Facial emotion recognition and borderline personality pathology

Meehan, Kevin B.; Panfilis, Chiara De; Cain, Nicole M.; Antonucci, Camilla; Soliani, Antonio; Clarkin, John F.; Sambataro, Fabio

doi:10.1016/j.psychres.2017.05.042

Cited by 36 publications

(47 citation statements)

References 41 publications

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“…Aligning with that, individuals with PTSD show high rates of child maltreatment, and child maltreatment is linked to negative interpretations of neutral facial expressions in children (Pollak, Cicchetti, Hornung, & Reed, 2000) and to impaired recognition of neutral facial expressions in adults (Wagner & Linehan, 1999). In individuals with child maltreatment, neutral expressions might trigger memories of neglect or abuse, potentially contributing to the above-mentioned findings (Daros et al, 2013;Meehan et al, 2017;Mitchell et al, 2014;Pollak et al, 2000;Schlumpf et al, 2013;Wagner & Linehan, 1999).…”

Section: Introductionmentioning

confidence: 77%

“…There is evidence in PTSD-related populations for abnormalities in the interpretation of neutral facial expressions. For example, individuals with BPD -who show a high prevalence of trauma and maltreatment during childhood (Battle et al, 2004;Wingenfeld et al, 2011) interpret neutral facial expressions as more negative than healthy controls and tend to interpret them as anger (Daros et al, 2013;Mitchell et al, 2014) or sadness (Meehan et al, 2017). Likewise, individuals with dissociative identity disorder show high rates of childhood traumatization.…”

Section: Introductionmentioning

confidence: 99%

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Are you angry at me? Negative interpretations of neutral facial expressions are linked to child maltreatment but not to posttraumatic stress disorder

Pfaltz

Passardi

Auschra

et al. 2019

European Journal of Psychotraumatology

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Background: Individuals with a high prevalence of child maltreatment, e.g. those with borderline personality disorder, tend to see neutral facial expressions as negative.Objective: Our aim was to assess whether this bias is present in individuals with posttraumatic stress disorder (PTSD) and whether it is linked to child maltreatment.Methods: Thirty-nine PTSD participants, 44 traumatized and 35 non-traumatized healthy controls watched 300 one-second movies showing 30 neutral and 270 emotional facial expressions, and indicated whether they interpreted each as a neutral or as one of nine emotional expressions.Results: PTSD individuals did not perform differently than the two control groups in the recognition and interpretation of neutral facial expressions (p’s < .300). Higher levels of childhood sexual and emotional abuse, and physical neglect were linked to more interpretations of neutral facial expressions as contempt (p’s < .043), and (for sexual abuse and physical neglect) to more interpretations of neutral facial expressions as anger (p’s < .014). Comparisons of statistical model fits suggested that childhood sexual abuse was the most relevant predictor of recognition accuracy in our sample. Alexithymia, state dissociation, interpersonal trauma, and number of experienced trauma types were not associated with deficits in the interpretation of neutral expressions.Conclusions: Child maltreatment, especially sexual abuse, may shape the interpretation of neutral facial expressions. Future research should explore whether the observed biases extend to real-life situations. If so, therapists might improve the therapeutic relationship with patients with a history of child maltreatment by paying more attention to their own non-verbal communication and their patients’ responses to it. Furthermore, similarly to individuals with high depressive and high social anxiety symptoms, facial expression recognition training might counteract negativity bias in individuals with a history of childhood (sexual and emotional) abuse, and (physical) neglect.

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Section: Introductionmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Are you angry at me? Negative interpretations of neutral facial expressions are linked to child maltreatment but not to posttraumatic stress disorder

Pfaltz

Passardi

Auschra

et al. 2019

European Journal of Psychotraumatology

View full text Add to dashboard Cite

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“…By using a fast Fourier transformation, the frequency features (60 power features, 16 power difference features) were prepared. In each channel, the power features were computed on four frequency bands, i.e., theta (4-8 Hz), alpha (8-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). Power difference features were employed to detect the variation in cerebral activity between the left and right cortical areas.…”

Section: Feature Extraction and The Target Emotion Classesmentioning

confidence: 99%

“…Past studies utilizing these indicators for HCI system emotion recognition show that subjects often intend to make their tones or manners in an exaggerated way to achieve a satisfactory performance [11]. Thus, emotion recognition via recording and analyzing physiological signals becomes a promising alternative [12]. Particularly, electroencephalography (EEG), with its non-invasive technique that easily yields input data for emotion classifiers, is becoming a preferred indicator [13].…”

Section: Introductionmentioning

confidence: 99%

Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals

2019

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Feature selection plays a crucial role in analyzing huge-volume, high-dimensional EEG signals in human-centered automation systems. However, classical feature selection methods pay little attention to transferring cross-subject information for emotions. To perform cross-subject emotion recognition, a classifier able to utilize EEG data to train a general model suitable for different subjects is needed. However, existing methods are imprecise due to the fact that the effective feelings of individuals are personalized. In this work, the cross-subject emotion recognition model on both binary and multi affective states are developed based on the newly designed multiple transferable recursive feature elimination (M-TRFE). M-TRFE manages not only a stricter feature selection of all subjects to discover the most robust features but also a unique subject selection to decide the most trusted subjects for certain emotions. Via a least square support vector machine (LSSVM), the overall multi (joy, peace, anger and depression) classification accuracy of the proposed M-TRFE reaches 0.6513, outperforming all other methods used or referenced in this paper.

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“…Previous research has focused primarily on the use of similar tasks to explore emotion perception in psychiatric patients. Most have relied on photographs of faces showing a particular emotion [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ], requiring the examinee to identify them by choosing from a selection of labels of emotions or to match two stimuli depicting the same emotion. While most of these used staged emotions expressed in static photographs for the purpose of the development of the particular tasks, others used naturalistic emotional expressions (i.e., photographs of real life facial expressions from magazines [ 28 ]).…”

Section: Introductionmentioning

confidence: 99%

What Is Going On? The Process of Generating Questions about Emotion and Social Cognition in Bipolar Disorder and Schizophrenia with Cartoon Situations and Faces

et al. 2018

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Regarding the notion of putative “best” practices in social neuroscience and science in general, we contend that following established procedures has advantages, but prescriptive uniformity in methodology can obscure flaws, bias thinking, stifle creativity, and restrict exploration. Generating hypotheses is at least as important as testing hypotheses. To illustrate this process, we describe the following exploratory study. Psychiatric patients have difficulties with social functioning that affect their quality of life adversely. To investigate these impediments, we compared the performances of patients with schizophrenia and those with bipolar disorder to healthy controls on a task that involved matching photographs of facial expressions to a faceless protagonist in each of a series of drawn cartoon emotion-related situations. These scenarios involved either a single character (Nonsocial) or multiple characters (Social). The Social scenarios were also Congruent, with everyone in the cartoon displaying the same emotion, or Noncongruent (with everyone displaying a different emotion than the protagonist should). In this preliminary study, both patient groups produced lower scores than controls (p < 0.001), but did not perform differently from each other. All groups performed best on the social-congruent items and worst on the social-noncongruent items (p < 0.001). Performance varied inversely with illness duration, but not symptom severity. Complete emotional, social, cognitive, or perceptual inability is unlikely because these patient groups could still do this task. Nevertheless, the differences we saw could be meaningful functionally and clinically significant and deserve further exploration. Therefore, we stress the need to continue developing novel, alternative ways to explore social cognition in patients with psychiatric disorders and to clarify which elements of the multidimensional process contribute to difficulties in daily functioning.

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Facial emotion recognition and borderline personality pathology

Cited by 36 publications

References 41 publications

Are you angry at me? Negative interpretations of neutral facial expressions are linked to child maltreatment but not to posttraumatic stress disorder

Are you angry at me? Negative interpretations of neutral facial expressions are linked to child maltreatment but not to posttraumatic stress disorder

Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals

What Is Going On? The Process of Generating Questions about Emotion and Social Cognition in Bipolar Disorder and Schizophrenia with Cartoon Situations and Faces

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