Borderline personality disorder (BPD) is characterized by emotion dysregulation, interpersonal dysfunction, and impulsivity (American Psychiatric Association, 2013; Gunderson et al., Reference Gunderson, Stout, McGlashan, Shea, Morey, Grilo, Zanarini, Yen, Markowitz, Sanislow, Ansell, Pinto and Skodol2011; Lieb et al., Reference Lieb, Zanarini, Schmahl, Linehan and Bohus2004) and is associated with many negative life outcomes, such as heightened risk for suicide (Paris & Zweig-Frank, Reference Paris and Zweig-Frank2001; Temes et al., Reference Temes, Frankenburg, Fitzmaurice and Zanarini2019). BPD affects 1–2% of the general population (Eaton & Greene, Reference Eaton and Greene2018) and is especially common among individuals in mental health outpatient (10%; Korzekwa et al., Reference Korzekwa, Dell, Links, Thabane and Webb2008; Zimmerman et al., Reference Zimmerman, Rothschild and Chelminski2005) and inpatient treatment (10–20%; Zimmerman et al., Reference Zimmerman, Chelminski and Young2008). BPD symptoms range from externalizing behaviors (e.g. anger outbursts) to internalizing states (e.g. emptiness) to interpersonal sensitivities (e.g. abandonment fears; American Psychiatric Association, 2013). Although symptoms of BPD likely peak in early adulthood (Aleva et al., Reference Aleva, Laceulle, Denissen, Hessels and van Aken2023), symptoms often emerge during adolescence (Sharp et al., Reference Sharp, Vanwoerden and Wall2018; Solmi et al., Reference Solmi, Radua, Olivola, Croce, Soardo, Salazar de Pablo, Il Shin, Kirkbride, Jones, Kim, Kim, Carvalho, Seeman, Correll and Fusar-Poli2022). Indeed, nonsuicidal self-injury and suicide attempts in individuals with BPD symptoms are as common in adolescence as in adulthood (Goodman et al., Reference Goodman, Tomas, Temes, Fitzmaurice, Aguirre and Zanarini2017), and most adults with BPD who self-harm report that this began in their youth (Zanarini et al., Reference Zanarini, Frankenburg, Ridolfi and Jager-Hyman2006).
Among those with BPD, difficulties in interpersonal relationships often precipitate negative emotions (Berenson et al., Reference Berenson, Gregory, Glaser, Romirowsky, Rafaeli, Yang and Downey2016; Koenigsberg et al., Reference Koenigsberg, Harvey, Mitropoulou, New, Goodman, Silverman, Serby, Schopick and Siever2001), behavioral dyscontrol (Sadikaj et al., Reference Sadikaj, Moskowitz, Russell, Zuroff and Paris2013; Scott et al., Reference Scott, Wright, Beeney, Lazarus, Pilkonis and Stepp2017), and – in extreme cases – suicidal behavior (Brodsky et al., Reference Brodsky, Groves, Oquendo, Mann and Stanley2006). Interpersonal difficulties often arise in the context of a missed social cue, such as instances of misreading a peer’s emotion expression. Decoding facial emotions depends on lower-level cognitive processes and studying these processes may reveal new insights into how facial processing abnormalities contribute to interpersonal problems in BPD.
The biopsychosocial theory of BPD: a developmental perspective
Developmental psychopathology perspectives on BPD view impulsivity and emotional sensitivity as biological vulnerabilities to the disorder and hold that whether an individual goes on to develop BPD as a young person depends on environmental factors (Crowell et al., Reference Crowell, Beauchaine and Linehan2009; Linehan, Reference Linehan1993). In particular, modern theories highlight the role of invalidation: temperamentally vulnerable children whose emotions are persistently met with invalidation do not learn effective emotion regulation strategies (Crowell et al., Reference Crowell, Beauchaine and Linehan2009; Linehan, Reference Linehan1993). Furthermore, if the child only receives support in response to extreme emotional displays, behaviors that escalate interpersonal exchanges are reinforced (Linehan, Reference Linehan1993). Importantly, peer and romantic relationships could provide an opportunity to correct these caregiver–child experiences and learn new, more adaptive, patterns of behavior (Hughes et al., Reference Hughes, Crowell, Uyeji and Coan2011).
The effect of positive peer relationships promises to be particularly impactful during adolescence, a developmental period typified by heightened neural plasticity (Fuhrmann et al., Reference Fuhrmann, Knoll and Blakemore2015; Luna et al., Reference Luna, Marek, Larsen, Tervo-Clemmens and Chahal2015) and a re-orienting toward peer relationships (Larson et al., Reference Larson, Richards, Moneta, Holmbeck and Duckett1996; Larson & Richards, Reference Larson and Richards1991). Yet, impulsive youth are more likely to be rejected by peers (Beauchaine et al., Reference Beauchaine, Klein, Crowell, Derbidge and Gatzke-Kopp2009; Hughes et al., Reference Hughes, Crowell, Uyeji and Coan2011). Thus, the very biological vulnerabilities that predispose a child to BPD may also undermine new learning opportunities. As a result, emotion dysregulation and ineffective interpersonal behaviors become further engrained in the child’s behavioral repertoire. Although there is evidence supporting distinct components of this model (Bortolla et al., Reference Bortolla, Cavicchioli, Fossati and Maffei2020; Carpenter & Trull, Reference Carpenter and Trull2013), much of this theory remains untested and much less is known about components of socioemotional processing that contribute to interpersonal difficulties in youth with BPD symptoms.
Socioemotional processing in BPD
Because people with BPD tend to react strongly to perceived invalidation and rejection (Berenson et al., Reference Berenson, Gregory, Glaser, Romirowsky, Rafaeli, Yang and Downey2016; Koenigsberg et al., Reference Koenigsberg, Harvey, Mitropoulou, New, Goodman, Silverman, Serby, Schopick and Siever2001), biases in socioemotional processing can catalyze a pattern of escalating emotions and ineffective interpersonal behaviors (Sadikaj et al., Reference Sadikaj, Moskowitz, Russell, Zuroff and Paris2013; Scott et al., Reference Scott, Wright, Beeney, Lazarus, Pilkonis and Stepp2017). To understand these interpersonal and affective processes, researchers have begun cataloguing the social cognitive biases that typify BPD (Daros et al., Reference Daros, Zakzanis and Ruocco2013; Domes et al., Reference Domes, Schulze and Herpertz2009; Mitchell et al., Reference Mitchell, Dickens and Picchioni2014; Schulze et al., Reference Schulze, Schmahl and Niedtfeld2016). At present, there is no clear consensus within the field. Whereas some studies reported heightened accuracy when identifying negative emotions (Berenson et al., Reference Berenson, Dochat, Martin, Yang, Rafaeli and Downey2018; Mier et al., Reference Mier, Lis, Esslinger, Sauer, Hagenhoff, Ulferts, Gallhofer and Kirsch2013; Schulze et al., Reference Schulze, Domes, Köppen and Herpertz2013; Scott et al., Reference Scott, Levy, Adams and Stevenson2011; Veague & Hooley, Reference Veague and Hooley2014; Wagner & Linehan, Reference Wagner and Linehan1999), others show a negativity bias (Bertsch et al., Reference Bertsch, Krauch, Stopfer, Haeussler, Herpertz and Gamer2017; Fenske et al., Reference Fenske, Lis, Liebke, Niedtfeld, Kirsch and Mier2015; Hidalgo et al., Reference Hidalgo, Oelkers-Ax, Nagy, Mancke, Bohus, Herpertz and Bertsch2016; Matzke et al., Reference Matzke, Herpertz, Berger, Fleischer and Domes2013; Mongeon & Gagnon, Reference Mongeon and Gagnon2017; Thome et al., Reference Thome, Liebke, Bungert, Schmahl, Domes, Bohus and Lis2016; van Dijke et al., Reference van Dijke, Wout, Ford and Aleman2016). To further complicate matters, other research produced conflicting findings (Dyck et al., Reference Dyck, Habel, Slodczyk, Schlummer, Backes, Schneider and Reske2009; Niedtfeld et al., Reference Niedtfeld, Defiebre, Regenbogen, Mier, Fenske, Kirsch, Lis and Schmahl2017), obtained inconclusive results (Hepp et al., Reference Hepp, Hilbig, Kieslich, Herzog, Lis, Schmahl and Niedtfeld2016; Jovev et al., Reference Jovev, Chanen, Green, Cotton, Proffitt, Coltheart and Jackson2011), or found that effects are contextually dependent (Minzenberg et al., Reference Minzenberg, Poole and Vinogradov2006). The few studies using adolescent samples recapitulate this pattern of mixed results: While some studies indicated reduced performance (Goueli et al., Reference Goueli, Nasreldin, Madbouly, Dziobek and Farouk2020; Robin et al., Reference Robin, Pham-Scottez, Curt, Dugre-Le Bigre, Speranza, Sapinho, Corcos, Berthoz and Kedia2012), others found enhanced performance (Berenschot et al., Reference Berenschot, van Aken, Hessels, de Castro, Pijl, Montagne and van Voorst2014).
Several factors likely contribute to these discrepant findings. First, BPD is a heterogenous disorder (Widiger & Trull, Reference Widiger and Trull2007), and growing evidence shows that BPD can be broken down into separable symptom dimensions of negative affectivity, interpersonal difficulties, and impulsivity (Wright et al., Reference Wright, Hallquist, Morse, Scott, Stepp, Nolf and Pilkonis2013, Reference Wright, Hopwood and Zanarini2015). Importantly, alterations in socioemotional processing may be driven by one dimension but not another. Because samples vary in their symptom profiles (e.g. one sample is more impulsive than another), analysis of group differences undermines replicability. Second, the field has typically relied on summary statistics of task behavior that suffer from poor reliability, undermining reproducibility (Haines et al., Reference Haines, Kvam, Irving, Smith, Beauchaine, Pitt, Ahn and Turner2020). Here, we address these limitations by (1) examining how BPD symptom dimensions of negative affectivity, interpersonal problems, and impulsivity relate to social cognition (rather than analyzing group differences) and (2) using advanced quantitative methods that promise to improve reliability and provide greater specificity for testing symptom-to-cognitive process relationships.
A final reason that these studies have yielded myriad conclusions is that they have employed various social cognitive tasks, each eliciting distinct cognitive processes (Minzenberg et al., Reference Minzenberg, Poole and Vinogradov2006). Although all tasks purport to measure social cognition, certain tasks may tap into social cognitive processes that are more relevant to interpersonal difficulties in BPD. Indeed, Minzenberg et al. (Reference Minzenberg, Poole and Vinogradov2006) show that social cognitive biases in BPD are most pronounced for complex social cognitive task conditions that depend on the integration of multiple, lower-level social cognitive capacities. To this end, we focus our attention on a relatively difficult complex social cognitive task: how to appraise another’s emotional expression when faced with discrepant socioemotional cues.
Decoding facial expressions of emotions is a key social cognitive capacity
Facial expressions of emotion serve a social communicative function (Crivelli & Fridlund, Reference Crivelli and Fridlund2018; Levenson, Reference Levenson2011), and decoding emotional expressions is critical for effective interpersonal behavior (Blakemore, Reference Blakemore2008). Decoding facial emotions is especially difficult, however, when there is conflicting information about that person’s state. As an example, Kaia notices that Kwame is upset: his brows are furrowed, and his face appears flushed. In Kaia’s experience, Kwame’s facial expression is indicative of anger. Yet, the tone of Kwame’s voice is soft, suggesting instead that he is fearful. To understand the cognitive processes that underlie how conflicting emotional information becomes resolved (i.e. stimulus–stimulus conflict; Stahl et al., Reference Stahl, Voss, Schmitz, Nuszbaum, Tüscher, Lieb and Klauer2014), Etkin et al. (Reference Etkin, Egner, Peraza, Kandel and Hirsch2006) developed an experimental paradigm in which individuals monitor and resolve conflict between alternative indicators of another’s emotional state. Relative to cognitive conflict (e.g. color-word interference; MacLeod, Reference MacLeod1991), resolving emotional conflict is more cognitively taxing and is believed to involve separable cognitive mechanisms. That is, conflict resolution depends on an ‘emotion control’ loop, rather than ‘cognitive control’ loop. Supporting this notion, the authors found evidence of distinct neural circuits for emotion – relative to cognitive – control (Egner et al., Reference Egner, Etkin, Gale and Hirsch2008).
Decomposing facial emotion decoding
How does a person reason about another’s emotional expression? Returning to our example, Kaia first notices Kwame’s brows are furrowed and then notices his cheeks are flushed. Both pieces of evidence indicate Kwame is angry. Next, Kaia notices that Kwame’s voice is soft, which suggests that he may instead be fearful. Critically, this process of Kaia gathering distinct bits of information (i.e. samples) unfolds over time. Once Kaia has sufficiently sampled, she comes to a decision about Kwame’s emotional state.
Yet, if Kaia is prone to snap judgments, she may insufficiently sample Kwame’s emotional state prior to making a judgment, relying on only the first few samples. Conversely, Kaia could be inefficient when gathering samples of Kwame’s emotional expression, prolonging the amount of time it takes to identify Kwame’s emotional state. Such cognitive processing biases can result in interpersonal difficulties. After all, if Kaia is both inefficient and prone to snap judgments, she may be especially likely to misidentify Kwame’s fearful expression as anger.
Dissecting a decision into its latent lower-level components, drift diffusion models (DDMs) quantify alterations in cognitive processes that would lead Kaia to misidentify Kwame’s emotional expression. We focus on two DDM parameters: drift rate and threshold. Whereas drift rate indexes efficiency of evidence accumulation, threshold is a measure of the level of evidence needed to execute a decision (Ratcliff et al., Reference Ratcliff, Smith, Brown and McKoon2016; Ratcliff & McKoon, Reference Ratcliff and McKoon2008). From a DDM perspective, if Kaia is prone to split-second but inaccurate decisions, Kaia may have a low threshold. Conversely, if Kaia tends to make slow judgments, Kaia’s drift rate could be low.
Divergent developmental pathways
Converging evidence suggests that there are normative changes in emotion control that occur throughout adolescence. Social cognitive capacities – including facial emotion processing (Lawrence et al., Reference Lawrence, Campbell and Skuse2015) – typically increase throughout adolescence (Blakemore, Reference Blakemore2008; Choudhury et al., Reference Choudhury, Blakemore and Charman2006; Kilford et al., Reference Kilford, Garrett and Blakemore2016; Lawrence et al., Reference Lawrence, Campbell and Skuse2015). These improvements are supported by the integration of brain regions and networks involved in face processing (Cohen Kadosh et al., Reference Cohen Kadosh, Johnson, Henson, Dick and Blakemore2013; McGivern et al., Reference McGivern, Andersen, Byrd, Mutter and Reilly2002), emotion processing (Crone & Dahl, Reference Crone and Dahl2012), and cognitive control (Luna et al., Reference Luna, Padmanabhan and O’Hearn2010). Adolescence is further marked by alterations in functional connectivity between ACC – a hub for emotion and cognitive control (Botvinick, Reference Botvinick2007; Egner et al., Reference Egner, Etkin, Gale and Hirsch2008) – and brain regions involved in social processing and in emotion regulation (Kelly et al., Reference Kelly, Di Martino, Uddin, Shehzad, Gee, Reiss, Margulies, Castellanos and Milham2009).
Preliminary evidence suggests that the developmental trajectory for healthy youth contrasts that of adolescents at risk for BPD. The reduced performance on complex social cognitive tasks that is found in adults with BPD (Minzenberg et al., Reference Minzenberg, Poole and Vinogradov2006) is also observed in adolescents (Goueli et al., Reference Goueli, Nasreldin, Madbouly, Dziobek and Farouk2020; Robin et al., Reference Robin, Pham-Scottez, Curt, Dugre-Le Bigre, Speranza, Sapinho, Corcos, Berthoz and Kedia2012; Sharp et al., Reference Sharp, Pane, Ha, Venta, Patel, Sturek and Fonagy2011). Reduced performance on these tasks has been linked to weaker PFC downregulation of limbic regions that are responsive to emotional stimuli (Domes et al., Reference Domes, Schulze and Herpertz2009). Downregulation of limbic regions depends on connectivity between PFC and subcortical structures (Banks et al., Reference Banks, Eddy, Angstadt, Nathan and Phan2007), including those involved in emotion control (Egner et al., Reference Egner, Etkin, Gale and Hirsch2008; Etkin et al., Reference Etkin, Egner, Peraza, Kandel and Hirsch2006), and PFC-limbic connectivity undergoes substantial changes during adolescent brain development (Tottenham & Gabard-Durnam, Reference Tottenham and Gabard-Durnam2017; Wu et al., Reference Wu, Kujawa, Lu, Fitzgerald, Klumpp, Fitzgerald, Monk and Phan2016). If an adolescent brain does not undergo such rewiring, then the adolescent may show attenuated PFC control of limbic structures and exhibit reduced emotion control. As a first step in testing whether those with BPD show an altered developmental trajectory, here we characterized age-related changes in emotion control among youth with BPD symptoms.
The present study
We recruited adolescents and young adults with and without BPD symptoms. Participants completed an emotional interference task (Figure 1a) designed to assess how individuals parse conflicting emotional stimuli (Etkin et al., Reference Etkin, Egner, Peraza, Kandel and Hirsch2006). To determine the specificity of our findings, participants completed a corresponding nonemotional interference task (Figure 1b; Egner et al., Reference Egner, Etkin, Gale and Hirsch2008; Egner & Hirsch, Reference Egner and Hirsch2005).
Figure 1. Facial emotion expressions used in the task were drawn from the set of Friesen and Ekman (Reference Friesen and Ekman1976). Participants provided their response to each trial using a button box.
Because adolescents typically show graded improvements in complex social cognitive capacities (Blakemore, Reference Blakemore2008; Kilford et al., Reference Kilford, Garrett and Blakemore2016), we expected age-related increases in drift rate on the emotional interference task, particularly for the difficult task conditions (e.g. conflict trials). Consistent with a growing body of work implicating lower drift rate in many different psychiatric disorders (Sripada & Weigard, Reference Sripada and Weigard2021; Weigard et al., Reference Weigard, Clark and Sripada2021), we anticipated that BPD symptom dimensions would be associated with a lower drift rate. Building on evidence that social cognitive biases are especially prominent for complex social cognitive capacities (Minzenberg et al., Reference Minzenberg, Poole and Vinogradov2006), we expected that BPD-related effects on drift rate would be most pronounced for difficult task conditions. Because evidence for altered threshold in psychiatric disorders is less consistent (e.g. Ziegler et al., Reference Ziegler, Pedersen, Mowinckel and Biele2016), we made no strong predictions about the effects of BPD symptom dimensions on threshold.
We also anticipated that the effect of age on social cognition would depend on BPD symptom dimensions. Specifically, emerging evidence highlights that social cognitive biases are present even among adolescents with BPD (Goueli et al., Reference Goueli, Nasreldin, Madbouly, Dziobek and Farouk2020; Robin et al., Reference Robin, Pham-Scottez, Curt, Dugre-Le Bigre, Speranza, Sapinho, Corcos, Berthoz and Kedia2012; Sharp et al., Reference Sharp, Pane, Ha, Venta, Patel, Sturek and Fonagy2011), aligning with theories on the developmental origins of BPD (Crowell et al., Reference Crowell, Beauchaine and Linehan2009; Kernberg, Reference Kernberg1967; Linehan, Reference Linehan1993). We thus anticipated that the effects of BPD symptom dimensions on drift rate would be observed even in the younger participants within our sample. That is, whereas individuals without clinically significant BPD symptomatology would show age-related improvements in facial emotion processing, we anticipated those with BPD symptoms would not evidence such graded improvements.
Methods
Participants
Participants were adolescents and young adults with BPD symptoms (N = 50) and healthy controls (N = 42), matched on age and sex. Six subjects were excluded because their task data did not meet quality checks (see Supplemental Methods for details). In total, 86 adolescents and young adults (26 males and 60 females) were retained for this study, 45 of whom had BPD symptoms. The average age of participants was 20.70 (range 13–30) years. Supplemental Tables S1 and S2 provide a demographic and clinical characterization of the sample.
Procedure
During an initial visit, participants completed informed consent followed by semi-structured psychiatric interviews supervised by the senior author (SCID-IV, First et al., Reference First, Spitzer, Gibbon and Williams1997; SIDP-IV, Pfohl et al., Reference Pfohl, Blum and Zimmerman1997). Participants in the BPD group met criteria for at least three BPD symptoms (reflective of an empirically derived threshold; Clifton & Pilkonis, Reference Clifton and Pilkonis2007), and participants in the control group were free of lifetime psychiatric disorders. In separate visits, participants completed self-report questionnaires and completed experimental tasks. The University of Pittsburgh Institutional Review Board approved all study procedures.
Materials
Personality assessment
Participants completed self-report questionnaires that assessed BPD symptom dimensions of negative affectivity, impulsivity, and interpersonal dysfunction: the Borderline Personality Questionnaire (Poreh et al., Reference Poreh, Rawlings, Claridge, Freeman, Faulkner and Shelton2006), the UPPS-P Impulsive Behavioral Scale (Lynam et al., Reference Lynam, Smith, Whiteside and Cyders2006; Whiteside et al., Reference Whiteside, Lynam, Miller and Reynolds2005), the Inventory of Interpersonal Problems (Horowitz et al., Reference Horowitz, Rosenberg, Baer, Ureño and Villaseñor1988; Pilkonis et al., Reference Pilkonis, Kim, Proietti and Barkham1996), and the NEO-Five Factor Inventory (McCrae & Costa, Reference McCrae and Costa2004). We entered subscales of each questionnaire into an exploratory factor analysis, and factor score estimates corresponding to each BPD symptom dimension were extracted for our individual difference analyses. As shown in Figure S2, the loadings in the EFA solution largely align with the expected dimensional decomposition of BPD (Wright et al., Reference Wright, Hallquist, Morse, Scott, Stepp, Nolf and Pilkonis2013, Reference Wright, Hopwood and Zanarini2015). Briefly though, it is worth noting that our impulsivity factor is largely reflective of emotion-related impulsivity (relative to nonaffective components; Lynam et al., Reference Lynam, Smith, Whiteside and Cyders2006; Whiteside et al., Reference Whiteside, Lynam, Miller and Reynolds2005): BPQ impulsivity, UPPS-P subscales, and the NEO conscientiousness scale were most strongly associated with this factor. Consequently, we refer to this factor as ‘emotion-related impulsivity’ throughout the remainder of the text. See Supplement for a more detailed treatment of this issue.
Experimental tasks
Participants completed an emotional interference task (Etkin et al., Reference Etkin, Egner, Peraza, Kandel and Hirsch2006) in which they viewed a happy, angry, or fearful face and were instructed to identify the emotion displayed by the face as quickly and accurately as possible. Overlaid the face was an emotion word that was either congruent or incongruent with the facial emotion (see Figure 1a), resulting in six conditions that corresponded to each face-word pairing. Relative to task conditions with congruent face-word pairings, incongruent conditions result in stimulus interference (Stahl et al., Reference Stahl, Voss, Schmitz, Nuszbaum, Tüscher, Lieb and Klauer2014) and engage emotion control.
Participants also completed a nonemotional interference task that assessed cognitive, rather than emotion, control (Egner et al., Reference Egner, Etkin, Gale and Hirsch2008; Egner & Hirsch, Reference Egner and Hirsch2005). This task used the same stimuli as the emotional interference task, but participants were instead tasked with identifying the gender of the image with a congruent or incongruent gender word overlaid (‘male’ or ‘female’; see Figure 1b). That is, the stimulus feature to be judged (emotion versus gender) changed but the facial stimuli did not. In this task, there were four conditions, corresponding to each face-word pairing. In both tasks, participants completed 24 trials of each condition, and conditions were interleaved across trials.
Analyses
Drift diffusion modeling
DDMs were fit to choices and reaction times on the decision tasks using hierarchical DDM (particularly the HDDMRegressor function from HDDM; Wiecki et al., Reference Wiecki, Sofer and Frank2013). HDDM is a multilevel Bayesian adaptation of the traditional DDM and produces more reliable parameter estimates with fewer trials (Ratcliff & Childers, Reference Ratcliff and Childers2015). Primary analyses focused on parameter estimates for the emotional interference task. To determine the specificity of our findings, secondary analyses use parameter estimates from the nonemotional interference task.
Bayesian distributional regression analyses
We employed distributional models in a Bayesian multilevel regression framework (brms; Bürkner, Reference Bürkner2017, Reference Bürkner2018) to examine how cognitive processes related to BPD symptom dimensions and age. In contrast to standard regression approaches, distributional models directly incorporate parameter uncertainty estimates of the dependent variable into the model. Specifically, we regressed the maximum a-posteriori (MAP) estimate on individual difference variables (Bürkner, Reference Bürkner2018) and used the standard error of individual-subject posteriors to weight the degree to which an individual MAP value influenced the group-level effect estimate (e.g. of this approach, see Hall et al., Reference Hall, Schreiber, Allen and Hallquist2021). Within this framework, we examined how age and BPD symptom dimensions relate to drift rate and threshold. In instances where we found evidence of an interaction between age and BPD symptom dimensions, we examined simple slopes to aid interpretation. We interpret effects whose 95% credible interval (CI) did not contain zero (denoted with square brackets; Gelman et al., Reference Gelman, Carlin, Stern, Dunson, Vehtari, Rubin, Carlin, Stern, Dunson, Vehtari and Rubin2013).
Sensitivity analyses
We tested whether our primary results held when accounting for demographic variables, particularly sex, race, and socioeconomic status. To ensure our results did not depend on the parametric assumptions of DDM, we conducted complementary mixed-effect analyses of RT and accuracy. The Supplement provides additional details about sensitivity analyses, as well as other methodological considerations in this study.
Results
Drift rate on the emotional interference task varied by condition (see Table 1 for estimates and ordering of task conditions by difficulty; Figure 2). Relative to positive emotions (i.e. happy), negative facial emotions (i.e. anger, fear) were associated with a lower drift rate (B = 1.63, [1.55, 1.74]), especially when the participant needed to identify a face as angry (B = 1.93, [1.82, 2.04]). Incongruency also predicted lower drift rate (B = 0.75, [0.67, 0.85]), although the effect of facial emotion was stronger (BInteraction = 0.87, [0.75, 1.01]).
Table 1. Group-level estimates of condition effects on drift rate for emotional interference task. N.B. We describe conditions with lower drift rate as more difficult because drift rate slows to more difficult task conditions (Ratcliff et al., Reference Ratcliff, Smith, Brown and McKoon2016; Ratcliff & McKoon, Reference Ratcliff and McKoon2008). Mixed-effects analyses of accuracy confirm this condition difficulty ordering (see Supplement for details; Figure S8)
Figure 2. Condition-level drift rate estimates. N.B. See Table 1 for condition name key.
Are there age-related changes in emotion control?
To understand normative age-related changes in emotion control, we examined the association of age with drift rate and threshold. Age predicted increases in drift rate across all conditions, B = 0.164, [0.071, 0.261], though the sharpest age-related increase was found for the most difficult condition, anger-fear (B = 0.003, [0.001, 0.005]). Young adults also exhibited a higher threshold than adolescents, B = 0.031, [0.004, 0.057], signifying that older participants required more information to execute a decision.
How do BPD symptom dimensions relate to emotion control?
We next tested whether BPD symptom dimensions were associated with drift rate or threshold on the emotional interference task. Emotion-related impulsivity predicted lower drift rate on the most difficult task condition, anger-fear (B = −0.003, [−0.0049, −0.0002]). We were further interested in understanding whether the association of BPD symptom dimensions with emotion control, particularly emotion-related impulsivity, depended on age. Emotion-related impulsivity interacted with age to predict threshold, B = 0.034, [0.004, 0.064]. When examining those low in emotion-related impulsivity (−1 SD), age was not associated with threshold, B = 0.003, [−0.033, 0.036]. Conversely, among those high in emotion-related impulsivity (+1 SD), age predicted increases in threshold, B = 0.066, [0.025, 0.108]. This effect was driven by impulsive adolescents, who had a particularly low threshold (see Figure 3a). Negative affectivity and interpersonal dysfunction were unrelated to cognitive processes involved in emotion control.
Figure 3. Emotion-related impulsivity and age interact to predict threshold in (a) emotional and (b) nonemotional interference tasks. N.B. For purpose of illustration, show effect of age for least impulsive (z = −1.71) and most impulsive (z = 2.55) individuals in sample.
Are these effects specific to emotion control?
To resolve whether our age- and BPD-related differences in DDM parameters were specific to emotion control, we ran parallel analyses for the nonemotional interference task (see Table 2). Two findings from these parallel analyses diverged from effects observed in the emotional interference task. 1) Emotion-related impulsivity predicted higher drift rate on incongruent trials (B = 0.003, [0.0002, 0.0059]). 2) Emotion-related impulsivity in adolescents was associated with a higher drift rate for a moderately difficult task condition (B = −0.043, [−0.081, −0.006]). Thus, our finding of emotion-related impulsivity being linked with lower drift rate for difficult task conditions is specific to emotion control.
Table 2. Group-level estimates of condition effects on drift rate for nonemotional interference task
These parallel analyses further revealed two key effects that were qualitatively similar to the emotional interference task: 1) Age predicted increases in drift rate across all conditions (B = 0.41, [0.24, 0.58]). 2) Age and emotion-related impulsivity interacted to predict threshold (B = 0.038, [0.003, 0.075]), and this effect was driven by impulsive adolescents with a low threshold (see Figure 3b). Thus, our findings of age-related increases in drift rate and of lower threshold in impulsive adolescents appear to be domain general.
Discussion
Discordant interpersonal interactions often precipitate life-threatening, impulsive behaviors in those with BPD (Brodsky et al., Reference Brodsky, Groves, Oquendo, Mann and Stanley2006). Among affected individuals, misreading another’s emotional expression can evoke strong emotions and promote interpersonal conflict (Sadikaj et al., Reference Sadikaj, Moskowitz, Russell, Zuroff and Paris2013). Furthermore, prior research has documented facial emotion processing abnormalities in BPD (Daros et al., Reference Daros, Zakzanis and Ruocco2013; Domes et al., Reference Domes, Schulze and Herpertz2009; Mitchell et al., Reference Mitchell, Dickens and Picchioni2014; Schulze et al., Reference Schulze, Domes, Köppen and Herpertz2013). Here, we examined whether component processes of emotion control – operationalized as the ability to suppress conflicting information about another’s emotion to correctly identify a facial emotion (Egner et al., Reference Egner, Etkin, Gale and Hirsch2008; Etkin et al., Reference Etkin, Egner, Peraza, Kandel and Hirsch2006) – are disrupted in BPD.
Because BPD is a heterogenous disorder, we examined its symptom dimensions of impulsivity, negative affectivity, and interpersonal dysfunction (Wright et al., Reference Wright, Hallquist, Morse, Scott, Stepp, Nolf and Pilkonis2013, Reference Wright, Hopwood and Zanarini2015). In alignment with studies linking impulsivity to inefficient processing (Sripada & Weigard, Reference Sripada and Weigard2021; Weigard et al., Reference Weigard, Clark and Sripada2021), impulsivity – particularly emotion-related impulsivity – predicted weaker efficiency of evidence accumulation on trials requiring emotion control to suppress irrelevant information (incongruent negative emotion word). This effect further accords with one study demonstrating that emotion processing biases are most pronounced for complex social cognitive capacities (Minzenberg et al., Reference Minzenberg, Poole and Vinogradov2006) and one study showing that those with BPD have greater difficulty discriminating negative emotions (Unoka et al., Reference Unoka, Fogd, Füzy and Csukly2011), though we note there are substantive discrepancies within the broader literature of social cognitive biases in BPD.
Crucially, BPD symptoms often emerge during adolescence (Sharp et al., Reference Sharp, Vanwoerden and Wall2018), yet little is known about socioemotional processing abnormalities in youth with BPD. Understanding social cognitive biases in this population is critical: individuals with BPD typically describe interpersonal difficulties as a trigger for suicidal behaviors and self-injury (Brodsky et al., Reference Brodsky, Groves, Oquendo, Mann and Stanley2006), and rates of these behaviors in adolescents have increased in recent years (Curtin, Reference Curtin2020). We were thus interested in understanding the development of emotion control during adolescence among affected individuals. Impulsive adolescents exhibited a lower decision boundary across both emotional and nonemotional interference tasks (see Figure 3), producing faster decisions but more errors (Ratcliff et al., Reference Ratcliff, Smith, Brown and McKoon2016; Ratcliff & McKoon, Reference Ratcliff and McKoon2008). When faced with conflicting information about another’s negative emotional state, impulsive adolescents were more likely to make quick, but at times inaccurate, decisionsFootnote 1. It is worth noting that the tendency for emotion-related impulsivity to predict a lower decision boundary was found in adolescents but not in young adults, suggesting that this decision-making abnormality normalizes by adulthood.
How might an alteration in social processing that is observed only in adolescence contribute to interpersonal difficulties in adulthood? One intriguing possibility is that this social cognitive bias canalizes unhelpful interpersonal behaviors and dynamics (Beauchaine et al., Reference Beauchaine, Klein, Crowell, Derbidge and Gatzke-Kopp2009; Crowell et al., Reference Crowell, Beauchaine and Linehan2009). Adolescents with BPD symptoms are often affected by dysfunctional family dynamics (Hallquist et al., Reference Hallquist, Hipwell and Stepp2015; Stepp et al., Reference Stepp, Whalen, Scott, Zalewski, Loeber and Hipwell2014), and peer relationships during adolescence provide an opportunity to learn more effective interpersonal behaviors (Hughes et al., Reference Hughes, Crowell, Uyeji and Coan2011). Yet, a low decision threshold could result in costly social mishaps that undermine the adolescent’s efforts to establish healthier relationships outside of the immediate family context. Even as a social cognitive bias normalizes by early adulthood, the canalization of ineffective interpersonal behaviors may lead to ongoing interpersonal difficulties. Future research using a longitudinal design could directly test this theory and may provide additional insights into the processes by which social cognitive biases during adolescence confer liability for interpersonal difficulties later in life.
Strengths and limitations
Our study provides new insights into age-related changes in the facial emotion processing abnormalities in BPD. Our unique sample of youth oversampled for BPD symptoms made this study well-suited to understand how BPD symptom dimensions relate to social cognitive biases during the transition from adolescence to adulthood. To our knowledge, this is the first study to examine whether the effects of BPD symptoms on cognitive processes involved in facial emotion processing vary with age.
We also employed an analytic approach that addresses methodological concerns regarding linking individual difference variables with task behavior. First, the heterogeneity of BPD symptom presentations motivated us to examine how symptom dimensions predicted social cognitive biases. This approach afforded us increased specificity: emotion-related impulsivity – but not negative affectivity or interpersonal dysfunction – moderated age-related changes in emotion control. Such specificity may also help to explain differences between prior studies of social cognition in BPD (Daros et al., Reference Daros, Zakzanis and Ruocco2013; Domes et al., Reference Domes, Schulze and Herpertz2009; Mitchell et al., Reference Mitchell, Dickens and Picchioni2014; Schulze et al., Reference Schulze, Schmahl and Niedtfeld2016): whereas some studies may have a BPD group higher in impulsivity by chance, others could have a BPD sample with higher levels of negative affectivity. Examining dimensions of BPD also helps to connect this study to a larger body of scientific work (such as Sripada & Weigard, Reference Sripada and Weigard2021; Weigard et al., Reference Weigard, Clark and Sripada2021), since impulsivity – especially emotion-related impulsivity – is elevated in many externalizing psychiatric disorders (Berg et al., Reference Berg, Latzman, Bliwise and Lilienfeld2015).
Second, we used hierarchical drift diffusion modeling to obtain parameters that indexed cognitive processes of interest (Wiecki et al., Reference Wiecki, Sofer and Frank2013). Relative to prior studies that examined summary statistics of behavior (Haines et al., Reference Haines, Kvam, Irving, Smith, Beauchaine, Pitt, Ahn and Turner2020; Rouder & Haaf, Reference Rouder and Haaf2019), our use of hierarchically estimated parameters promises to yield more reliable and replicable effects (Brown et al., Reference Brown, Chen, Gillan and Price2020; Wiecki et al., Reference Wiecki, Sofer and Frank2013). Further, compared to traditional approaches that do not account for measurement error in dependent variables (here, DDM parameter estimates), we used Bayesian distributional multilevel regression models that directly model uncertainty in the outcome variable (Bürkner, Reference Bürkner2018). We also conducted a series of sensitivity analyses that demonstrated our results did not depend on the DDM specification and held when accounting for demographic variables.
Despite these strengths, there are a few noteworthy limitations. First, although our sample is comparable or larger than other case–control studies of social cognition in BPD, our sample size is relatively small for a study linking personality variables with outcomes (Soto, Reference Soto2019). Effect sizes for personality–behavioral outcome relationships are typically modest (especially impulsivity; Sharma et al., Reference Sharma, Markon and Clark2014), underscoring the need to replicate this research in a larger sample. Our sampling frame also poses a limitation: although our work highlights the role of impulsivity, we cannot ascertain whether our effects would be seen in a sample recruited for variation in levels of trait impulsivity, as opposed to BPD symptoms. Relatedly, the term impulsivity is used to describe dissociable personality traits (Lynam et al., Reference Lynam, Smith, Whiteside and Cyders2006) and behavioral tendencies (e.g. temporal vs. reflection impulsivity; Caswell et al., Reference Caswell, Bond, Duka and Morgan2015). Our study largely speaks to links between emotion-related impulsivity and alterations in facial emotion processing. Additionally, we only assessed certain behavioral components of impulsivity, such as reflection impulsivity, and did not directly assess other subtypes of impulsivity that have been previously linked to BPD (Barker et al., Reference Barker, Romaniuk, Cardinal, Pope, Nicol and Hall2015). Even within our tasks, there are aspects of emotion control – such as conflict adaptation (Etkin et al., Reference Etkin, Egner, Peraza, Kandel and Hirsch2006) – that may bear relevance for understanding interpersonal difficulties in BPD but were unexplored in the current project. Future research that uses alternative sampling strategies and links various forms of behavioral and trait impulsivity is warranted.
Third, we were interested in how the development of social cognitive processes related to BPD symptoms, but our design was cross-sectional. Longitudinal research is needed to elucidate within-person developmental trajectories. Fourth, experimental studies of social cognition in BPD are constrained by the cognitive constructs they purport to measure. Our tasks provided insight into interference control, and future work should assess other domains of social cognition and cognitive control (Blakemore, Reference Blakemore2008; Frith & Frith, Reference Frith and Frith2007). Our null findings for negative affectivity and interpersonal dysfunction do not necessarily mean that there are no social cognitive biases associated with those BPD symptom dimensions. Instead, negative affectivity and interpersonal dysfunction could be associated with other social cognitive biases that were not elicited in the context of our experimental tasks. Finally, our study focused on facial emotion decoding, yet emotions are communicated in a variety of ways (e.g. vocal tone, gestures; Scherer & Moors, Reference Scherer and Moors2019). Future research should manipulate different aspects of emotion expression and use other experimental paradigms (e.g. audiovisual recordings of dyadic interactions; Levenson, Reference Levenson2024) to explore whether individuals with BPD show similar alterations when decoding other components of emotional expressions.
Conclusion
In a sample of adolescents and young adults selected for BPD symptoms, we show that impulsive adolescents are liable to make quicker, but less accurate, decisions. Furthermore, decreased efficiency when resolving conflicting cues about negative emotional stimuli leaves impulsive adolescents prone to misreading another’s expression of negative emotion. Altogether, our findings shed light on the lower-level social cognitive processes that contribute to interpersonal problems in adolescents with BPD symptoms.
Supplementary material
The supplementary material for this article can be found at http://doi.org/10.1017/S0033291725000595.
Open access
