Mechanisms of Empathic Behavior in Children with Callous-Unemotional Traits:
Eye Gaze and Emotion Recognition
Lauren A. Delk
Thesis submitted to the faculty of the Virginia Polytechnic Institute and State University in
partial fulfillment of the requirements for the degree of
Master of Science
In
Psychology
Bradley A. White, Chair
Jungmeen Kim-Spoon
Susan W. White
November 16, 2016
Blacksburg, VA
Keywords: emotion recognition, prosocial behavior, callous-unemotional traits, empathy
Mechanisms of Empathic Behavior in Children with Callous-Unemotional Traits:
Eye Gaze and Emotion Recognition
Lauren A. Delk
ABSTRACT
The presence of callous-unemotional (CU) traits (e.g., shallow affect, lack of empathy) in children
predicts reduced prosocial behavior. Similarly, CU traits relate to emotion recognition deficits,
which may be related to deficits in visual attention to the eye region of others. Notably, recognition
of others’ distress necessarily precedes sympathy, and sympathy is a key predictor in prosocial
outcomes. Thus, visual attention and emotion recognition may mediate the relationship between
CU traits and deficient prosocial behavior. Elucidating these connections furthers the development
of treatment protocols for children with behavioral problems and CU traits. This study seeks to:
(1) extend this research to younger children, including girls; (2) measure eye gaze using infrared
eye-tracking technology; and (3) test the hypothesis that CU traits are linked to prosocial behavior
deficits via reduced eye gaze, which in turn leads to deficits in fear recognition. Children (n = 81,
ages 6-9) completed a computerized, eye-tracked emotion recognition task and a standardized
prosocial behavior task while parents reported on the children’s CU traits. Results partially
supported hypotheses, in that CU traits predicted less time focusing on the eye region for fear
expressions, and certain dimensions of eye gaze predicted accuracy in recognizing some emotions.
However, the full model was not supported for fear or distress expressions. Conversely, there was
some evidence that the link between CU traits and deficient prosocial behavior is mediated by
reduced recognition for low intensity happy expressions, but only in girls. Theoretical and practical
implications for these findings are considered.
Mechanisms of Empathic Behavior in Children with Callous-Unemotional Traits:
Eye Gaze and Emotion Recognition
Lauren A. Delk
GENERAL AUDIENCE ABSTRACT
Callous-unemotional (CU) traits are defined as experiencing limited emotion and empathy for
others. Children with CU traits are less likely to show prosocial behavior, such as sharing or
helping others. Similarly, children with CU traits also have more difficulty recognizing emotions
compared to peers. This may be related to less attention to the eye region of other’s faces, which
conveys emotional information. Notably, accurate recognition of others’ distress is necessary for
children to feel concern for others and want to engage in prosocial behavior. Elucidating these
connections furthers the development of interventions for children with and CU traits, which often
related to behavior problems. This study seeks to: (1) extend this research to younger children,
including girls; (2) measure eye gaze using eye-tracking technology; and (3) test the hypothesis
that CU traits predict prosocial behavior deficits due to reduced eye gaze and subsequent deficits
in fear and distress recognition. While this hypothesis was not fully supported, results did indicate
that CU traits predicted less time focusing on the eye region for fear expressions, and certain forms
of eye gaze predicted better emotion recognition accuracy for some emotions. Instead, results
indicated that eye gaze and recognition of subtle happy expressions played a role linking CU traits
and prosocial behavior, but only in girls. Results suggest that CU traits relate to less attention to
the eye region and poorer emotion recognition across emotions, and that these mechanisms may
operate differently in boys and girls.
Acknowledgements
I would like to thank the children and their caregivers who took the time to participate in the
study. I am also very grateful to Dr. Robin Panneton for her expert advice and her generous use
of laboratory space and materials, as well as Drs. Thomas Ollendick and Alison Heck for their
essential consultation in creating the protocol for this study. Additional thanks to Stephanie
Roldan and her assistance with the Matlab code for analyzing the eye tracking data. This thesis
was supported with funding from the Virginia Tech Institute for Society, Culture and
Environment (ISCE) and the Center for Peace Studies and Violence Prevention (CPSVP) grants
(B.White, PI), and we thank them for supporting this study.
I am very grateful to Dr. Bradley White, my thesis chair and advisor, who has provided invaluable
assistance with this project in addition to continued support throughout my time at Virginia Tech.
I would also like to thank Drs. Susan White and Jungmeen Kim-Spoon for serving on my thesis
committee and offering helpful suggestions to strengthen the project. Finally, I would like to thank
my husband, Kaleb, and my family for their encouragement and support.
iv
Table of Contents
Introduction ......................................................................................................................................1
Methods..........................................................................................................................................12
Results ............................................................................................................................................20
Discussion ......................................................................................................................................26
Conclusions ....................................................................................................................................35
References ......................................................................................................................................37
v
List of Tables
Table 1: Trial Types for Prosocial Behavior Task .........................................................................55
Table 2: Descriptive Statistics for Primary Variables and Accuracy ............................................55
Table 3: Descriptive Statistics of Initial Fixation ..........................................................................56
Table 4: Descriptive Statistics of Gaze Duration ...........................................................................57
Table 5: Paired Samples T tests for Emotion Recognition ............................................................58
Table 6: Paired Samples T tests for Prosocial Behavior Task .......................................................58
Table 7: Bivariate Correlations ......................................................................................................59
Table 8: Correlations with Accuracy .............................................................................................60
Table 9: Correlations with Initial Fixation.....................................................................................61
Table 10: Correlations with Duration ............................................................................................62
Table 11: Steiger Z tests for Significant ICU and Eye Gaze Correlations ....................................63
vi
List of Figures
Figure 1: Hypothesis 1 ...................................................................................................................64
Figure 2: Hypothesis 2 ...................................................................................................................64
Figure 3: Hypothesis 3 ...................................................................................................................64
Figure 4: Hypothesis 4 ...................................................................................................................65
Figure 5: Sample AOI Placement on NimStim image (Tottenham et al., 2009) ...........................65
Figure 6: Moderated Mediation of CU traits on Recognition of Low Intensity Happy
Expressions ....................................................................................................................................65
Figure 7: Interaction between Gender and Initial Fixation on the Eyes Predicting Accuracy of
Recognition for Happy Faces at 50% intensity .............................................................................66
Figure 8: Moderated Mediation of Eye Gaze on Prosocial Behavior ............................................66
Figure 9: Serial Mediation Model of CU Traits Predicting Prosocial Behavior Through Initial
Fixation on the Eyes of Happy Faces at 50% ...............................................................................67
vii
Mechanisms of Empathic Behavior in Children with Callous-Unemotional Traits:
Eye Gaze and Emotion Recognition
Introduction
For a society to function successfully, antisocial behavior must be limited and prosocial
tendencies (e.g., compassion, cooperation) promoted. In every culture, this struggle against
disruptive behavior and toward cooperation is evidenced (Lykken, 1995), a struggle influenced
not only by broader sociocultural influences, but also by differences in individual patterns of
cognition, emotion, and behavior, which can manifest and change in certain ways over the course
of development. Certain individual traits appear to play a particularly important role in both
antisocial and prosocial tendencies, defined as actions chosen to benefit others (Eisenberg,
Fabes, & Spinrad, 2006).
Historically, Hervey Cleckley was among the first to detail in his book, The Mask of
Sanity (Cleckley, 1976), vignettes of psychiatric inpatients who seemed to “know the words but
not the music” regarding healthy interpersonal feelings and interactions. These patients
superficially seemed psychologically healthy, but further exploration revealed that they did not
experience guilt or empathy and had remarkable histories of irresponsible, reckless behavior.
Cleckley labeled this personality style as “psychopathic.” These observations led Robert Hare to
create, and later revise, the Psychopathy Checklist (PCL-R; Hare et al., 1990) which identified
two distinct facets of psychopathy, interpersonal-affective deficits (e.g., superficial charm,
manipulativeness, lack of remorse) and antisocial deviant lifestyle (e.g., impulsivity,
irresponsibility, delinquency) which have been further differentiated and alternately
conceptualized in subsequent models (e.g., Cooke & Michie, 2001).
Despite the obvious importance of psychopathy in both antisocial and prosocial
tendencies, the processes by which psychopathy facets and precursors (e.g., callous-unemotional
traits) are connected to the development of social behavior, particularly prosocial tendencies, is
poorly understood. However, emerging evidence suggests that visual attention and emotion
recognition processes may play key roles (e.g., Bowen, Morgan, Moore, & van Goozen, 2014;
Dadds, Jambrak, Pasalich, Hawes, & Brennan, 2011; Dawel, O’Kearney, McKone, & Palermo,
2012). The main objectives of the present study were therefore to address whether callousunemotional (CU) traits predict deficits in the recognition of others’ emotional expressions (e.g.,
distress) by reducing visual attention (i.e. eye gaze) to the eye region of others, and whether
these reductions in visual attention may lead to prosocial behavior deficits by interfering with
recognition of others’ emotional expressions that otherwise could serve as social cues for
prosocial responding.
Callous-Unemotional Traits
Callous-unemotional traits, which include lack of empathy, disregard for others, and lack
of emotional experience, are typically considered developmental precursors to the affective core
deficits observed in adult psychopathy (Frick & Ellis, 1999). CU traits are conceptualized as a
constellation of behavioral patterns including uncaring (i.e., disregard for one’s performance or
others’ feelings), callous (i.e., disregard for responsibilities and lack of remorse), and
unemotional traits (i.e., lack of emotional expression; Byrd, Kahn, & Pardini, 2013; Frick, 2004).
They are frequently associated with trait fearlessness, lack of distress for punishment or
consequences (Barry et al., 2000), and elevated sensitivity to reward (Pardini, Lochman, & Frick,
2003).
2
CU traits in youth (Ezpeleta, Osa, Granero, Penelo, & Domènech, 2013; Willoughby,
Mills-Koonce, Waschbusch, & Gottfredson, 2015), like psychopathic traits in adults (Edens,
Marcus, Lilienfeld, & Poythress, 2006), exist on a continuum in the community and appear to be
important risk factors for relatively early, severe, and persistent disruptive and antisocial
behavior (Frick, Ray, Thornton, & Kahn, 2014; Moffitt, 1993). By late childhood, CU traits tend
to be moderately stable (Fontaine, McCrory, Boivin, Moffitt, & Viding, 2011; Frick, Kimonis,
Dandreaux, & Farell, 2003; López-Romero, Romero, & Villar, 2014; Muratori et al., 2016).
These early developing patterns increase the impetus for improving understanding of their
nature, with such knowledge hopefully translating to improvement of early interventions.
Theories regarding the link between CU traits and disruptive behavior emphasize the role
of deficient affective responding. Fowles and Kochanska (2000) propose that low fear response
and limited affective responsivity to others increases the chances for deficient development of
conscience, defined as a self-governed decision making, separate from outside influence.
Moreover, longitudinal studies indicate that moral emotions (e.g., guilt or discomfort from one’s
transgressions) increase moral conduct (e.g., rule following), two primary components in the
development of conscience which are predicative of self-regulation (Kochanska & Aksan, 2006).
Blair (1995) builds upon this theory when describing his Violence Inhibition Mechanism (VIM)
model, which suggests that low personal distress for others reduces the likelihood that the
individual will inhibit behavior that could be aggressive or otherwise antisocial towards others.
Like Fowles & Kochanska (2000), others have since proposed that CU traits relate to deficits in
conscience development, which impedes empathic concern and promotes antisocial behavior
(Frick, Blair, & Castellanos, 2013).
3
In addition, there is evidence of low fear response associated with CU traits (Pardini &
Frick, 2013). Recent work supports these claims by demonstrating that CU traits are also
associated with low physiological reactivity, regardless of levels of conduct problems (Fanti,
Panayiotou, Lazarou, Michael, & Georgiou, 2015). When examined in the context of limiting an
individual’s response to other’s distress, fearlessness may be a path by which conduct problems
develop.
Despite the term “unemotional” and fact that CU traits are typically associated with
fearlessness (Fanti et al., 2015; Frick, 2012, Marsh et al., 2011), children with CU traits
sometimes exhibit elevated negative affect. For instance, cluster analyses have suggested that
some youth with CU traits are also high on anxiety, especially those with a history of abuse
(Kahn et al., 2013; Salihovic, Kerr, & Stattin, 2014). Furthermore, the callousness dimension in
particular is positively associated with depression and anxiety (Hawes et al., 2014), and there is
evidence that as CU traits and behavior problems increase, so do anxiety and depression,
accompanied by decreases in self-esteem (Eisenbarth, Demetriou, Kyranides, & Fanti, 2016).
While internalizing symptoms may also accompany CU traits, internalizing symptoms are not
frequently considered or controlled in studies of social information processing or behavioral
problems in youth with CU traits. In such instances, the relative impact of CU traits and
internalizing can be difficult to separate.
Prosocial Behavior: Nature and Correlates
While much research on CU traits emphasizes their role in disruptive behavior, they are
also clearly associated with prosocial behavior deficits (Blair, 2006), as reflected in their label in
the new Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric
Association, 2013) Conduct Disorder specifier, “With limited prosocial emotions,” (Kimonis et
4
al., 2015). Reduced prosocial behavior is related to CU traits, regardless of whether or not
conduct problems are statistically controlled for (Herpers et al., 2014). This finding suggests that
prosocial behavior deficits cannot simply be attributed to disruptive behavior problems and must
be better understood in their own right, in order to identify ways to foster prosocial behavior in
youth with CU traits.
Since prosocial, like other complex behaviors, can be defined and operationalized in
different ways, prior studies focus on specific forms or functions of prosocial behavior, such as
prosocial reasoning (e.g., responses to transgressive behavior) or moral maturity (e.g., responses
to moral statements (Herpers, Scheepers, Bons, Buitelaar, & Rommelse, 2014). Sharing
resources is a specific form of prosocial behavior that develops over the course of early
childhood (Fehr, Bernhard, & Rockenbach, 2008) and is itself nuanced. Economists distinguish
two conditions of unequal sharing: disadvantageous inequality (DI), in which the other person in
a pair receives more resources than the subject, and advantageous inequality (AI), in which the
subject receives more resources than the other person (Fehr & Schmidt, 1999). In each condition
(AI or DI), sharing may come at no cost to oneself (e.g., giving away an extra ticket to a show
that one would otherwise not use) or with substantial self-sacrifice or risk (e.g., entering a
burning building to save a stranger). In “no-cost” type scenarios, the subject experiences no
personal cost for choosing to distribute resources equally between oneself and the other person,
whether the alternative choice to equal sharing is AI (getting the same amount as one would via
equal sharing, but giving the other person less than oneself) or DI (getting the same amount as
one would via equal sharing, but giving the other person more than oneself). In “cost” type
scenarios, the participant must forfeit resources in order to achieve equal sharing between oneself
and the other person, whether the alternative choice to equal sharing is AI (getting more than one
5
would have via equal sharing by giving the other person less than oneself) or DI (getting more
than one would have via equal sharing by giving the other person more than oneself; Williams,
O’Driscoll, & Moore, 2014).
Thus the different conditions are separated as follows: AI no-cost scenarios compare
sharing equally vs refusing the other resources so the self would benefit, AI cost scenarios
compare sharing equally or benefiting notably more than the other by giving the other nothing,
DI no-cost scenarios compare sharing equally or giving the other more resources, and DI cost
scenarios compare sharing equally or giving the other more resources than oneself, but also
gaining more (e.g., win-win). Notably, in cases of DI, choosing equal sharing may result in
withholding resources the partner would otherwise receive, which is not the most prosocial
decision, even allowing for withholding resources to one’s own detriment, potentially driven by
envious motives (Williams et al., 2014). Therefore, while sharing tasks typically assess
perceptions of resource equality, they have also been used to measure prosocial decision making
(e.g., generosity; Shaw, Choshen-Hillel, & Caruso, 2016) in children from early through middle
childhood.
Prior studies suggest that children as young as three years of age tend to find DI aversive
and (across cost and no-cost scenarios) thus typically choose equal sharing in these scenarios
(LoBue, Nishida, Chiong, DeLoache, & Haidt, 2011), even giving up resources for themselves as
well as for the partner in order to achieve equality (DI cost scenarios; Sheskin, Bloom, & Wynn,
2014). However, with age, children are more likely to make unequal sharing DI choices that
benefit the partner (Shaw et al., 2016). To a lesser degree, children also show increased aversion
to AI as they mature, such that by age 7 or 8 they increasingly share in order to avoid accruing
more resources than their partner, a pattern observed slightly more often in cost trials than no-
6
cost trials (Fehr et al., 2008; Fehr & Schmidt, 1999; Sheskin et al., 2014; Williams & Moore,
2016). Notably, the context (e.g., familiarity with the sharing partner) can also influence sharing
decisions and thus is typically controlled in lab-based behavioral economic paradigms, such as
by enlisting an unfamiliar confederate as the partner (Williams & Moore, 2016).
Empathic Concern and Emotion Recognition
While prosocial behavior can have various motives (White, 2014), typical antecedents for
prosocial behavior, particularly those motivated by genuine concern and a desire to be supportive
or helpful, are: (1) the experience of empathy, defined as the ability to both comprehend the
emotional state of another person (sometimes labeled perspective-taking, or cognitive empathy)
and to emotionally share in their experience (sometimes termed affective empathy; Feshbach,
1978) and (2) sympathy, an other-oriented reaction to another person’s emotional state that
includes feelings of concern and wanting the person to feel better (Eisenberg & Eggum, 2009).
Empathy and sympathy are generally positively related to prosocial behavior outcomes
(Eisenberg, Eggum, & Di Giunta, 2010; Malti, Gummerum, Keller, & Buchmann, 2009; Malti &
Krettenauer, 2013), which increases the importance of understanding mechanisms associated
with these experiences.
The capacity to recognize others’ distress is a necessary initial step for developing
sympathy for others (Eisenberg, Huerta, Edwards, 2012). Thus, the extent to which children
understand other’s emotional expressions may impact prosocial behavior. Accurate emotion
recognition is predictive of increased prosocial behavior such as prosocial roles (i.e. defender,
mediator, and consoler by teacher report), social functioning in school, and social abilities in
preschoolers (Belacchi, & Farina, 2012). Emotion recognition is also related to lower self-reports
of negative social experiences in a sample of kindergarten and first graders (Miller et al., 2005).
7
Notably, accurate emotion recognition is predictive of positive social adjustment, particularly for
girls (Leppänen & Hietanen, 2001). Therefore, emotion recognition is an important predictor to
later prosocial behavior in a variety of contexts, including prosocial roles and social adjustment.
There is also some evidence that visual attention to others’ faces influences emotion
recognition. The eyes are an especially salient region of the face for the conveyance of cues
signalizing fear (Adolphs et al., 2005). Prior studies suggest that the amygdala controls visual
orienting to the eye region of faces (Han, Alders, Greening, Neufeld, & Mitchell, 2012). Damage
to the amygdala relates to decreased attending to the eye region and poorer emotion recognition
accuracy (Spezio, Huang, Castelli, & Adolphs, 2007), suggesting that attention to different
regions of others’ faces is important for emotion recognition.
Deficits in Empathic Concern: CU Traits and Emotion Recognition
Children with CU traits do not respond empathically to other’s distress, a deficit which
may interfere with prosocial responding. Limited empathic concern is associated with risk for
development of conduct problems (e.g, Aspan, Vida, Gadoros, & Halasz, 2013; Leist & Dadds,
2009; Sharp, 2008). Foundational studies in emotional responding in adult psychopathy
demonstrated a blunted startle response and reduced autonomic reactivity to emotionally
valenced images in psychopaths, compared to healthy individuals (e.g., Hare, Williamson, &
Harpur, 1988; Patrick, Bradley, & Lang, 1993).
Children with elevated CU traits demonstrate deficits identifying fearful emotional
expressions in others (Blair, Colledge, Murray & Mitchell, 2001; Herpers et al., 2014),
potentially due to deficits in amygdala functioning (Lozier, Cardinale, VanMeter, & Marsh,
2014; Marsh et al., 2008). Similar deficits exist in recognizing fearful body language (Muñoz,
2009). There is additional evidence that CU traits predict deficits identifying expression of
8
distress more broadly (i.e. fear, sadness; Dawel et al., 2012; Stevens, Charman, & Blair, 2001).
However, studies examining the extent to which emotion recognition deficits generalize in youth
with CU traits have not to our knowledge been extended yet to younger children in early to
middle childhood.
Similar to individuals with amygdala damage (Adolphs et al., 2005), individuals with CU
traits demonstrate idiosyncrasies in their eye gaze which may result in deficits identifying fear or
distress (Dadds et al., 2006; Marsh et al., 2008; Viding et al., 2012). Interestingly, when
explicitly instructed to “look at the eyes” of static emotional faces viewed on a screen, emotion
recognition improved for pre-adolescent and adolescent boys (8-17 years) with elevated CU
traits toward normal levels, whereas instructions to “look at the mouth” predicted poorer fear
recognition (Dadds et al., 2006). Thus, preliminary evidence suggested that CU traits in children
are related to deficit fear recognition, potentially mediated through reduced attention to the eye
region of others’ faces (Dadds et al., 2006). In a follow-up study, Dadds and colleagues directly
examined the relationship between CU traits and eye contact directly using eye tracking (Dadds,
Masry, Wimalaweera, & Gustella, 2008). Results demonstrated that elevated CU traits predict a
lower probability of initial focusing on the eye region, less time overall focusing on the eye
region, and less shifting of attention back to the eye region, all predicting poorer recognition of
fear expression.
Gaps in the Literature
The majority of prior studies of emotion information processing deficits in youth with
CU traits have focused mostly on boys (e.g., Blair et al., 2001; Dadds et al., 2006/2008),
resulting in a dearth of evidence regarding the relationships between CU traits and emotion
recognition in girls. However, differences in levels of empathy and prosocial behavior are
evident across genders (Hicks et al., 2012). Specifically, girls on average tend to rate negatively
9
affective stimuli as more unpleasant than boys (Sharp, van Goozen, & Goodyer, 2006). In
addition, girls tend to show elevated psychophysiological responses to aggression compared to
boys (Murray-Close & Crick, 2007), suggesting that elevated physiological reactions in response
to other’s negative emotions may be typical of girls, and thus could influence their behavior,
however, more research is necessary to clarify these relationships in females.
In addition, prior studies on relationships between CU traits and their socioemotional
correlates have also tended to focus on pre-adolescent and adolescent samples (e.g. Aspan et al.,
2013; Dadds et al, 2008; Leist & Dadds, 2009; Muñoz, 2009; Sharp, 2008). Furthermore,
relationships between the observed deficits in eye gaze, emotion recognition, and behavioral
outcomes is underexplored. In particular, to the best of our knowledge, no published studies have
examined the potential roles of eye gaze and emotion recognition in associations between CU
traits and prosocial behavior deficits. Furthermore, much of the literature on behavioral problems
in youth with elevated CU traits rests upon caregiver and teacher reports of behavioral problems,
which may be confounded with reporting on CU traits in themselves via shared method variance.
Based on this body of literature and its limitations, the current study focused instead on a
younger sample (ages 6-9 years), which allows for a downward extension of the previously
observed relationships, at a time when CU traits are stabilizing but potentially still malleable
(e.g., Fontaine et al., 2011; López-Romero et al., 2014). Notably, around age six or seven,
children are able to differentiate between themselves and others, show increasing perspective
taking skills, and develop better understanding of various forms of reciprocity (Harter, 1983).
Moreover, children ages 5-7 and 8-9 did not have significantly different abilities in a study
examining developmental changes in children’s abilities to identify mixed emotions (Zajdel,
Bloom, Fireman, & Larsen, 2013), suggesting emotion recognition skills are relatively stable
10
across the 6-9-year age range, which is consistent with by Rosenqvist and colleagues (2014)
finding that the impact of age on emotion recognition begins to decelerate around 5-6 years of
age. Thus, while we were able to consider age impacts if preliminary analyses suggested it was
necessary, selecting this range also helped constrain the potential impact of age on emotion
recognition abilities.
Current Study
The present study had several goals. First, we sought to establish if a relationship exists
between CU traits and emotion recognition that predicts prosocial behavior. Next, we attempted
to replicate the prior findings that CU traits predict emotion recognition deficits in a younger
sample of children using a new emotion recognition paradigm. We also aimed to identify the role
of initial eye gaze fixation in emotion recognition and prosocial behavior. Finally, we intended to
establish a serial mediation relationship between CU traits, eye gaze patterns, emotion
recognition and prosocial behavior, which has previously been shown to be negatively related to
CU traits (e.g., Herpers et al., 2014).
In summary, the four hypotheses of the current study are as follows:

Hypothesis 1: CU traits predict less prosocial behavior due to poor emotion recognition
(Figure 1).

Hypothesis 2: CU traits also predict failure to sufficiently visually attend (i.e. “eye gaze”) to
emotional information conveyed by others’ eye region, which in turn results in deficient
emotion recognition for distress (Figure 2).

Hypothesis 3: Failure to attend (reduced gaze) to others’ eyes leads to reduced prosocial
behavior due to limited recognition of distress-related emotions (i.e. fear and sadness), which
mediates their relationship (Figure 3).
11

Hypothesis 4: A serial mediation process exists, by which CU traits predict reduced prosocial
behavior through failure to sufficiently visually attend (i.e. “eye gaze”) to emotional
information conveyed by others’ eye region, which in turn results in deficient emotion
recognition for distress (Figure 4).
Methods
Participants
Participants were recruited through advertisement at day cares, camps, and through local
organizations in the rural southeastern region of the United States. The final sample ranged in
age from 6 – 9 years old (n=81, 34 boys, 72% White). The average age of the sample was 7.82
years (SD = 1.15 years).
Following Fritz and MacKinnon (2007), for adequate power, the final sample included 81
participants. Similar studies indicate that the standardized regression coefficients for the
relationship between CU traits (or eye gaze) and emotion recognition ranged from .21 to .52
(medium to large effect sizes; Aspan et al., 2013; Dadds et al., 2008; Leist and Dadds, 2009;
Muñoz, 2008). The regression coefficients for emotion recognition predicting prosocial behavior
range from .26 to .52 (medium to large effect sizes; Barth & Bastiani, 1997; Blair & Coles, 2000;
Leppänen & Hietanen, 2001). Therefore, assuming the medium effect size for both paths a and b,
based upon the medium effect size observed in prior studies, Fritz and MacKinnon’s (2007)
article suggests a sample size of 78 for the percentile bootstrap test for a simple mediation,
without covariates. Thus, the current study was adequately powered to detect a moderate
mediation effect in the initial models, but underpowered to include covariates or detect moderate
serial mediation effects.
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Procedures
IRB approval was obtained prior to recruitment or data collection. Recruitment entailed
posting flyers in community centers and day-cares in the region, posting electronic notices, and
sending mailing to potential participants, identified through a departmental database of
individuals who previously consented to be contacted with information for research studies.
Parents or guardians who were interested in participating contacted the investigator and
completed a phone screen, including verbal consent, prior to scheduling the laboratory session.
Information on current diagnoses were obtained, and if the child required corrective lenses the
parent was notified that glasses may interfere with tracking, thus disqualifying participants.
Exclusion criteria included a prior diagnosis of Autism Spectrum Disorder, Intellectual
Disability, or Bipolar Disorder, because such diagnoses have significantly impact on emotion
recognition abilities. In addition, recruitment was monitored to achieve a gender balance within
the sample (no more than a 60-40% difference) to increase the odds of identifying gender
differences where they exist.
As an incentive and reimbursement for participating, caregivers received $20 each for
participation in this study and children received a small toy from a prize box. Following
informed consent and assent procedures, caregivers completed questionnaires about their
children’s demographics and behavior, including the Inventory of Callous-Unemotional Traits
(ICU; Frick, 2004), while children completed the emotion recognition and prosocial behavior
tasks.
Similar to Dadds and colleagues (2008), children’s visual attention was tracked using a
Tobii T60 infrared eye tracker with 17-inch display, maximum resolution of 1,280 x 1,024
pixels, and a sampling rate of 60 Hz. Each participating child was seated at a fixed distance of
13
approximately 60 cm in front of the eye-tracker monitor and a five-point calibration was
completed, followed immediately by the emotion recognition task. Afterward, the child was
escorted to a neighboring room for the prosocial behavior task.
Measures
Inventory of Callous Unemotional Traits – Parent Version (ICU; Frick, 2004). The
ICU is a 24 item questionnaire with a 4-point Likert scale. The ICU was designed to improve
upon several psychometric aspects of the Antisocial Process Screening Device (APSD; Frick &
Hare, 2002) and is routinely used in assessment of CU traits in children (Frick, 2004). Although
few studies have used the parent-report version of the ICU, its use is on the rise (Benesch, GörtzDorten, Breuer, & Döpfner, 2014). The ICU has good internal consistency across 18 months at
six-month intervals (Cronbach’s αs ranging .86 to.87) for children ages 6-12 years (Fanti &
Centifanti, 2014; Kimonis, Fanti, & Singh, 2014). In addition, in a recent meta-analysis of
studies examining CU traits (Waller, Gardner, & Hyde, 2013), most (n=23 of 30, 76.7%) used
parent report to measure CU traits, most often through items on the ASPD. Moreover, of those
studies that used parent report to measure CU traits, 18 overlapped with the targeted age range of
this study.
Research on the internal structure of the ICU has produced mixed results. While many
studies support a three-factor bifactor model (Essau, Sasagawa, & Frick, 2006; Ezpeleta et al.,
2013; Frick & Ray, 2014), reflected by one overarching general factor and three underlying
specific factors (uncaring, callous, and unemotional; e.g.,), more recent studies support various
2-factor models based on shortened versions of the ICU (Hawes et al., 2014; Houghton, Hunter,
& Crow, 2013; Waller et al., 2015; Willoughby et al., 2015). Still others highlight concerns
about method variance on the ICU that may affect apparent factor structure, leading to
14
recommendations to focus on the ICU total score (Paiva-Salisbury, Gill, & Stickle, 2016; Ray,
Frick, Thornton, Steinberg, & Cauffman, 2016). Thus, the present study completed analyses with
the total score for the ICU. In the current study, Cronbach’s α = .86 for the ICU total score.
Child Behavior Checklist (CBCL; Achenbach & Rescorla, 2001). The CBCL/6-18 is
a parent-report instrument commonly used to assess psychopathology in youth. It consists of 113
questions, scored on a three-point Likert scale (0=absent, 1= occurs sometimes, 2=occurs often).
The Anxiety Problems scale was used to assess whether internalizing symptoms may confound
relationships between primary variables of interest (CU traits, emotion recognition, eye gaze, and
prosocial behavior) in light of prior research showing that anxiety can co-occur with CU traits
and thus may impact behavior (Hawes et al., 2014; Kahn et al., 2013; Salihovic et al., 2014).
Sensitivity to Punishment and Sensitivity to Reward Questionnaire for Children
(SPSRQ-C; Colder & O'Connor, 2004). This questionnaire measures reinforcement sensitivity
in children through parent report and contains 33 items. It comprises a Punishment Sensitivity
scale, and three Reward Sensitivity scales: Reward Responsivity, Impulsivity/Fun-Seeking, and
Drive. Each item is scored on a 5-point Likert scale (1=strongly disagree, 5=strongly agree). It
has demonstrated good internal reliability for all four scales in children ages 6-13 (Luman, van
Meel, Oosterlaan, & Geurts, 2012). More recent analyses identified a Fear/Shyness scale (Colder
et al., 2011), which was used to test fearfulness as a potential confound, in light of prior evidence
that low fear response is associated with both CU traits (Fanti et al., 2015; Pardini & Frick,
2013), as well as development of conscience which impact prosocial behavior outcomes (Fowles
& Kochanska, 2000). Thus, a child’s level of fear may act as a confound when examining these
variables. In the current study, α = .87 for the SPSRQ-C Fear/Shyness scale score.
15
Emotion recognition task. To assess emotion recognition, we created a new
computerized emotion recognition paradigm based on the UNSW Facial Emotion Recognition
Task (Dadds, Hawes, & Merz, 2004). However, because stimuli for that task were not
standardized, which could confound emotion recognition, we elected to instead use previously
validated stimuli from the NimStim set (Tottenham et al., 2009) that had been rated at 71% or
higher agreement that the image was representative of the target emotion. Three adult male faces
and adult three female faces were selected that were rated highly on each emotion (using happy,
fear, anger, sad, and neutral) expression, counterbalancing for mouth position (open vs closed)
across genders and emotions. There were 54 trials in total, with nine trials per model (four at
50%, four at 100% intensity, and one neutral face). Only Caucasian faces were used in this initial
investigation to reduce potential confound of implicit racial bias.
Recently, multiple researchers have created their own stimuli at various intensity levels in
order to reduce the likelihood of ceiling effects (Gao, Maurer, & Nishimura, 2010; Gillespie,
Rotshtein, Satherly, Beech, & Mitchell, 2015; Tell, Davidson, & Camras, 2014). For this study,
we followed the protocol described by Calder, Young, Perrett, Etcoff, and Rowland (1996) and
used the Morpheus Photo Morpher (as used by Tell et al., 2014) to ‘morph’ the prototypical
images of a neutral expression with each of the other emotional expressions, selecting the frame
associated with 50% of the full emotion and the neutral emotion combined. The final task
included 50% intensity trials followed by 100% intensity trials. We further adapted it for use
with younger participants by requiring a verbal numeric response to a visual response set
(“Which number matches how they are feeling?”), rather than having participants name the
expressions (Dadds et al., 2006). This modification was implemented to permit measurement of
emotion recognition while controlling for individual differences in ability to accurately label
16
emotions. A single female face rated most gender neutral by three independent judges was used
to display all emotions in the response key. We counterbalanced response key display
order (emotions labeled 1and 2 on top and 4,5,6, directly below them) across trials of the same
emotion within each intensity block (50% and 100%), in order to avoid confounding emotion
type with response location.
Immediately prior to the emotion recognition task, the participants completed a shapematching task on the same Tobii monitor to ensure they understood the directions for the
emotion recognition task. Each image was displayed for 2 seconds and the response options were
displayed visually by including a range of emotional faces from a single model, paired with
corresponding number choices. During the task, the participants responded verbally with the
name of the number corresponding to the selection. Correct identification was coded as 1,
incorrect identification was coded as 0, and accuracy scores were computed by averaging the
points across trials for each emotion.
Eye tracking. Following Dadds and colleagues (2008), we examined on each Emotion
Recognition Task trial the location of initial fixation and duration of fixation to each facial
stimulus as indices of eye gaze patterns. In the literature it is most common to define a fixation
as eye gaze confined for at least 100 ms to a given pixel radius or within an Area of Interest
(AOI). This standard has been used in participants from 4 months to 24 years (Amso, Haas, &
Markant, 2014; Best, Yim, & Sloutsky, 2013; in 9-17 year olds; Guyer et al., 2008; Hunnius, de
Wit, Vrins, & von Hofsten, 2011). Individual trials missing more than 50% of tracking data were
coded as missing data for that participant (7.36% of all trials). Three cases were removed from
the data set prior to analyses due to significant data loss (i.e., less than 2 complete trials for an
emotion). Following methodology from similar studies (Leitzke & Pollak, 2016) AOI’s included
17
the eyes, mouth, or the remainder of the face region (Figure 5) with the eye region defined by an
ellipse from the upper nose to the brow and from temple to temple and the mouth region defined
as the area immediately surrounding the mouth and lips extending up to just under the tip of the
nose. Initial fixation and duration scores were derived by averaging these scores across trials for
each relevant emotion. Data reduction code was developed in MATLAB.
Prosocial behavior task. In order to assess prosocial behavior through a sharing
paradigm, we followed a protocol for emotion induction set forth by Williams and colleagues
(2014) which was previously validated in children as young as three. Using the identical stimulus
video from this study, the children were exposed to an empathy-induction scenario. Specifically,
the video detailed the story of another child (“Jenny”) who first plays with her dog, but then she
describes in a sad tone that he ran away. She is visibly upset as she makes posters and looks for
her dog. The child participant was then asked how Jenny felt and to reflect on how he or she felt
during the video, before completing the resource-allocation (sticker-sharing) task. After
completion of the resource-allocation task each child was reassured that Jenny’s dog returned
home.
During the resource-allocation task, which was previously validated for this age range
(Fehr & Schmidt, 1999; LoBue, Nishida, Chiong, DeLoache, & Haidt, 2011; Malti et al., 2009;
Sheskin, Bloom, & Wynn, 2014), the children were presented with several dichotomous choices
in which they decided how many stickers to keep for themselves and how many to ostensibly
mail to Jenny. These trials were divided into two groups with two subtypes each, AI cost and no
cost as well as DI cost and no cost. All trials were scored such that the more prosocial decision
earned one point and less prosocial decisions earned no points. In AI no cost trials the child can
choose to give Jenny a sticker to make it equal (vs one sticker for themselves and none for
18
Jenny). In the AI cost trials, the child had to choose to give up an extra sticker for themselves in
order to achieve equality (by giving one sticker to Jenny). In the DI no cost conditions, the child
could choose to give more stickers to Jenny (although they received no more stickers themselves,
thereby violating equality). Whereas in the DI cost condition they could give Jenny two more
stickers and receive an extra sticker themselves (again, violating equality but a more prosocial
choice). Each trial was paired with an equal option (one sticker each). Each child completed 4
rounds of each trial type which were counterbalanced over two blocks, separated by a coloring
task (Table 1). The children placed their selection for the other child into envelopes to encourage
a sense of privacy and reduce the likelihood of response bias.
Data Analysis
First, univariate outliers were identified based on a criterion of 3 SDs above or below the
mean. These outliers were winsorized to a value of 3 standard deviations from the mean. The
data were then examined for multivariate outliers using Mahlnobis distances, although there
were no outliers identified. All predictor variables demonstrated limited deviance from normality
except for initial fixation on the mouth for angry (50%) faces and accuracy for happy (100%)
faces (George & Mallery, 2010). Bivariate correlations were examined to consider potential
confounds between variables of interest (CU traits, eye gaze, emotion recognition, and prosocial
behavior) and common correlates (gender, age, race, anxiety, and fearfulness), and to examine at
the bivariate level the hypothesized relationships between CU traits, emotion recognition, eye
gaze patterns (fixation and duration), and prosocial behavior. Paired sample t-tests were
completed for emotion recognition scores to determine if general distress and total emotion
recognition scores could be computed (vs. examining each emotion separately). Similarly, paired
19
sample t-tests were computed for the prosocial behavior task scores to determine if the different
trial types should be combined.
Multiple regression analyses were used to examine the relationships between scores on
the ICU, the point of initial eye gaze fixation, accuracy of emotion recognition and prosocial
behavior. Hayes’ PROCESS macro (2013) was used to test mediation hypotheses by
bootstrapping estimates of hypothesized indirect effects. In each mediation model, “Path c”
refers to the total effect of X on Y, “Path c'” refers to the direct effect of X on Y when M is
included in the model, and with regard to the indirect (mediation) effect, “Path a” refers to the
effect of X on M, and “Path b” to the effect of M on Y (controlling for X), where X is the
independent variable, M is the moderator, and Y is the dependent variable of interest.
Results
Descriptive statistics for the ICU, demographics, the prosocial behavior task, and
accuracy of the emotion recognition task are presented in Table 2. Descriptive statistics for initial
fixation and gaze duration are presented in Tables 3 and 4, respectively. ICU mean total scores
(M = 16.88, SD = 8.06; Table 2) followed similar patterns to prior studies using the parent-report
form of the ICU (Kimonis et al., 2014). In addition, 13 children (6 boys, 7 girls; 16% of the final
sample) surpassed the clinical cutoff of 24 points, indicating parental report of significant levels
of CU traits (Kimonis et al., 2014).
Mean accuracy of emotion recognition ranged from 51% (for sad expressions) to 74%
(fear expressions) for the 50% intensity stimuli (Table 2). As expected, mean accuracy for
emotion recognition was higher for the 100% intensity expressions, ranging from 77% (fear/sad)
to 93% (happy). T-test comparisons of means accuracy across emotion expressions for 50%
intensity stimuli indicated that recognition for each emotion expression type significantly
20
differed from one another (all ps < .01) except for fear and angry expressions (p = .969; see
Table 5). For the 100% stimuli, recognition of sadness was higher than at 50% and not
significantly different from fear recognition at 100% intensity. Also at 100% intensity, angry
expression accuracy scores were not significantly different from recognition of happy
expressions but did show a nonsignificant trend (p = .080) with recognition for happy
expressions being marginally higher than for angry faces. Eye gaze patterns to the eye AOIs and
mouth AOIs differed in some instances across emotions and intensities. For 50% intensity,
duration of gaze to the eye region AOI and to the mouth region AOI differed across several but
not all emotions. Specifically, participants fixated longer on the mouth for sad compared to fear
expression (p < .001). Initial fixation to the eye or mouth AOI did not sig differ for any emotions
at 50% intensity (ps > .081). For emotions at 100% intensity, duration and initial fixation on eye
AOIs did not differ based on emotion (all ps > .070). However, participants spent more time
looking at the mouth during fear compared to sad expressions at 100% intensity (p = .013), and
they were more likely to look at the mouth first during fear compared to sad expressions at 100%
intensity (p = .020). Due to the observed significant mean differences in emotion recognition
accuracy and in eye gaze patterns across the emotional expressions and intensity conditions,
separate analyses were run for each emotional expression and intensity level, rather than
combining scores to compute general distress (fear + sad expressions) or total emotion
recognition score.
Next, a paired samples t-test was completed for the AI and DI conditions to compare
scores on the trial types (cost vs. no cost) comprising these conditions. In our study, the means of
the cost vs no-cost trial type in both AI and DI conditions were significantly different (Table 6).
These significant differences among trial types across conditions and their medium correlations
21
with one another were similar to those of other studies (Williams & Moore, 2016). However
prior studies only use one trial type (cost or no cost) to represent each condition (AI or DI), or
did not test for differences among the trial type means (LoBue et al., 2011; Sheskin et al., 2014).
Williams and Moore’s (2016) primary research question regarded preference for equality and
thus they argued that the intercorrelations were high enough to justify combining the trial types,
in spite of the statistically significant differences in means (r’s for AI trials ranged from .40 to
.58, r’s for DI trials ranged from .47 to .64). We instead separated the trials to permit
consideration of potential distinctions in prosocial behavior among the four (AI/DI; no-cost/cost)
sharing scenarios.
Bivariate correlations (Table 7) were considered next, first to determine whether potential
confounds required statistical control. The CBCL anxiety problems scale significantly correlated
with ICU total scores (r = .24, p = .032), but not with outcome variables of interest (i.e., emotion
recognition accuracy, AI, or DI scores). Similarly, gender related to some gaze patterns, but not
to any outcome variables of interest. Age significantly correlated with DI no-cost and DI cost
scores (r = .36, p = .001 and r = .32, p = .004, respectively) as well as several measures of
fixation and accuracy, and thus was included as a covariate in the associated analyses. The
fear/shyness scale only correlated with recognition of angry faces at 50% intensity (r = -.27, p =
.014; Table 8).
In relation to our primary hypotheses, at the bivariate correlation level, ICU total scores
did not correlate with any of the four prosocial behavior (sticker sharing) types (all ps > .32).
ICU total scores were negatively associated with recognition of happy faces at 100% intensity (r
= -.28, p = .012; Table 8), but did not correlate with recognition of fear or any other emotional
expression types at either intensity (all ps > .05). Notably, nonsignificant trends (p < .1) included
22
the relationship between ICU total scores and recognition of 50% happy expressions (r = -.19, p
= .082), and ICU total scores and recognition of 100% sad expressions (r = -.19, p = .09). ICU
total scores negatively correlated with initial fixation on the eyes only for happy and sad
expressions at 50% intensity (r = -.23, p = .037, and r = -.25, p = .024, respectively, Table 9; all
other ps > .09). A nonsignificant trend occurred between ICU total scores and initial fixation on
the mouth for 50% angry expressions (r = .19, p = .094). In addition, ICU total scores
significantly correlated with duration of gaze to the eye region for fear and sad expressions at
50% intensity (both rs = -.25, p = .027 and .024, respectively, Table 10, all other ps > .111).
Furthermore, emotion recognition for any emotional expression at either intensity did not
correlate with any prosocial behavior type; (all ps > .05; Table 7).
Regression results for our mediation hypotheses were considered next. The first
hypothesis was that CU traits predict poorer emotion recognition, which in turn predicts
prosocial deficits. This mediation model was not supported for fear expressions. Specifically,
there was no evidence of a total effect of CU traits on prosocial behaviors (Path c: all ps > .305)
or on emotion recognition for fearful faces at either 50% (Path a: p = .128) or 100% intensity (p
= .279). Furthermore, emotion recognition for fearful faces at faces at either 50% or 100%
intensity failed to predict any of the four sharing behavior scores (Path b: all ps > .617), and the
95% C.I.s of the bootstrapped estimate of the indirect effect contained zero, thus failing to
support mediation. Results were similarly nonsignificant for the same model for sad faces (as the
other component of distress-related emotional expressions), as well was for other emotional
expressions considered (happy, angry).
Our second hypothesis was that deficient visual attention to others’ eyes mediates the
relationship between CU traits and emotion recognition deficits. This mediation model was not
23
clearly supported for fear expressions, although some pathways in the model were supported.
Specifically, there was no evidence of a total effect of CU traits on emotion recognition for
fearful faces at either 50% (Path c: all ps > .128) or 100% intensity (all ps > .279). ICU total
scores did inversely predict as expected children’s duration of fixation on the eye region for
fearful faces displayed at 50% intensity (Path a: b = -7.6135, p = .027), but ICU total scores did
not predict duration of fixation on the eyes for fear faces at 100% intensity, or initial fixation on
the eyes at either 50% or 100% (all ps > .280). Also, duration of gaze to the eye region for
fearful faces inversely predicted as expected children’s emotion recognition for fear expressions
displayed at 100% intensity (Path b: b = -.0003, p = .030) but not for fear at 50% intensity or for
initial fixation at either 50% or 100% intensity (all ps > .077). Furthermore, the 95% C.I.s of the
bootstrapped estimate of the indirect effect contained zero, thus failing to support mediation.
For sad expressions, as the other component of distress-related emotional expressions,
results indicated that ICU total scores predicted both duration and initial fixation on the eyes for
sad expressions at 50% intensity (Path a: b = -7.8802, p = .024, and b = -.0080, p = .024,
respectively). Otherwise, results were nonsignificant for the remained of the model for sad faces,
as well was for angry expressions.
Despite these null findings of the second hypothesis for fear and sadness expressions,
mediation of the CU trait – emotion recognition relationship by eye gaze was supported for
happy expressions at 50%. Specifically, as illustrated in Figure 6, the relationship between ICU
total score and recognition of happy faces at 50% intensity (Path c: b = -.0056, p = .082) was
qualified by a significant moderated mediation, with ICU total score predicting fixation to the
eyes (Path a: b = -.0066, p = .0370, and the relationship between fixation to the eyes and
24
recognition of happy expressions at 50% moderated by gender (Figure 6 & 7). The indirect effect
(Path c') was only significant for females (Figure 6; b = -.0037, 95% CI [-.0078, -.0004]).
The third hypothesis was that less gaze to the eye region of others relates to poorer
recognition of fear emotions, which in turn predicts prosocial deficits. This mediation model was
partially supported for fear expressions. While there was no evidence of a total effect of limited
gaze to the eye region predicting prosocial behavior deficits (Path c: all ps > .144), there was
evidence of an effect of duration of gaze to the eye region on emotion recognition for 100%
intensity fearful faces (Path a; b = .0003, p = .022) similar to Path b in hypothesis 2. However, as
in hypothesis 1, emotion recognition for fearful faces at faces at either 50% or 100% intensity
failed to predict any of the four sharing behavior scores (Path b: all ps > .354), and the 95% C.I.s
of the bootstrapped estimate of the indirect effect contained zero, thus failing to support
mediation. Results were similarly nonsignificant for the same model for sad faces (as the other
component of distress-related emotional expressions), as well was for angry expressions.
As in hypothesis 2, while there was not substantial evidence for relationships with fear or
sad expression, the third hypothesis was supported for happy expressions. Gender again
significantly moderated the relationship between fixation and recognition in a moderated
mediation of the relationship between first fixation on the eyes and prosocial behavior for DI no
cost trials (Figure 8). As before, this relationship was only significant for girls (b = .7014, 95%
CI [.0277, 1.7092]).
Our final hypothesis 4 proposed that a serial mediation process exists, in that CU traits
predict reduced prosocial behavior via failure to attend to the eyes (reduced eye gaze) which in
turn results in deficient emotion recognition for fear. This mediation model was only partially
supported for fear expressions. Specifically, there was no evidence of a total effect of CU traits
25
on prosocial behaviors (Path c: all ps > .319). However, as noted earlier, there was some
evidence that CU traits predict less time focusing on the eyes for 50% fear faces (b = -7.6135, p
= .027; also Path a of hypothesis 2), as well as a relationship between duration of gaze to the eye
region and emotion recognition for 100% intensity fearful faces (b = .0003, p = .03; also Path b
of hypothesis 2). Regardless, emotion recognition for fearful faces at either 50% or 100%
intensity failed to predict any of the four sharing behavior scores (Path b: all ps > .429), and the
95% C.I.s of the bootstrapped estimate of the indirect effect contained zero, thus failing to
support mediation. Results were similarly nonsignificant for sad and angry expressions.
On the other hand, serial mediation was supported for recognition of happy faces at 50%
intensity (Figure 9). The ICU total score predicted the probability of first fixating on the eyes for
happy faces at 50% intensity (b = -.0066, p = .037). In turn, first fixation on the eyes predicting
recognition of happy faces at 50% intensity (controlling for ICU total score, b = .2375, p = .038).
Finally, controlling for all previous variables, recognition for happy expressions predicted
prosocial behavior specifically in the DI no cost trials (b = 1.2949, p = .068). The indirect effect
of this serial mediation was significant (Path c': b = -.0022, 95% CI [-.0105, -.0001]). As there
was evidence for gender moderation of the path from eye gaze to emotion recognition, the full
model in Figure 9 is likely primarily accounted for by this association in females, although we
did not test for moderation of this pathway due to limited power.
Discussion
In the current study, we proposed a series of mediation analyses, based on prior literature,
that CU traits in young boys and girls are associated with prosocial behavior deficits
(specifically, sharing with a sad, same-aged peer) because youth with CU traits have difficulty
26
visually attending to the eye region of others’ faces, thus impeding recognizing distress in others.
This study posed several specific hypotheses to test this model.
With regard to the first hypothesis, we predicted that that elevated CU traits in children
relate to poorer emotion recognition, compared to children with low CU traits (Dadds et al.,
2008), thereby predicting less prosocial behavior. However, this hypothesis was not supported.
First, CU traits did not predict lower levels of prosocial behavior on the resource allocation task.
This null result may have been due to social desirability, as the child participants made their
sticker-sharing selections on the prosocial behavior task in front of the experimenter (Heyman,
Barner, Heumann, & Schenck, 2014). Alternatively, it may be because prosocial behaviors and
the processes driving them (e.g., sympathy, moral motivation; Malti, Gummerum, Keller, &
Buchmann, 2009) have a variety of influences, not just visual attention and emotion recognition.
Alternatively, it could have been due to the limited number of children with elevated CU traits in
the sample. Notably, while the current study employed regression analyses, Dadds and
colleagues (2008) used an ANOVA-based approach including only youth scoring high (75 th
percentile) and low (25th percentile) on CU traits, which may have impacted results. In addition,
studies of CU traits assess for them in various ways (Waller et al., 2013), and the approach by
Dadds and colleagues (2006, 2008) differed from our own. These differences, as well as
differences among the samples (age and gender) may have resulted in divergence between our
findings and those of Dadds and colleagues with regard to CU links to visual attention as well,
described below.
Another potential explanation for null results is children’s aversion to inequality, (Fehr et
al., 2008; Williams & Moore, 2016). This literature suggests that, around age seven, children
avoid both inequality that favors them (advantageous) and inequality that favors others
27
(disadvantageous; Fehr et al., 2008). However, children’s reactions to DI may change throughout
development. Williams and Moore (2016) suggested that aversion to DI may stem more from
envy as children age, but also note that this relationship may be qualified by one’s relationship to
the interactional partner (e.g., friend vs. stranger). Therefore, it could be that the desire for
equality has more influence on sharing behavior than CU traits do in this age range and context.
Also in this sample, there were significant relationships between DI cost and no cost trials with
age, indicating that older children were more likely to make prosocial sharing decisions,
regardless of CU trait levels. Thus emergence of individual differences related to CU traits in
prosocial responding on this resource allocation task might be more discernable in an older
sample.
Our second hypothesis stated that CU traits would relate to reduced visual attention to the
eye region of others’ faces, resulting in poorer emotion recognition. We anticipated this
relationship to be strongest for fearful expression recognition, based on prior work by Dadds and
colleagues (2006, 2008). While CU traits did not predict reduced initial fixation on the eyes for
fear faces, in contrast to findings by Dadds and colleagues (2008), children with elevated CU
traits were significantly less likely to first fixate on the eyes for relatively subtle (i.e. 50%
intensity) happy and sad expressions. CU traits also predicted less overall time fixating on the
eyes for subtle fear and sad expressions. These results appear to be in support of arguments that
CU traits are predictive of deficient attention and emotion recognition related to all emotions, not
just fear (Dawel et al., 2012; Dawel et al., 2015). Notably, CU traits were not related to increased
attention to the mouth region, replicating Dadds and colleagues (2008) conclusions that CU traits
are predictive of reduced attention to the eyes, rather than increased attention to the mouth region
per se. In addition, it is interesting that these relationships were only significant for subtler (50%
28
intensity) stimuli. This may suggest that individuals with CU traits are most vulnerable to
overlooking subtle emotion cues (Blair et al., 2004; Bowen et al., 2014). However, when
comparing ICU score to gaze patterns for 50% intensity stimuli with the similar correlations for
full-intensity stimuli, only duration of gaze to the eyes for sad faces (at partial and full intensity)
had significantly different relationships with ICU scores (Table 11.; Steiger, 1980). Thus, there is
not enough evidence in the present study to support the theory that CU traits predict deficient
attention for subtle emotional cues, but future research should consider and test such
possibilities.
The second part of this second hypothesis was partially supported, in terms of gaze
patterns predicting emotion recognition. While first fixation on the eyes for fear expressions did
not predict poorer accuracy, more initial fixation on the mouth region predicted poorer
recognition of fear faces for 100% intensity. While unexpected, initial fixation on the eyes for
relatively subtle happy expressions predicted improved recognition for these expressions. Also
consistent with hypotheses, duration of fixation on the eyes for fear faces predicted better
recognition for full-intensity fear expressions. In our stimulus set, the whites of the eyes are
notably less pronounced in subtle fear expressions than full-intensity fear expressions, and
perhaps provide less relevant emotional cuing information to participants. This positive
relationship between gaze and emotion recognition for full emotions supports the notion that
widened eyes, allowing more visible sclera, are important for recognition of fear expressions
(Dadds et al., 2011).
Support for the proposed mediation model, that CU traits would predict reduced eye gaze
resulting in poorer emotion recognition, was only found for subtle happy expressions.
Furthermore, the relationship between CU traits and emotion recognition via reduced initial
29
fixation to the eye region was only significant for girls viewing subtle happy expressions. Gender
moderated the path between initial fixation and emotion recognition, indicating a significant
relationship for girls but not boys in this sample. Notably, mean scores for emotion recognition
were not significantly higher for girls than boys in this sample. It may first appear contradictory
that initial fixation on the eyes improves recognition for happy expressions, since happy
expressions are commonly denoted by a grinning expression. However, Frank, Ekman, and
Friesen (1993) suggest that there is important information conveyed in the orbicularis oculi
action (eye muscles), such that movement around the eyes conveys additional salient emotional
information accompanying a smile. Furthermore, the relatively subtle (50%) happy faces did not
show a smile as clearly as in the full-intensity stimuli. Thus girls may be better at detecting
tension in these ocular muscles as clues to the emotion than boys. We revisit discussion of
gender differences in interpreting findings for our third hypothesis.
Our third hypothesis stated that less attention to the eye region would predict less
prosocial behavior through poorer emotion recognition. While a link between gaze and emotion
recognition was not evidenced for fear or sad expressions, there was a significant relationship for
subtle happy faces. This relationship was qualified by a gender interaction, in that girls who first
fixated on the eyes of subtle happy expressions were more likely to correctly identify the happy
expression, whereas there was no relationship for boys. This suggests that females may have
some advantages in emotion recognition, although such gender differences are likely small
(Lawrence, Campbell, & Skuse, 2015). The significant moderated mediation model
demonstrated that attention to the eye region for subtle happy emotions statistically predicts
more prosocial behavior from girls when presented with the opportunity to share with a peer.
Thus, eye gaze potentially explains why emotion recognition would predict prosocial outcomes
30
for girls (Leppänen & Hietanen, 2001). In addition, there is substantial prior work which
indicates that girls report more affective empathy than boys (Michalska, Kinzler, & Decety,
2013) and that girls are more likely than boys to demonstrate prosocial behavior with same-sex
peers (Feshbach, & Roe, 1968; Stuijfzand, De Wied, Kempes, Van de Graaff, Branje, & Meeus,
2016).
Notably, our selected prosocial behavior task focused upon responding to a sad rather
than fearful child, which may have impacted our findings. Furthermore, while Kochanska (1993)
and others have emphasized the ability to experience others’ distress as an antecedent to
prosocial behavior, happy expressions also serve communicatory and reinforcement functions of
increasing the probability of one’s behaviors that elicit others’ happiness (Blair, 2003; Matthews
& Wells, 1999). We thus propose that, in addition to recognizing others’ distress, recognizing
others’ positive affect may also motivate prosocial behavior by allowing others’ positive affect to
serve as a reinforcing social cue when one acts in a caring or supportive manner toward others.
Interestingly, exogenous administration of oxytocin may improve both recognition of others’
happiness (Marsh, Henry, Pine, & Blair, 2010) and prosocial behavior (Campbell, 2010),
although these relationships may be specific to individuals with existing social-emotion deficits
and particular contexts (Bartz, Zaki, Bolger, & Ochsner, 2011). There is also some evidence that
recognition of both happy and sad expressions increases approach behavior (Mizokawa,
Minemoto, Komiya, & Noguchi, 2013; Seidela, Habela, Kirschnerc, Gurd, & Derntl, 2010), with
approach being required for many (although not all or exclusively) prosocial responses,
including sharing of resources. Therefore, if one cannot recognize the positive impact of one’s
prosocial behavior on others via their displays of happiness, then one may be less likely to
engage in prosocial behavior. Consistent with this conjecture, we observed in the present study
31
that in girls specifically, less attention to the subtle information from the eye muscle region
appeared to potentially reduce recognition of happiness, and was associated with a lower
likelihood of choosing a prosocial response, even when there is no difference in resources they
themselves would receive (i.e., DI no cost scenario). For the girls with better eye contact, the
relationship was reversed, and associated with higher levels of prosocial behavior.
The hypothesized serial mediation model was only supported for first fixation to the eyes
of subtle happy expressions predicting less prosocial decisions during no-cost disadvantageous
inequality trials. This is contrary to prior work, which indicates that children in this age range are
more likely to choose the equal-sharing (i.e. less prosocial) option during no cost trials (Williams
et al., 2014; Williams & Moore, 2016). However, there was a significant relationship between
CU traits and reduced initial fixations on the eyes, suggesting that in both boys and girls, CU
traits predict less attention for the eye region while viewing subtle happy faces. The significant
gender moderation suggests that this serial mediation is primarily accounted for by the
relationship between initial fixation and emotion recognition in girls. Notably, there were not
differences in mean accuracy based on gender. Therefore, initial fixation on the eyes did not
improve girls’ recognition of low intensity happy faces beyond boys’ abilities, but there was a
significant positive relationship. In turn, girls who did not initial fixate on the eye region were
less likely to choose prosocial sharing even though it did not change the outcome for themselves.
According to theories of conscience, their actions may be driven by limited feelings of sympathy
when confronted with the lost dog scenario (Eisenberg & Eggum, 2009; Eisenberg et al., 2012;
Fowles, & Kochanska, 2000).
There are several noteworthy limitations of the current study. First, it is cross-sectional.
Others have inferred (Dadds et al., 2011; Viding et al., 2012) that attention to the eyes must
32
occur before recognition, and emotions must be recognized before an empathic prosocial
response can be generated, implying causal directionality. However, temporal precedence cannot
be formally tested in the current study. More so, although CU traits are often treated as
predisposing temperamental features, there is not definitive evidence of what point in
development these traits arise or are solidified, and what processes are involved. Thus, this crosssectional study cannot make conclusions as to the impact of CU in the manifestation of current
eye gaze patterns. Alternative plausible scenarios include deficient eye gaze and emotion
recognition leading to CU traits.
Another limitation of this study is the sample size, which limited power, reducing the
likelihood of detecting smaller effects, including moderating or serial mediation effects,
increasing the risk of type II errors. Conversely, the number of correlations tested inflates type I
error rate. If Bonferroni corrections were made the following relationships would remain
significant: the relationship between first fixation on the mouth and emotion recognition for fear
expressions at 100% intensity (r = -.38, p = .001), as well as the relationship between age and DI
no cost and cost trials (r = .36, p = .001 and r = .32, p = .004, respectively). Given that most of
the standardized regression coefficients for the mediation paths in this study ranged from .15 to
.25, according to Fritz and MacKinnon (2007) we would need over 400 participants to achieve
power of .80. Determining the necessary N for a serial mediation is more complicated, given that
even if individual paths are highly powered, the mediated effects are often small and thus tests of
them are often underpowered (Thoemmes, MacKinnon, & Reiser, 2010).
In this preliminary investigation, emotion recognition accuracy was considered for each
expression without consideration to the nature of mistakes made. Future studies should examine
33
confusion matrices to compare error patterns of emotion categorization and consider potential
systematic recognition errors (e.g., mistaking fearful for angry expressions).
Finally, this study did not take into account the role of different forms of prosocial
behavior (Dunfield, 2014) and that such subtypes may relate differently to both emotion
recognition and the context of the sharing task. Even from infancy, neural correlates relate
differently to helping compared to comforting behaviors (Paulus, Kühn-Popp, Licata, Sodian, &
Meinhardt, 2013). As this study only focused on one form of prosocial behavior, we cannot make
conclusions about other subtypes (e.g., helping, comforting). In addition, separating the emotion
recognition task from the prosocial behavior task limits and our cross-sectional design our ability
to make conclusions about directionality of these mechanism. Potential improvements to our
protocol are discussed further below.
Implications and Future Directions
This study was the first that we know of to examine the empathic behavioral outcomes of
eye gaze, CU traits, and emotion recognition. While our results were similar to prior studies
(e.g., Williams & Moore, 2016), relationships between CU traits and prosocial behavior were
minimal, at best, in contrast to the view that CU traits reflect a lack of prosocial emotions (DSM5; APA, 2013). This discrepancy suggests that this prosocial behavior task may be different from
prosocial behaviors observed in children’s everyday social and school settings (e.g., Belacchi, &
Farina, 2012) and that perception of equality may play a role (Shaw et al., 2016). It is possible
that naturalistic settings include additional social cues that would alter prosocial responding,
such as context or vocal and postural communication, which were absent in the paradigm we
selected.
34
Future studies should strive to include such social information in their paradigms as well
as creating a paradigm that could test eye gaze, emotion recognition, and prosocial responding
with the same stimulus. This would allow for testing of mediation. Further, longitudinal studies
can test the role of CU traits in emotion recognition and prosocial behavior throughout
development, focusing on identifying if these are indeed causal relationships. In addition, future
studies might examine distinct facets of CU traits, as well as isolating the impact of empathy.
As accuracy for fear recognition was significantly worse for those who initially fixated
on the mouth, results suggest that emotion recognition training programs that call attention to the
eye regions might be helpful for improving recognition of high valence fear expressions
(Schönenberg et al., 2014), yet they may be less effective for subtle expressions. There is some
preliminary evidence that implicit emotion recognition training for low intensity expressions
increases overall bias for detecting the trained emotions, however it does not increase recognition
accuracy (Griffiths, Jarrold, Penton-Voak & Munafò, 2015). On the other hand, an explicit
emotion recognition training task using subtle emotions did show significant increase in emotion
recognition and decreases in severity of subsequent crimes (Hubble, Bowen, Moore & Van
Goozen, 2015). Clearly there are promising new interventions on the horizon and investigation
of the differences in design is necessary in order to improve the likelihood of success.
Conclusions
This study demonstrated that for girls, CU traits predict less initial fixation on the eyes
which corresponds to poorer emotion recognition of low intensity happy expressions, which in
turn corresponds to less prosocial sharing behavior. While findings are preliminary in terms of
application of a new emotion recognition task in combination with a prosocial sharing task in
young female and male children, this study provides insight that eye gaze patterns and emotion
35
recognition for positive emotions may mediate the negative relationship between CU traits and
prosocial behavior in girls in this age range. This preliminary investigation demonstrated the
feasibility of implementing a new emotion recognition paradigm with more standardized stimuli
on a younger child sample, and suggests potential improvements for further testing these
relationships and their temporal relationships, which ultimately could inform future
interventions.
36
References
Achenbach, T. M., & Rescorla, L. (2001). ASEBA school-age forms & profiles. Burlington, VT:
Aseba.
Adolphs, R., Gosselin, F., Buchanan, T. W., Tranel, D., Schyns, P., & Damasio, A. R. (2005). A
mechanism for impaired fear recognition after amygdala damage. Nature, 433(7021), 6872.
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders
(5th ed.). Arlington, VA: American Psychiatric Publishing.
Amso, D., Haas, S., & Markant, J. (2014). An eye tracking investigation of developmental
change in bottom-up attention orienting to faces in cluttered natural scenes. PloS
one, 9(1), e85701.
Aspan, N., Vida, P., Gadoros, J., & Halasz, J. (2013). Conduct symptoms and emotion
recognition in adolescent boys with externalization problems. The Scientific World
Journal, 199(3), 1-6.
Barry, C. T., Frick, P. J., DeShazo, T. M., McCoy, M. G., Ellis, M., & Loney, B. R. (2000). The
importance of callous–unemotional traits for extending the concept of psychopathy to
children. Journal of Abnormal Psychology, 109, 335–340.
Barth, J. M., & Bastiani, A. (1997). A longitudinal study of emotion recognition and preschool
children's social behavior. Merrill-Palmer Quarterly 43(1), 107-128.
Bartz, J. A., Zaki, J., Bolger, N., & Ochsner, K. N. (2011). Social effects of oxytocin in humans:
context and person matter. Trends in cognitive sciences, 15(7), 301-309.
Belacchi, C., & Farina, E. (2012). Feeling and thinking of others: Affective and cognitive
empathy and emotion comprehension in prosocial/hostile preschoolers. Aggressive
behavior, 38(2), 150-165.
Benesch, C., Görtz-Dorten, A., Breuer, D., & Döpfner, M. (2014). Assessment of CallousUnemotional Traits in 6 to 12 Year-Old Children with Oppositional Defiant
Disorder/Conduct Disorder by Parent Ratings. Journal of Psychopathology and
Behavioral Assessment, 36(4), 519-529.
Best, C. A., Yim, H., & Sloutsky, V. M. (2013). The cost of selective attention in category
learning: Developmental differences between adults and infants. Journal of experimental
child psychology, 116(2), 105-119.
Blair, R. J. R. (1995). A cognitive developmental approach to morality: Investigating the
psychopath. Cognition, 57(1), 1-29.
Blair, R. J. R. (2003). Facial expressions, their communicatory functions and neuro–cognitive
substrates. Philosophical Transactions of the Royal Society B: Biological Sciences,
358(1431), 561-572.
Blair, R. J. R. (2006). The emergence of psychopathy: Implications for the neuropsychological
approach to developmental disorders. Cognition, 101, 414–442.
Blair, R. J. R., & Coles, M. (2000). Expression recognition and behavioural problems in early
adolescence. Cognitive development, 15(4), 421-434.
Blair, R. J., Colledge, E., Murray, L., & Mitchell, D. G. (2001). A selective impairment in the
processing of sad and fearful expressions in children with psychopathic tendencies.
Journal of Abnormal Child Psychology, 29(6), 491–8.
38
Blair, R. J. R., Mitchell, D. G. V., Peschardt, K. S., Colledge, E., Leonard, R. A., Shine, J. H., ...
& Perrett, D. I. (2004). Reduced sensitivity to others’ fearful expressions in psychopathic
individuals. Personality and Individual Differences, 37(6), 1111-1122.
Bowen, K. L., Morgan, J. E., Moore, S. C., & van Goozen, S. H. (2014). Young offenders’
emotion recognition dysfunction across emotion intensities: explaining variation using
psychopathic traits, conduct disorder and offense severity. Journal of psychopathology
and behavioral assessment, 36(1), 60-73.
Byrd, A. L., Kahn, R. E., & Pardini, D. A. (2013). A validation of the inventory of callousunemotional traits in a community sample of young adult males. Journal of
psychopathology and behavioral assessment, 35(1), 20-34.
Calder, A., Young, A., Perrett, D., Etcoff, N., & Rowland, D. (1996). Categorical perception of
morphed facial expressions. Visual Cognition, 3(2), 81-117.
http://doi.org/10.1080/713756735
Campbell, A. (2010). Oxytocin and human social behavior. Personality and Social Psychology
Review, 14(3) 281–295.
Cleckley, H. (1976). The mask of sanity 5th edn. St Louis: Mosby.
Colder, C. R., & O'Connor, R. M. (2004). Gray's reinforcement sensitivity model and child
psychopathology: Laboratory and questionnaire assessment of the BAS and BIS. Journal
of Abnormal Child Psychology,32(4), 435-451.
Colder, C. R., Trucco, E. M., Lopez, H. I., Hawk, L. W., Read, J. P., Lengua, L. J., ... & Eiden,
R. D. (2011). Revised reinforcement sensitivity theory and laboratory assessment of BIS
and BAS in children. Journal of Research in Personality, 45(2), 198-207.
39
Cooke, D. J., & Michie, C. (2001). Refining the construct of psychopathy: towards a hierarchical
model. Psychological assessment, 13(2), 171-188.
Dadds, M. R., Allen, J. L., Oliver, B. R., Faulkner, N., Legge, K., Moul, C., ... & Scott, S.
(2012). Love, eye contact and the developmental origins of empathy v. psychopathy. The
British Journal of Psychiatry, 200(3), 191-196.
Dadds, M. R., Hawes, D. & Merz, S. (2004) The UNSW Facial Emotion Task. Sydney:
University of New South Wales.
Dadds, M. R., Jambrak, J., Pasalich, D., Hawes, D. J., & Brennan, J. (2011). Impaired attention
to the eyes of attachment figures and the developmental origins of psychopathy. Journal
of Child Psychology and Psychiatry, 52(3), 238-245.
Dadds, M. R., El Masry, Y., Wimalaweera, S., & Guastella, A. J. (2008). Reduced eye gaze
explains “fear blindness” in childhood psychopathic traits. Journal of the American
Academy of Child & Adolescent Psychiatry, 47(4), 455-463.
Dadds, M. R., Perry, Y., Hawes, D. J., Merz, S., Riddell, A. C., Haines, D. J., …
Abeygunawardane, A. I. (2006). Attention to the eyes and fear-recognition deficits in
child psychopathy. The British Journal of Psychiatry : The Journal of Mental Science,
189, 280–281.
Dawel, A., O’Kearney, R., McKone, E., & Palermo, R. (2012). Not just fear and sadness: metaanalytic evidence of pervasive emotion recognition deficits for facial and vocal
expressions in psychopathy. Neuroscience and Biobehavioral Reviews, 36(10), 2288–
2304.
Dawel, A., McKone, E., O’Kearney, R., Sellbom, M., Irons, J., & Palermo, R. (2015). Elevated
levels of callous unemotional traits are associated with reduced attentional cueing, with
40
no specificity for fear or eyes. Personality Disorders: Theory, Research, and
Treatment, 6(3), 216-228.
Dunfield, K. A. (2014). A construct divided: prosocial behavior as helping, sharing, and
comforting subtypes. Frontiers in Psychology, 5 (958), 111-123.
Edens, J. F., Marcus, D. K., Lilienfeld, S. O., & Poythress Jr, N. G. (2006). Psychopathic, not
psychopath: Taxometric evidence for the dimensional structure of psychopathy. Journal
of Abnormal Psychology, 115, 131-144.
Eisenbarth, H., Demetriou, C. A., Kyranides, M. N., & Fanti, K. A. (2016). Stability Subtypes of
Callous–Unemotional Traits and Conduct Disorder Symptoms and Their
Correlates. Journal of youth and adolescence, 45(9), 1889-1901.
Eisenberg, N., & Eggum, N. D. (2009). Empathic responding: Sympathy and personal
distress. In J. Decety & W. Ickes, The social neuroscience of empathy, (pp.71-83). MIT
Press.
Eisenberg, N., Eggum, N. D., & Di Giunta, L. (2010). Empathy related responding: Associations
with prosocial behavior, aggression, and intergroup relations. Social Issues and Policy
Review, 4, 143–180.
Eisenberg, N., Fabes, R. A., & Spinrad, T. L. (2006). Prosocial development. In N. Eisenberg &
W. Damon (Eds.), Handbook of child psychology. 6th edition (646-718). New York:
Wiley.
Eisenberg, N., Huerta, S., & Edwards, A. (2012). Relation of empathy-related responding to
children’s and adolescents’ social competence. Empathy: From bench to bedside, 148163.
41
Eisenberg, N., Spinrad, T. L., & Sadovsky, A. (2006). Empathy-related responding in
children. Handbook of moral development, 517-549.
Ezpeleta, L., Osa, N. D. L., Granero, R., Penelo, E., & Domènech, J. M. (2013). Inventory of
callous-unemotional traits in a community sample of preschoolers. Journal of Clinical
Child & Adolescent Psychology, 42(1), 91-105.
Fanti, K. A., & Centifanti, L. C. M. (2014). Childhood callous-unemotional traits moderate the
relation between parenting distress and conduct problems over time. Child Psychiatry &
Human Development, 45(2), 173-184.
Fanti, K. A., Panayiotou, G., Lazarou, C., Michael, R., & Georgiou, G. (2015). The better of two
evils? Evidence that children exhibiting continuous conduct problems high or low on
callous–unemotional traits score on opposite directions on physiological and behavioral
measures of fear. Development and psychopathology, 28, 185-198.
Fehr, E., Bernhard, H., & Rockenbach, B. (2008). Egalitarianism in young children. Nature, 454,
1079–1084.
Fehr, E., & Schmidt, K. (1999). A theory of fairness, competition, and cooperation. Quarterly
Journal of Economics, 114, 817–868.
Feshbach, N.D. (1978). Studies of empathic behaviour in children. Progress in experimental
personality research, 8, 1-47.
Feshbach, N. D., & Roe, K. (1968). Empathy in six-and seven-year-olds. Child development,
133-145.
Fontaine, N. M., McCrory, E. J., Boivin, M., Moffitt, T. E., & Viding, E. (2011). Predictors and
outcomes of joint trajectories of callous–unemotional traits and conduct problems in
childhood. Journal of abnormal psychology, 120(3), 730.
42
Fowles, D. C., & Kochanska, G. (2000). Temperament as a moderator of pathways to conscience
in children: The contribution of electrodermal activity. Psychophysiology, 37, 788-795.
Frank, M. G., Ekman, P., & Friesen, W. V. (1993). Behavioral markers and recognizability of the
smile of enjoyment. Journal of personality and social psychology, 64(1), 83-93.
Frick, P. J. (2004). The Inventory of Callous-Unemotional Traits. Unpublished rating scale.
Frick, P. J. (2009). Extending the construct of psychopathy to youth: Implications for
understanding, diagnosing, and treating antisocial children and adolescents. Canadian
Journal of Psychiatry/Revue canadienne de psychiatrie, 31(12), 803.
Frick, P. J. (2012). Developmental pathways to conduct disorder: Implications for future
directions in research, assessment, and treatment. Journal of Clinical Child & Adolescent
Psychology, 41(3), 378-389.
Frick, P. J., Blair, R. J., & Castellanos, F. X. (2013). Callous-unemotional traits and
developmental pathways to the disruptive behavior disorders. In Disruptive behavior
disorders (69-102). New York: Springer.
Frick, P. J., & Ellis, M. (1999). Callous–unemotional traits and subtypes of conduct disorder.
Clinical Child and Family Psychology Review, 2, 149–168.
Frick, P., & Hare, R. (2002). The Antisocial Process Screening Device. Toronto: Multi-Health
Systems.
Frick, P. J., Kimonis, E. R., Dandreaux, D. M., & Farell, J. M. (2003). The 4 year stability of
psychopathic traits in non-referred youth. Behavioral Science and the Law, 21, 713–736.
Frick, P. J., & Ray, J. V. (2014). Evaluating Callous‐Unemotional Traits as a Personality
Construct. Journal of personality. DOI: 10.1111/jopy.12114
43
Frick, P. J., Ray, J. V., Thornton, L. C., & Kahn, R. E. (2014). Annual Research Review: A
developmental psychopathology approach to understanding callous‐unemotional traits in
children and adolescents with serious conduct problems. Journal of child Psychology and
Psychiatry, 55(6), 532-548.
Fritz, M. S., & MacKinnon, D. P. (2007). Required sample size to detect the mediated
effect. Psychological science, 18(3), 233-239.
Gao, X., Maurer, D., & Nishimura, M. (2010). Similarities and differences in the perceptual
structure of facial expressions of children and adults. Journal of Experimental Child
Psychology, 105(1-2), 98–115. http://doi.org/10.1016/j.jecp.2009.09.001
Gillespie, S. M., Rotshtein, P., Satherley, R.-M., Beech, A. R., & Mitchell, I. J. (2015).
Emotional expression recognition and attribution bias among sexual and violent
offenders: a signal detection analysis. Frontiers in Psychology, 6(May), 1–12.
http://doi.org/10.3389/fpsyg.2015.00595
Griffiths, S., Jarrold, C., Penton-Voak, I. S., & Munafò, M. R. (2015). Feedback training induces
a bias for detecting happiness or fear in facial expressions that generalises to a novel task.
Psychiatry Research, 230(3), 951-957.
Guyer, A. E., Monk, C. S., McClure-Tone, E. B., Nelson, E. E., Roberson-Nay, R.,... & Ernst, M.
(2008). A developmental examination of amygdala response to facial
expressions. Journal of Cognitive Neuroscience, 20(9), 1565-1582.
Han, T., Alders, G. L., Greening, S. G., Neufeld, R. W. J., & Mitchell, D. G. V. (2012). Do
fearful eyes activate empathy-related brain regions in individuals with callous traits?
Social Cognitive and Affective Neuroscience, 7(8), 958–68.
44
Hare, R. D., Harpur, T. J., Hakstian, A. R., Forth, A. E., Hart, S. D., & Newman, J. P. (1990).
The revised Psychopathy Checklist: reliability and factor structure. Psychological
Assessment: A Journal of Consulting and Clinical Psychology, 2(3), 338.
Hare, R. D., Williamson, S. E., & Harpur, T. J. (1988). Psychopathy and language. In T. E.
Moffitt, & S. A. Mednick, Biological Contributions to Crime Causation (68-92).
Dordrecht, Netherlands: Martinus Nijhoff.
Harter, S. (1983). Children's understanding of multiple emotions: A cognitive-developmental
approach. In Overton, W. S. (Ed.), The relationship between social and cognitive
development, 147-194. Psychology Press.
Hawes, S. W., Byrd, A. L., Henderson, C. E., Gazda, R. L., Burke, J. D., Loeber, R., & Pardini,
D. A. (2014). Refining the parent-reported Inventory of Callous–Unemotional Traits in
boys with conduct problems. Psychological assessment, 26(1), 256.
Hawes, D. J., & Dadds, M. R. (2007). Stability and malleability of callous-unemotional traits
during treatment for childhood conduct problems. Journal of Clinical Child and
Adolescent Psychology, 36(3), 347-355.
Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A
regression-based approach. Guilford Press.
Herpers, P. C. M., Scheepers, F. E., Bons, D. M. a, Buitelaar, J. K., & Rommelse, N. N. J.
(2014). The cognitive and neural correlates of psychopathy and especially callousunemotional traits in youths: a systematic review of the evidence. Development and
Psychopathology, 26(1), 245–73.
Heyman, G., Barner, D., Heumann, J., & Schenck, L. (2014). Children's sensitivity to ulterior
motives when evaluating prosocial behavior. Cognitive science, 38(4), 683-700.
45
Hicks, B. M., Carlson, M. D., Blonigen, D. M., Patrick, C. J., Iacono, W. G., & Mgue, M.
(2012). Psychopathic personality traits and environmental contexts: Differential
correlates, gender differences, and genetic mediation. Personality Disorders, 3(3), 209–
27.
Houghton, S., Hunter, S. C., & Crow, J. (2013). Assessing callous unemotional traits in children
aged 7-to 12-years: A confirmatory factor analysis of the inventory of callous
unemotional traits. Journal of Psychopathology and Behavioral Assessment, 35(2), 215222.
Hubble, K., Bowen, K. L., Moore, S. C., & Van Goozen, S. H. (2015). Improving negative
emotion recognition in young offenders reduces subsequent crime. PloS one, 10(6),
e0132035.
Hunnius, S., de Wit, T. C., Vrins, S., & von Hofsten, C. (2011). Facing threat: Infants' and adults'
visual scanning of faces with neutral, happy, sad, angry, and fearful emotional
expressions. Cognition and emotion, 25(2), 193-205.
Kahn, R. E., Frick, P. J., Youngstrom, E. A, Youngstrom, J. K., Feeny, N. C., & Findling, R. L.
(2013). Distinguishing primary and secondary variants of callous-unemotional traits among
adolescents in a clinic-referred sample. Psychological Assessment, 25(3), 966–78.
http://doi.org/10.1037/a0032880
Kimonis, E. R., Fanti, K. A., & Singh, J. P. (2014). Establishing Cut-off Scores for the ParentReported Inventory of Callous-Unemotional Traits. Archives of Forensic
Psychology, 1(1), 27-48.
Kimonis, E. R., Fanti, K. A., Frick, P. J., Moffitt, T. E., Essau, C., Bijttebier, P., & Marsee, M.
A. (2015). Using self‐reported callous‐unemotional traits to cross‐nationally assess the
46
DSM‐5 ‘With Limited Prosocial Emotions’ specifier. Journal of Child Psychology and
Psychiatry, 56(11), 1249-1261.
Kochanska, G. (1993). Toward a synthesis of parental socialization and child temperament in
early development of conscience. Child development, 64(2), 325-347.
Kochanska, G., & Aksan, N. (2006). Children's conscience and self‐regulation. Journal of
Personality, 74(6), 1587-1618.
Lawrence, K., Campbell, R., & Skuse, D. (2015). Age, gender, and puberty influence the
development of facial emotion recognition. Frontiers in psychology, 6(761), 1-14.
Leist, T., & Dadds, M. R. (2009). Adolescents' ability to read different emotional faces relates to
their history of maltreatment and type of psychopathology. Clinical Child Psychology
and Psychiatry, 14(2), 237-250.
Leitzke, B. T., & Pollak, S. D. (2016). Developmental changes in the primacy of facial cues for
emotion recognition. Developmental psychology, 52(4), 572.
Leppänen, J. M., & Hietanen, J. K. (2001). Emotion recognition and social adjustment in school–
aged girls and boys. Scandinavian journal of psychology,42(5), 429-435.
LoBue, V., Nishida, T., Chiong, C., DeLoache, J. S., & Haidt, J. (2011). When getting something
good is bad: Even three‐year‐olds react to inequality. Social Development, 20(1), 154170.
López-Romero, L., Romero, E., & Villar, P. (2014). Assessing the stability of psychopathic
traits: adolescent outcomes in a six-year follow-up. The Spanish journal of
psychology, 17, E97.
Lozier, L. M., Cardinale, E. M., VanMeter, J. W., & Marsh, A. a. (2014). Mediation of the
relationship between callous-unemotional traits and proactive aggression by amygdala
47
response to fear among children with conduct problems. JAMA Psychiatry, 71(6), 627–
36.
Luman, M., van Meel, C. S., Oosterlaan, J., & Geurts, H. M. (2012). Reward and punishment
sensitivity in children with ADHD: validating the sensitivity to punishment and
sensitivity to reward questionnaire for children (SPSRQ-C).Journal of abnormal child
psychology, 40(1), 145-157.
Lykken, D. T. (1995). The antisocial personalities. Psychology Press.
Malti, T., Gummerum, M., Keller, M., & Buchmann, M. (2009). Children’s moral motivation,
sympathy, and prosocial behavior. Child development, 80(2), 442-460.
Malti, T., & Krettenauer, T. (2013). The Relation of Moral Emotion Attributions to Prosocial and
Antisocial Behavior: A Meta‐Analysis. Child development, 84(2), 397-412.
Marsh, A. A., Finger, E. C., Mitchell, D. G. V., Reid, M., Sims, C., Kosson, D. S., et al. (2008).
Reduced amygdala response to fearful expressions in children and adolescents with
callous–unemotional traits and disruptive behaviour disorders. American Journal of
Psychiatry, 165, 712–720.
Marsh, A. a., Finger, E. C., Schechter, J. C., Jurkowitz, I. T. N., Reid, M. E., & Blair, R. J. R.
(2011). Adolescents with psychopathic traits report reductions in physiological responses
to fear. Journal of Child Psychology and Psychiatry and Allied Disciplines, 52, 834–841.
http://doi.org/10.1111/j.1469-7610.2010.02353.x
Marsh, A. A., Henry, H. Y., Pine, D. S., & Blair, R. J. R. (2010). Oxytocin improves specific
recognition of positive facial expressions. Psychopharmacology, 209(3), 225-232.
Matthews, G. & Wells, A. 1999 The cognitive science of attention and emotion. In Handbook of
cognition and emotion (ed. T. Dalgleish & M. J. Power), pp. 171–192. New York: Wiley.
48
Michalska, K. J., Kinzler, K. D., & Decety, J. (2013). Age-related sex differences in explicit
measures of empathy do not predict brain responses across childhood and
adolescence. Developmental cognitive neuroscience, 3, 22-32.
Miller, A. L., Gouley, K. K., Seifer, R., Zakriski, A., Eguia, M., & Vergnani, M. (2005).
Emotion Knowledge Skills in Low‐income Elementary School Children: Associations
with Social Status and Peer Experiences. Social Development,14(4), 637-651.
Mizokawa, A., Minemoto, K., Komiya, A., & Noguchi, M. (2013). The Effects of Infants’ and
Adults’ Facial Expressions on Approach-Avoidance Behavior. Psychologia, 56(1), 33-44.
Moffitt, T. E. (1993). Adolescence-limited and life-course-persistent antisocial behavior: a
developmental taxonomy. Psychological review, 100(4), 674.
Muñoz, L. C. (2009). Callous-unemotional traits are related to combined deficits in recognizing
afraid faces and body poses. Journal of the American Academy of Child & Adolescent
Psychiatry, 48(5), 554-562.
Muratori, P., Lochman, J. E., Manfredi, A., Milone, A., Nocentini, A., Pisano, S., & Masi, G.
(2016). Callous unemotional traits in children with disruptive behavior disorder:
predictors of developmental trajectories and adolescent outcomes. Psychiatry
research, 236, 35-41.
Murray-Close, D., & Crick, N. R. (2007). Gender differences in the association between
cardiovascular reactivity and aggressive conduct. International Journal of
Psychophysiology : Official Journal of the International Organization of
Psychophysiology, 65(2), 103–13.
49
Paiva-Salisbury, M. L., Gill, A. D., & Stickle, T. R. (2016). Isolating Trait and Method Variance
in the Measurement of Callous and Unemotional Traits. Assessment,
doi:10.1177/1073191115624546.
Paulus, M., Kühn-Popp, N., Licata, M., Sodian, B., & Meinhardt, J. (2013). Neural correlates of
prosocial behavior in infancy: different neurophysiological mechanisms support the
emergence of helping and comforting. Neuroimage, 66, 522-530.
Pardini, D., & Frick, P. J. (2013). Multiple developmental pathways to conduct disorder: Current
conceptualizations and clinical implications. Journal of the Canadian Academy of Child
and Adolescent Psychiatry, 22(1), 20.
Pardini, D. A., Lochman, J. E., & Frick, P. J. (2003). Callous/unemotional traits and socialcognitive processes in adjudicated youths. Journal of the American Academy of Child &
Adolescent Psychiatry, 42(3), 364-371.
Patrick, C. J., Bradley, M. M., & Lang, P. J. (1993). Emotion in the Criminal Psychopath: Startle
Reflex Modulation. Journal of Abnormal Psychology, 82-92.
Ray, J. V., Frick, P. J., Thornton, L. C., Steinberg, L., & Cauffman, E. (2016). Positive and
negative item wording and its influence on the assessment of callous-unemotional traits.
Psychological Assessment, 28(4), 394-404.
Rosenqvist, J., Lahti-Nuuttila, P., Laasonen, M., & Korkman, M. (2014). Preschoolers’
recognition of emotional expressions: Relationships with other neurocognitive
capacities. Child Neuropsychology, 20(3), 281-302.
Salihovic, S., Kerr, M., & Stattin, H. (2014). Under the surface of adolescent psychopathic traits:
high-anxious and low-anxious subgroups in a community sample of youths. Journal of
Adolescence, 37(5), 681–9. http://doi.org/10.1016/j.adolescence.2014.03.002
50
Schönenberg, M., Christian, S., Gaußer, a-K., Mayer, S. V, Hautzinger, M., & Jusyte, a. (2014).
Addressing perceptual insensitivity to facial affect in violent offenders: first evidence for
the efficacy of a novel implicit training approach. Psychological Medicine, 44(5), 1043–
52.
Schönenberg, M., Mayer, S. V., Christian, S., Louis, K., & Jusyte, A. (2015). Facial affect
recognition in violent and nonviolent antisocial behavior subtypes. Journal of Personality
Disorders, 29, 1-12.
Seidela, E. M., Habela, U., Kirschnerc, M., Gurd, R. C., & Derntl, B. (2010). The impact of
facial emotional expressions on behavioral tendencies in females and males. J Exp
Psychol Hum Percept Perform, 36(2), 500-507.
Sharp, C., van Goozen, S., & Goodyer, I. (2006). Children’s subjective emotional reactivity to
affective pictures: Gender differences and their antisocial correlates in an unselected
sample of 7-11-year-olds. Journal of Child Psychology and Psychiatry and Allied
Disciplines, 47, 143–150.
Sharp, C. (2008). Theory of Mind and conduct problems in children: Deficits in reading the
“emotions of the eyes”. Cognition and Emotion, 22(6), 1149-1158.
Shaw, A., Choshen-Hillel, S., & Caruso, E. M. (2016). The development of inequity aversion
understanding when (and why) people give others the bigger piece of the
pie. Psychological Science, 0956797616660548.
Sheskin, M., Bloom, P., & Wynn, K. (2014). Anti-equality: Social comparison in young
children. Cognition, 130(2), 152-156.
51
Spezio, M. L., Huang, P.-Y. S., Castelli, F., & Adolphs, R. (2007). Amygdala damage impairs
eye contact during conversations with real people. The Journal of Neuroscience : The
Official Journal of the Society for Neuroscience, 27(15), 3994–3997.
Steiger, J. H. (1980). Tests for comparing elements of a correlation matrix. Psychological
Bulletin, 87, 245-251.
Stevens, D., Charman, T., & Blair, R. J. R. (2001). Recognition of emotion in facial expressions
and vocal tones in children with psychopathic tendencies. The Journal of genetic
psychology, 162(2), 201-211.
Stuijfzand, S., De Wied, M., Kempes, M., Van de Graaff, J., Branje, S., & Meeus, W. (2016).
Gender Differences in Empathic Sadness towards Persons of the Same-versus Other-sex
during Adolescence. Sex Roles, 1-13.
Székely, E., Tiemeier, H., Jaddoe, V. W., Hofman, A., Verhulst, F. C., & Herba, C. M. (2014).
Associations of Internalizing and Externalizing Problems with Facial Expression
Recognition in Preschoolers: The Generation R Study. Social Development, 23(3), 611630.
Tell, D., Davidson, D., & Camras, L. a. (2014). Recognition of emotion from facial expressions
with direct or averted eye gaze and varying expression intensities in children with autism
disorder and typically developing children. Autism Research and Treatment, 2014, 1-11.
http://doi.org/10.1155/2014/816137
Thoemmes, F., MacKinnon, D. P., & Reiser, M. R. (2010). Power analysis for complex
mediational designs using Monte Carlo methods. Structural Equation Modeling, 17(3),
510-534.
52
Tottenham, N., Tanaka, J. W., Leon, A. C., McCarry, T., Nurse, M., Hare, T. A., ... & Nelson, C.
(2009). The NimStim set of facial expressions: judgments from untrained research
participants. Psychiatry research, 168(3), 242-249.
Viding, E., Frick, P. J., & Plomin, R. (2007). Aetiology of the relationship between callous–
unemotional traits and conduct problems in childhood. The British Journal of
Psychiatry, 190(49), 33-38.
Viding, E., Sebastian, C. L., Dadds, M. R., Lockwood, P. L., Cecil, C. a M., De Brito, S. a, &
McCrory, E. J. (2012). Amygdala response to preattentive masked fear in children with
conduct problems: the role of callous-unemotional traits. The American Journal of
Psychiatry, 169(10), 1109–16.
Waller, R., Gardner, F., & Hyde, L. W. (2013). What are the associations between parenting,
callous–unemotional traits, and antisocial behavior in youth? A systematic review of
evidence. Clinical Psychology Review, 33(4), 593-608.
Waller, R., Wright, A. G., Shaw, D. S., Gardner, F., Dishion, T. J., Wilson, M. N., & Hyde, L.
W. (2015). Factor structure and construct validity of the parent-reported inventory of
callous-unemotional traits among high-risk 9-year-olds. Assessment, 22(5), 561-580.
White, B. A. (2014). Who cares when nobody is watching? Psychopathic traits and empathy in
prosocial behaviors. Personality and Individual Differences, 56, 116-121.
Williams, A., O’Driscoll, K., & Moore, C. (2014). The influence of empathic concern on
prosocial behavior in children. Frontiers in Psychology, 5, 1–8.
Williams, A., & Moore, C. (2016). A longitudinal exploration of advantageous and
disadvantageous inequality aversion in children. Journal of Experimental Child
Psychology, 152, 294-306.
53
Willoughby, M. T., Mills-Koonce, W. R., Waschbusch, D. A., & Gottfredson, N. C. (2015). An
examination of the parent report version of the inventory of callous-unemotional traits in
a community sample of first-grade children. Assessment, 22(1), 76-85.
Zajdel, R. T., Bloom, J. M., Fireman, G., & Larsen, J. T. (2013). Children's understanding and
experience of mixed emotions: The roles of age, gender, and empathy. The Journal of
genetic psychology, 174(5), 582-603.
54
Table 1
Trial Types for Prosocial Behavior Task
Child, Jenny vs. Child, Jenny
AI No Cost- 4x
1, 0
1, 1
AI Cost- 4x
2, 0
1, 1
DI No Cost- 4x
1, 2
1, 1
DI Cost- 4x
2 ,3
1, 1
Note: AI = Advantageous Inequality,
DI = Disadvantageous Inequality
Table 2
Descriptive Statistics for Primary Variables and Accuracy
Std
S.E.
S.E.
Variable
Mean
Dev Kurtosis Kurt
Skew Skew Min
Max
.27
Sex
.58
.50
-1.94
.53
-.33
.00
1.0
.53
.27 72.00 119.0
Age (months)
93.84
13.83
-1.28
-.04
.53
.27 3.04
AI
7.11
1.36
1.03
-1.49
8.0
.53
.27
DI
3.73
2.80
-1.31
.02
.00
8.0
.53
.27 1.00
AI No Cost
3.72
.58
6.28
-2.33
4.0
.53
.27
AI Cost
3.40
1.00
1.53
-1.57
.00
4.0
.53
.27
DI No Cost
1.54
1.51
-1.29
.42
.00
4.0
.53
.27
DI Cost
2.19
1.64
-1.58
-.27
.00
4.0
.53
.27 3.00
ICU Total
16.88
8.06
1.32
1.03
41.0
.53
.27 8.00
Fearfulness
19.25
5.66
-.13
.22
36.0
.53
.27 50.00
Anxiety Problems
54.61
7.30
1.35
1.58
76.8
Accuracy at 50%
Fear
.62
.27
-.31
.53
-.60
.27
.00
1.0
Happy
.74
.23
.01
.53
-.79
.27
.04
1.0
Sad
.51
.24
-.74
.53
.18
.27
.00
1.0
Angry
.61
.18
.59
.53
-.73
.27
.05
1.0
Accuracy at 100%
Fear
.77
.25
1.06
.53
-1.22
.27
.01
1.0
Happy
.93
.13
2.56
.53
-1.82
.27
.50
1.0
Sad
.77
.24
.34
.53
-1.04
.27
.04
1.0
Angry
.89
.15
1.22
.53
-1.37
.27
.41
1.0
Note. Sex: 0 = male, 1 = female, Race: 0 = White, 1 = other, ICU total = Inventory of
Callous-Unemotional Traits Total Score, Fear/Shyness = Fear/Shyness scale of the
SPSRQ-C, Anxiety Problems = Anxiety Problems Subscale of the CBCL, AI =
Advantageous Inequality, DI = Disadvantageous Inequality
55
Table 3.
Descriptive Statistics of Initial Fixation
Variable Mean Std Dev Kurtosis S.E. Kurt Skew S.E. Skew Min Max
Proportion of Initial Fixation on the Eyes vs. Elsewhere on Face at 50% Stimuli Intensity
Fear
.63
.24
-.06
.53
-.49
.27
0 1.00
Happy
.60
.23
-.49
.53
-.28
.27
0 1.00
Sad
.63
.26
-.56
.53
-.37
.27
0 1.00
Angry
.62
.24
-.26
.53
-.10
.27
0 1.00
Proportion of Initial Fixation on the Eyes vs. Elsewhere on Face at 100% Stimuli Intensity
Fear
.59
.27
-.93
.53
-.07
.27
0 1.00
Happy
.61
.24
-.42
.53
-.28
.27
0 1.00
Sad
.63
.24
.04
.53
-.47
.27
0 1.00
Angry
.62
.25
-.29
.53
-.51
.27
0 1.00
Proportion of Initial Fixation on the Mouth vs. Elsewhere on Face at 50% Stimuli Intensity
Fear
.14
.18
.79
.53
1.29
.27
0
.67
Happy
.16
.20
1.54
.53
1.37
.27
0
.80
Sad
.15
.18
.52
.53
1.11
.27
0
.67
Angry
.17
.20
2.00
.53
1.26
.27
0 1.00
Proportion of Initial Fixation on the Mouth vs. Elsewhere on Face at 100% Stimuli Intensity
Fear
.22
.23
.07
.53
.99
.27
0
.83
Happy
.16
.20
1.41
.53
1.30
.27
0
.79
Sad
.17
.22
.51
.53
1.18
.27
0
.83
Angry
.16
.21
1.26
.53
1.42
.27
0
.78
56
Table 4.
Descriptive Statistics of Gaze Duration
Std
S.E.
S.E.
Variable
Mean
Dev Kurtosis Kurt Skewness Skew
Duration of Gaze on the Eyes at 50%
.27
Fear
858.61 250.41
.49
.53
.17
.53
.27
Happy
751.72 252.40
.43
.25
.53
.27
Sad
824.43 253.23
-.04
.52
.53
.27
Angry
865.44 262.06
-.13
.56
Duration of Gaze on the Eyes at 100%
.53
.27
Fear
842.09 235.33
1.32
.89
.53
.27
Happy
872.53 277.15
.47
.43
.53
.27
Sad
862.20 279.18
.54
-.06
.53
.27
Angry
882.88 247.13
-.01
.58
Duration of Gaze on the Mouth at 50%
.53
.27
Fear
544.97 206.92
.78
.44
.53
.27
Happy
640.63 238.93
.54
-.02
.53
.27
Sad
608.66 224.30
.88
.97
.53
.27
Angry
567.09 220.92
-.18
.37
Duration of Gaze on the Mouth at 100%
.53
.27
Fear
620.66 217.08
.57
.26
.53
.27
Happy
565.93 255.96
.37
.59
.53
.27
Sad
565.39 260.02
.85
.69
.53
.27
Angry
566.14 202.67
.58
.72
57
Min
Max
94.80
.00
280.61
366.74
1464.18
1414.17
1453.07
1522.53
316.73
266.72
21.90
325.07
1549.50
1715.40
1520.30
1525.31
.00
.00
191.71
150.03
1186.30
1300.26
1303.20
1186.35
180.04
.00
.00
244.49
1289.70
1347.80
1330.82
1180.30
Table 5.
Paired Samples T tests for Emotion Recognition
Paired Differences
95% Confidence Interval
of the Difference
Std. Std. Error
Pairs
Mean Dev. mean
Lower
Upper
50% Emotion Intensity
Fear-Happy
-.13
.30
.03
-.20
-.06
Fear-Sad
.11
.33
.04
.03
.18
Fear-Angry
.00
.26
.03
-.06
.06
Happy-Sad
.24
.31
.03
.17
.31
Happy-Angry
.13
.24
.03
.08
.18
Sad-Angry
-.11
.29
.03
-.17
-.04
100% Emotion Intensity
Fear-Happy
-.16
.27
.03
-.22
-.10
Fear-Sad
.00
.30
.03
-.07
.06
Fear-Angry
-.12
.22
.02
-.17
-.07
Happy-Sad
.15
.25
.03
.10
.21
Happy-Angry
.04
.20
.02
.00
.08
Sad-Angry
-.11
.29
.03
-.18
-.05
T
df
Sig (2tailed)
-3.83
2.93
0.04
6.99
4.88
-3.37
80
80
80
80
80
80
<.001
.004
.969
<.001
<.001
.001
-5.31
-0.14
-4.70
5.41
1.78
-3.47
80
80
80
80
80
80
<.001
.888
<.001
<.001
.080
.001
Table 6.
Paired Samples T tests for Prosocial Behavior Task
Paired Differences
95% Confidence Interval
of the Difference
Std. Std. Error
Sig (2Pairs
Mean Dev. mean
Lower
Upper
T
df tailed)
AI-DI
3.38 3.24
.36
2.67
4.10 9.39 80 <.001
AI Cost vs.
.33
.86
.10
.14
.52 3.44 80
.001
No Cost
DI Cost vs.
-.64 1.44
.16
-.96
-.32 -4.00 80 <.001
No Cost
Note. AI = Advantageous Inequality, DI = Disadvantageous Inequality
Table 7.
Bivariate Correlations
Sex
Age (months)
Race
ICU total
Fear/Shyness
Anxiety Problems
Sex
Age
(months)
1.00
.12
-.02
-.04
.19*
.15
.12
1.00
-.21*
-.09
-.07
.11
Race
ICU
total
Fear/
Anxiety
Shyness Problems
-.02 -.04
.19*
-.21* -.09
-.07
1.00
.06
.13
.06 1.00
.03
.13
.03
1.00
.05
.24** .32***
.15
.11
.05
.24**
.32***
1.00
AI No
Cost
.19*
-.10
-.07
-.09
.19*
.07
AI Cost
DI No
Cost
DI Cost
.04
.11
.00
-.11
.12
.21*
.09
.36***
.10
-.06
-.15
.02
.11
.32***
.11
-.11
.05
-.11
AI No Cost
.19*
-.10
-.07 -.09
.19*
.07
1.00
.46*** -.14
.02
AI Cost
.04
.11
.00 -.11
.12
.21*
.46*** 1.00
-.04
-.16
DI No Cost
.09
.36***
.10 -.06
-.15
.02
-.14
-.04
1.00
.58***
DI Cost
.11
.32***
.11 -.11
.05
-.11
.02
-.16
.58*** 1.00
Note. Sex: 0 = male, 1 = female, Race: 0 = White, 1 = other, ICU total = Inventory of Callous-Unemotional Traits Total
Score, Fear/Shyness = Fear/Shyness scale of the SPSRQ-C, Anxiety Problems = Anxiety Problems Subscale of the CBCL,
AI = Advantageous Inequality, DI = Disadvantageous Inequality
*p<.1, **p<.05, ***p<.01
Table 8.
Correlations with Accuracy
Age
AI No
Sex (Months) Race
Cost
Accuracy of Emotion Recognition at 50%
Fear -.06
.20*
.02
-.01
Happy .06
.18
.07
-.12
Sad .03
.19*
.12
-.07
Angry -.03
-.06
.02
-.06
Accuracy of Emotion Recognition at 100%
AI Cost
.00
.07
.18
-.12
DI No
Cost
DI Cost
ICU total
Fear/shy
Anx. Prob.
.05
.21*
.22*
.04
.00
.01
.15
.04
-.17
-.19*
-.11
-.09
-.15
-.12
.02
-.27**
-.15
-.17
.01
-.14
Fear -.02
.11
-.07
.06
-.02
.05
.03
-.12
-.05
-.08
Happy .14
.20*
.07
-.10
.03
.21*
.17
-.28**
.04
.01
Sad .13
.21*
-.15
.03
.02
.10
.09
-.19*
-.06
-.15
Angry -.11
.10
.04
-.03
-.13
.06
.13
.00
-.14
.03
Note. Sex: 0 = male, 1 = female, Race: 0 = White, 1 = other, ICU total = Inventory of Callous-Unemotional Traits
Total Score, Fear/Shyness = Fear/Shyness scale of the SPSRQ-C, Anxiety Problems = Anxiety Problems Subscale of
the CBCL, AI = Advantageous Inequality, DI = Disadvantageous Inequality
*p<.1, **p<.05, ***p<.01
60
Table 9.
Correlations with Initial Fixation
Age
AI No
DI No
Anxiety
Emotion
Sex
(months)
Race
Cost AI Cost
Cost DI Cost ICU total Fear/shy
Problems Accuracy
First Fixation on the Eyes at 50% Intensity
Fear
.07
.16
.05
-.03
.06
-.06
-.04
.02
-.12
-.17
.14
Happy
.36***
.09
.05
.19*
.11
.04
.13
-.23**
.05
-.09
.27**
Sad
-.01
.32***
-.11
-.06
.13
.09
.02
-.25**
-.12
.10
-.01
Angry
.19*
.31***
-.12
.10
.08
.25**
.18
-.16
-.03
-.16
-.09
First Fixation on the Eyes at 100% Intensity
Fear
.06
.15
-.04
-.18
-.03
.15
.00
.00
.00
.01
.20*
Happy
.00
.03
.04
-.16
-.01
.06
-.07
.00
-.07
-.09
.15
Sad
.06
.14
.05
-.05
.08
.07
.10
-.12
-.11
-.16
.00
Angry
-.09
.15
.01
-.01
.06
.24**
.12
-.10
-.10
-.16
.09
First Fixation on the Mouth at 50% Intensity
Fear
-.07
-.28**
-.07
.02
-.08
-.16
-.10
-.01
.19*
.13
-.20*
Happy
-.27**
-.09
-.03
-.11
-.02
-.18
-.25**
.13
-.03
.12
-.32***
Sad
-.17
-.23**
-.02
.01
-.16
-.14
-.12
.08
-.07
-.13
-.06
Angry
-.19*
-.28**
.03
-.11
-.10
-.07
-.15
.19*
.05
.12
-.04
First Fixation on the Mouth at 100% Intensity
Fear
-.06
-.12
-.01
.13
.10
-.13
-.10
-.07
.08
.12
-.38***
Happy
-.12
-.05
-.11
.07
.01
-.14
-.01
.08
.01
.02
-.04
Sad
-.03
-.17
.00
.05
-.05
-.08
-.07
.10
.09
.12
-.03
Angry
-.07
-.29***
.04
.03
-.05
-.21*
-.22**
.15
.14
.18
-.19*
Note. Sex: 0 = male, 1 = female, Race: 0 = White, 1 = other, ICU total = Inventory of Callous-Unemotional Traits Total Score,
Fear/Shyness = Fear/Shyness scale of the SPSRQ-C, Anxiety Problems = Anxiety Problems Subscale of the CBCL, AI =
Advantageous Inequality, DI = Disadvantageous Inequality
*p<.1, **p<.05, ***p<.01
61
Table 10.
Correlations with Duration
Age
AI No
DI No
Emotion
Sex (months) Race
Cost AI Cost
Cost DI Cost ICU total Fear/shy Anx. Prob Accuracy
Duration of Gaze on the Eyes at 50% Intensity
Fear
.19*
.04
-.02
.05
.03
.12
.02
-.25**
.08
-.11
.16
Happy
.13
.02
.09
.18
.13
.19*
.02
-.15
.05
-.13
.21*
Sad
.18
.16
-.08
.11
.13
.17
.01
-.25**
-.06
-.16
.01
Angry
.16
.04
.05
.08
.04
.11
.00
-.13
.05
-.10
.13
Duration of Gaze on the Eyes at 100% Intensity
Fear
.08
.10
-.01
-.05
.07
.13
-.14
-.12
-.05
-.08
.25**
Happy
.23**
.18
-.01
-.04
.07
.14
.03
-.18
.06
-.21*
.15
Sad
.10
.01
-.12
.12
.12
.06
-.14
-.06
.16
-.07
-.02
Angry
.08
.13
.08
.10
.06
.18
.06
-.15
.07
-.20*
-.17
Duration of Gaze on the Mouth at 50% Intensity
Fear
.05
-.04
.03
.16
.12
-.04
-.04
.04
.17
.12
-.16
Happy
.10
.02
-.02
.11
.14
.01
.05
-.01
.02
.31***
-.13
Sad
.06
-.04
-.06
.13
.10
.00
-.04
.18
.02
.23**
.05
Angry
.13
.07
-.04
.05
.10
.06
.09
.06
-.01
.20*
-.08
Duration of Gaze on the Mouth at 100% Intensity
Fear
.04
-.02
-.12
.10
.00
-.06
.06
-.05
.10
.22*
-.27**
Happy
.00
.01
-.03
.00
-.01
-.07
-.01
-.08
.05
.18
.03
Sad
.04
.04
-.10
.16
.05
-.03
.14
-.11
.05
.02
-.01
Angry
.02
.07
-.18
-.12
.02
.03
.03
-.09
-.02
.25**
-.08
Note. Sex: 0 = male, 1 = female, Race: 0 = White, 1 = other, ICU total = Inventory of Callous-Unemotional Traits Total Score,
Fear/Shyness = Fear/Shyness scale of the SPSRQ-C, Anxiety Problems = Anxiety Problems Subscale of the CBCL, AI =
Advantageous Inequality, DI = Disadvantageous Inequality
*p<.1, **p<.05, ***p<.01
62
Table 11.
Steiger Z tests for Significant ICU and Eye Gaze Correlations
ICU and Happy Initial Fixation
ICU and Sad Initial Fixation
ICU and Fear Duration
ICU and Sad Duration
ICU and Fear Accuracy
ICU and Sad Accuracy
ICU and happy Accuracy
Correlation for
50% intensity
-.23
-.25
-.25
-.25
-.17
-.11
-.19
Correlation for
100% intensity
.00
-.12
-.12
-.06
-.12
-.19
-.28
63
Correlation
between 50%
and 100%
.19
.42
.64
.62
.52
.29
.15
z-score
-1.625
-1.094
-1.386
-1.967
-.457
.603
.636
2-tail p
.104
.274
.166
.049
.648
.547
.525
Figure 1. Hypothesis 1
Figure 2. Hypothesis 2
Figure 3. Hypothesis 3
Figure 4. Hypothesis 4
Figure 5. Sample AOI Placement on NimStim image (Tottenham et al., 2009).
Figure 6. Moderated Mediation of CU traits on Recognition of Low Intensity Happy
Expressions. Note: ap<.1, *p < .05
65
Figure 7. Interaction between Gender and Initial Fixation on the Eyes Predicting Accuracy of
Recognition for Happy Faces at 50% intensity
Accuracy of Recognition for Happy
Faces at 50% Intensity
Males
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Females
b = .0032, p = .986
0.3709
0.6012
0.8316
Proportion First Fixating on the Eyes Compared to the Rest of the Face
Figure 8. Moderated Mediation of Eye Gaze on Prosocial Behavior. Note: ap<.1, *p < .05
66
Figure 9. Serial Mediation Model of CU Traits Predicting Prosocial Behavior Through Initial
Fixation on the Eyes of Happy Faces at 50% Intensity. Note: ap<.1, *p < .05
67