Introduction: This secondary analysis of quality control data assessed principal components of personality dysfunction and their relationship to mentalizing in a sample of treatment-seeking women with severe personality disorders. Methods: The Schedule for Nonadaptive and Adaptive Personality (SNAP) and the Movie for the Assessment of Social Cognition (MASC) were administered to 37 females in routine quality assessments of a specialized residential treatment program. Principal component analysis (PCA) of SNAP scores was used to determine dimensions of personality most significantly contributing to overall maladaptive personality functioning. Bootstrapped stepwise regression tested the relationship of dimensional personality indices to hypermentalizing and hypomentalizing on the MASC controlling for general psychiatric severity. Results: Four principal components (PCs) explained 71.4% of the variance in personality dysfunction, mapping onto antisocial, obsessive compulsive, borderline, and narcissistic personality constellations. The borderline and antisocial PCs were positively predictive of hypermentalizing. The obsessive-compulsive PC was positively predictive of hypomentalizing, while the antisocial PC was negatively predictive of hypomentalizing. Conclusion: The study reiterates prior findings of a relationship between hypermentalizing and borderline and antisocial personality profiles. It also contributes evidence to the limited research on hypomentalizing as a clinical indicator and potential treatment target for obsessive-compulsive personality, and shows evidence of a negative relationship between antisocial personality disorder and hypomentalizing. These findings provide clinical indications for enhancing and regulating mentalizing via attention to and interpretations of internal and interpersonal events in individuals with personality disorders. Further research is needed to replicate these associations in larger, more representative clinical samples.

Deficits in mentalizing are proposed as the fundamental disturbance in personality disorders (PDs), particularly borderline personality disorder (BPD), which may be a prototype and severity indicator of personality dysfunction broadly [1‒3]. Disruptions in the capacity to understand mental states, both in oneself and others, behind observable actions flexibly and accurately in context may underlie the emotional, interpersonal, and behavioral instabilities diagnostic of BPD and other severe PDs [4, 5]. As diagnostic systems evolve to incorporate dimensional considerations of the many general and specific factors within a patient’s personality functioning, understanding the association between different mentalizing problems and factors determining personality pathology may help refine therapeutic interventions.

Little empirical research exists to characterize the mentalizing deficits of specific PDs aside from BPD. Mentalizing errors have been systematically evaluated with the Movie for the Assessment of Social Cognition (MASC) [6], a video-based measure that models implicit social cognition [7]. Overly elaborated imagination of mental states based on limited observable evidence is known as hypermentalizing, which reflects excessive production of poorly modulated interpretations of social situations. Hypermentalizing is associated with BPD and psychopathology more generally [8‒10] and is especially salient in BPD as early as adolescence [7, 11‒17], extending to adulthood [18‒22]. When individuals with BPD experience emotional and attachment activation, cognition and interpretation of experience are distorted, engendering hypermentalizing and further emotional dysregulation [15, 23].

Challenges with differentiation, or recognition of the subjectivity and hypothetical nature of one’s representations, are characteristic of patients with BPD, even while controlling for general symptom severity [24‒26]. Specifically, in addition to having dysregulated cognitions in highly aroused states, individuals with BPD demonstrate biased perceptions of others’ facial expressions, with heightened threat sensitivity [27]. General psychiatric severity is associated with disrupted frontolimbic dysfunction associated with cognitive disinhibition and disorganization, but the additional specific disturbance in reading social interactions appears to be more distinguishing of borderline and other PDs [28‒30].

Patients with a range of personality problems have tendencies toward schema-driven, overly certain attributions and assumptions about others’ mental states [31]. Beyond BPD, adolescent patients with affective components of psychopathy [32], as well as with traits of suspiciousness, withdrawal, callousness, and deceitfulness [33], displayed hypermentalizing on the MASC. The literature on metacognition, a related construct, provides broader evidence of deficits in ability to reflect on mental states as a core mechanism in PDs, considering integration or fragmentation of sense of self and others across changing contexts [34, 35]. Impairment in metacognition was elevated in outpatients with PDs compared to patients seeking treatment at the same clinic who met criteria for at least one symptom disorder but not a PD [35].

Hypomentalizing, or reduced attention, interest, and investment in considering mental states in oneself and others, is not as often the subject of empirical investigation. Hypomentalizing, assessed with the MASC, has been observed in violent offenders with and without antisocial personality disorder (ASPD) [36], yet in adolescents was negatively associated with traits of manipulation, lying, grandiosity [32], and risk taking [33]. Severity in the form of extensive prior treatment, comorbid PTSD, number of BPD criteria, and poorer clinical improvement over time related to hypomentalizing in BPD in a longitudinal study [21]. Notably, patients with BPD in this study exhibiting both hyper- and hypomentalizing had poorer clinical outcomes than those who demonstrated only hypermentalizing [21]. Longer illness duration in patients with BPD predicted a higher number of hypomentalizing errors [37] which, in conjunction with research in adolescent populations with BPD showing a predominance of hypermentalizing, may suggest a trajectory of excessive to insufficient mentalizing throughout the course of illness.

In sum, mentalizing deficits are apparent in patients with personality pathology and thus far, are most studied empirically in patients with BPD, with limited exploration of mentalizing errors in other personality presentations. Clinical samples generally contain a high degree of comorbidity between axis II disorders [38‒40], indicating the importance of parsing the relationship between complex and dimensional forms of personality problems and mentalizing. To our knowledge, only one analysis has been conducted using the MASC and a dimensional measure in a personality-focused treatment setting [33].

The current study utilizes a dimensional measure to explore relations between profiles of personality traits and overly imaginative or restricted mentalizing in a sample enriched with personality pathology. Utilizing quality assurance data from a sample of treatment-seeking women in a specialized residential treatment program for severe PDs, we had two aims. First, in an exploratory manner, we used principal component analysis (PCA) to determine which dimensions of personality traits and temperament loaded together in our sample. Second, we examined how the clusters of personality variance identified with PCA and general psychiatric severity related to mentalizing on the MASC, aiming to replicate and extend prior work and to further elucidate potential relationships between PD clusters and hypomentalizing given the relative dearth of literature.

Participants

In this small observational investigation, participants were 37 patients in a residential treatment program for women with severe PDs. The program is part of a larger hospital system and treats patients who have extensive prior treatment histories and complex constellations of personality pathology. Patients receive individual, family, and group therapy, in addition to case management and skills coaching (see [41] for details of the treatment structure). 83.8% of the sample met criteria for BPD on structured interviews (N = 16 on the Structured Clinical Interview for DSM-5 and N = 15 on the International Personality Disorder Examination), 13.5% (N = 5) did not meet criteria for BPD on a structured clinical interview, and 2.7% (N = 1) did not have a structured clinical interview for BPD on file. The average age was 25.9 (SD = 6.4), and the sample was predominately white, highly educated, and of high socioeconomic status (the program does not accept insurance reimbursement). See Table 1 for demographics.

Table 1.

Demographics

CharacteristicN%
Gender 
 Female 37 100 
Race 
 White 32 81 
 Asian 11 
 Multiethnic 
 Hispanic/Latino 
Education 
 Some college 20 54 
 Post-college education 19 
 College graduate 14 
 High school graduate 
 Missing data 
Hospitalization in 6 months prior 
 Yes 22 60 
 No 12 32 
 Missing data 
Employment status in month prior 
 Unemployed 32 86 
 Part-time work 
 Full-time work 
 Missing data 
CharacteristicN%
Gender 
 Female 37 100 
Race 
 White 32 81 
 Asian 11 
 Multiethnic 
 Hispanic/Latino 
Education 
 Some college 20 54 
 Post-college education 19 
 College graduate 14 
 High school graduate 
 Missing data 
Hospitalization in 6 months prior 
 Yes 22 60 
 No 12 32 
 Missing data 
Employment status in month prior 
 Unemployed 32 86 
 Part-time work 
 Full-time work 
 Missing data 
MeanSD
Age 25.9 6.4 
MeanSD
Age 25.9 6.4 

Measures

Personality traits and temperament were measured with the Schedule for Nonadaptive and Adaptive Personality-2 (SNAP-2) [42], a 375-item self-report questionnaire with a true/false format. The Computerized Adaptive Version was used, which demonstrated test-retest reliability and convergent and discriminant validity comparable to the pen and paper version [43]. The SNAP measures 15 trait and temperament scales, including negative temperament, mistrust, manipulativeness, aggression, self-harm, eccentric perceptions, dependency, positive temperament, exhibitionism, entitlement, detachment, disinhibition, impulsivity, propriety, and workaholism. Trait scales cluster into three higher-order factors: negative temperament, positive temperament, and disinhibition [42, 44].

Mentalizing was assessed with the Movie for the Assessment of Social Cognition [6], which presents participants with a 15-min video of a dinner party scene depicting interactions amongst four white characters of European descent. The video is sequentially paused at 45 distinct timepoints for questions assessing how the viewer perceives the characters’ mental states (i.e., emotions, thoughts, intentions). For example, questions ask, “What is Sandra feeling?” and “Why is Michael saying this?” At each timepoint, a four-choice multiple choice format presents the viewer with answers that are reflective of either hypermentalizing, hypomentalizing, no mentalizing (i.e., with little or no reference to mental states), or intact mentalizing (i.e., accurate response). The measure produces scores for each type of error and a number of correct responses. The MASC is ecologically valid [45] and sufficiently complex and emotionally arousing to prompt deficits in mentalizing in individuals with BPD [23].

General psychiatric severity was assessed with the Behavior and Symptom Identification Scale (BASIS-24) [46, 47]. The measure surveys six areas of distress and symptoms in the past week including depression, relationships, self-harm, emotional lability, psychosis, and substance use. Overall score at initiation of treatment was utilized as a control variable in the present analysis.

Statistical Procedures

To determine which personality traits load together, PCA was performed on the raw SNAP data, with two records removed for invalidity and missing data resulting in N = 37. PCA achieves dimension reduction by mapping the raw SNAP measures into several uncorrelated “principal components” that capture most of the variability in the SNAP data. A scree plot was examined to identify the optimal number of PCs for the downstream analysis based on a prespecified cutoff of 70% for the total proportion of variance explained. Ultimately, four PCs were retained for regression with eigenvalues exceeding 1.5. Data from the MASC, capturing mentalizing, were converted to error ratios, namely, the number of hypermentalizing responses to correct responses and the number of hypomentalizing responses to correct responses, and log transformed. Errors of no mentalizing were not included in the regression analyses due to insufficient nonzero data. The PCs of personality, mentalizing error ratios, and BASIS-24 score of general psychiatric severity were then input into the function boot.stepAIC in R [48] with 5,000 samples to perform backward elimination for variable selection and bootstrap resampling to determine the stability and variability of the selected model [49]. In this backward elimination technique, all predictor variables (all four PCs and general severity) are initially included in the regression model, and then the predictor whose deletion leads to the largest decrease in the Akaike information criterion (AIC) is removed. This process is repeated until all four variables are removed or none of the remaining variables can be removed to further decrease the AIC. This procedure was repeated 5,000 times on bootstrapped samples of the full dataset, providing an estimate of how often each predictor variable would survive the variable selection. The stability of the prediction power of each predictor, then, was determined by the percentage of bootstrapped samples in which it survived. Literature suggests a cutoff of 60% for consideration as a stable predictor [49].

In the PC analysis, the first four PCs comprised approximately 71.4% of the total variance in personality traits and temperament (PC1: 26%; PC2: 20%; PC3: 13%; PC4: 12%). These PCs map onto clinical syndromes identified with antisocial, obsessive compulsive, borderline, and narcissistic PDs. Table 2 contains the trait loadings of each PC. Figure 1 illustrates the relationship of each PC to each trait measured by the SNAP. Characterization of the clustering of features that hang together from two perspectives, that of section II diagnoses in the DSM-5 and section III Alternative DSM-5 Model of Personality Disorders (AMPD), is provided in Table 3 [50].

Table 2.

Principal component loadings

TraitPrincipal component
antisocial (PC1)obsessive-compulsive (PC2)borderline (PC3)narcissistic (PC4)
Positive temperament 11 32 42 28 
Propriety 08 39 05 14 
Workaholism 00 53 04 07 
Aggression 31 18 03 31 
Eccentric perceptions 25 15 17 08 
Dependency 04 38 19 19 
Negative temperament 24 09 42 33 
Self-harm 09 02 60 11 
Mistrust 12 34 25 32 
Detachment 09 16 26 58 
Entitlement 27 15 18 40 
Disinhibition 44 21 07 13 
Impulsivity 41 22 08 10 
Manipulativeness 43 02 11 02 
Exhibitionism 34 00 17 13 
TraitPrincipal component
antisocial (PC1)obsessive-compulsive (PC2)borderline (PC3)narcissistic (PC4)
Positive temperament 11 32 42 28 
Propriety 08 39 05 14 
Workaholism 00 53 04 07 
Aggression 31 18 03 31 
Eccentric perceptions 25 15 17 08 
Dependency 04 38 19 19 
Negative temperament 24 09 42 33 
Self-harm 09 02 60 11 
Mistrust 12 34 25 32 
Detachment 09 16 26 58 
Entitlement 27 15 18 40 
Disinhibition 44 21 07 13 
Impulsivity 41 22 08 10 
Manipulativeness 43 02 11 02 
Exhibitionism 34 00 17 13 
Fig. 1.

Heatmap of identified PCs from SNAP traits.

Fig. 1.

Heatmap of identified PCs from SNAP traits.

Close modal
Table 3.

Principal component trait loadings by AMPD and DSM-5 categorizations

Principal componentSection II personality disorderAlternative model for personality disorders (AMPD)
PC1 (antagonism) 
 Disinhibition Antisocial Criterion B: disinhibition 
 Manipulativeness  Manipulativeness (antagonism) 
 Impulsivity  Impulsivity (disinhibition) 
 Exhibitionism 
 Aggression  Hostility (antagonism) 
 Entitlement  Irresponsibility (disinhibition) 
PC2 (over-controlled) 
 Workaholism Obsessive compulsive Criterion A: sense of self derived from work (identity) 
 Propriety 
 Mistrust Criterion A: rigid standards of behavior (self-direction) 
 Positive temperament 
PC3 (borderline) 
 Self-harm Borderline Criterion B: negative affectivity 
 Negative temperament 
 Detachment Criterion A: close relationships marked by mistrust, neediness (intimacy) 
 Mistrust 
 Dependency 
PC4 (narcissistic) 
 Detachment Narcissistic Criterion A: impaired ability to identify w/others (empathy) 
 Entitlement Criterion B: grandiosity (antagonism) 
 Mistrust 
Principal componentSection II personality disorderAlternative model for personality disorders (AMPD)
PC1 (antagonism) 
 Disinhibition Antisocial Criterion B: disinhibition 
 Manipulativeness  Manipulativeness (antagonism) 
 Impulsivity  Impulsivity (disinhibition) 
 Exhibitionism 
 Aggression  Hostility (antagonism) 
 Entitlement  Irresponsibility (disinhibition) 
PC2 (over-controlled) 
 Workaholism Obsessive compulsive Criterion A: sense of self derived from work (identity) 
 Propriety 
 Mistrust Criterion A: rigid standards of behavior (self-direction) 
 Positive temperament 
PC3 (borderline) 
 Self-harm Borderline Criterion B: negative affectivity 
 Negative temperament 
 Detachment Criterion A: close relationships marked by mistrust, neediness (intimacy) 
 Mistrust 
 Dependency 
PC4 (narcissistic) 
 Detachment Narcissistic Criterion A: impaired ability to identify w/others (empathy) 
 Entitlement Criterion B: grandiosity (antagonism) 
 Mistrust 

With regard to mentalizing, subscale means and standard deviations can be found in Table 4. Hypermentalizing was the most frequent error type in our sample. Supplementary Table 1 (MASC subscale mean data in adult clinical samples) situates our sample's scores in relation to other adult clinical samples (for all online suppl. material, see https://doi.org/10.1159/000543363). The mentalizing abilities of our patient group appear to be relatively strong, though impairment in both excessive and insufficient mentalizing is nonetheless evident. In brief, amongst adult samples with at least one MASC subscale score reported in relation to personality pathology the average number of correct responses in our sample was in range, leaning high. The average number of hypermentalizing and hypomentalizing responses in our sample (5.27 and 3.78, respectively) were comparable to those in the literature, yet on the lower end (hypermentalizing range: 4.40–8.17; hypomentalizing range: 3.10–6.99).

Table 4.

MASC subscale scores

MASC subscalesMSD
Correct responses 34.35 4.63 
Hypermentalizing responses 5.27 2.77 
Hypomentalizing responses 3.78 2.69 
No mentalizing responses 1.59 1.72 
MASC subscalesMSD
Correct responses 34.35 4.63 
Hypermentalizing responses 5.27 2.77 
Hypomentalizing responses 3.78 2.69 
No mentalizing responses 1.59 1.72 

To determine relationships between the PCs of personality and mentalizing, two bootstrapped stepwise regressions were run with antisocial, obsessive-compulsive, borderline, and narcissistic PCs as predictors and hypermentalizing and hypomentalizing errors as the respective outcomes. In the analysis of predictors of hypomentalizing, the obsessive-compulsive and antisocial PCs appeared to be robust predictors (see Table 5). Controlling for general psychiatric severity, higher levels of the obsessive-compulsive covariates were considered predictive of greater hypomentalizing in approximately 73% of bootstrapped samples, with a positive sign in 99% of samplings. The antisocial covariates were considered predictive in about 76% of samples, and their relationship to hypomentalizing was negative in 99% of samplings. That is, higher levels of the composition of antisocial traits represented in the PC predicted less hypomentalizing. The consistency of these coefficient signs further supports the stability of the relationships between the corresponding PCs and hypomentalizing. The narcissistic PC was selected into the hypomentalizing model with a frequency of 52% and a consistently positive sign. We focus our subsequent discussion on the two predictors that met the suggested cutoff of 60% for consideration as stable. Lastly, the borderline PC was selected into the model with a frequency of 12% and was not considered significant.

Table 5.

Bootstrapped backward selection model results: Stability of PC and BASIS as predictors of mentalizing errors

Predictor% covariates selected% coefficients positiveStat significance
Hypermentalizing 
 PC1 83.02 99.93 77.14 
 PC2 21.88 77.51 30.99 
 PC3 87.08 99.77 85.32 
 PC4 14.68 34.88 21.66 
 BASIS 100 34.78 8.64 
Hypomentalizing 
 PC1 76.08 55 84.81 
 PC2 72.28 99.53 69.54 
 PC3 12.02 15.64 25.79 
 PC4 52.60 97.76 47.00 
 BASIS 100 90.66 29.88 
Predictor% covariates selected% coefficients positiveStat significance
Hypermentalizing 
 PC1 83.02 99.93 77.14 
 PC2 21.88 77.51 30.99 
 PC3 87.08 99.77 85.32 
 PC4 14.68 34.88 21.66 
 BASIS 100 34.78 8.64 
Hypomentalizing 
 PC1 76.08 55 84.81 
 PC2 72.28 99.53 69.54 
 PC3 12.02 15.64 25.79 
 PC4 52.60 97.76 47.00 
 BASIS 100 90.66 29.88 

Adjusting for general psychiatric severity, hypermentalizing was positively predicted by the borderline and antisocial PCs (see Table 5). These PCs were selected into the model, respectively, with frequencies of around 87% and 83%. The obsessive-compulsive PC was not a stable predictor of hypermentalizing, as it contributed predictive value to the regression model in only 22% of bootstrapped samples. The narcissistic PC, similarly, was not significantly or stably predictive of hypermentalizing. The proportion at which the narcissistic PC was selected into the model was 14%, not reaching significance. Figure 2 shows the strength and direction of each PC’s relationship to hyper- and hypomentalizing.

Fig. 2.

Frequency with which each PC was selected in two bootstrapped, stepwise regressions with hyper- and hypomentalizing as the respective outcomes, controlling for general psychiatric severity. Solid lines denote values above the commonly used 60% cutoff for a stable predictor; red lines indicate coefficient was positive in >50% of samplings; blue lines indicate coefficient was negative in >50% of samplings.

Fig. 2.

Frequency with which each PC was selected in two bootstrapped, stepwise regressions with hyper- and hypomentalizing as the respective outcomes, controlling for general psychiatric severity. Solid lines denote values above the commonly used 60% cutoff for a stable predictor; red lines indicate coefficient was positive in >50% of samplings; blue lines indicate coefficient was negative in >50% of samplings.

Close modal

The present analysis extends previous work by utilizing a dimensional measure to identify clusters of personality pathology and relating them to mentalizing tendencies in a sample of treatment-seeking women with severe PDs, allowing for nuanced exploration of nonadaptive personality traits. Our analysis yielded several important findings which warrant discussion. First, four PCs emerged in personality problems, which we classify as representing antisocial, obsessive-compulsive, borderline, and narcissistic variance. Second, consistent with previously published literature, the borderline and antisocial personality profiles predicted hypermentalizing. Third, the antisocial personality profile was inversely associated hypomentalizing. Lastly, the obsessive-compulsive personality PC was positively predictive of hypomentalizing, which to our knowledge is a novel finding with preliminary clinical implications.

The first PC contains a composition of traits of disinhibition and antagonism captured well by antisocial personality. The traits of entitlement, aggression, and manipulativeness in this PC represent key facets of antagonism, while disinhibition encompasses the traits of both impulsivity and disinhibition. Some argue that antagonism forms the core of psychopathy, as well as of antisocial and narcissistic PDs [51]. Although NPD and ASPD share overlapping features such as lack of empathy, deceitfulness, and exploitativeness, there are distinguishing traits [52]. In our PCA, aggression was a strong contributor to the antisocial but not the narcissistic PC, mirroring a prior finding of aggression as a differentiating factor between ASPD and NPD [52]. Furthermore, entitlement contributed comparatively less to the ASPD component than the NPD component in our analysis, in accordance with Stanton and Zimmerman’s factor analysis [52]. This PC with exhibitionistic, manipulative, impulsive, and disinhibited traits contributed the most variance to the overall SNAP score, followed by the overcontrolled component. The prototypically borderline constellation of facets including self-harm, negative temperament, mistrust and dependency, and the narcissistic PC with detachment, entitlement and mistrust only contributed 13% and 12% of variance, respectively.

In our analysis, the antisocial personality profile, which was the highest contributor to maladaptive personality functioning, positively predicted hypermentalizing and negatively predicted hypomentalizing. These findings align with and extend past research [32, 33] using an adult clinical sample [32, 33]. Sharp and Vanwoerden, for example, found a significant relationship between hypermentalizing and callous-unemotional traits in a regression model accounting for overall psychiatric severity [32]. Furthermore, the contribution of antisocial traits to general personality dysfunction was shown to be mediated by difficulty seeing one’s own thoughts as subjective and in understanding others’ minds using a metacognition scale [53]. This trouble with perspective taking was demonstrated in our antisocial personality profile as well. As opposed to the imaginative but misaligned interpretations present in patients with BPD, those with antisocial traits may make hostile attributions.

The strongest positive contributor to PC2 is workaholism, a key feature of obsessive-compulsive personality disorder (OCPD) [54, 55]. In our review, no prior studies were found to have examined mentalizing using the MASC in obsessive-compulsive personality. Our finding that this profile of over-controlled maladaptive personality traits predicted hypomentalizing may suggest that patients with such presentations would benefit from interventions that address hypomentalizing (or in other words, those that foster mentalizing). The MASC measures only mentalization of others, as opposed to oneself, which is likely to present more evident difficulty for patients who use attentional regulation toward self-mastery of concrete facets of reality and away from relational or internal psychological focus, which is clinically compatible with the profile of individuals with OCPD. Patients with predominant traits of workaholism, propriety, and mistrust as captured by this PC may be less focused on mental states and more preoccupied with observable markers or achievements as indicators of worth, predictability, and meaning. Indeed, limited empathic perspective taking in patients with OCPD compared to controls and sensitivity to warmth [56] supports this notion. Reported personal distress was high in Cain et al.’s sample of patients with OCPD, indicating an ability to reflect on self-related mental states which may be less impaired than other-focused mentalizing.

Personality presentations of overcontrol such as OCPD, one of the most prevalent PDs [57], have received comparably little attention in the literature [58]. This PC accounted for the second largest portion of the total variance, indicating the high prevalence of these traits in our sample. In the present sample of highly educated patients with severe PDs, overcontrol may enable some adaptive functional advantages and be rewarded and reinforced over time. But to date, no evidence-based treatments for OCPD have been developed and adequately trialed. While treatments such as radically open dialectical behavior therapy and cognitive behavioral therapy target problems of overcontrol [59‒61], none have been adequately tested in a randomized controlled trial for OCPD. A group intervention for enhancing metacognitive abilities in patients with emotional withdrawal and overcontrol, including those with OCPD, resulted in sustained improvements in emotional awareness and regulation at follow up [62].

Moving to the third PC in our analysis, this profile was dominated by self-harm and negative temperament [63], in addition to dependency as well as mistrust and detachment, reflecting the oscillations of BPD [64]. It is pertinent to note that the self-harm scale of the SNAP incorporates both low self-esteem and suicide proneness subscales, capturing the tendency to harm oneself in the context of self-loathing [42]. This BPD PC predicted hypermentalizing, as is well documented in the literature [7, 10, 12‒14, 16‒18]. Hypermentalizing responses were the most frequent error type in our data, which follows from almost 84% of the sample meeting criteria for BPD. At the level of specialized residential care sought by our sample, severity, and acuity are high. The clinical presentation of the majority of patients in this sample relates to frequent states of high arousal which limit frontolimbic regulation of emotions and cognition optimal for mentalizing. Our results contribute evidence to support the notion that antisocial and borderline trait profiles specifically predict hypermentalizing beyond the variance of general psychiatric severity. This contradicts some work proposing hypermentalizing as a broad, transdiagnostic state of cognitive dysfunction related to severity and general psychopathology rather than as a product of a particular personality constellation [8‒10, 65]. Our statistical technique of controlling for overall psychiatric severity may explain this contradiction. In McLaren and colleagues’ meta-analysis of associations between hypermentalizing on the MASC and various forms of psychopathology, for example, BPD was no more strongly related to hypermentalizing than other forms of psychopathology were. But the included studies did not systematically control for severity. While a relationship clearly exists between general psychopathology and hypermentalizing, our findings suggest an additional specific association with the personality traits characteristic of BPD, which may be detectable only once the effects of severity are removed.

Counter to our expectation, the narcissistic PC was not significantly predictive of either hypomentalizing or hypermentalizing in our sample using a threshold of 60% of covariates selected into the model [49]. This may relate to limitations of the MASC in identifying participant-specific self-esteem threats that influence deficits in mentalizing. The challenge of appraising mentalizing in patients with narcissistic traits may also in part be explained by the variability and heterogeneity of narcissism. Narcissistic personality contains both individual differences in presentation and within-person dynamic oscillation between grandiose and vulnerable states [66, 67]. A broad range of functioning is observed in narcissism, and narcissistic grandiosity may be protective while narcissistic vulnerability is linked to dysfunction, impairment, and psychopathology [68‒70]. Narcissistic personality disturbances span a broader range of severity than other severe PDs such as borderline or antisocial PDs [71]. Trull and colleagues demonstrated a significant drop in the observed prevalence of NPD in a large epidemiological sample when factoring in the requirement for significant distress and impairment [57]. The estimated rate was drastically reduced from 6% to 1% following this adjustment in the distress and impairment criterion, a similar percentage decrease to the one observed for obsessive-compulsive PD and a greater percentage decrease than was observed for borderline or antisocial PD. A study aiming to clarify the diagnostic concept of lack of empathy in narcissism discerned an association with impairment in emotional but not cognitive empathy, as measured by the MASC, once comorbidity with BPD was controlled for [72]. Furthermore, patients with NPD demonstrated impaired monitoring of emotional states on a metacognitive measure [73]. No difference was found in a later study between patients with NPD and other PD in metacognition. Notably, however, entitlement related to challenges in subdomains of understanding others’ minds and differentiation of one’s own thoughts [74]. Contextualizing our results, the mentalizing deficits found in conjunction with narcissistic traits may not be easily measurable with the MASC.

Measures under the umbrella of metacognition diverge in how they assess perception of mental states. The MASC measures mentalization by evaluating how an individual perceives others’ thoughts and feelings, while the Metacognition Assessment Scale (MAS), for example, assesses understanding of both one’s own and others’ minds [24]. The mentalizing deficits evident in our results may differ from those assessed by another measure intended to capture a broader picture of self and other mental state appraisal.

Mentalization-based treatment (MBT) was found to diverge from generalist treatment in terms of positive recovery outcomes with increasing severity and complexity of PD [75]. Characterizing dimensionally assessed personality traits of treatment-seeking patients may be an important clinical consideration to aid in identifying specific mentalizing-based treatment targets. Indeed, mentalizing may be malleable with treatment, as evidenced by improvement in hypermentalizing measured by the MASC from baseline to discharge in a study of adolescent in patients with BPD [7]. Hypermentalizing entails overly imaginative attempts to understand others’ minds, losing touch with accurate assessment of reality, and may indicate broad neurocognitive problems. Lowering affective arousal is important for allowing processing of social information more accurately. Compared to controls, patients with BPD have shown poorer performance on behavioral inhibition tasks in the context of negative emotion, associated with lower ventromedial prefrontal cortex activity [76]. Promoting top-down regulation may support mentalizing in patients with underlying borderline functioning and is a crucial component of treatment.

Limitations

This outcomes analysis has several limitations. First, the sample size of 37 is small. Second, the sample is treatment-seeking, predominately white, all female, highly educated, and of high socioeconomic status, and thus not representative of a community PD sample. Further research should be done to replicate these findings in a larger and more diverse sample. Additionally, personality traits and temperament were measured using a self-report assessment, creating the possibility of bias. This was mitigated by parsing the SNAP validity indices and removing one record for analysis accordingly. Although the MASC has been shown to be ecologically valid due to its emotional saliency, the measure is culturally biased and features white characters which may affect the performance of non-white patients. As discussed in relation to narcissism, the extent to which deficits in mentalizing are measurable by the MASC may depend on which vulnerabilities the scenes target. Additionally, the MASC is limited in its assessment of in-vivo self- and other-focused mentalizing capacity. Responses to the MASC administered in a treatment setting may not reflect spontaneous mentalizing in daily life and assessing mentalizing with any measure has challenges [77]. Lastly, confounding variables included in the present analysis were limited to general psychiatric severity, precluding examination of the impact of factors such as emotion dysregulation on reflection on others’ mental states.

Despite the limitations, this study is characterized by several strengths. Our sample of patients with complex and severe PDs is a unique sample that is otherwise hard to recruit beyond this type of treatment setting. Utilizing a dimensional measure of personality traits, the present study provides preliminary evidence that hypomentalizing is predicted by an overcontrolled personality profile, signaling potential benefit from intervention focused on fostering mentalizing. MBT aims to enhance mentalizing ability and addresses hypo- or hypermentalizing through regulation of attention to reappraise or reconsider one’s perspective using metacognitive process. By promoting curiosity and attention to the quickly drawn conclusions made without sufficient consideration of one’s own and others’ mental states, new perspectives and reappraisals of interpretation are possible.

Lastly, we also provide supportive evidence for borderline and antisocial personality profiles as predictive of hypermentalizing as measured by the MASC in this sample of high acuity, even when controlling for general psychiatric severity. This finding correlates to the major indications for MBT or other treatments that promote metacognition for these severe personality diagnoses. Further research is needed to replicate our findings with a larger and more representative sample and to continue exploration of the relationship between dimensional personality traits and mentalizing.

We thank Evan Iliakis, Gabrielle Ilagan, and Jacob MacDonald for their contributions to data collection. We thank the staff at the Gunderson Residence of McLean Hospital for their clinical management of patients that participated in this analysis. In particular, we thank Brandon Unruh and Karen Jacob, who replaced Lois Choi-Kain as program leadership at the tail end of this data collection.

Use of the data analyzed in this study was approved by the Institutional Review Board of Mass General Brigham (#2016P001610). Due to the focus on PDs and relevant psychological processes, the residential program from which these data were collected requested and received approval to augment the standard clinical symptom assessments included in the hospital’s clinical measurement initiative with additional measures tailored to PD symptoms and treatment. This study is a retrospective analysis of these data classified as exempt and approved by Mass General IRB. The need for individual consent to use archived data was waived.

Lois Choi-Kain receives royalties from American Psychiatric Association Publishing and Springer, as well as consulting fees from Boehringer Ingelheim and Tetricus. All other authors have no conflicts of interest to declare.

Two different anonymous donors contributed to an access to care Grant No. (#2015I0000034) and the Gunderson Legacy Grant No. (#2017I0000086). The access to care grant funded the creation of the quality assurance battery by Drs. Choi-Kain and Masland. The Gunderson Legacy Grant funded statistical consultation and research staff time. Dr. Traynor’s time was funded by an anonymous gift to the Gunderson Research Fund (#2019A003740). None of these funders had a role in the design, data collection, data analysis, and reporting of this study.

Study conception and design: Lois W. Choi-Kain and Sara R. Masland. Data analysis and interpretation: Julia Jurist, Jenna M. Traynor, Grace E. Murray, Boyu Ren, Sara R. Masland, Sam A. Mermin, Kevin B. Meehan, Lois W. Choi-Kain. Drafting: Julia Jurist, Lois W. Choi-Kain, Sam A. Mermin. Review and final approval of manuscript: Julia Jurist, Jenna M. Traynor, Grace E. Murray, Boyu Ren, Sara R. Masland, Sam A. Mermin, Kevin B. Meehan, Lois W. Choi-Kain.

The data are not available because it is part of a clinical assessment initiative, and, although it has been deidentified, consent has not been given for it to be shared publicly. Further inquiries can be directed to the corresponding author.

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