The wandering mind in borderline personality disorder:
Instability in self- and other-related thoughts
Philipp Kanskea*, Lars Schulzeb, Isabel Dziobekc, Hannah Scheibnerc, Stefan Roepked#, Tania
Singera#
a
Department of Social Neuroscience, Max Planck Institute for Human Cognitive and Brain
Sciences, Leipzig, Germany
b
Department of Educational Sciences and Psychology, Free University Berlin, Germany
c
d
Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Germany
Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Department of Psychiatry,
Berlin, Germany
#
These authors contributed equally.
Running title: Mind-wandering in borderline personality
*Correspondence to
Philipp Kanske, Department of Social Neuroscience, Max Planck Institute for Human
Cognitive and Brain Sciences, Stephanstr. 1a, 04103 Leipzig, Germany, email:
kanske@cbs.mpg.de, Tel: +49-(0)341 9940-2693; Fax: +49 (0)341 9940-2356
1
Abstract
Diagnostic criteria for borderline personality disorder (BPD) include instability in identity
and interpersonal relationships. Here, we probed whether instability is already present in BPD
patients’ thoughts about themselves and others. We tested BPD patients (N=27) and healthy
controls (N=25) with a mind-wandering task that assesses content and variability of stimulusindependent self-generated thoughts. Multi-level modeling revealed that while BPD patients
and healthy controls mind-wander to a similar extent, BPD patients’ thoughts are colored
predominantly negatively. Most importantly, although their thoughts concerned the self and
others as much as in controls, they fluctuated more strongly in the degree to which their
thoughts concerned themselves and others and also gave more extreme ratings. Self- and
other related thoughts that were more extreme were also more negative in valence. The
increased variability supports current conceptualizations of BPD and may account for the
instability in identity and interpersonal relationships.
Keywords: borderline personality disorders, mind-wandering, self-generated thought,
identity, self-other representations, interpersonal relationships
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1. Introduction
Borderline personality disorder (BPD) is a severe and debilitating mental disorder that affects
between 0.7 and 2.7% of the general population (Torgersen et al., 2001; Trull et al., 2010).
Patients with BPD are characterized by impulsivity and affective dysregulation, but also by
instability in identity and interpersonal relations (American Psychiatric Association, 2013;
Gunderson, 2007; Sanislow et al., 2002). The DSM-5 alternative model for BPD further
highlights self and interpersonal problems as crucial for the disorder. These may play out in
dramatic shifts in self-image or sense of self and unstable, intense relationships. Across the
theoretical spectrum, unstable self-other representations are integral parts of etiological
models of BPD (Beck et al., 2003; Horowitz, 2004; Levy et al., 2006; Livesley and Jang,
2000) and have also been hypothesized to constitute the fundamental impairment that gives
rise to BPD symptomatology, including affective dysregulation and impulsivity (Bender and
Skodol, 2007).
Empirical investigations of unstable self and other representations have mainly
focused on self and observer evaluations (e.g., of interview or projective data; Porcelli et al.,
2006; Tramantano et al., 2003) or specific stimulus-induced reactions of the patients (e.g., of
others’ facial expressions; Lerner and St Peter, 1984; Westen et al., 1990). Clinicians’ ratings
of their patients’ self-concept, for example, revealed that BPD patients have a more
incoherent and inconsistent self-concept compared to other personality disorder patients and
non-clinical control groups (Wilkinson-Ryan and Westen, 2000). BPD patients’ selfdescriptions in questionnaires are typically very negative (Klein et al., 2001; Rüsch et al.,
2007; Sieswerda et al., 2005), and they also report lower self-concept clarity (Roepke et al.,
2011), increased alexithymia (i.e., an inability to identify one’s own emotions New et al.,
2012), and describe themselves more in terms of opposites than in terms of salient attributes
in repertory grid tests (de Bonis et al., 1995). Using a card sorting task, Vater et al. (Vater et
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al., 2015) recently showed that BPD patients have a more compartmentalized and negative
self-concept, that is, they have a tendency to organize knowledge about the self into discrete,
extremely negatively valenced categories (Showers, 1992).
BPD patients’ representations of others also differ from healthy controls (HC)
(Herpertz and Bertsch, 2014). Neutral or ambiguous facial expressions, for example, are
evaluated as more negative (Domes et al., 2008; Domes et al., 2009; Dyck et al., 2009), as are
film characters (Barnow et al., 2009; Sieswerda et al., 2013) and words, irrespective of
comorbid depression (Kurtz and Morey, 1998). Moreover, their descriptions of others are
characterized by multidimensional dichotomous thinking, a cognitive style that refers to the
tendency to evaluate experiences in terms of mutually exclusive categories rather than falling
along continua (Beck et al., 2001). Film characters, for example, are rated as either extremely
negative or positive (Napolitano and Mckay, 2007; Veen and Arntz, 2000), as are real
interaction partners (Arntz and ten Haaf, 2012).
The studies reported above investigated BPD patients’ self-other representations at a
given point in time and therefore represent a momentary snapshot. A direct test of instability
would, however, require the assessment of self- and other-related thoughts at multiple points
and explore how they change over time. Furthermore, if trait judgments are requested,
momentary snapshots also reflect patients’ own integration of previous experience and,
therefore, rely on meta-cognitive capabilities. As has been demonstrated for affective
dysregulation, as well as dissociation and paranoid ideation, interpersonal disturbances and
suicidality, sampling from the actual momentary experience of participants and integrating
the data statistically may give a more realistic and valid characterization of BPD patients’
psychopathology (Ebner-Priemer et al., 2015; Ebner-Priemer et al., 2007; Santangelo et al.,
2014).
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Therefore, we utilized an established mind-wandering paradigm that, because of low
task demands, induces stimulus-independent, self-generated thoughts and probed these at
multiple points across the task (Ruby et al., 2013a). To increase the number of sampling
points, the task was performed twice approximately ten days apart. In contrast to previous
studies that tested participants’ responses to specific stimuli such as faces, the mindwandering paradigm gives an indication of patients’ self-generated mental contents. In the
general population, such self-generated thought, mind-wandering, is highly prevalent,
occurring in up to 50% of waking time (Kane et al., 2007; Killingsworth and Gilbert, 2010).
While mind-wandering has been linked to negative mental health outcome (Killingsworth
and Gilbert, 2010; Smallwood and Schooler, 2015), we know little about mind-wandering
activity and specific mind-wandering content in psychopathology (Ottaviani et al., 2015;
Smallwood, 2013). So far, no studies have investigated mind-wandering in BPD.
Given the reported predominance of negative self and other evaluations, we
hypothesized that patients in the BPD group would have more negative and less positive
thoughts compared to healthy matched controls. Crucially, we also expected to find evidence
for the hypothesized instability in self and other representations. This instability should be
reflected in greater variation of the ratings of self- and other-related thoughts. Specifically,
we explored two measures of variability, fluctuations in ratings between different, successive
thought probes, and the extremity of the ratings. The reported negative self and other
evaluations in BPD also gave rise to the hypothesis that self- and other-related thoughts and
their variability during mind-wandering may be colored more negatively in the BPD group.
We therefore related thought valence (negative, positive) to the ratings and variability of selfand other-related thoughts. Furthermore, we included additional ratings that asked for the
temporal focus of the self-generated thoughts (past- or future-oriented). We had no explicit
hypotheses regarding these ratings in BPD, but included them to more comprehensively
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characterize the thought space (Smallwood and Schooler, 2015). To assess current mood, we
also included mood ratings, hypothesizing more negative mood in BPD patients. Finally, in
addition to testing differences between BPD patients and controls, we also tested whether the
effects would be associated with symptom severity in BPD patients only.
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2. Methods
2.1 Participants
2.1.1 BPD group
Thirty patients with Borderline Personality Disorder according to DSM-IV-TR (American
Psychiatric Association, 2000; Saß et al., 2003) were recruited at the Department of
Psychiatry, Charité – Universitätsmedizin Berlin, Germany. All were inpatients at the Charité
hospital and admitted for specialized BPD treatment from a waiting list; all BPD patients had
outpatient status before admission, none of the patients were transferred from another
institution to our hospital or admitted for acute care. Testing was performed in the first two
weeks after admission in a laboratory at the hospital. Because of technical and timing
difficulties (specifically, a software problem with the experimental computer), three patients
could not complete the mind-wandering protocol, yielding a final sample of 27 patients (for
demographics, see Table 1).
2.1.2 Control group
Thirty-four healthy control participants were recruited and matched to the patients in age,
gender, and education. Because of technical and timing difficulties (as for the patient group),
data from nine control participants were missing, yielding a final sample of 25 healthy
controls (see Table 1).
The presence or absence of individual diagnoses in patients and controls was established with
the German versions of the Mini International Neuropsychiatric Interview (Ackenheil et al.,
1999; Lecrubier et al., 1997) and the Structured Clinical Interview II (First et al., 1997;
Wittchen et al., 1997). All interviewers were psychiatrists or clinical psychologists, trained in
the application of the SCID II and MINI interview and received supervision on the SCIDs
and MINIs. In a previous study, interrater reliability of SCID-II BPD diagnoses by the same
interviewers employed in the current study was good, κ=0.82 (Ritter et al., 2014). Exclusion
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criteria for the patients included any psychotic disorder, current substance abuse/
dependency, mental retardation, epilepsy/ organic brain disease, and age younger than 18. For
controls, the same exclusion criteria applied and the presence of any current mental disorder
additionally led to exclusion from the study.
All participants gave written informed consent prior to participation. The study was approved
by the ethics committee of the Charité – Universitätsmedizin Berlin.
2.2 Task
We used an established mind-wandering paradigm that probes self-generated thoughts during
a choice reaction time task (Baird et al., 2012; Ruby et al., 2013a; Smallwood et al., 2013a).
A series of black digits between 1 and 8 was presented. One sixth of the digits (randomly
selected) was presented in red, signaling to participants that they should indicate via button
press if this number was odd or even. Black digits were presented for 1000 ms and red digits
for 2000 ms. Responses had to be made while the colored digits were still present on the
screen. Stimuli were separated by a fixation cross of variable duration (2200–4400 ms).
Thought probes were presented between four and nine times at random intervals during the
choice reaction time task. At each probe time point, participants were asked to rate their
current thoughts using nine-point Likert scales on several separate dimensions including (1)
how positive, (2) how negative, (3) how self-related, (4) how other-related, (5) how pastoriented, (6) how future-oriented, and (7) how off task their thoughts at that point in time had
been. The extent to which participants rated their thoughts as off task is interpreted as an
indicator of mind-wandering. Additionally, participants rated their current mood (i.e., how
positive and how negative they felt). (For the exact phrasing of all rating questions, please see
Ruby et al., 2013).
The entire task lasted approximately 20 min. Stimuli were presented using E-prime 2.0
(Psychology Software Tools, Inc., Sharpsburg, PA, USA).
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The time of day for testing was matched for the two groups (mean experiment start time was
1:42 pm for BPD patients and 1:26 pm for control participants).
In contrast to most previous mind-wandering studies, each participant completed the task
twice in two identical sessions approximately 10 days apart (mean time difference 9.0 ± 8.2
days for BPD patients and 11.6 ± 9.4 days for healthy control participants; t(50) = -1.1,
p > .25) to increase the number of thought probes (combined mean number of probes 11.2 ±
2.0 for BPD patients and 11.9 ± 2.5 for healthy controls; t(50) = -1.1, p > .25).
2.3 Questionnaire
The German version of the Personality Assessment Inventory - Borderline Features (PAIBOR) was used to measure symptom severity in BPD patients (Groves and Engel, 2007;
Hopwood et al., 2013; Morey, 1991). The PAI-BOR assesses core aspects of BPD, that is,
affective instability, identity problems, negative relationships, and self-harm. Previous
validation studies indicated good-to-excellent internal consistency and illustrated the
usefulness of the PAI-BOR for determining features of BPD in clinical samples and the
general population (e.g., (BellPringle et al., 1997; Gardner and Qualter, 2009; Stein et al.,
2007). Internal consistency of the PAI-BOR in the present sample was excellent, with a
Cronbach’s alpha of 0.95.
2.4 Data analysis
All statistical analyses were performed using SPSS (IBM Corp. Released 2013. IBM SPSS
Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp.).
2.4.1 Performance
We analyzed the reaction times and accuracy of participants’ responses in the choice reaction
time task with independent samples t-tests.
2.4.2 Thought probes
2.4.2.1 Principal components analysis
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To test for congruency with previous reports on the contents of self-generated thoughts in
healthy participants, we first calculated a principal components analysis (as described in
Ruby et al., 2013a), which yielded a three-component solution that conformed with the
literature (see Supplementary Table 1; Ruby et al., 2013b; Smallwood et al., 2013b).
Specifically, component 1 mainly includes positive and negative ratings, component 2
includes past and other ratings, and component 3 includes future and self ratings. For a more
fine-grained characterization of self-generated thought in BPD, we here focused on analyzing
the ratings individually.
2.4.2.2 Rating levels
To analyze the ratings, we used multi-level models because they take correlated observations
within individuals into account and perform well with missing data or unequal numbers of
data points within individuals (Jahng et al., 2008). The models were specified to test for
associations of the group factor (BPD group vs. control group, coded as 0 and 1, respectively)
with the thought probe ratings. The ratings on each dimension (i.e., how other-related, selfrelated, negatively valenced, positively valenced, past-oriented, future-oriented and how off
task the thoughts were) were entered as dependent variable to be predicted in separate
models. The main predictor was the group factor. Significant effects of group would indicate
that BPD is, for instance, associated with higher levels of negative thoughts. Additional
covariates included the number of the particular sampling point within the session (e.g.,
sample count 5, indicating that this rating value was obtained during the fifth probe for this
participant) to control for changes due to the repetition of the rating questions, and session
(i.e., session 1 or 2) to control for changes due to the repetition of the whole procedure.
2.4.2.3 Fluctuations in ratings
In addition to the rating level (e.g., to what extent a certain thought was negative), we
investigated two indices of instability: fluctuations and extremity of ratings. To obtain a
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measure of how much individuals fluctuate in their ratings from one thought probe to the
next, we calculated squared successive differences, an established procedure in experience
sampling studies (Ebner-Priemer et al., 2007; Jahng et al., 2008; Skirrow et al., 2014; Trull et
al., 2008). As for the rating levels, we then calculated multi-level models with fluctuations as
dependent variable and group (BPD group vs. control group) as predictor. Sample count and
session were again included as covariates.
2.4.2.4 Extremity of ratings
To obtain a measure of the extremity of the individual ratings, we calculated the squared
difference of each rating from the mean for that variable. While this measure is naturally
related to the fluctuations (e.g., very strong fluctuations will also lead to high extremity), it
does not take into account the extent of these successive changes, but rather indicates the
degree to which a certain rating differs from the “norm” (e.g., fluctuations may be low, but
extremity still high if ratings are uniformly high during the first half and low during the
second half of a session). As for the rating levels, we then calculated multi-level models with
extremity as dependent variable and group (BPD group vs. control group) as predictor.
Sample count and session were included as covariates.
2.4.2.5 Testing the valence of self- and other-related thoughts
A new set of models tested for relations between the variables that showed significant
differences between BPD patients and controls. For instance, we tested whether extremity of
self-related ratings was related to negativity. To this end, rating levels, fluctuations, and
extremity of self and other ratings were entered as dependent variable in separate models.
Again, group (BPD group vs. control group) was used as a predictor, but the ratings on how
positive and negative the thoughts were, were entered as additional predictors. Crucially, the
interaction of group with the additional predictors was included to test, for instance, if the
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association of extremity in self-relatedness and negativity of a thought was stronger/weaker
in BPD patients.
2.4.3 Mood probes
The ratings of how positive and how negative participants’ current mood was were analyzed
in the same way as the ratings, fluctuations, and extremity of the thought probes.
2.4.4 Relation to symptom severity
For models where group was a significant predictor, follow-up analyses were used to
examine symptoms severity as a predictor in the BPD group only. We included the PAI-BOR
scores as covariates in the models described above, only testing BPD patients in this analysis
and replacing group (BPD group vs. control group) with the PAI-BOR scores as a predictor.
All multi-level models included a random intercept and random effects for sample count and
order. Maximum Likelihood (ML) was used as estimation method.
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3. Results
3.1 Performance
Independent samples t-tests of performance measures in the choice reaction time task showed
no significant differences between BPD patients and HCs in accuracy (t(50) = -0.17, p >
0.85; BPD: 89.2 % ± 14.5, HC: 89.9 % ± 15.1) or reaction times (t(50) = 0.86, p > .35; BPD:
904.8 milliseconds (ms) (169.4), HC: 869.4 ms (120.5)).
3.2 Thought probes
3.2.1 Rating levels
Multi-level models revealed that BPD patients did not differ from HCs in the extent of offtask thoughts, that is, in the amount of reported mind-wandering (see Fig.1A and Table 2).
However, the content of the self-generated thoughts differed. BPD patients rated their
thoughts to be more negative (Cohen’s d = 1.85) and less positive than HCs (Cohen’s d =
1.70). While HCs showed a positive bias (i.e., more positive than negative thoughts; t(24) = 8.2, p < 0.001), BPD patients showed a negative bias (i.e., more negative than positive
thoughts; t(26) = 2.1, p < 0.05). Including current mood (see below) as a covariate (positive
mood: b = 0.594, S.E. = 0.037, p < 0.001, negative mood: b = 0.483, S.E. = 0.032, p < 0.001)
showed significant relations between mood and thought valence, but did not change the group
differences in positive (b = -13.265, S.E. = 2.844, p < 0.001) and negative thoughts (b =
21.869, S.E. = 2.970, p < 0.001). There were no group differences regarding how self- and
other-related or past- and future-oriented the rated thoughts were.
3.2.2 Fluctuations in ratings
As a measure of how much individuals fluctuate in their ratings from one thought probe to
the next, squared successive differences (Ebner-Priemer et al., 2007; Jahng et al., 2008;
Skirrow et al., 2014; Trull et al., 2008) were subjected to multi-level models (see Fig. 1B and
Table 2). The results show increased fluctuations in BPD patients compared to HCs in self-
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and other-related thoughts (Cohen’s d = 0.89 and d = 0.45, respectively). There were no
further differences between the groups.
3.2.3 Extremity of ratings
As a measure of the extremity of the individual ratings, the squared differences of each rating
from the mean were subjected to multi-level models (see Fig. 1C and Table 2). As for
fluctuations, we also observed significantly increased extremity of self- and other-related
thoughts in BPD patients compared to HCs (Cohen’s d = 2.15 and d = 1.79, respectively).
Additionally, BPD patients showed more extreme ratings of off-task thoughts (Cohen’s d =
0.60).
3.2.4 Testing the valence of self- and other-related thoughts
The previous analyses showed increased negative and decreased positive thoughts in BPD
patients, as well as increased fluctuations and extremity of self- and other-related thoughts.
Here we tested whether the valence of thoughts (negative, positive) was associated with the
rating levels, fluctuations, and extremity of self- and other-related thoughts (Table 3).
The results show that the more negative a thought, the more it was also self-related. This was
true across both BPD patients and HCs, there was no interaction with group. There was no
relation to positive thoughts or between other-related thoughts and the level of positive and
negative thoughts.
With regard to fluctuations in self- and other-related thoughts, there were no significant
relations to the level of negative or positive thoughts.
Regarding the extremity of self- and other-related thoughts, interactions of group and
negative thoughts indicated greater extremity of how self- and other-related the negative
thoughts were in BPD patients, that is, self- and other-related thoughts that were more
extreme were also more negative in valence in BPD patients compared to HCs (see Fig. 2).
The interaction of group and positive thoughts was not significant.
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negative valence of thoughts positively predicted the extremity ratings of self and other
relatedness, that is, self- and other-related thoughts that were more extreme were also more
negative in valence.
3.3 Mood probes
Multi-level models revealed that BPD patients showed elevated levels of negative and
decreased levels of positive mood (see Supplementary Fig. 1 and Table 2). Fluctuations and
extremity of mood ratings were not significantly different between groups.
3.4 Relation to symptom severity
To test for a relation of the observed effects to symptom severity in the BPD group, we
included the PAI-BOR scores as covariates in the models (only testing BPD patients in this
analysis). PAI-BOR was a predictor of increased levels of negative thoughts (b = 1.377, S.E.
= 0.520, p = 0.014) and of extremity of other-related thoughts (b = 21.537, S.E. = 9.590, p =
0.028). Numerically, but not significantly, PAI-BOR also predicted reduced levels of positive
thoughts (b = -0.896, S.E. = 0.439, p = 0.051), reduced positive mood (b = -0.861, S.E. =
0.467, p = 0.077), and fluctuations in self- (b = 23.941, S.E. = 14.196, p = 0.093) and otherrelated thoughts (b = 35.156, S.E. = 20.099, p = 0.095). All other effects were not
significantly related to PAI-BOR scores (all p > 0.30).
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4. Discussion
The present study aimed at investigating the self-generated thoughts of patients with BPD
and utilized a standard mind-wandering task that probes the amount and specific content of
self-generated thoughts (Ruby et al., 2013a). The results reveal that BPD patients and HCs
mind-wander to a similar extent. BPD patients also think of the past, the future, themselves,
and others as often as healthy individuals; however, their thoughts are colored predominantly
negatively, while the thoughts of HCs are colored positively. Crucially, BPD patients are
more unstable in their self- and other-related thoughts, as indicated by increased fluctuations
between successive thought probes and by more extreme values of these ratings. The more
these ratings of self- and other-related thoughts differed from the mean, the more negative
they were in BPD patients.
The observation of more negative thoughts in BPD patients is in line with previous
reports of a negative bias in emotional face recognition (Dyck et al., 2009) and in
questionnaire evaluations of themselves and others (Klein et al., 2001; Rüsch et al., 2007;
Sieswerda et al., 2013). Critically, it extends these findings to more negative self-generated
mental content, as assessed with multiple probes of ongoing mind-wandering. As previously
reported, BPD patients also showed more negative mood (Nisenbaum et al., 2010). The
negative bias in thought contents remained, however, when controlling for current mood,
which suggests some specificity of mood and thought contents. Given the negative biases in
information processing across different psychopathologies (e.g., in major depressive disorder
(Raes et al., 2006), generalized anxiety disorder (Mogg and Bradley, 2005), or social anxiety
disorder (Joormann and Gotlib, 2006)), it is possible that the predominance of negative
thoughts observed here in BPD patients is a more general characteristic of psychopathology,
which should be tested in future studies (for a recent report in depression, see Hoffmann et
al., 2016). Nevertheless, it may represent an important problem also in BPD. While studies
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on the consequences of specific thought patterns are still rare, there is some indication that
more negative thoughts increase the stress-related cortisol response to psycho-social stressors
(Engert et al., 2014). Future studies should therefore elucidate if a negative bias constitutes a
psychopathogenic factor and how it may be improved.
BPD patients also showed more variability in self- and other-related thoughts.
Variability was measured as within-subject fluctuations between successive thought probes
and as the extremity of these ratings, the latter being related to particularly negative thoughts
in BPD patients. The increased variability points towards instability in self- and other-related
mental representations and supports accounts of BPD across the theoretical spectrum that
posit these instabilities as central to the disorder (Beck et al., 1961; Horowitz, 2004; Levy et
al., 2006; Livesley and Jang, 2000). However, it also extends these accounts by showing that
BPD patients already differ in how much they vary in thinking of themselves and others, not
only in whether their thoughts of themselves and others vary more strongly in valence. It is
conceivable that this variability is related to other characteristics of BPD such as diminished
self-concept clarity (Roepke et al., 2011). In contrast to previous empirical investigations of
disturbed self (de Bonis et al., 1995; Roepke et al., 2011; Vater et al., 2015; Wilkinson-Ryan
and Westen, 2000) and other representations in BPD (Arntz and ten Haaf, 2012; Napolitano
and Mckay, 2007; Veen and Arntz, 2000), the present study tested (a) the contents of selfgenerated mental activity and (b) sampled ongoing mental activity at multiple time points.
Testing at multiple time points may be more valid than single measurements, as has been
demonstrated for affective dysregulation in BPD (Ebner-Priemer et al., 2015; Ebner-Priemer
et al., 2007), and it also allows a direct test of instability because participants are not asked to
integrate across experiences themselves in their judgments.
A better understanding of the self-generated thoughts of BPD patients is also
important because of the high prevalence of mind-wandering, occurring in 25 to 50% of
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waking time (Kane et al., 2007; Killingsworth and Gilbert, 2010). Beyond impairment in
current task performance, which is mainly affected by the extent of off-task thoughts
(Smallwood et al., 2003; Smallwood et al., 2007), the content of this prevalent mental activity
may also have far-reaching consequences, as indicated by studies on stress reactivity or on
the effects of habitual rumination styles (Engert et al., 2014; Hertel, 1998; Schick et al.,
2013). Investigating the specific consequences of instability in self- and other-related
thoughts, as observed here for BPD patients, could elucidate their etiological role for the
disorder, for example by linking unstable other-related thoughts to problems in social
cognition and ultimately in interpersonal relationships.
Neuroimaging studies have found that mind-wandering is associated with increased
activity in the default-mode network of cortical regions that are active during resting state,
where the amount of self-generated thought correlates with activation (Mason et al., 2007).
Patients with BPD show increased “trait-level” functional connectivity within the defaultmode network at rest (Kluetsch et al., 2012; Wolf et al., 2011), and it would be interesting to
test the association between this increased connectivity and the increased variability in
thought content. During external stimulation, however, in particular when reasoning about the
affective states of others, activity within this network is reduced in BPD (Dziobek et al.,
2011; Mier et al., 2013). Future research could investigate whether the altered trait activity
within this network, which may be due to instability in self- and other-related thoughts,
hampers the functioning of this network under external demands, leading to impaired social
cognition.
While BPD patients’ self-generated thoughts showed a negative bias and increased
variability in how self- and other-related they were, the extent of off-task thoughts and the
degree to which thoughts were past- or future-oriented did not differ from healthy controls.
First and foremost, this speaks for specificity of the observed alterations. Given that
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borderline personality features have been linked to increased rumination (Baer and Sauer,
2011), increased overall off-task thoughts, that is, mind-wandering, with a past focus could
have been expected in addition to a negative bias. As rumination in BPD has so far only been
studied with trait questionnaires, the results may suggest that BPD patients momentary and
trait self-judgments diverge, which should be explicitly tested in future investigations.
Because of the methodological difficulties associated with studying the content of
stimulus-independent mental activity, the investigation of mind-wandering has only really
gained momentum in the last decade (Smallwood and Schooler, 2015). Regarding
psychopathology, this advancement offers the chance to gain better understanding not only of
the amount of mind-wandering, as has been attempted in depression (Watts et al., 1988),
ADHD (Shaw and Giambra, 1993), and schizophrenia (Elua et al., 2012), but also of the
specific content of self-generated thoughts, which has so far not been investigated. The
present study demonstrates the feasibility of using mind-wandering tasks as a tool in clinical
psychological research. Furthermore, while the mind-wandering literature has so far focused
on the extent of off-task thoughts and increasingly also its content (Smallwood, 2013), the
present study shows that investigating the variability of mind-wandering across time yields an
additional interesting characteristic of self-generated thoughts and allows insight into the
(in)stability of mental representations.
There are some limitations to the present study. We could not include a matched
clinical control group. Therefore, the specificity of the observed results to BPD could not be
tested. The correlation of the observed effects to symptom severity, as measured with the
PAI-BOR, is some indication of a relation to BPD, but future studies should probe whether
the effects remain specific in comparison to other clinical groups. Furthermore, a larger
sample would have been advantageous, in particular with regard to analyzing subgroups with
different comorbidities or medication. As most BPD patients were treated with psychotropic
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medication, influence of medication on the data could not be evaluated. Similarly, the
subgroups with or without different comorbidities (e.g., current or past depression, PTSD,
eating disorder) were very small. Future studies should include larger samples that allow
comparison of these subgroups, because comorbidity or medication may impact the effects,
for instance the observed negativity bias. Additionally, the small number of men in the
present sample hampers generalizations of the results to all BPD patients irrespective of
gender.
The present results have several clinical implications. An increasing body of research
investigates the effects of mindfulness practices for stabilizing the mind (Lutz et al., 2007),
and mindfulness exercises are also implemented in one of the most effective
psychotherapeutic treatments for BPD, dialectical behavioral therapy (Linehan, 1987;
Linehan et al., 2006). Given that mindfulness training has been shown to reduce mindwandering (Mrazek et al., 2013), the present results suggest that mindfulness exercises are an
important component of BPD treatment, possibly through their effect on mind-wandering.
Mentalization-based therapy for BPD, in contrast, focuses on elaborating the capacity to
implicitly and explicitly interpret the actions of oneself and others (Bateman and Fonagy,
2004, 2010). As an element of increasing the patient’s mentalization capacity, it may
necessary to also stabilize the thoughts related to self and others. It may also prove fruitful to
directly target the negative bias in self-generated thoughts observed in BPD, because a
negative cognitive bias has been consistently linked to negative outcome and targeted
interventions are available, such as “cognitive bias modification” or mental imagery based
interventions (see e.g. Beck et al., 2003; Hallion and Ruscio, 2011; Lang et al., 2012).
To conclude, using a mind-wandering paradigm we observed more negatively colored
thoughts and more instability in self- and other-related thoughts in patients with BPD. In
BPD patients, more extreme ratings of self- and other-related thoughts were also more
20
negative. Given the high prevalence of such self-generated thoughts, these alterations may
have consequences, for example on patients’ stress responsivity and interpersonal
relationships, and may thereby play an important etiological role for BPD.
21
Acknowledgments
We wish to thank Lisa Deunert and Tom Dreßler for their help in data collection.
22
References
Ackenheil, M., Stotz, G., Dietz-Bauer, R., Vossen, A., 1999. Mini International
Neuropsychiatric Interview. German version 5.0.0. Psychiatrische Universitätsklinik
München, Munich, Germany.
American Psychiatric Association, 2000. Diagnostic and Statistical Manual of Mental
Disorders (4th ed.) - Text Revision. American Psychiatric Publishing, Inc., Washington, D.C.
American Psychiatric Association, 2013. Diagnostic and Statistical Manual of Mental
Disorders (5th ed.). American Psychiatric Publishing, Inc., Washington, D.C.
Arntz, A., ten Haaf, J., 2012. Social cognition in borderline personality disorder: evidence for
dichotomous thinking but no evidence for less complex attributions. Behav Res Ther 50 (11),
707-718.
Baer, R.A., Sauer, S.E., 2011. Relationships Between Depressive Rumination, Anger
Rumination, and Borderline Personality Features. Personality Disorders-Theory Research and
Treatment 2 (2), 142-150.
Baird, B., Smallwood, J., Mrazek, M.D., Kam, J.W., Franklin, M.S., Schooler, J.W., 2012.
Inspired by distraction: mind wandering facilitates creative incubation. Psychol Sci 23 (10),
1117-1122.
Barnow, S., Stopsack, M., Grabe, H.J., Meinke, C., Spitzer, C., Kronmuller, K., Sieswerda,
S., 2009. Interpersonal evaluation bias in borderline personality disorder. Behav Res Ther 47
(5), 359-365.
Bateman, A.W., Fonagy, P., 2004. Mentalization-based treatment of BPD. J Pers Disord 18
(1), 36-51.
Bateman, A.W., Fonagy, P., 2010. Mentalization based treatment for borderline personality
disorder. World Psychiatry 9 (1), 11-15.
Beck, A.T., Butler, A.C., Brown, G.K., Dahlsgaard, K.K., Newman, C.F., Beck, J.S., 2001.
Dysfunctional beliefs discriminate personality disorders. Behav Res Ther 39 (10), 1213-1225.
Beck, A.T., Freeman, A., Davis, D.D., 2003. Cognitive Therapy of Personality Disorders
(2nd ed.). Guilford Press, New York.
Beck, A.T., Ward, C.H., Mendelson, M., Mock, J., Erbaugh, J., 1961. An inventory for
measuring depression. Arch Gen Psychiatry 4, 561-571.
BellPringle, V.J., Pate, J.L., Brown, R.C., 1997. Assessment of borderline personality
disorder using the MMPI-2 and the personality assessment inventory. Assessment 4 (2), 131139.
Bender, D.S., Skodol, A.E., 2007. Borderline personality as a self-other representational
disturbance. J Pers Disord 21 (5), 500-517.
23
de Bonis, M., De Boeck, P., Lida-Pulik, H., Feline, A., 1995. Identity disturbances and selfother differentiation in schizophrenics, borderlines, and normal controls. Compr Psychiatry
36 (5), 362-366.
Domes, G., Czieschnek, D., Weidler, F., Berger, C., Fast, K., Herpertz, S.C., 2008.
Recognition of facial affect in borderline personality disorder. J Pers Disord 22 (2), 135-147.
Domes, G., Schulze, L., Herpertz, S.C., 2009. Emotion recognition in borderline personality
disorder-a review of the literature. J Pers Disord 23 (1), 6-19.
Dyck, M., Habel, U., Slodczyk, J., Schlummer, J., Backes, V., Schneider, F., Reske, M.,
2009. Negative bias in fast emotion discrimination in borderline personality disorder. Psychol
Med 39 (5), 855-864.
Dziobek, I., Preissler, S., Grozdanovic, Z., Heuser, I., Heekeren, H.R., Roepke, S., 2011.
Neuronal correlates of altered empathy and social cognition in borderline personality
disorder. Neuroimage 57 (2), 539-548.
Ebner-Priemer, U.W., Houben, M., Santangelo, P., Kleindienst, N., Tuerlinckx, F., Oravecz,
Z., Verleysen, G., Van Deun, K., Bohus, M., Kuppens, P., 2015. Unraveling affective
dysregulation in borderline personality disorder: a theoretical model and empirical evidence.
J Abnorm Psychol.
Ebner-Priemer, U.W., Kuo, J., Kleindienst, N., Welch, S.S., Reisch, T., Reinhard, I., Lieb, K.,
Linehan, M.M., Bohus, M., 2007. State affective instability in borderline personality disorder
assessed by ambulatory monitoring. Psychol Med 37 (7), 961-970.
Elua, I., Laws, K.R., Kvavilashvili, L., 2012. From mind-pops to hallucinations? A study of
involuntary semantic memories in schizophrenia. Psychiatry Research 196 (2-3), 165-170.
Engert, V., Smallwood, J., Singer, T., 2014. Mind your thoughts: associations between selfgenerated thoughts and stress-induced and baseline levels of cortisol and alpha-amylase. Biol
Psychol 103, 283-291.
First, M.B., Gibbon, M., Spitzer, R.L., Williams, J.B.W., Benjamin, L., 1997. Structured
Clinical Interview for DSM-IV Axis II Personality Disorders (SCID-II). American
Psychiatric Publishing, Inc., Arlington.
Gardner, K., Qualter, P., 2009. Reliability and validity of three screening measures of
borderline personality disorder in a nonclinical population. Personality and Individual
Differences 46 (5-6), 636-641.
Groves, J.A., Engel, R.R., 2007. The German Adaptation and standardization of the
Personality Assessment Inventory (PAI). J Pers Assess 88 (1), 49-56.
Gunderson, J.G., 2007. Disturbed relationships as a phenotype for borderline personality
disorder. Am J Psychiatry 164 (11), 1637-1640.
Hallion, L.S., Ruscio, A.M., 2011. A meta-analysis of the effect of cognitive bias
modification on anxiety and depression. Psychol Bull 137 (6), 940-958.
24
Herpertz, S.C., Bertsch, K., 2014. The social-cognitive basis of personality disorders. Curr
Opin Psychiatry 27 (1), 73-77.
Hertel, P.T., 1998. Relation between rumination and impaired memory in dysphoric moods. J
Abnorm Psychol 107 (1), 166-172.
Hoffmann, F., Banzhaf, C., Kanske, P., Bermpohl, F., Singer, T., 2016. Where the depressed
mind wanders: Self-generated thought patterns as assessed through experience sampling as a
state marker of depression. J Affect Disord 198, 127-134.
Hopwood, C.J., Wright, A.G., Krueger, R.F., Schade, N., Markon, K.E., Morey, L.C., 2013.
DSM-5 pathological personality traits and the personality assessment inventory. Assessment
20 (3), 269-285.
Horowitz, L.M., 2004. Interpersonal Foundations of Psychopathology. American
Psychological Association, Washington, D.C.
Jahng, S., Wood, P.K., Trull, T.J., 2008. Analysis of affective instability in ecological
momentary assessment: Indices using successive difference and group comparison via
multilevel modeling. Psychol Methods 13 (4), 354-375.
Joormann, J., Gotlib, I.H., 2006. Is this happiness I see? Biases in the identification of
emotional facial expressions in depression and social phobia. J Abnorm Psychol 115 (4), 705714.
Kane, M.J., Brown, L.H., McVay, J.C., Silvia, P.J., Myin-Germeys, I., Kwapil, T.R., 2007.
For whom the mind wanders, and when - An experience-sampling study of working memory
and executive control in daily life. Psychol Sci 18 (7), 614-621.
Killingsworth, M.A., Gilbert, D.T., 2010. A wandering mind is an unhappy mind. Science
330 (6006), 932.
Klein, M.H., Wonderlich, S.A., Crosby, R., 2001. Self-concept correlates of the personality
disorders. J Pers Disord 15 (2), 150-156.
Kluetsch, R.C., Schmahl, C., Niedtfeld, I., et al., 2012. Alterations in default mode network
connectivity during pain processing in borderline personality disorder. Archives of General
Psychiatry 69 (10), 993-1002.
Kurtz, J.E., Morey, L.C., 1998. Negativism in evaluative judgments of words among
depressed outpatients with borderline personality disorder. J Pers Disord 12 (4), 351-361.
Lang, T.J., Blackwell, S.E., Harmer, C.J., Davison, P., Holmes, E.A., 2012. Cognitive Bias
Modification Using Mental Imagery for Depression: Developing a Novel Computerized
Intervention to Change Negative Thinking Styles. European Journal of Personality 26 (2),
145-157.
Lecrubier, Y., Sheehan, D.V., Weiller, E., Amorim, P., Bonora, I., Sheehan, K., Janavs, J.,
Dunbar, G., 1997. The mini international neuropsychiatric interview (MINI). A short
diagnostic structured interview: reliability and validity according to CIDI. European
Psychiatry 12, 224-231.
25
Lerner, H.D., St Peter, S., 1984. Patterns of object relations in neurotic, borderline and
schizophrenic patients. Psychiatry 47 (1), 77-92.
Levy, K.N., Clarkin, J.F., Yeomans, F.E., Scott, L.N., Wasserman, R.H., Kernberg, O.F.,
2006. The mechanisms of change in the treatment of borderline personality disorder with
transference focused psychotherapy. J Clin Psychol 62 (4), 481-501.
Linehan, M.M., 1987. Dialectical behavior therapy for borderline personality disorder:
Theory and method. Bulletin of the Menninger Clinic 51, 261-276.
Linehan, M.M., Comtois, K., Murray, A.M., et al., 2006. TWo-year randomized controlled
trial and follow-up of dialectical behavior therapy vs therapy by experts for suicidal
behaviors and borderline personality disorder. Archives of General Psychiatry 63 (7), 757766.
Livesley, W.J., Jang, K.L., 2000. Toward an empirically based classification of personality
disorder. J Pers Disord 14 (2), 137-151.
Lutz, A., Dunne, J.D., Davidson, R.J., 2007. Meditation and the neuroscience of
consciousness, in: Zelazo, P., Moscovitch, M., Thompson, E. (Eds.), Cambridge Handbook of
Consciousness. Cambridge University Press, Campbridge, UK.
Mason, M.F., Norton, M.I., Van Horn, J.D., Wegner, D.M., Grafton, S.T., Macrae, C.N.,
2007. Wandering minds: The default network and stimulus-independent thought. Science 315
(5810), 393-395.
Mier, D., Lis, S., Esslinger, C., Sauer, C., Hagenhoff, M., Ulferts, J., Gallhofer, B., Kirsch,
P., 2013. Neuronal correlates of social cognition in borderline personality disorder. Soc Cogn
Affect Neurosci 8 (5), 531-537.
Mogg, K., Bradley, B.P., 2005. Attentional bias in generalized anxiety disorder versus
depressive disorder. Cognitive Therapy and Research 29 (1), 29-45.
Morey, L.C., 1991. The Personality Assessment Inventory: Professional Manual.
Psychological Assessment Resources, Lutz, FL.
Mrazek, M.D., Franklin, M.S., Phillips, D.T., Baird, B., Schooler, J.W., 2013. Mindfulness
Training Improves Working Memory Capacity and GRE Performance While Reducing Mind
Wandering. Psychol Sci 24 (5), 776-781.
Napolitano, L.A., Mckay, D., 2007. Dichotomous thinking in borderline personality disorder.
Cognitive Therapy and Research 31 (6), 717-726.
New, A.S., aan het Rot, M., Ripoll, L.H., Perez-Rodriguez, M.M., Lazarus, S., Zipursky, E.,
Weinstein, S.R., Koenigsberg, H.W., Hazlett, E.A., Goodman, M., Siever, L.J., 2012.
Empathy and alexithymia in borderline personality disorder: clinical and laboratory
measures. J Pers Disord 26 (5), 660-675.
Nisenbaum, R., Links, P.S., Eynan, R., Heisel, M.J., 2010. Variability and predictors of
negative mood intensity in patients with borderline personality disorder and recurrent suicidal
behavior: Multilevel analyses applied to experience sampling methodology. J Abnorm
Psychol 119 (2), 433-439.
26
Ottaviani, C., Shahabi, L., Tarvainen, M., Cook, I., Abrams, M., Shapiro, D., 2015.
Cognitive, behavioral, and autonomic correlates of mind wandering and perseverative
cognition in major depression. Frontiers in Neuroscience 8.
Porcelli, J.H., Shahar, G., Blatt, S.J., Ford, R.Q., Mezza, J.A., Greenlee, L.M., 2006. Social
cognition and object relations scale: Convergent validity and changes following intense
inpatient treatment. Personality and Individual Differences 41, 407-417.
Raes, F., Hermans, D., Williams, J.M.G., 2006. Negative bias in the perception of others'
facial emotional expressions in major depression - The role of depressive rumination. Journal
of Nervous and Mental Disease 194 (10), 796-799.
Ritter, K., Vater, A., Rusch, N., Schroder-Abe, M., Schutz, A., Fydrich, T., Lammers, C.H.,
Roepke, S., 2014. Shame in patients with narcissistic personality disorder. Psychiatry
Research 215 (2), 429-437.
Roepke, S., Schroder-Abe, M., Schutz, A., Jacob, G., Dams, A., Vater, A., Ruter, A., Merkl,
A., Heuser, I., Lammers, C.H., 2011. Dialectic behavioural therapy has an impact on selfconcept clarity and facets of self-esteem in women with borderline personality disorder. Clin
Psychol Psychother 18 (2), 148-158.
Ruby, F.J.M., Smallwood, J., Engen, H., Singer, T., 2013a. How self-generated thought
shapes mood--the relation between mind-wandering and mood depends on the socio-temporal
content of thoughts. PLoS One 8 (10), e77554.
Ruby, F.J.M., Smallwood, J., Sackur, J., Singer, T., 2013b. Is self-generated thought a means
of social problem solving? Front Psychol 4.
Rüsch, N., Lieb, K., Gottler, I., Hermann, C., Schramm, E., Richter, H., Jacob, G.A.,
Corrigan, P.W., Bohus, M., 2007. Shame and implicit self-concept in women with borderline
personality disorder. Am J Psychiatry 164 (3), 500-508.
Sanislow, C.A., Morey, L.C., Grilo, C.M., Gunderson, J.G., Shea, M.T., Skodol, A.E., Stout,
R.L., Zanarini, M.C., McGlashan, T.H., 2002. Confirmatory factor analysis of DSM-IV
borderline, schizotypal, avoidant and obsessive-compulsive personality disorders: findings
from the Collaborative Longitudinal Personality Disorders Study. Acta Psychiatr Scand 105
(1), 28-36.
Santangelo, P., Bohus, M., Ebner-Priemer, U.W., 2014. Ecological momentary assessment in
borderline personality disorder: a review of recent findings and methodological challenges. J
Pers Disord 28 (4), 555-576.
Saß, H., Wittchen, H.-U., Zaudig, M., Houben, I., 2003. Diagnostisches und Statistisches
Manual Psychischer Störungen – Textrevision – DSM-IV-TR Hogrefe, Göttingen, Germany.
Schick, A., Wessa, M., Vollmayr, B., Kuehner, C., Kanske, P., 2013. Indirect assessment of
an interpretation bias in humans: neurophysiological and behavioral correlates. Front Hum
Neurosci 7, 272.
Shaw, G.A., Giambra, L., 1993. Task-unrelated thoughts of college-students diagnosed as
hyperactive in childhood. Dev Neuropsychol 9 (1), 17-30.
27
Showers, C., 1992. Compartmentalization of positive and negative self-knowledge: keeping
bad apples out of the bunch. J Pers Soc Psychol 62 (6), 1036-1049.
Sieswerda, S., Arntz, A., Wolfis, M., 2005. Evaluations of emotional noninterpersonal
situations by patients with borderline personality disorder. Journal of Behavior Therapy and
Experimental Psychiatry 36 (3), 209-225.
Sieswerda, S., Barnow, S., Verheul, R., Arntz, A., 2013. Neither dichotomous nor split, but
schema-related negative interpersonal evaluations characterize borderline patients. J Pers
Disord 27 (1), 36-52.
Skirrow, C., Ebner-Priemer, U., Reinhard, I., Malliaris, Y., Kuntsi, J., Asherson, P., 2014.
Everyday emotional experience of adults with attention deficit hyperactivity disorder:
evidence for reactive and endogenous emotional lability. Psychol Med 44 (16), 3571-3583.
Smallwood, J., 2013. Distinguishing how from why the mind wanders: a process-occurrence
framework for self-generated mental activity. Psychol Bull 139 (3), 519-535.
Smallwood, J., Baracaia, S.F., Lowe, M., Obonsawin, M., 2003. Task unrelated thought
whilst encoding information. Conscious Cogn 12 (3), 452-484.
Smallwood, J., Fishman, D.J., Schooler, J.W., 2007. Counting the cost of an absent mind:
Mind wandering as an underrecognized influence on educational performance. Psychonomic
Bulletin & Review 14 (2), 230-236.
Smallwood, J., Ruby, F.J.M., Singer, T., 2013a. Letting go of the present: mind-wandering is
associated with reduced delay discounting. Conscious Cogn 22 (1), 1-7.
Smallwood, J., Ruby, F.J.M., Singer, T., 2013b. Letting go of the present: Mind-wandering is
associated with reduced delay discounting. Conscious Cogn 22 (1), 1-7.
Smallwood, J., Schooler, J.W., 2015. The science of mind wandering: empirically navigating
the stream of consciousness. Annu Rev Psychol 66, 487-518.
Stein, M.B., Pinsker-Aspen, J.H., Hilsenroth, M.J., 2007. Borderline pathology and the
personality assessment inventory (PAI): An evaluation of criterion and concurrent validity. J
Pers Assess 88 (1), 81-89.
Torgersen, S., Kringlen, E., Cramer, V., 2001. The prevalence of personality disorders in a
community sample. Archives of General Psychiatry 58 (6), 590-596.
Tramantano, G., Javier, R.A., Colon, M., 2003. Discriminating among subgroups of
borderline personality disorder: an assessment of object representations. Am J Psychoanal 63
(2), 149-175.
Trull, T.J., Jahng, S., Tomko, R.L., Wood, P.K., Sher, K.J., 2010. Revised NESARC
personality disorder diagnoses: gender, prevalence, and comorbidity with substance
dependence disorders. J Pers Disord 24 (4), 412-426.
Trull, T.J., Solhan, M.B., Tragesser, S.L., Jahng, S., Wood, P.K., Piasecki, T.M., Watson, D.,
2008. Affective instability: measuring a core feature of borderline personality disorder with
ecological momentary assessment. J Abnorm Psychol 117 (3), 647-661.
28
Vater, A., Schroder-Abe, M., Weissgerber, S., Roepke, S., Schutz, A., 2015. Self-concept
structure and borderline personality disorder: evidence for negative compartmentalization. J
Behav Ther Exp Psychiatry 46, 50-58.
Veen, G., Arntz, A., 2000. Multidimensional dichotomous thinking characterizes borderline
personality disorder. Cognitive Therapy and Research 24 (1), 23-45.
Watts, F.N., Macleod, A.K., Morris, L., 1988. Associations between phenomenal and
objective aspects of concentration problems in depressed-patients. British Journal of
Psychology 79, 241-250.
Westen, D., Ludolph, P., Silk, K., Kellam, A., Gold, L., Lohr, N., 1990. Object relations in
borderline adolescents and adults: developmental differences. Adolesc Psychiatry 17, 360384.
Wilkinson-Ryan, T., Westen, D., 2000. Identity disturbance in borderline personality
disorder: an empirical investigation. Am J Psychiatry 157 (4), 528-541.
Wittchen, H.-U., Zaudig, M., Fydrich, T., 1997. SKID-II. Strukturiertes Klinisches Interview
für DSM-IV [Structural Clinical Interview for DSM-IV Axis II Disorders]. Hogrefe,
Göttingen.
Wolf, R.C., Sambataro, F., Vasic, N., Schmid, M., Thomann, P.A., Bienentreu, S.D., Wolf,
N.D., 2011. Aberrant connectivity of resting-state networks in borderline personality
disorder. J Psychiatry Neurosci 36 (6), 402-411.
29
Figure captions
Figure 1: (A) Level of reported off-task thoughts, other- and self-related, positive and
negative, and future- and past-oriented thoughts (rating scale numbers without unit varying
between 0 and 100). (B) Fluctuations between thought probes (squared successive
differences). (C) Extremity of ratings (squared deviations from mean ratings). * p < 0.05; # p
< 0.10
Figure 2: Scatterplot depicting the relation of the level of negative thoughts to the extremity
(squared deviations from mean ratings) of (A) self-related and (B) other-related thoughts,
separately for BPD patients and HCs. Intra-individual standard errors are displayed in grey
and model predictions from the multi-level model as lines in the respective color.
30
Figure 1
31
Figure 2
32
Tables
Table 1: Demographics and clinical characteristics of BPD patients and matched healthy
control participants (means and standard deviations or absolute number and percent are given
without and with parentheses, respectively).
N
Gender (female/male)
Age
Years of education
PAI-BOR
MDE
Lifetime
Current
Current dysthymia
Substance use disorder
Any anxiety disorder
Current PTSD
Any eating disorder
Any cluster A PD
Any other Cluster B PD
Any cluster C PD
No psychotropic medication
Current medication
Antidepressants
Atypical antipsychotics
Mood stabilizer
BPD
27
25/2
32.1 (9.7)
11.3 (2.3)
78.7 (7.5)
HC
25
21/4
31.2 (10.1)
12.5 (2.6)
44.4 (10.9)
statistic
21 (77.8)
5 (18.5)
1 (3.7)
9 (33.3)
2 (7.4)
9 (33.3)
9 (33.3)
0
1 (3.7)
4 (14.8)
7 (25.9)
-
-
15 (55.6)
6 (22.2)
2 (7.4)
-
-
Χ2(1) = .33, p > 0.40
t(50) = .35, p > 0.70
t(50) = -1.82, p > 0.07
t(50) = 13.2, p < 0.001
33
Table 2: Differences between BPD patients and HCs in rating levels, fluctuations in ratings, and extremity of ratings as estimated with
multi-level modeling. Model parameters for the group predictor are displayed. For full model information, see Supplementary Table 2.
off-task
other
self
negative
positive
past
future
negative mood
positive mood
rating levels
b
S.E.
2.794
6.016
-3.080
5.094
4.693
4.962
32.633
5.063
-27.821
4.704
5.151
4.714
-0.938
5.729
21.536
5.590
-24.178
5.648
p-value
0.644
0.548
0.349
0.000
0.000
0.280
0.871
0.000
0.000
fluctuations in ratings
b
S.E.
p-value
115.281
164.766
0.485
402.132
152.093
0.008
571.535
124.799
0.000
22.618
99.668
0.821
143.081
156.439
0.361
204.707
152.534
0.186
232.985
261.637
0.373
-15.397
117.913
0.896
191.759
104.121
0.070
extremity of ratings
b
S.E.
p-value
168.021
79.384
0.035
320.553
93.109
0.001
468.330
95.780
0.000
320.778
166.038
0.059
67.784
101.421
0.504
211.564
140.037
0.137
82.129
139.681
0.559
-59.828
120.937
0.622
-18.824
173.275
0.914
34
Table 3: Differences between BPD patients and HCs in the relation of rating levels, fluctuations, and extremity of self- and otherrelated ratings to the negativity of thoughts estimated as interactions in mutli-level models. Model parameters for valence predictors
and their interaction with group are displayed. For full model information, see Supplementary Table 3.
self
other
self
other
Predictors
negative
negative*Group
negative
negative*Group
positive
positive*Group
positive
positive*Group
b
rating levels
S.E.
p-value
fluctuations in ratings
b
S.E.
p-value
extremity of ratings
b
S.E.
p-value
0.143
0.065
0.028
4.958
4.124
0.230
-5.686
2.157
0.009
0.061
0.091
0.503
-6.791
5.172
0.190
8.184
2.706
0.003
-0.035
0.080
0.657
-0.705
5.336
0.895
-5.173
2.277
0.023
0.128
0.104
0.221
-1.389
6.799
0.838
10.491
2.873
0.000
-0.069
0.073
0.344
-6.355
3.974
0.110
1.661
2.004
0.408
0.029
0.093
0.751
8.820
5.128
0.086
-4.954
2.602
0.057
-0.017
0.081
0.836
-8.515
5.524
0.124
1.973
2.264
0.384
0.013
0.104
0.897
11.606
7.037
0.100
-5.681
2.908
0.051
35
Supplement
Supplementary Table 1: Principal components analysis showing the component loadings for each
rating question. Three components were observed: The affect component positively weighted on
positive ratings and negatively on negative ratings, the socio-temporal past other component
weighted positivelyon other and past ratings, and the socio-temporal future self component
weighted positively on self and future.
off-task
other
self
negative
positive
past
future
F1
affect
0,273
0,019
-0,073
-0,920
0,940
-0,147
0,022
F2
past-other
-0,622
0,797
0,067
0,159
-0,096
0,754
0,101
F3
future-self
-0,326
0,284
0,727
0,071
0,001
-0,207
0,866
36
Supplementary Table 2: Differences between BPD patients and HCs in ratings levels, fluctuations in ratings and extremity in ratings
as estimated with multi-level modelling
off task
other
self
negative
positive
past
Model parameters
rating levels
Predictors
b
S.E.
Intercept
52.694
5.662
Group
2.794
6.016
Session
10.736
3.384
Sample
-1.586
0.425
Intercept
40.773
4.875
Group
-3.080
5.094
Session
-6,436
3.316
Sample
1.123
0.417
Intercept
31.156
4.309
Group
4.693
4.962
Session
5.672
3.667
Sample
1.056
0.383
Intercept
22.456
3.366
Group
32.633
5.063
Session
-2.524
2.804
Sample
0.226
0.299
Intercept
70.455
3.805
Group
-27.821
4.704
Session
-0.370
2.561
Sample
-0.372
0.303
Intercept
23.931
3.916
Group
5.151
4.714
Session
-2.105
3.462
Sample
0.592
0.369
p-value
0.000
0.644
0.003
0.000
0.000
0.548
0.057
0.007
0.000
0.349
0.128
0.006
0.000
0.000
0.372
0.450
0.000
0.000
0.886
0.221
0.000
0.280
0.546
0.109
fluctuations in ratings
b
S.E.
799.759
212.261
115.281
164.766
-61.119
180.354
-9.323
33.358
499.961
191.175
402.132
152.093
-1.858
152.772
58.301
37.207
313.413
156.867
571.535
124.799
212.037
125.356
5.434
30.530
436.059
125.278
22.618
99.668
137.093
100.113
-16.314
24.382
460.652
161.947
143.081
156.439
-177.552
122.879
15.588
25.556
547.808
165.580
204.707
152.534
27.019
144.032
3.573
28.403
p-value
0.000
0.485
0.735
0.780
0.009
0.008
0.990
0.118
0.046
0.000
0.091
0.859
0.001
0.821
0.171
0.504
0.005
0.361
0.152
0.542
0.001
0.186
0.852
0.900
extremity of ratings
b
S.E.
500.623
100.018
168.021
79.384
258.725
79.688
-9.796
17.333
902.185
84.858
320.553
93.109
-261.131
61.667
12.894
18.744
757.377
81.636
468.330
95.780
-151.509
75.389
-4.759
18.250
866.964
132.635
320.778
166.038
-158.732
56.232
-2.854
12.447
898.414
80.079
67.784
101.421
-206.513
90.510
-2.580
18.857
719.115
127.268
211.564
140.037
-47.705
117.659
2.618
12.985
p-value
0.000
0.035
0.001
0.572
0.000
0.001
0.000
0.493
0.000
0.000
0.047
0.795
0.000
0.059
0.005
0.819
0.000
0.504
0.026
0.891
0.000
0.137
0.687
0.840
37
future
negative
mood
positive
mood
Intercept
Group
Session
Sample
Intercept
Group
Session
Sample
Intercept
Group
Session
Sample
43.085
-0.938
-2.680
0.941
27.779
21.536
0.796
1.063
70.052
-24.178
-3.418
-1.059
5.202
5.729
3.461
0.426
4.603
5.590
2.621
0.264
4.694
5.648
2.611
0.236
0.000
0.871
0.442
0.027
0.000
0.000
0.762
0.000
0.000
0.000
0.197
0.000
903.833
232.985
294.698
-36.194
411.628
-15.397
72.666
-8.574
88.677
191.759
129.848
18.717
258.846
261.637
192.771
36.399
141.951
117.913
105.024
23.917
94.822
104.121
69.235
25.114
0.000
0.373
0.127
0.320
0.004
0.896
0.492
0.720
0.351
0.070
0.072
0.458
1068.024
82.129
-58.920
-3.418
1146.016
-59.828
-215.965
-4.396
959.761
-18.824
-242.025
4.291
128.088
139.681
120.316
13.240
93.681
120.937
167.026
9.926
139.883
173.275
97.640
10.328
0.000
0.559
0.626
0.796
0.000
0.622
0.196
0.658
0.000
0.914
0.016
0.678
38
Supplementary Table 3: Differences between BPD patients and HCs in the relation of rating levels, fluctuations and extremity of selfand other-related ratings to the negativity of thoughts as estimates as interactions in mutli-level models
Model parameters
self
other
self
other
Predictors
Intercept
Group
Session
Sample
negative
negative*Group
Intercept
Group
Session
Sample
negative
negative*Group
Intercept
Group
Session
Sample
positive
positive*Group
Intercept
Group
Session
Sample
positive
positive*Group
rating levels
b
S.E.
p-value
fluctuations in ratings
b
S.E.
p-value
extremity of ratings
b
S.E.
p-value
27.570
3.870
0.000
211.055
178.000
0.236
818.476
89.658
0.000
-3.086
6.882
0.658
785.220
235.179
0.001
282.939
132.881
0.034
6.656
4.617
0.155
192.240
126.606
0.129
-123.647
84.836
0.153
1.020
0.367
0.006
6.196
30.585
0.840
-0.006
12.993
1.000
0.143
0.065
0.028
4.958
4.124
0.230
-5.686
2.157
0.009
0.061
0.091
0.503
-6.791
5.172
0.190
8.184
2.706
0.003
41.200
4.812
0.000
501.601
239.494
0.036
1003.133
96.490
0.000
-8.474
6.348
0.185
511.525
344.074
0.137
-81.833
141.267
0.563
-5.994
3.427
0.086
-13.458
156.009
0.931
-231.401
61.598
0.000
1.092
0.418
0.009
66.339
34.433
0.054
11.820
18.357
0.521
-0.035
0.080
0.657
-0.705
5.336
0.895
-5.173
2.277
0.023
0.128
0.104
0.221
-1.389
6.799
0.838
10.491
2.873
0.000
36.277
6.803
0.000
771.579
327.092
0.019
638.940
164.971
0.000
1.463
6.911
0.832
33.401
328.531
0.919
713.431
186.147
0.000
5.523
2.810
0.049
188.896
125.816
0.134
-141.482
84.208
0.098
1.004
0.378
0.008
3.701
30.555
0.904
-5.392
16.864
0.749
-0.069
0.073
0.344
-6.355
3.974
0.110
1.661
2.004
0.408
0.029
0.093
0.751
8.820
5.128
0.086
-4.954
2.602
0.057
41.894
7.775
0.000
1097.332
454.484
0.016
759.051
183.415
0.000
-3.685
8.227
0.654
-370.597
488.916
0.449
610.644
199.183
0.002
-6.561
3.825
0.094
17.801
161.792
0.913
-254.415
61.527
0.000
1.117
0.407
0.006
71.014
44.879
0.117
13.209
18.706
0.481
-0.017
0.081
0.836
-8.515
5.524
0.124
1.973
2.264
0.384
0.013
0.104
0.897
11.606
7.037
0.100
-5.681
2.908
0.051
39
Supplementary Figure 1:
Figure 3: Level (A) of reported positive and negative mood, as well as (B) fluctuations between
probes (squared successive differences) and (C) extremity of ratings (squared deviations from
mean ratings). * p < 0.05; # p < 0.10
40