -.366 .715
PrimeDummy _ -.O14 .021 -.055 -.652 .516
COMBXPrimeD .020 .054 .047 .367 .714

 

 

 

 

 

 

 

a. Dependent Variable: DifENtRT

Table 33. Regression Analysis on EN Untransformed COMB Reaction Time Scores

 

 

 

Coefficients”
Unstandardized Standardized
Coefficients Coefficients
Model B Std. Error Beta t Sig.
'1__'(?Tmstant) 452.841 16.072 -9.510 .000
COMBcent 43.309 43.175 -.040 -.308 .758
PrimeDummy -7.329 22.403 -.028 -.327 .744
COMBXPrimeD 25.108 57.276 .056 .438 .662

 

 

 

 

 

 

 

 

a. Dependent Variable: DifENRT

Table 34. Regression Analysis on EE Transformed COMB Accuracy Scores

 

 

 

Coefficients’
Unstandardized Standardized
Coefficients Coefficients
Model B Std. Error Beta t Sig.

T—(Eonstant) .095 .022 4.343 .000
COMBcent -.1 15 .059 -.247 -1.954 .053
PrimeDummy .027 .030 .073 .876 .382
COMBXPrimeD .108 .078 .175 1.392 .166

 

 

 

 

 

 

 

 

a. Dependent Variable: DifEEAcT

Table 35. Regression Analysis on EE Untransformed COMB Accuracy Scores

64

Coefficientsa

 

 

 

 

 

 

 

 

 

Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta t Sig.

1 (Constant) .060 .014 4.343 .000
COMBcent -.O73 .037 -.247 -1 .954 .053
PrimeDummy .017 .019 .073 .876 .382
COMBXPrimeD .069 .050 .175 1.392 .166

 

a. Dependent Variable: DifEEAc

Table 36. Regression Analysis on EE Transformed COMB Reaction Time Scores

 

 

 

 

 

 

 

 

 

Coefficientsa
Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta t Si .

1 (Constant) -.100 .015 -6.749 .000
COMBcent -.005 .040 -.015 -.120 .905
PrimeDummy -.006 .021 -.024 -.284 .777
COMBXPrimeD .01 1 .053 .027 .208 .836

 

a. Dependent Variable: DifEEtRT

Table 37. Regression Analysis on EE Untransformed COMB Reaction Time Scores

 

 

 

 

 

 

 

 

 

Coefficients“
Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta t Sig.

1 (Constant) -118.521 18.104 -6.547 .000
COMBcent -5.438 48.637 -.014 -.112 .911
PrimeDummy -13.296 25.236 -.044 -.527 .599
COMBXPrimeD 31 .281 64.521 .062 .485 .629

 

a. Dependent Variable: DifEERT

65

 

 

 

Appendix C

Need to Belong Scale
(Leary, Kelly, Cottrell, & Schreindorfer, 2005)
Instructions: For each of the statements below, indicate the degree to which you agree
or disagree with the statement by writing a number in the space beside the question
using the scale below:
1 = Strongly disagree
2 = Moderately disagree
3 = Neither agree nor disagree
4 = Moderately agree
5 = Strongly agree
1. If other people don't seem to accept me, I don't let it bother me.
2. I try hard not to do things that will make other people avoid or reject me.
3. I seldom worry about whether other people care about me.
4. I need to feel that there are people I can turn to in times of need.
5. I want other people to accept me.
6. I do not like being alone.
7. Being apart from my friends for long periods of time does not bother me.
8. I have a strong need to belong.

9. It bothers me a great deal when I am not included in other people's plans.

10. My feelings are easily hurt when I feel that others do not accept me.

Rejection Sensitivity Questionnaire (Downey & F eldman, 1996)

66

Each of the items below describes things college students sometimes ask of other
people. Please imagine that you are in each situation. You will be asked to answer the
following questions:

1) How concerned or anxious would you be about how the other person would
respond?

2) How do you think the other person would be likely to respond?

1. You ask someone in class if you can borrow his/her notes.

How concerned or anxious would you be over whether very unconcerned very
concerned

or not the person would want to lend you his/her notes? I 2 3 4 5
6

I would expect that the person would willingly give me very unlikely very
likely

his/her notes. I 2 3 4 5
6

2. You ask your boyfriend/girlfriend to move in with you.

How concerned or anxious would you be over whether very unconcerned very
concerned

or not the person would want to move in with you? I 2 3 4 5
6
. I would expect that he/she would want to move in very unlikely very
likely

with me. I 2 3 4 5
6

3. You ask your parents for help in deciding what programs to apply to.

How concerned or anxious would you be over whether very unconcerned very
concerned

or not your parents would want to help you? 1 2 3 4 5
6

I would expect that they would want to help me. very unlikely very
likely

I 2 3 4 5

6

4. You ask someone you don’t know well out on a date.

How concerned or anxious would you be over whether very unconcerned very
concerned
or not the person would want to go out with you? I 2 3 4 5

6

67

I would expect that the person would want to go out with very unlikely very
likely

me. I 2 3 4 5
6

5. Your boyfriend/girlfriend has plans to go out with friends tonight, but you
really want to spend the evening with him/her, and you tell him/her so.

How concerned or anxious would you be over whether very unconcerned very
concerned

or not your boyfriend/girlfriend would decide to stay in? l 2 3 4 5
6

I would expect that the person would willingly choose very unlikely very
likely

to stay in. l 2 3 4 5
6

6. You ask your parents for extra money to cover living expenses.

How concerned or anxious would you be over whether very unconcerned very
concerned

or not your parents would help you out? I 2 3 4 5
6

I would expect that my parents would not mind helping very unlikely very
likely

me out. 1 2 3 4 5

6 7. After class, you tell your professor that you have been having some trouble
with a section of the course and ask if he/she can give you some extra help.

How concerned or anxious would you be over whether very unconcerned very
concerned

or not your professor would want to help you out? 1 2 3 4 5
6

I would expect that my professor would want to help very unlikely very
likely

me out. 1 2 3 4 5
6

8. You approach a close friend to talk after doing or saying something that
seriously upset him/her.

How concerned or anxious would you be over whether very unconcerned very
concerned

or not your friend would want to talk with you? I 2 3 4 5
6

I would expect that he/she would want to talk with me very unlikely very
likely

to try to work things out. I 2 3 4 5
6

68

9. You ask someone in one of your classes to coffee.

How concerned or anxious would you be over whether very unconcerned very
concerned

or not the person would want to go? I 2 3 4 5
6

I would expect that the person would want to go very unlikely very
likely

with me. 1 2 3 4 5
6

10. After graduation, you can’t find a job and ask your parents if you can live at
home for a while.

How concerned or anxious would you be over whether very unconcerned very
concerned

or not your parents would want you to come home? I 2 3 4 5
6

I would expect I would be welcome at home. very unlikely very
likely

I 2 3 4 5

6

11. You ask your friend to go on a vacation with you over Spring Break.

How concerned or anxious would you be over whether very unconcerned very
concerned

or not your friend would want to go with you? I 2 3 4 5
6

I would expect that he/she would want to go with me. very unlikely very
likely

I 2 3 4 5

6

12. You call your boyfriend/girlfriend after a bitter argument and tell him/her
you want to see him/her.

How concerned or anxious would you be over whether very unconcerned very
concerned

or not your boyfriend/girlfriend would want to see you? 1 2 3 4 5
6

I would expect that he/she would want to see me. very unlikely very
likely

I 2 3 4 5

6

13. You ask a friend if you can borrow something of his/hers.

How concerned or anxious would you be over whether very unconcerned very
concerned

69

or not your friend would want to loan it to you? I 2 3 4 5

I would expect that he/she would willingly loan me it. very unlikely very
likely
I 2 3 4 5
6

14. You ask your parents to come to an occasion important to you.

How concerned or anxious would you be over whether very unconcerned very
concerned

or not your parents would want to come? 1 2 3 4 5
6

I would expect that my parents would want to come. very unlikely very
likely

I 2 3 4 5

6

15. You ask a friend to do you a big favor.

How concerned or anxious would you be over whether very unconcerned very
concerned

or not your friend would do this favor? l 2 3 4 5
6

I would expect that he/she would willingly do very unlikely very
likely

this favor for me. 1 2 3 4 5
6

16. You ask your boyfriend/girlfriend if he/she really loves you.

How concerned or anxious would you be over whether very unconcerned very
concerned

or not your boyfriend/girlfriend would say yes? I 2 3 4 5
6

I would expect that he/she would answer yes sincerely. very unlikely very
likely

I 2 3 4 5

6

17. You go to a party and notice someone on the other side of the room and then
you ask them to dance.

How concerned or anxious would you be over whether very unconcerned very
concerned

or not the person would want to dance with you? I 2 3 4 5
6

I would expect that he/she would want to dance with me. very unlikely very
likely

I 2 3 4 5

6

70

18. You ask your boyfriend/girlfriend to come home to meet your parents.

How concerned or anxious would you be over whether very unconcerned very
concerned

or not your boyfriend/girlfriend would want to meet I 2 3 4 5
6

your parents?

I would expect that he/she would want to meet my very unlikely very
likely

parents. I 2 3 4
6

71

Adult Attachment Scale (Collins & Read, 1990)
Each item rated from not at all characteristic (1) to very characteristic (5)

I find it difficult to allow myself to depend on others*

People are never there when you need them*

I am comfortable depending on others.

I know that others will be there when I need them.

I find it difficult to trust others completely*

I am not sure that I can always depend on others to be there when I need them*
I do not often worry about being abandoned*

I often worry that my partner does not really love me.

I find others are reluctant to get as close as I would like.

10. I often worry that my partner will not want to stay with me.

11. I want to merge completely with another person.

12. My desire to merge sometimes scares people away.

13. I find it relatively easy to get close to others.

14. I do not often worry about someone getting too close to me.

15. I am somewhat uncomfortable being close to others*

16. I am nervous when anyone gets too close to me*

17. I am comfortable having others depend on me.

18. Often, love partners want to be more intimate than I feel comfortable being*

95”.“???pr

Subscales
Depend — 1 through 6
Anxiety — 7 through 12
Close — 13 through 18

*Asterisk denotes that item needs to be reverse coded

72

Attachment Style Questionnaire (F eeney, Nollar, & Hanrahan, 1994)

Scale ranges from totally disagree (1) to totally agree (6)

WNQV‘PPPT‘

9

10.
11.
12.
13.
14.
15.
l6.
I7.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.

Overall, I am a worthwhile person.

I am easier to get to know than most people.

I feel confident that people will be there when I need them.

I prefer to depend on myself rather than other people.

I prefer to keep to myself

To ask for help is to admit that you’re a failure.

People’s worth should be judged by what they achieve.

Achieving things is more important than building relationships.

Doing your best is more important than getting on with others.

If you’ve got a job to do, you should 0 it no matter who gets hurt.

It’s important to me that others like me.

It’s important to me to avoid doing things that others won’t like.

I find it hard to make a decision unless I know what other people think.
My relationships with other are generally superficial.

Sometimes I think I am no good at all.

I find it hard to trust other people.

I find it difficult to depend on others.

I find that others are reluctant to get as close as I would like.

I find it relatively easy to get close to other people.

I find it easy to trust others.

I feel comfortable depending on other people.

I worry that others won’t care about me as much as I care about them.
I worry about people getting too close.

I worry that I won’t measure up to other people.

I have mixed feelings about being close to others.

While I want to get close to others, I feel uneasy about it.

I wonder why people would want to know me.

It’s very important to me to have a close relationship.

I worry a lot about my relationships.

I wonder how I would cope without someone to love me.

I feel confident about relating to others.

I often feel left out or alone.

I often worry that I do not really fit in with other people.

Other people have their own problems so I don’t bother them with mine.
When I talk over my problems with others I generally feel ashamed or foolish.
I am too busy with other activities to put much time into relationships.
If something is bothering me, others are generally aware and concerned.
I am confident that other people will like and respect me.

I get fi'ustrated when others are not available when 1 need them.

Other people often disappoint me.

Anxiety subscale — 24, 22, 32, 33, 27, 13, 18, 15, 11, 38, 29, 30, 31

73

Discomfort subscale — 20, 16, 21, 19, 5, 17, 14, 10, 25, 3, 4, 9, 23, 37, 8, 34
Items 20, 21, 19, 3, 37, 38, and 31 need to be reverse coded

74

Appendix D
Neutral supraliminal priming task:
Instructions: For each set of words below, make a grammatical four word sentence and

write it down in the space provided.

For example:
flew eagle the plane around

The eagle few around

1. send 1 mail it over

2. saw hammer he train the

3. ate it selfishly all he

4. the machine wash frequently clothes
5. they obedient him often meet

6. ball the throw toss silently

7. prepare neatly gift the wrap

8. picked throw apples hardly the

9. sky the seamless red is

10. a have June holiday wedding

Rejection related supraliminal priming task:

Instructions: For each of the set of words below, make a grammatical four word

sentence and write it down in the space provided.

75

For example:

8.

9.

flew eagle the plane around

The eagle few around

. was the comfortless sat baby

. cast he him it out

. hermit was far isolated the

. behavior is this disgusting turned

. rejected effort the remained idea

smaller the help one exclude
them avoid need usually I
eat ran was banished the

the lonesome prisoners these were

10. that should we him ignore

Neutral primes: pencil, brick, calendar, doorbell

Rejection-related primes: loser, excluded, rejected, weirdo

76

REFERENCES

Aronoff, J ., Barclay, A.M., & Stevenson, LA. (1988). The recognition of threatening
facial stimuli. Journal of Personality and Social Psychology. Vol, 54(4), 647-
655.

Aronoff, J ., Woike, B.A., & Hyman, L.M. (1992). Which are the stimuli in facial
displays of anger and happiness? Configurational bases of emotion recognition.
Journal of Personality and Social Psychology. Vol, 62(6), 1050-1066.

Barrera M., Maurer D., (1981). Recognition of mother's photographed face by the
three-month-old infant, Child Development, 52, 714 - 716

Bargh, J.A., & Chartrand, T.L. (2000). The mind in the middle: A practical guide to
priming and automaticity research. In H.T. Reis & C.M. Judd (Eds) Handbook
of research methods in social and personality psychology (pp. 253-285). New
York: Cambridge University Press.

Baumeister, R.F., & Leary, MR. (1995). The need to belong: Desire for interpersonal
attachments as a fundamental human motivation. Psychological Bulletin,
11 7(3), 497-529.

Baumeister, R.F., & Tice, D.M. (1990). Anxiety and social exclusion. Journal of Social
and Clinical Psychology. Vol, 9(2), 165-195.

Borland, C., Zadro, L., & Richardson, R. (2004). Eflects of ostracism on normals and
social phobics over time. University of New South Wales, Unpublished
manuscript.

Bouhuys, A.L., Bloem, G.M., & Groothuis, T.G.G. (1995). Induction of depressed and
elated mood by music influences the perception of facial emotional expressions
in healthy subjects. Journal of Aflective Disorders. Vol, 33(4), 215-226.

Bradbury, T.N., & Fincham, FD. (1990). Attributions in maniage: Review and
critique. Psychological Bulletin. Vol, 107(1), 3-33.

Bruner, J. (1992). Another look at New Look 1. American Psychologist, 47, 780-783.

Cohen, J. (1992). Statistical Power Analysis. Current Directions in Psychological
Science (3): 98 - 101.

Collins, N., Read, 8., (1990). Adult attachment, working models, and relationship

quality in dating couples. Journal of Personality and Social Psychology, 58(4),
644-663.

77

Downey, G., & Feldman, SJ. (1996). Implications of rejection sensitivity for intimate
relationships. Journal of Personality and Social Psychology. Vol, 70(6), 1327-
1343.

Eisenberger, N.I., Lieberman, M.D., & Williams, K.D. (2003). Does Rejection Hurt?
An fMRI Study of Social Exclusion. Science. Vol, 302(5643), 290-292.

Feeney, J., Noller, P., and Hanrahan, M. (1994), 'Assessing adult attachment'. In W.
Berman and M. Sperling (eds.), Attachment in adults: Clinical and
developmental perspectives, Guilford Press, New York.

Fox, E., Victoria, L., Russo, R., Bowles, R.J., Pichler, A., & Dutton, K. (2000). Facial
Expressions of Emotion: Are Angry Faces Detected More Efficiently?
Cognition and Emotion, 14(1), 61-92.

Gardner, W.L., Pickett, C.L., & Brewer, MB. (2000). Social exclusion and selective
memory: How the need to belong influences memory for social events.
Personality and Social Psychology Bulletin. Vol, 26(4), 486-496.

Gardner, W.L., Pickett, C.L., & Knowles, M. (2004). Social Snacking and shielding:
Using social symbols, selves, and surrogates in the service of belonging needs.
In K. D. Williams, J. P. Forgas & W. von Hippel (Eds.), The Social Outcast:
Ostracism, Social Exclusion, Rejection, and Bullying.

Gonsalkorale, K., & Williams, K. D. (2004). The KKK won’t let me play: Ostracism by
a despised outgroup hurts. Unpublished manuscript, University of New South
Wales, Sydney, Australia.

Hampton, C., Purcell, D.G., Bersine, L., Hansen, C.H., & et al. (1989). Probing "pop-
out": Another look at the face-in-the-crowd effect. Bulletin of the Psychonomic
Society. Vol, 27(6), 563-566.

Hansen, C., & Hansen, RD. (1988). Finding the Face in the Crowd: The Anger
Superiority effect. Journal of Personality and Social Psychology, 54(6), 917-
924.

Hazan, C., & Shaver, P. R. (1987). Romantic love conceptualized as an attachment
process. Journal of Personality and Social Psychology, 52, 511-524.

Homa, D., Haver, B., & Schwartz, T. (1976). Perceptibility of schematic face stimuli:
evidence for a perceptual Gestalt. Memory and Cognition, 4 (2), 176—185.

J uth, P., Lundqvist, D., Karlsson, A., & Ohman, A. (2005). Looking for foes and

friends: Perceptual and emotional factors when finding a face in the crowd.
Emotion, 5, 379-395

78

Inzlicht, M., Kaiser, C. R., & Major, B. (2006). The face of chauvinism: How prejudice scripts
shape perceptions of facial affect. Manuscript under review at Journal of Experimental
Social Psychology.

Kobak, R. R., & Hazan, C. (1991). Attachment in marriage: Effects of security and
accuracy of working models. Journal of Personality and Social Psychology, 60,
861—869.

LaBarbera, J .D., lzard, C.E., Vietze, P., & Parisi, SA. (1976). Four- and six-month-old
infants' visual responses to joy, anger, and neutral expressions. Child
Development. Vol, 47(2), 535-538.

Lakin, J., & Chartrand, T.L. (2003). Using nonconscious behavioral mimicry to create
affiliation and rapport. Psychological Science, 14, 334-339.

LeDoux, J .E. (1996) The Emotional Brain. New York, Simon and Schuster.

Lundqvist, D., & Ohman, A. (2005). Emotion regulates attention: The relation between
facial configurations, facial emotion, and visual attention. Visual Cognition.
Vol, 12(1), 51-84.

Mogg, K., Bradley, B. P. & Williams, R. (1995). Attentional bias in anxiety and
depression: the role of awareness. British Journal of Clinical Psychology, 34,
17-36.

Nadasi, C. (1995). The eflects of social ostracism on verbal and non-verbal behavior in
introverts and extraverts. Honors thesis, University of Toledo, Toledo, OH.

Noller, P., & F eeney, J. A. (1994). Relationship satisfaction, attachment, and nonverbal
accuracy in early marriage. Journal of Nonverbal Behavior, 18, 199-221.

Nothdurft, HQ (1993). Faces and facial expressions do not pop out . Perception, 22,
1287-1298.

Northdurft, HQ (2002). Attention shifts to salient targets. Vision Research. Vol,
42(10), 1287-1306.

Nowicki, S., & Duke, MP. (1992). The association of children's nonverbal decoding
abilities with their popularity, locus of control, and academic achievement.
Journal of Genetic Psychology. Vol, 153(4), 385-393.

Ohman, A., F lykt, A., & Esteves, F. (2001). Emotion drives attention: Detecting the
snake in the grass. Journal of Experimental Psychology: General. Vol, 130(3),
466-478.

Perlman, D., & Peplau, LA. (1984). Loneliness research: A survey of empirical
findings. In Peplau, Letitia Anne (Ed); Goldston, Stephen E (Ed). (1984).

79

 

Preventing the harmful consequences of severe and persistent loneliness. (pp.
13 46). Rockville, MD, US: National Institute of Mental Health. xi, 131 pp.

Pickett, C.L., & Gardner, W.L. (2004). The Social Monitoring System. In K. D.
Williams, J. P. Forgas & W. von Hippel (Eds.), The Social Outcast: Ostracism,
Social Exclusion, Rejection, and Bullying.

Pickett, C.L., Gardner, W.L., & Knowles, M. (2004). Getting a Cue: The Need to
Belong and Enhanced Sensitivity to Social Cues. Personality and Social
Psychology Bulletin, 30(9), 1095-1107.

Postman, L., Bnmer, J. S., McGinnies, E. (1948). Personal values as selective factors in
perception. J. Abnormal Social Psychology 43: 142-54

Pratto, F. & John, O. P. (1991). Automatic vigilance: The attention-grabbing power of
negative social information. Journal of Personality and Social Psychology, 61,
380—391.

Purcell, DC. (1996). It takes a confounded face to pop out of a crowd. Perception, 25 ,
1091-1108.

Schwartz, G.M., lzard, C.E., & Ansul, SE. (1985). The 5-month-old's ability to
discriminate facial expressions of emotion. Infant Behavior and Development.
Vol, 8(1), 65-77.

Serrano, J .M., lglesias, J ., & Loeches, A. (1992). Visual discrimination and recognition
of facial expressions of anger, fear, and surprise in 4- to 6-month-old infants.
Development Psychobiology, 25, 41 1-425.

Simpson, J. (1990). The influence of attachment styles on romantic relationships.
Journal of Personality and Social Psychology, 59, 971-980.

Smith, A., & Williams, K. D. (2003). R U There? Ostracism by cell phone text
messages. Unpublished manuscript, Macquarie University, Sydney, Australia.

Twenge, J .M., Baumeister, R.F., Tice, D.M., & Stucke, TS. (2001). If you can't join
them, beat them: Effects of social exclusion on aggressive behavior. Journal of
Personality and Social Psychology. Vol, 81(6), 1058-1069.

Warburton, W. A. (2002). Aggressive responding to ostracism: The moderating roles of
control motivation and narcissistic vulnerability, and the mediating role of
negative affect. Honors thesis. Macquarie University, Sydney, Australia.

Warburton, W. A., Williams, K. D., & Cairns, D. (2004). Swept away by a loss of
control: When ostracism leads to aggression. Unpublished manuscript,
Macquarie University.

80

 

 

ll ..r. - ..n

Warburton, W.A., & Williams, K.D. (2005). Ostracism: When competing motivations
collide. In F orgas, Joseph P (Ed)

White, M.J. (1995). Preattentive analysis of facial express ions of emotion. Cognition
and Emotion, 9, 439- 460.

Williams, Kipling D (Ed); Laham, Simon M (Ed). (2005). Social motivation:
Conscious and unconscious processes. mp. 294 313). New York, NY, US:
Cambridge University Press. xx, 386 pp.

Williams, K.D., Shore, W.J., & Grahe, J .E. (1998). The silent treatment: Perceptions of
its behaviors and associated feelings. Group Processes and Intergroup
Relations. Vol, 1(2), 117-141.

Williams, K.D., & Sommer, KL. (1997). Social ostracism by coworkers: Does
rejection lead to loafing or compensation? Personality and Social Psychology
Bulletin. Vol, 23(7), 693-706.

Williams, K. D., Cheung, C. K. T., & Choi, W. (2000). Cyberostracism: Effects of
being ignored over the Internet. Journal of Personality and Social Psychology,
79, 748—762.

Williams, K.D., & Zadro, L. (2001). Ostracism: On being ignored, excluded, and
rejected. In Leary, Mark R (Ed). (2001 ). Interpersonal rejection. (pp. 21 -53).
New York, NY, US: Oxford University Press. xii, 334 pp.

Williams, K.D., & Zadro, L. (2004). Ostracism: The Indiscriminate Early Detection
System. In K. D. Williams, J. P. Forgas & W. von Hippel (Eds), The Social
Outcast: Ostracism, Social Exclusion, Rejection, and Bullying. New York:
Psychology Press.

Wyer, R.S., & Srull, T.K. (Eds.), Handbook of Social Cognition: Laurence Erlbaum &
Associates.

Zadro, L., Williams, K.D., & Richardson, R. (2004). How low can you go? Ostracism
by a computer is sufficient to lower self-reported levels of belonging, control,
self-esteem, and meaningful existence. Journal of Experimental Social
Psychology. Vol, 40(4), 560-567.

Zadro, L., Williams, K.D., & Richardson, R. (2005). Riding the '0' Train: Comparing
the Effects of Ostracism and Verbal Dispute on Targets and Sources. Group
Processes and Intergroup Relations. Vol, 8(2), 125-143.

Zajonc, R. B., and Markus. H. 1984. Affect and Cognition: The Hard Interface, in

Emotions, Cognition, and Behavior. Eds. Carroll E. lzard, Jerome Kagan, and
Robert B. Zajonc. Cambridge: Cambridge University Press, 73-102.

81