Mental state decoding abilities in young adults with borderline personality disorder traits.

Abstract

Previous studies have demonstrated that patients with borderline personality disorder (BPD) tend to misattribute malevolence to benign social stimuli, including facial expressions. Yet, facial emotion recognition studies examining those with BPD have yielded mixed results, with some studies showing impaired accuracy and others demonstrating enhanced accuracy in the recognition of emotions or mental states. The current study examined the ability to decode mental states from photographs of just the eye region of faces in a nonclinical sample of young adults who exhibited BPD traits (high BPD) compared with those who did not (low BPD). Group differences in mental state decoding ability depended on the valence of the stimuli. The high-BPD group performed better for negative stimuli compared with the low-BPD group, but did not perform significantly different from the low-BPD group for stimuli of neutral or positive valence. The high-BPD group also demonstrated a response bias for attributing negative mental states to facial stimuli. In addition, findings suggested that the group difference in accuracy for negative stimuli could not be explained by response bias, because the group difference in response bias for negative stimuli did not reach significance. These findings suggest that BPD traits may be associated with enhanced ability to detect negative emotions and a bias for attributing negative emotions to nonnegative social stimuli.

Full text

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Mental State Decoding Abilities in Young Adults With Borderline
Personality Disorder Traits
Lori N. Scott, Kenneth N. Levy, Reginald B. Adams Jr., and Michael T. Stevenson
Pennsylvania State University
Previousstudieshavedemonstratedthatpatientswithborderlinepersonalitydisorder(BPD)
tend to misattribute malevolence to benign social stimuli, including facial expressions. Yet,
facial emotion recognition studies examining those with BPD have yielded mixed results,
with some studies showing impaired accuracy and others demonstrating enhanced accuracy
in the recognition of emotions or mental states. The current study examined the ability to
decode mental states from photographs of just the eye region of faces in a nonclinical
sample of young adults who exhibited BPD traits (high BPD) compared with those who did
not (low BPD). Group differences in mental state decoding ability depended on the valence
of the stimuli. The high-BPD group performed better for negative stimuli compared with
the low-BPD group, but did not perform significantly different from the low-BPD group for
stimuli of neutral or positive valence. The high-BPD group also demonstrated a response
bias for attributing negative mental states to facial stimuli. In addition, findings suggested
that the group difference in accuracy for negative stimuli could not be explained by
response bias, because the group difference in response bias for negative stimuli did not
reach significance. These findings suggest that BPD traits may be associated with enhanced
ability to detect negative emotions and a bias for attributing negative emotions to nonneg-
ative social stimuli.
Keywords: borderline personality disorder, mental state decoding, emotion recognition, response
bias, Reading the Mind in the Eyes Test
Borderline personality disorder (BPD) is a
chronic and debilitating disorder characterized
by emotional instability, interpersonal dysfunc-
tion, identity disturbance, impulsivity, self-
injury, and suicidality. BPD is estimated to oc-
cur in 1% to 6% of the general population
(Grant et al., 2008; Lenzenweger, Lane, Lor-
anger, & Kessler, 2007; Samuels et al., 2002;
Taylor & Reeves, 2007; Torgerson, Kringlen, &
Cramer, 2001) and is especially prevalent in
young adults under the age of 35 (Stone, 1990).
Emotional dysregulation, particularly within in-
terpersonal contexts, is one of the most prob-
lematic and enduring features of BPD (Linehan,
1993; McGlashan et al., 2005). Both clinicians
and investigators have noted that negative affec-
tive states among BPD patients are often precipi-
tated by real or imagined events in relationships
(Ebner-Priemer et al., 2007; Herpertz, 1995; Stigl-
mayr et al., 2005). Evidence also suggests that
BPD features are associated with increased inter-
personal stress (Daley, Hammen, Davila, &
Burge, 1998; Trull, 1995), intense affective re-
sponses to social stimuli (Herpertz et al., 1997),
and amygdala hyperactivation in response to so-
cially relevant stimuli (Donegan et al., 2003; Her-
pertz et al., 2001; Silbersweig et al., 2007).
These clinical and empirical observations
have led several theorists to suggest that im-
paired social cognition is a core mechanism
underlying the development and maintenance of
BPD (Bateman & Fonagy, 2003; Bender &
Skodol, 2007; Levy, 2005; Levy et al., 2006;
Westen, 1991). Bateman and Fonagy (2003)
have proposed that BPD is characterized by a
deficit or inhibition in the capacity for mental-
ization, a social–cognitive ability that allows
one to reflect on one’s own mental experience
and the mental experiences of others in terms of
This article was published Online First March 7, 2011.
Lori N. Scott, Kenneth N. Levy, Reginald B. Adams, Jr.,
and Michael T. Stevenson, Department of Psychology,
Pennsylvania State University.
The authors would like to thank Stevie Grassetti, Zach
Infantolino, Justin Meyer, Lauren Testa, and Rachel Tomko
for their assistance with data collection and data entry.
Correspondence concerning this article should be addressed
to Lori N. Scott, 545 Moore Building, Department of Psychol-
ogy, Penn State University, University Park, PA 16802. E-
mail: lscott@psu.edu; or to Kenneth N. Levy, 521 Moore
Building, Department of Psychology, Penn State University,
University Park, PA 16802. E-mail: klevy@psu.edu
Personality Disorders: Theory, Research, and Treatment
2011, Vol. 2, No. 2, 98–112
© 2011 American Psychological Association
1949-2715/11/$12.00 DOI: 10.1037/a0020011
98

Page 2

underlying emotional states. Mentalization is a
concept that is closely related to theory of mind,
defined broadly as the ability to ascribe mental
states to others and to understand and predict
others’ social behavior (e.g., Baron-Cohen,
1989; Langdon, Coltheart, Ward, & Catts,
2002). However, mentalization also involves
reflection on the mental states of oneself, in
addition to those of others. Linehan (1993) has
discussed deficits in a meta-social–cognitive
capacity called mindfulness, which involves ob-
serving, reflecting, and describing emotional
experiences while developing focused attention.
Several other theorists have discussed similar
social–cognitive deficits as central to BPD (e.g.,
Blatt, Auerbach, & Levy, 1997; Gunderson,
1996; Kernberg, 1984; Levy & Blatt, 1999;
Westen, 1991; Young, Klosko, & Weishaar,
2003). Despite distinctions between many of
these theories, they all share a common hypoth-
esis that BPD patients lack the ability to ade-
quately process and appraise emotional infor-
mation, particularly within social contexts. In
addition, a growing body of evidence demon-
strates cognitive problems in BPD patients, and
several authors have suggested that these difficul-
ties may interact with experiences of childhood
maltreatment or neglect, potentially creating vul-
nerability for problems with mentalization and
flexible cognitive processing of social information
(Fertuck, Lenzenweger, Clarkin, Hoermann, &
Stanley, 2006; Fonagy, Gergely, Jurist, & Target,
2002; Judd, 2005; Judd & McGlashan, 2003;
Minzenberg, Poole, & Vinogradov, 2008).
Both clinicians and researchers have noted
that one manifestation of social–cognitive dys-
function in BPD might be impairment in the
ability to decode mental states or emotions from
perceivable social information, such as human
facial expressions (for a review, see Domes,
Schulze, & Herpertz, 2009). The capacity for
mental state reasoning based on facial expres-
sions is a socioperceptual ability that has been
described as an important component of theory
of mind (Sabbagh, 2004; Tager-Flusberg,
2001). The ability to accurately infer emotional
states based on features of the human face al-
lows one to more accurately predict behaviors
of others, respond empathically and appropri-
ately in social situations, and regulate one’s
own emotional state in social contexts. Thus,
impaired facial emotion recognition may trigger
extreme emotional reactions and interpersonal
conflict. In patients with BPD, such impairment
may lead to aggressive, impulsive, and self-
destructive behaviors that are potentially life-
threatening, functionally debilitating, and dam-
aging to relationships (Domes et al., 2009; Silk,
2000; Yeomans & Levy, 2002).
Empirical studies of facial emotion recognition
among individuals with BPD have generally
yielded mixed results. There is some evidence that
BPD patients may be impaired at recognizing
basic emotions in static facial expressions (e.g.,
Bland, Williams, Scharer, & Manning, 2004;
Levine, Marziali, & Hood, 1997). However, other
studies suggest that BPD patients are not impaired
in the recognition of basic emotions unless the
stimuli are complex and multifaceted (Minzen-
berg, Poole, & Vinogradov, 2006) or the task
requires them to rapidly discriminate between
negative or neutral expressions (Dyck et al.,
2009). A recent study (Lynch et al., 2006) using
facial morphing stimuli suggested that BPD pa-
tients might have a lower emotion detection
threshold than healthy controls, which suggests
enhanced sensitivity to detection of emotion in
facial expressions; on the other hand, a similar
investigation (Domes et al., 2008) found no dif-
ferences between women with BPD and healthy
controls in emotion detection threshold or accu-
racy. However, the BPD group demonstrated en-
hanced learning in terms of reduced detection
thresholds over the course of the experiment
(Domes et al., 2008).
Although it remains unclear whether BPD pa-
tients show broad impairments or enhancements
in emotion recognition, a consistent and robust
finding across a number of studies is that BPD
patients tend to imbue benign or neutral facial
stimuli with negative emotion (Domes et al.,
2008; Donegan et al., 2003; Dyck et al., 2009;
Meyer, Pilkonis, & Beevers, 2004; Silbersweig et
al., 2007; Wagner & Linehan, 1999). This nega-
tivity bias is consistent with evidence that individ-
uals with BPD tend to perceive others as untrust-
worthy, rejecting, abandoning, and neglectful
(Arntz, Dietzel, & Dreessen, 1999; Butler, Brown,
Beck, & Grisham, 2002; Jovev & Jackson, 2004;
Nordahl, Holthe, & Haugum, 2005). Taken to-
gether, these findings suggest that BPD symptoms
may be associated with a subtle and specific ab-
normality in processing of emotions from perceiv-
able social cues. In particular, BPD symptoms
might be associated with a tendency to project
negative emotions onto ambiguous or even benev-
99MENTAL STATE DECODING AND BPD

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olent social stimuli. Whether or not this perceptual
bias results in impaired or enhanced emotion rec-
ognition accuracy remains an open question.
Most previous studies of facial emotion rec-
ognition among those with BPD have used
stimuli comprising the entire face and represent-
ing only basic emotions, which can be insensi-
tive to the detection of subtle individual differ-
ences in mental state decoding. However, the
revised Reading the Mind in the Eyes task
(RME; Baron-Cohen, Wheelwright, Hill, Raste,
& Plumb, 2001) is a relatively new method for
examining emotion recognition abilities that in-
volves the identification of complex mental
states based on pictures of the eye region only
(i.e., from the nose to the brow). In the RME
task, participants are asked to select the mental
state word that best describes the emotion por-
trayed in the picture from among four word
choices. Unlike previously established facial
emotion recognition stimuli that were rated
based on coding systems (e.g., Ekman & Fri-
esen, 1978), the RME task was developed based
on consensus ratings. Because it is considerably
more difficult than standard emotion recogni-
tion tasks, the RME is exceptionally sensitive to
subtle individual differences in facial emotion
recognition without being susceptible to floor or
ceiling effects.
To date, only one published study (Fertuck et
al., 2009) has examined RME performance of
BPD patients as compared to a healthy control
group. In this study, those with BPD actually
performed better on the task relative to healthy
comparison participants, and these effects were
partially mediated by depression. These results are
surprising given that Fonagy and Bateman (2008)
have cited unpublished findings that BPD patients
are impaired on the RME task relative to non-
BPD patients. In addition, other empirical evi-
dence suggests that RME performance is also
impaired among those with clinically significant
depression (Lee, Harkness, Sabbagh, & Jacobsen,
2005) as well as in several other psychiatric pop-
ulations (e.g., Bora et al., 2005; Craig, Hatton,
Craig, & Bentall, 2004; Farzin et al., 2006; Kele-
men et al., 2005; Schmidt & Zachariae, 2009),
although there is evidence that dysphoria among
college students is associated with enhanced RME
performance (Harkness, Sabbagh, Jacobson,
Chowdrey, & Chen, 2005).
Due to the myriad inconsistencies in previous
studies of facial emotion recognition in BPD
patients, the association between BPD symp-
toms and complex mental state decoding abili-
ties deserves further exploration. In addition,
few previous studies have examined emotion
recognition accuracy, response time, and re-
sponse bias within the same task. Therefore, the
current study examined mental state decoding
accuracy, response time, and response biases
using the RME task in a sample of college
students who endorse a high number of BPD
traits as compared to those who endorse few
BPD traits. The investigation of BPD traits in a
young nonclinical sample is relevant given the
prevalence of BPD features in nonclinical pop-
ulations, particularly among young adults (Len-
zenweger, Loranger, Korfine, & Neff, 1997;
Taylor & Reeves, 2007; Trull, 1995; Trull,
Useda, Conforti, & Doan, 1997).
Based on empirical ratings obtained during pi-
lot testing, RME stimuli without their correspond-
ing mental state terms were categorized by va-
lence in order to examine differences in accuracy
for stimuli of different valence categories. Va-
lence ratings were also obtained for all mental
statewordsthatappearedintheRMEtaskinorder
to examine response biases. Considering previous
findings of increased sensitivity to negative emo-
tional displays and the misattribution of negativity
to benign social stimuli among those with BPD
(Domesetal.,2008;Doneganetal.,2003;Dycket
al., 2009; Meyer et al., 2004; Silbersweig et al.,
2007; Wagner & Linehan, 1999; see Domes et al.,
2009 for a review), we predicted that those with
BPD traits would demonstrate enhanced accuracy
for negative RME stimuli, as well as a bias toward
attributing negative mental states to facial expres-
sions. However, consistent with a negativity bias,
we predicted that those with BPD traits would
show impaired accuracy for neutral and positive
RME stimuli. Response time, present-state affect,
and state and trait anxiety were measured in order
to control for the influence of speed–accuracy
trade-offs and affective experiences on mental
state attributions.
Method
Participants
In exchange for credit toward their introduc-
tory psychology class research participation re-
quirement, 242 students from the introductory
psychology participant pool at a major north-
100SCOTT, LEVY, ADAMS, AND STEVENSON

Page 4

eastern university participated in the study. All
participants were administered a modified ver-
sion of the McLean Screening Instrument for
BPD (MSI-BPD; Zanarini et al., 2003) in order
to derive study groups. Those scoring at least
one SD above the mean on the MSI-BPD (n ?
40) were identified for the high-BPD group, and
those scoring at least one SD below the mean on
the MSI-BPD (n ? 50) were identified for the
low-BPD group. Statistical outliers at p ? .01
were identified in each group based on standard-
ized scores more than 2.58 SD below the group
mean for RME accuracy or 2.58 SD above or
below the group mean for response time (i.e.,
those with unusually low accuracy or high/low
response time). Using these criteria, six outliers
were identified (four low-BPD participants and
two high-BPD participants). After these partic-
ipants’ data were excluded from analysis, there
were 38 participants in the high-BPD group
and 46 participants in the low-BPD group. The
demographics characteristics of each sample are
presented in Table 1.
Measures
Demographics questionnaire.
tionnaire contained questions regarding sex,
age, ethnicity, race, and years having lived in
the United States.
This ques-
BPD symptom severity.
measured using a 21-item modified version of
the MSI-BPD (Zanarini et al., 2003). The orig-
inal MSI-BPD (Zanarini et al., 2003) is a 10-
item self-report screener for BPD features with
demonstrated test–retest reliability, internal
consistency, validity, and diagnostic efficiency
for identifying the presence of Diagnostic and
Statistical Manual of Mental Disorders, Fourth
Edition (DSM–IV) BPD in respondents between
the ages of 18 and 59. Items were rewritten in
the first-person for self-administration, and
some items from the original MSI-BPD were
broken up into separate items for more precise
assessment. For example, the original MSI-
BPD item, “Have you often felt that you had no
idea of who you are or that you have no iden-
tity?” was presented as two items: “I have often
felt that I had no idea who I am” and “I have
often felt that I have no identity”. All items
were rated on a 4-point scale (0 ? False, not at
all true; 3 ? Very true). The sum of all items
was calculated in order to yield a continuous
scale score, with internal consistency (coeffi-
cient alpha) of ? ? .93.
State Positive and Negative Affect
(PANAS).
The PANAS (Watson, Clark, &
Tellegen, 1988) was administered to assess state
affective experiences just before engaging in the
experimental task. The PANAS consists of two
BPD traits were
Table 1
Demographics and Clinical Characteristics of Each Group
Low-BPD (n ? 46) High-BPD (n ? 38)
tM SDMSD
Age
Years lived in United States
MSI-BPD
STAI-S
STAI-T
PANAS-NA
PANAS-PA
Gender
Male
Female
Race/ethnicity
Caucasian
Non-Caucasian
18.85
17.67
2.00
32.75
33.02
12.93
28.43
n
15
31
n
36
10
1.26
4.25
1.28
7.38
8.06
2.21
7.97
%
33
67
%
78
22
19.63
19.42
28.37
43.16
47.26
19.16
29.05
n
13
25
n
33
5
2.82
3.05
5.96
11.52
10.31
8.02
10.03
%
34
66
%
87
13
1.69
2.12?
29.25???
5.01???
7.11???
5.04???
0.32
?2
0.02
?2
1.04
Note.
Anxiety Inventory, State Anxiety; STAI-T ? State-Trait Anxiety Inventory, Trait Anxiety; PANAS-NA ? Positive and
Negative Affect Schedule, Negative Affect; PANAS-PA ? Positive and Negative Affect Schedule, Positive Affect.
?p ? .05.
BPD ? borderline personality disorder; MSI ? McLean Screening Instrument for BPD; STAI-S ? State-Trait
??p ? .01.
???p ? .001.
101 MENTAL STATE DECODING AND BPD

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10-item subscales, one for state positive affect
(PANAS-PA) and the other for state negative
affect (PANAS-NA). Two additional items,
“happy” and “unhappy”, were added to the
PANAS for the current study. Each item is rated
on a 5-point scale (1 ? very slightly; 5 ? ex-
tremely). Each subscale was calculated based on
the sum of the 11 items corresponding to the scale
(PANAS-PA, ? ? .90; PANAS-NA, ? ? .87).
State and Trait Anxiety.
administered the State–Trait Anxiety Inventory
(STAI; Spielberger, Gorsuch, Lushene, Vagg,
& Jacobs, 1983), which is a widely used, 40-
item self-report measure of state and trait anx-
iety. Each item is rated on a 4-point scale (1 ?
Not at all; 4 ? Very much so). Subscales were
calculated based on the sum of the 20 items
corresponding to state anxiety (STAI-S; ? ?
.92) and trait anxiety (STAI-T; ? ? .94).
Participants were
Procedures
Participants arrived at the laboratory either
individually or in groups of up to four people.
All participants were seated individually at their
own desk and computer terminal for the entirety
of the study. These work stations were well-
separated to ensure that participants had ade-
quate privacy during the experiment. Partici-
pants who were run in small groups all began
the procedures at the same time. Of the final
sample (n ? 84), 3 participants completed the
task individually, 10 participants completed the
task in groups of 2, 18 participants completed
the task in groups of 3, and 53 participants
completed the task in groups of 4. After a com-
plete description of the study, participants pro-
vided written informed consent. Next, partici-
pants completed the self-report questionnaires
(Demographics Questionnaire, MSI-BPD,
PANAS, and STAI) and the RME task, de-
scribed below. Participants were then debriefed,
thanked, and dismissed by the examiner. The
protocol for this study was approved by the
university’s Office for Research Protections.
RME task.
The RME task (Baron-Cohen
et al., 2001) consists of 36 black-and-white pho-
tographs (15 cm ? 6 cm) of the eye region of
faces from just above the eyebrows to halfway
down the bridge of the nose. The following
instructions were presented to participants on
the computer screen prior to the task:
You will see a series of photographs of faces. Your
task is to decide what each person is thinking or
feeling. For each face, enter the number on the key-
board that corresponds with the number of the word
that best describes what the person in the photograph is
thinking or feeling. You may feel that more than one
word is applicable, but please just choose one word
which you consider to be the most suitable. Before
making your choice, make sure that you have read all 4
words.
The stimuli were presented in the center of
the computer screen, preceded by a fixation
cross, with a white background. The four de-
scriptive mental state terms (one target and
three distracters) appeared at the four corners of
each photograph, equally spaced from the cen-
ter of the screen, and participants were asked to
select the term that best described the mental
state portrayed in the photograph. Participants
responded by pressing one of four keys (1, 2, 3,
4) corresponding to the four terms. The 36 trials
for the RME task were presented in randomized
order. Participants’ responses and response
times (in milliseconds) were digitally recorded.
Classification of stimuli.
tal state decoding accuracy for stimuli of par-
ticular emotional valence, the 36 RME stimuli
were classified into positive, neutral, and nega-
tive valence categories based on ratings of the
stimuli that we obtained from a separate sample
of 40 undergraduate students. Whereas previous
classifications of RME stimuli by valence were
obtained by presenting each eyes photograph
alongside its target mental state term (Harkness
et al., 2005), this method potentially confounds
photograph valence ratings with implied va-
lence of the attached mental state word (e.g.,
eyes stimuli may be rated more negative if
presented next to the target word ”upset“ than if
the stimuli are presented alone). For this reason,
we obtained stimulus ratings without target
mental state words attached to the photographic
stimuli, which provides valence categorizations
of the RME stimuli that are based solely on the
valence of the photographs.
In the stimulus valence rating procedure, we
presented the 36 stimuli (photographs only) one
at a time in random order in the center of the
computer screen on a white background, and
asked participants to rate each of the photo-
graphs on a 7-point scale (1 ? very negative;
7 ? very positive). Following the stimulus clas-
sification procedures employed by Harkness et
al. (2005), those stimuli with mean ratings signif-
To examine men-
102SCOTT, LEVY, ADAMS, AND STEVENSON

Page 6

icantly below neutral (one-sample t(39) ? 2.02,
? ? 4, p ? .05, uncorrected) were classified as
negative, those with mean ratings significantly
above neutral were classified as positive, and
those that did not differ from neutral were classi-
fied as neutral. This procedure resulted in the
classification of 10 stimuli as negative, 17 stimuli
as neutral, and 9 stimuli as positive. The classifi-
cation results for the 36 RME stimuli are pre-
sented in Table 2. An interrater reliability analysis
using the Kappa (?) statistic demonstrated very
lowconcordancebetweenourRMEstimulusclas-
sifications and those from Harkness et al. (2005),
? ? 0.14, p ? .26, 95% confidence interval
(CI) ? ?0.128, 0.398. This suggests that our
classification of the RME stimuli is not re-
dundant with the classification produced by
Harkness and colleagues.
RME accuracy scores for negative, neutral,
and positive stimuli were calculated as the per-
centage of items of a particular valence on
which participants selected the correct (target)
mental state word (Baron-Cohen et al., 2001).
Response times for negative, neutral, and positive
stimuli were calculated as the mean of response
times for stimuli in each valence category.
Calculation of response valence.
acterize the valence of responses on the RME
task, an additional sample of 40 undergraduate
To char-
students rated each of the 99 mental state terms
(targets and distracters) that appeared during the
RME task on a 7-point valence scale (1 ? very
negative; 7 ? very positive). Each term was
presented one at a time in random order on a
white computer screen. These ratings were used
to calculate a mean valence level for each men-
tal state term. Due to space considerations, the
mean valence ratings for the 99 mental state
terms are not presented here, but are available
upon request from the first author. For our anal-
ysis, response valence corresponds to the mean
valence of the mental state term that was chosen
by a participant as a response for any RME
item. The means of these response valence val-
ues for positive, neutral, and negative stimuli
were then calculated for each participant, with
higher values indicating the choice of more
positive mental states and lower values indicat-
ing the choice of more negative mental states.
Results
Item Analysis
To confirm the validity of each item in the
RME task, we conducted a series of Bonfer-
onni-corrected binomial tests comparing the
proportion of participants who selected the cor-
Table 2
Reading the Mind in the Eyes Task (RME) Stimulus Classification Results From Single-Sample T-Tests
(N ? 40)
ValenceItem no.Target
t
Item no.Target
t
Negative2
4
5
8
Upset
Insisting
Worried
Despondent
Skeptical
Desire
Fantasizing
Uneasy
Preoccupied
Cautious
Regretful
Thoughtful
Doubtful
Decisive
Playful
Anticipating
Contemplative
Tentative
Friendly
?7.82
?4.72
?3.98
?5.35
?3.12
1.66
?0.10
0.85
?0.84
0.11
?0.25
1.83
?0.24
?0.19
5.81
2.37
4.60
5.54
9.16
14
26
32
33
36
22
23
24
27
29
30
31
35
Accusing
Hostile
Serious
Concerned
Suspicious
Preoccupied
Defiant
Pensive
Cautious
Reflective
Flirtatious
Confident
Nervous
?4.28
?12.89
?17.64
?3.10
?3.37
?1.71
?1.18
0.48
0.56
0.90
1.70
1.16
?1.11
12
Neutral3
6
7
9
10
11
16
17
18
Positive1 21
25
28
34
Fantasizing
Interested
Interested
Distrustful
6.15
4.33
3.40
2.99
13
15
19
20
103MENTAL STATE DECODING AND BPD

Page 7

rect response to the proportion that would be
expected by chance (p ? .25). More partici-
pants selected the target than would be expected
by chance for all items (at least 34 of 84 par-
ticipants, binomial test, p ? .0013). Thus, all 36
items were retained for calculation of RME
accuracy scores. Mean overall RME task accu-
racy rates for our low-BPD and high-BPD
groups were 73.85% and 74.71% correct
(SDs ? 8.50 and 8.54), respectively. Based on
single-sample t tests (95% confidence interval),
these accuracy rates were not significantly dif-
ferent from the average mean accuracy rate
(73.1% according to Fertuck et al., 2009) of
healthy control groups across six studies (Bar-
on-Cohen et al., 2001; Domes, Heinrichs, Mi-
chel, Berger, & Herpertz, 2007; Harkness et al.,
2005; Kelemen, Ke ´ri, Must, Benedek, & Janka,
2004; Lee et al., 2005; Richell et al., 2003),
t(45) ? 0.60, p ? .55 and t(37) ? 1.16, p ? .25,
respectively.
Preliminary Data Analysis
Data analyses were conducted using
SPSS 17.0 (SPSS Inc., Chicago). Statistical sig-
nificance was set at p ? .05, and Bonferroni
corrections were applied where required for
multiple comparisons. In the case of inhomoge-
neity of variance, the Greenhouse-Geisser
(G-G) correction was applied.
?2analyses demonstrated that the two groups
did not differ in distributions of men and
women or ethnicity/race (Table 1). After check-
ing the Gaussian distribution of the data by
Kolmogorov–Smirnov tests, the groups were
compared in continuous demographic and clin-
ical parameters with two-tailed t tests. Groups
did not differ in the number of participants who
completed the task in the room at the same time
(low-BPD M ? 3.35, SD ? 0.88; high-BPD
M ? 3.55, SD ? 0.80; t(82) ? 1.11, ns). As
shown in Table 1, the high-BPD group scored
significantly higher than the low-BPD group in
BPD symptom severity (MSI-BPD), state nega-
tive affect (PANAS-NA), state anxiety (STAI-S),
and trait anxiety (STAI-T). In addition, the
high-BPD group was significantly higher in
number of years having lived in the United
States. There were no significant differences
between groups in age or state positive affect
(PANAS-PA).
Pearson correlations were used to examine
relationships between all dependent variables
(i.e., RME response time [RT], RME accuracy,
and response valence) and demographic vari-
ables, number of participants who completed
the task in the room at the same time, and state
and trait affect scales. Participant age was neg-
atively correlated with average RME accuracy,
r(84) ? ?.31, p ? .005. Hence, age was en-
tered as a covariate in the data analysis for
accuracy. However, none of the other variables
were correlated with any of the dependent vari-
ables at significant or trend levels (all ps ? .10).
RT was also uncorrelated with task accuracy.
Therefore, results are reported without control-
ling for these factors.
Experimental Task Analyses
RME RT.
differences in RT, we conducted a mixed model
analysis of variance (ANOVA) with valence
(negative, neutral, and positive) as the within-
participants factor, group (high and low BPD)
as the between-participants factor, and RT as
the dependent measure. There was no signifi-
cant main effect of group, F(1, 82) ? 0.71, p ?
.40, ?p
groups did not differ in RT overall when col-
lapsed across valence. However, there was a
within-participants main effect of valence on
RT, F(1.83, 146.36) ? 3.99, p ? .02, G-G
corrected, ?p
paired comparison tests demonstrated that par-
ticipants responded significantly faster for neg-
ative (M ? 6603.35, SE ? 196.75) than for
neutral stimuli (M ? 6975.99, SE ? 200.25),
p ? .05; RTs did not differ significantly be-
tween negative (M ? 6603.35, SE ? 196.75)
and positive stimuli (M ? 7029.02, SE ?
242.71), p ? .08. There was no significant va-
lenceby group
146.36) ? 0.50, p ? .59, G-G corrected, ?p
.01. Hence, there were no differences between
groups in RT whether collapsed across valence
or at specific valence levels, suggesting that any
group differences in task accuracy cannot be
explained by a speed–accuracy trade-off. Figure
1 illustrates group means and standard errors for
response times at each valence level.
RME accuracy.
To examine valence and
group differences in RME task performance, we
conducted a mixed model analysis of covari-
To examine valence and group
2? .01, demonstrating that the two
2? .05. Bonferroni-corrected
interaction,
F(1.83,
2?
104SCOTT, LEVY, ADAMS, AND STEVENSON

Page 8

ance (ANCOVA) with stimulus valence as the
within-participants factor, group as the be-
tween-participants factor, and percent accuracy
on the RME task as the dependent measure. As
previously mentioned, participants’ age was
significantly correlated with task accuracy, and
therefore, was retained as a covariate in the anal-
ysis. However, as recommended for repeated-
measures ANCOVA (e.g., Annaz, Karmiloff-
Smith, Johnson, & Thomas, 2009), because the
within-participants main effects of valence are in-
dependent of the effects of a between-participants
covariate such as age, the analysis was first con-
ductedexcludingthecovariateinordertoexamine
pure within-participants main effects. The main
effect of valence on accuracy was significant,
F(2, 164) ? 17.30, p ? .001, ?p
Bonferroni-corrected paired comparison tests
demonstrated that, collapsed across groups,
accuracy was higher for negative stimuli
(M ? 81.07%, SE ? 1.48) than for both
neutral (M ? 70.66%, SE ? 1.23) and posi-
tive (M ? 73.41%, SE ? 1.60) stimuli, ps ?
.001, but accuracy for neutral stimuli did not
differ from accuracy for positive stimuli, p ?
.55. After controlling for the between-partici-
pants effect of age, F(1, 81) ? 11.32, p ? .001,
?p
was not significant, F(1, 81) ? 0.80, p ? .38,
?p
low-BPD groups did not differ in overall task
accuracy when collapsed across valence.
2? .17.
2? .12, the main effect of group on accuracy
2? .01, indicating that the high-BPD and
Most importantly, there was a significant va-
lence by group interaction effect, F(2,
162) ? 3.18, p ? .04, ?p
groups differed in accuracy depending on va-
lence. Examination of the task accuracy means
(shown in Figure 2) and the significant linear
contrast effect for the interaction term, F(1,
81) ? 5.16, p ? .03, ?p
for negative stimuli only, the high-BPD group
appears to show enhanced accuracy relative to
the low-BPD group. Examination of the
ANOVA model parameters for group differ-
ences in accuracy at each level of valence dem-
onstrated that the low-BPD group was less ac-
curate than the high-BPD group at detecting
mental states from negative stimuli, t(81) ?
?2.13, p ? .04, d ? .47, but the group differ-
ences in accuracy were not significant for neu-
tral, t(81) ? ?0.86, p ? .39, d ? .19, or
positive stimuli, t(81) ? 1.10, p ? .28, d ? .24.
Response valence.
there were group differences in the valence of
responses (i.e., a response bias) on the RME
task across the three types of stimuli, we con-
ducted a mixed model ANOVA with stimulus
valence as the within-participants factor, group
as the between-participants factor, and valence
of responses (“response valence”) as the depen-
dent measure (Figure 3). There was a significant
main effect of group on response valence, F(1,
82) ? 4.73, p ? .03, ?p
2? .04, suggesting that
2? .06, suggested that
To determine whether
2? .06. Averaged across
Figure 2.
personality disorder (high-BPD; n ? 38) and low-BPD (n ?
46) groups for Reading the Mind in the Eyes task (RME)
stimuli in each valence category. Groups differed significantly
in accuracy for negative stimuli (?p ? .05), but not for neutral
or positive stimuli (ps ? .05). SEs are represented in the figure
by the error bars attached to each column.
Mean percent task accuracy in high-borderline
Figure 1.
personality disorder (high-BPD; n ? 38) and low-BPD (n ?
46) groups for Reading the Mind in the Eyes task (RME)
stimuli in each valence category. No significant group dif-
ferences in response times were found. SEs are represented
in the figure by the error bars attached to each column.
Mean response times (ms) in high-borderline
105MENTAL STATE DECODING AND BPD

Page 9

stimuli of all valence categories, the high-BPD
group’s response choices were more negative in
valence (M ? 3.81, SE ? 0.03) compared to the
low-BPD group’s response choices (M ? 3.89,
SE ? 0.03). These results suggest that the high-
BPD group showed a response bias character-
ized by the tendency to choose more negatively
valenced mental state terms. Furthermore, the
main effect of stimulus valence on response
valence was highly significant, F(2, 164) ?
1592.42, p ? .001, ?p
corrected pairwise comparisons demonstrated
that, collapsed across groups, the mean valence
of responses for each set of stimuli were signif-
icantly different from each other, all ps ? .001,
with responses being more negative for nega-
tive stimuli (M ? 2.76, SE ? 0.02), more pos-
itive for positive stimuli (M ? 4.71, SE ? 0.03),
and in the middle for neutral stimuli (M ? 4.07,
SE ? 0.03). This pattern was expected given
that response choices on the RME should be
similar in valence to corresponding stimuli. The
valence by group interaction effect was not sig-
nificant, F(2, 164) ? 2.16, p ? .12, ?p
This finding, combined with the significant
main effect of group, suggests that the high-
BPD group tended to show a negative bias for
all stimuli, regardless of the valence of the
stimuli.
2? .95. Bonferroni-
2? .03.
The analyses so far suggest that the group
difference in accuracy for negative stimuli
could be driven by a negative response bias,
even when looking at negative stimuli. There-
fore, we examined the group differences in re-
sponse valence for negative stimuli. However,
the group effect for response valence at the level
of negative stimuli was not significant,
t(82) ? 1.20, p ? .23, d ? .27. Hence, the
group difference in accuracy for negative stim-
uli could not be explained by any group differ-
ence in response bias at the level of negative
stimuli in particular.
Discussion
We examined mental state decoding abilities
as measured by the RME task in young adults
who were high in BPD traits as compared to
those who were low in BPD traits. Based on
previous studies, we predicted that those with
BPD traits would demonstrate enhanced accu-
racy for negative RME stimuli and impaired
accuracy for neutral and positive RME stimuli,
as well as a bias toward attributing negative
mental states to facial expressions. Our results
partially supported these hypotheses. Specifi-
cally, the high-BPD group was more accurate
than the low-BPD group in decoding mental
states from negative stimuli on the RME, but
there was no significant difference between
groups in task accuracy for neutral or positive
stimuli. Consistent with our hypotheses, analy-
sis of the valence of RME task responses
showed that those who were high in BPD traits
tended to ascribe more negative mental states to
social stimuli than those who were low in BPD
traits. Further analysis suggested that the rela-
tionship between BPD traits and enhanced ac-
curacy for negative stimuli was not attributable
to a group difference in response bias when
looking specifically at negative stimuli. Prelim-
inary correlational analyses suggested that task
performance was not attributable to RT or af-
fective states, and we controlled for any influ-
ence of age on accuracy.
Our results are partially consistent with those
of Fertuck et al. (2009), who found that BPD
patients performed better on the RME task than
healthy comparisons regardless of stimulus va-
lence. Our findings are also partially consistent
with those of Harkness et al. (2005), who found
enhanced RME task accuracy among dysphoric
Figure 3.
sonality disorder (high-BPD; n ? 38) and low-BPD (n ?
46) groups for Reading the Mind in the Eyes task (RME)
stimuli in each valence category. The main effect of group
was significant, with the high-BPD group showing a nega-
tive response bias (p ? .03). There was no significant
interaction between valence and group (p ? .05). SEs are
represented in the figure by the error bars attached to each
column.
Mean response valence in high-borderline per-
106SCOTT, LEVY, ADAMS, AND STEVENSON

Page 10

college students regardless of stimulus valence.
However, our accuracy results suggest that the
advantage in mental state decoding abilities
among college students with BPD traits is spe-
cific to negative facial stimuli and does not
generalize to neutral or positive stimuli. Among
studies using whole-face and static picture
methods, our results are at least partially con-
sistent with those of Wagner and Linehan
(1999), who found enhanced accuracy in the
detection of fearful expressions among BPD
patients. Our results are also partially consistent
with those of Lynch et al. (2006) who found
evidence of enhanced sensitivity in emotion de-
tection among BPD patients.
Many of the discrepancies between our re-
sults and those of other researchers may be
attributable to methodological differences be-
tween the studies and/or heterogeneity in re-
search samples. Although our RME accuracy
findings are discrepant from those of some re-
searchers (e.g., Bland et al., 2004; Levine et al.,
1997) who have found impairment among BPD
patients in facial emotion recognition, those
studies have used very different methods for
assessing emotion recognition that involve the
identification of basic emotions in whole-face,
static stimuli that are based on a different sci-
entific framework than the RME task. In com-
parison to the only published study on RME
performance in patients with BPD versus
healthy comparisons (Fertuck et al., 2009), our
study differed in sample type (i.e., nonclinical
vs. clinical), presentation of stimuli (i.e., com-
puter vs. card administration), and classification
of stimuli according to valence (i.e., our own
classification system vs. Harkness et al.’s
(2005) system). Moreover, previous studies
suggest that BPD patients’ emotion recognition
performance might be impaired on speeded
tasks and enhanced on tasks that allow unlim-
ited time or the ability to change answers (for a
review, see Domes et al., 2009). Although we
did not tell participants to emphasize either
speed or accuracy in our task instructions, par-
ticipants were not given the option to return to
previous items in order to change a response
after their response was given, which may have
attenuated mental state decoding strengths in
the high-BPD group relative to the low-BPD
group.
Our finding of a bias toward the attribution of
negative mental states to RME stimuli in the
high-BPD group is consistent with numerous
studies demonstrating an association between
BPD symptoms and the tendency to misattrib-
ute negative emotional states to benign social
information (Domes et al., 2008; Donegan et al.,
2003; Dyck et al., 2009; Meyer et al., 2004;
Silbersweig et al., 2007; Wagner & Linehan,
1999; see Domes et al., 2009, for a review). Of
note, the high- and low-BPD groups did not
differ in the valence of their responses for neg-
ative stimuli, nor did they not differ in RT for
negative stimuli. These findings suggest that the
enhanced accuracy for negative stimuli ob-
served in the high-BPD group is unlikely to be
driven by either response bias or RT. Hence,
other variables should be explored in future
research as potential mechanisms of enhanced
negative emotion recognition accuracy in those
with BPD features.
Our finding of enhanced negative emotion
detection and a response bias toward the per-
ception of negative emotions among those with
BPD traits is consistent with conceptualizations
of the phenomenological affective experiences
of BPD patients as tending to be dominated by
negative affect with relatively few experiences
of positive affect (Depue & Lenzenweger,
2001; Ebner-Premier et al., 2007; Kernberg,
1984). These results could also be understood in
the context of response expectancy theory
(Kirsch, 1997). From this perspective, height-
ened sensitivity to negative or threatening social
cues may represent an overlearned response set
resulting from an accumulation of negative in-
terpersonal experiences. Consistent with this in-
terpretation, a high percentage of those with
BPD have histories of childhood maltreatment
(Brodsky, Cloitre, & Dulit, 1995; Zanarini,
2000). Furthermore, there is evidence that mal-
treated children are more sensitive to the detec-
tion of threat in facial stimuli, even when threat
is not present (Pollak, Cicchetti, Hornung, &
Reed, 2000; Pollak & Sinha, 2002). In addition,
Wagner and Linehan (1999) found that patients
with a history of childhood sexual abuse (both
with and without BPD) were less accurate in
their identification of neutral emotion in facial
expressions and more likely to misattribute neg-
ative emotions to neutral expressions. It is pos-
sible that increased vigilance to threat cues in
human facial expressions represents a type of
adaptation for survival in an abusive or emo-
tionally challenging environment. A child who
107MENTAL STATE DECODING AND BPD

Page 11

is able to quickly detect negative emotion in a
volatile parent may be more equipped to pre-
dict, cope with, or avoid abuse or neglect. As
suggested by Harkness et al. (2005), hyper-
vigilance to emotion in facial expressions may
result from attempts to regain a sense of control
over the environment (Weary & Edwards,
1994). However, this adaptation may become
maladaptive when even neutral or positive so-
cial cues are imbued with malevolence without
flexibility or the recognition that one’s interpre-
tations may be erroneous.
Our study has several strengths and limita-
tions that deserve mention. First, we have mea-
sured mental state decoding abilities using a
task that is sensitive to subtle individual differ-
ences in this capacity. Second, we have mea-
sured RT, state and trait anxiety, and state neg-
ative and positive affect, and we found that
these results were not attributable to these vari-
ables. Third, our groups did not differ signifi-
cantly in distributions of gender or ethnicity/
race, and we controlled for the effect of age on
accuracy. Although education and intelligence
were not measured in this study, our groups can
also be assumed to have similar education and
intelligence levels given that they were drawn
from the same introductory psychology subject
pool and they were approximately the same age.
In addition, we developed and utilized a clas-
sification of RME stimuli according to valence
that is independent of the valence of the target
mental state terms that correspond to the eyes
photographs. Our classification of stimuli also
allows for the investigation of response bias
during the task. Based on our classification of
stimuli, we found that RTs were faster and
accuracy was higher for negative stimuli in
comparison to other stimuli. This was consistent
with findings by Harkness et al. (2005), even
though our own classifications of stimuli were
generally not concordant with those of Hark-
ness and colleagues. The strong within-
participants effect of valence on accuracy and
RT suggests that researchers using the RME to
investigate mental state decoding should not
only examine overall RME accuracy, but should
also examine accuracy at particular valence
levels.
Perhaps the most notable limitation of the
current study is the use of a homogeneous and
nonclinical sample derived from a self-report
method of assessing BPD traits. Although stud-
ies have shown that young adults with BPD
traits experience significant amounts of distress
and dysfunction (Lenzenweger et al., 1997;
Taylor & Reeves, 2007; Trull, 1995; Trull et al.,
1997), these results have limited generalizabil-
ity to heterogeneous clinical populations of
treatment-seeking patients. In addition, our
sample size for group comparisons was rela-
tively small, and did not provide significant
power to detect small effects. Moreover, given
the evidence that depressive symptoms may en-
hance RME accuracy in some samples (e.g.,
Harkness et al., 2005; Fertuck et al., 2009), our
lack of measurement of depression is a signifi-
cant limitation. Furthermore, we did not assess
childhood maltreatment in the current study,
and evidence suggests that experiences of mal-
treatment could be an important potential mech-
anism of emotion detection from social infor-
mation. There are multiple other variables that
were not measured in this study and that may
influence social perception in those with BPD
features, such as other psychopathological
symptoms on both Axis I and II, educational
and intelligence level, treatment history, and
medication status.
An additional potential limitation to the in-
terpretation of these results is that we did not
provide participants with a glossary so that they
could check the meaning of words in the task
(as incorporated in the original RME) because
of the possibility that checking a glossary might
influence RT. However, the lack of a glossary
did not appear to invalidate any of the items on
the task, as evidenced by our preliminary item
analysis, and both groups demonstrated accu-
racy rates that were commensurate with those
found in nonclinical samples both with and
without the use of a glossary. One might also
expect that RTs would be much longer and
accuracy rates much lower for participants who
had difficulty understanding words in the task,
and we excluded any participants who had un-
usually long RTs or extremely low accuracy
rates from data analysis. Nonetheless, the pos-
sibility that some participants’ performance
might have suffered due to poor understanding
of the words in the task cannot be ruled out.
The results of the current study suggest that
young adults with significant BPD traits may be
particularly sensitive to detecting negative emo-
tions in social situations based on perceivable
social cues. However, our findings also imply
108SCOTT, LEVY, ADAMS, AND STEVENSON

Page 12

that BPD features are related to negatively bi-
ased appraisals of social information, including
neutral or positive social information. Our re-
sults add to the emerging body of evidence that
those with significant BPD features may dem-
onstrate strengths rather than deficits in certain
aspects of social perception (e.g., Fertuck et al.,
2009; Lynch et al., 2006). Considering the mul-
tiple theories and treatments for BPD that focus
on social–cognitive impairment as a core char-
acteristic of the disorder, it appears to be im-
portant for researchers to investigate domains of
social cognition other than emotion recognition
in order to better understand these processes as
putative mechanisms underlying BPD. Studies
that investigate social–cognitive abilities
among BPD patients under conditions that re-
semble real-life experiences may be particularly
informative, such as under time pressure, with
multimodal stimuli, with personally relevant
stimuli, or under conditions of heightened emo-
tional intensity. Potential relationships between
neurocognitive problems and difficulties with
social cognition should also be explored. Addi-
tionally, given the mixed evidence with regard
to emotion recognition abilities in those with
BPD, the heterogeneity of the disorder might be
examined as a potential moderator of these abil-
ities. It is possible that certain BPD features
predict better or worse performance in emotion
recognition, and this may have implications for
understanding the heterogeneity of interper-
sonal functioning in patients with BPD.
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