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The common view of emotional expressions is that certain configurations of facial-muscle movements reliably reveal certain categories of emotion. The principal exemplar of this view is the Duchenne smile, a configuration of facial-muscle movements (i.e., smiling with eye constriction) that has been argued to reliably reveal genuine positive emotion. In this paper, we formalized a list of hypotheses that have been proposed regarding the Duchenne smile, briefly reviewed the literature weighing on these hypotheses, identified limitations and unanswered questions, and conducted two empirical studies to begin addressing these limitations and answering these questions. Both studies analyzed a database of 751 smiles observed while 136 participants completed experimental tasks designed to elicit amusement, embarrassment, fear, and physical pain. Study 1 focused on participants’ self-reported positive emotion and Study 2 focused on how third-party observers would perceive videos of these smiles. Most of the hypotheses that have been proposed about the Duchenne smile were either contradicted by or only weakly supported by our data. Eye constriction did provide some information about experienced positive emotion, but this information was lacking in specificity, already provided by other smile characteristics, and highly dependent on context. Eye constriction provided more information about perceived positive emotion, including some unique information over other smile characteristics, but context was also important here as well. Overall, our results suggest that accurately inferring positive emotion from a smile requires more sophisticated methods than simply looking for the presence/absence (or even the intensity) of eye constriction.

The words a person uses while describing salient life events/experiences can provide a wealth of information about their cognitive and emotional functioning. Linguistic analyses can thus play an important role in clinical settings by providing insights into patients' mental health. The current paper applies such techniques to the study of posttraumatic stress disorder (PTSD). It uses a combination of computational and human rating methods to analyze the lexical and emotional content of trauma appraisal essays (i.e., impact statements) written by a treatment-seeking clinical sample (N=94). More frequent use of first-person plural pronouns (e.g., 'we', 'our') was associated with lower self-reported PTSD and depressive severity, higher ratings of expressed anger were associated with higher clinician-rated PTSD severity, and higher ratings of expressed vulnerable emotions were associated with higher self-reported PTSD and depressive severity. Text-mining approaches also provided insights into common themes across participants' trauma impact statements (e.g., safety, trust, and self-esteem).

Van Oest (2019) developed a framework to assess interrater agreement for nominal categories and complete data. We generalize this framework to all four situations of nominal or ordinal categories and complete or incomplete data. The mathematical solution yields a chance-corrected agreement coefficient that accommodates any weighting scheme for penalizing rater disagreements and any number of raters and categories. By incorporating Bayesian estimates of the category proportions, the generalized coefficient also captures situations in which raters classify only subsets of items; that is, incomplete data. Furthermore, this coefficient encompasses existing chance-corrected agreement coefficients: the S-coefficient, Scott's pi, Fleiss' kappa, and Van Oest's uniform prior coefficient, all augmented with a weighting scheme and the option of incomplete data. We use simulation to compare these nested coefficients. The uniform prior coefficient tends to perform best, in particular, if one category has a much larger proportion than others. The gap with Scott's pi and Fleiss' kappa widens if the weighting scheme becomes more lenient to small disagreements and often if more item classifications are missing; missingness biases play a moderating role. The uniform prior coefficient often performs much better than the S-coefficient, but the S-coefficient sometimes performs best for small samples, missing data, and lenient weighting schemes. The generalized framework implies a new interpretation of chance-corrected weighted agreement coefficients: These coefficients estimate the probability that both raters in a pair assign an item to its correct category without guessing. Whereas Van Oest showed this interpretation for unweighted agreement, we generalize to weighted agreement. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Objectives This study aimed to (1) determine the feasibility of collecting behavioral data from participants hospitalized with acute psychosis and (2) begin to evaluate the clinical information that can be computationally derived from such data. Methods Behavioral data was collected across 99 sessions from 38 participants recruited from an inpatient psychiatric unit. Each session started with a semi-structured interview modeled on a typical “clinical rounds” encounter and included administration of the Positive and Negative Syndrome Scale (PANSS). Analysis We quantified aspects of participants' verbal behavior during the interview using lexical, coherence, and disfluency features. We then used two complementary approaches to explore our second objective. The first approach used predictive models to estimate participants' PANSS scores from their language features. Our second approach used inferential models to quantify the relationships between individual language features and symptom measures. Results Our predictive models showed promise but lacked sufficient data to achieve clinically useful accuracy. Our inferential models identified statistically significant relationships between numerous language features and symptom domains. Conclusion Our interview recording procedures were well-tolerated and produced adequate data for transcription and analysis. The results of our inferential modeling suggest that automatic measurements of expressive language contain signals highly relevant to the assessment of psychosis. These findings establish the potential of measuring language during a clinical interview in a naturalistic setting and generate specific hypotheses that can be tested in future studies. This, in turn, will lead to more accurate modeling and better understanding of the relationships between expressive language and psychosis.

Physical attractiveness plays a central role in psychosocial experiences. One of the top research priorities has been to identify factors affecting perceptions of physical attractiveness (PPA). Recent work suggests PPA derives from different sources (e.g., target, perceiver, stimulus type). Although smiles in particular are believed to enhance PPA, support has been surprisingly limited. This study comprehensively examines the effect of smiles on PPA and, more broadly, evaluates the roles of target, perceiver, and stimulus type in PPA variation. Perceivers (n = 181) rated both static images and 5-sec videos of targets displaying smiling and neutral-expressions. Smiling images were rated as more attractive than neutral-expression images (regardless of stimulus motion format). Interestingly, perceptions of physical attractiveness were based more on the perceiver than on either the target or format in which the target was presented. Results clarify the effect of smiles, and highlight the significant role of the perceiver, in PPA.

BackgroundYouth with bipolar disorder (BD) are at high risk for suicidal thoughts and behaviors and frequently experience interpersonal impairment, which is a risk factor for suicide. Yet, no study to date has examined the longitudinal associations between relationship quality in family/peer domains and suicidal thoughts and behaviors among youth with BD. Thus, we investigated how between-person differences - reflecting the average relationship quality across time - and within-person changes, reflecting recent fluctuations in relationship quality, act as distal and/or proximal risk factors for suicidal ideation (SI) and suicide attempts.MethodsWe used longitudinal data from the Course and Outcome of Bipolar Youth Study (N=413). Relationship quality variables were decomposed into stable (i.e., average) and varying (i.e., recent) components and entered, along with major clinical covariates, into separate Bayesian multilevel models predicting SI and suicide attempt. We also examined how the relationship quality effects interacted with age and sex.ResultsPoorer average relationship quality with parents (β=-.33, 95% Bayesian highest density interval (HDI) [-0.54, -0.11]) or friends (β=-.33, 95% HDI [-0.55, -0.11]) was longitudinally associated with increased risk of SI but not suicide attempt. Worsening recent relationship quality with parents (β=-.10, 95% HDI [-0.19, -0.03]) and, to a lesser extent, friends (β=-.06, 95% HDI [-0.15, 0.03]) was longitudinally associated with increased risk of SI, but only worsening recent relationship quality with parents was also associated with increased risk of suicide attempt (β=-.15, 95% HDI [-0.31, 0.01]). The effects of certain relationship quality variables were moderated by gender but not age.ConclusionsAmong youth with BD, having poorer average relationship quality with peers and/or parents represents a distal risk factor for SI but not suicide attempts. Additionally, worsening recent relationship quality with parents may be a time-sensitive indicator of increased risk for SI or suicide attempt.

Recent progress in artificial intelligence has led to the development of automatic behavioral marker recognition, such as facial and vocal expressions. Those automatic tools have enormous potential to support mental health assessment, clinical decision making, and treatment planning. In this paper, we investigate nonverbal behavioral markers of depression severity assessed during semi-structured medical interviews of adolescent patients. The main goal of our research is two-fold: studying a unique population of adolescents at high risk of mental disorders and differentiating mild depression from moderate or severe depression. We aim to explore computationally inferred facial and vocal behavioral responses elicited by three segments of the semi-structured medical interviews: Distress Assessment Questions, Ubiquitous Questions, and Concept Questions. Our experimental methodology reflects best practise used for analyzing small sample size and unbalanced datasets of unique patients. Our results show a very interesting trend with strongly discriminative behavioral markers from both acoustic and visual modalities. These promising results are likely due to the unique classification task (mild depression vs. moderate and severe depression) and three types of probing questions.

Objectives Psychosocial factors and responses to injury modify the transition from acute to chronic pain. Specifically, posttraumatic stress disorder symptoms (PTSS; reexperiencing, avoidance, and hyperarousal symptoms) exacerbate and co-occur with chronic pain. Yet no study has prospectively considered the associations among these psychological processes and pain reports using experience sampling methods (ESM) during the acute aftermath of injury. This study applied ESM via daily text messaging to monitor and detect relationships among psychosocial factors and post-injury pain across the first 14-days after emergency department (ED) discharge. Methods We recruited 75 adults (59% male; M age = 33) who experienced a potentially traumatic injury (i.e., involving life threat or serious injury) in the past 24-hours from the EDs of two Level 1 trauma centers. Participants received 5 questions per day via text messaging from Day-1 to Day-14 post-ED discharge; three questions measured PTSS, one question measured perceived social support, and one question measured physical pain. Results Sixty-seven participants provided sufficient data for inclusion in the final analyses, and the average response rate per subject was 86%. Pain severity score decreased from a mean of 7.2 to 4.4 over 14 days and 50% of the variance in daily pain scores was within-person. In multilevel structural equation models, pain scores decreased over time, and daily fluctuations of hyperarousal (b = 0.22, 95% CI [0.08, 0.36]) were uniquely associated with daily fluctuations in reported pain level within each person. Conclusions Daily hyperarousal symptoms predict same-day pain severity over the acute post-injury recovery period. We also demonstrated feasibility to screen and identify patients at risk for pain chronicity in the acute aftermath of injury. Early interventions aimed at addressing hyperarousal (e.g. anxiolytics) could potentially aid in reducing experience of pain. This article is protected by copyright. All rights reserved.

Relationships are among the most salient factors affecting happiness and wellbeing for individuals and families. Relationship science has identified the study of dyadic behavioral patterns between couple members during conflict as an important window in to relational functioning with both short-term and long-term consequences. Several methods have been developed for the momentary assessment of behavior during interpersonal transactions. Among these, the most popular is the Specific Affect Coding System (SPAFF), which organizes social behavior into a set of discrete behavioral constructs. This study examines the interpersonal meaning of the SPAFF codes through the lens of interpersonal theory, which uses the fundamental dimensions of Dominance and Affiliation to organize interpersonal behavior. A sample of 67 couples completed a conflict task, which was video recorded and coded using SPAFF and a method for rating momentary interpersonal behavior, the Continuous Assessment of Interpersonal Dynamics (CAID). Actor partner interdependence models in a multilevel structural equation modeling framework were used to study the covariation of SPAFF codes and CAID ratings. Results showed that a number of SPAFF codes had clear interpersonal signatures, but many did not. Additionally, actor and partner effects for the same codes were strongly consistent with interpersonal theory's principle of complementarity. Thus, findings reveal points of convergence and divergence in the 2 systems and provide support for central tenets of interpersonal theory. Future directions based on these initial findings are discussed. (PsycINFO Database Record

© The Author(s) 2018. The Continuous Assessment of Interpersonal Dynamics (CAID) is a method in which trained observers continuously code the dominance and warmth of individuals who interact with one another in dyads. This method has significant promise for assessing dynamic interpersonal processes. The purpose of this study was to examine the impact of individual sex, dyadic familiarity, and situational conflict on patterns of interpersonal warmth, dominance, and complementarity as assessed via CAID. We used six samples with 603 dyads, including two samples of unacquainted mixed-sex undergraduates interacting in a collaborative task, two samples of couples interacting in both collaborative and conflict tasks, and two samples of mothers and children interacting in both collaborative and conflict tasks. Complementarity effects were robust across all samples, and individuals tended to be relatively warm and dominant. Results from multilevel models indicated that women were slightly warmer than men, whereas there were no sex differences in dominance. Unfamiliar dyads and dyads interacting in more collaborative tasks were relatively warmer, more submissive, and more complementary on warmth but less complementary on dominance. These findings speak to the utility of the CAID method for assessing interpersonal dynamics and provide norms for researchers who use the method for different types of samples and applications.

Suicide is one of the leading causes of death in the modern world. In this digital age, individuals are increasingly using social media to express themselves and often use these platforms to express suicidal intent. Various studies have inspected suicidal intent behavioral markers in controlled environments but it is still unexplored if such markers will generalize to suicidal intent expressed on social media. In this work, we set out to study multimodal behavioral markers related to suicidal intent when expressed on social media videos. We explore verbal, acoustic and visual behavioral markers in the context of identifying individuals at higher risk of suicidal attempt. Our analysis reveals that frequent silences, slouched shoulders, rapid hand movements and profanity are predominant multimodal behavioral markers indicative of suicidal intent1.

The Duchenne smile hypothesis is that smiles that include eye constriction (AU6) are the product of genuine positive emotion, whereas smiles that do not are either falsified or related to negative emotion. This hypothesis has become very influential and is often used in scientific and applied settings to justify the inference that a smile is either true or false. However, empirical support for this hypothesis has been equivocal and some researchers have proposed that, rather than being a reliable indicator of positive emotion, AU6 may just be an artifact produced by intense smiles. Initial support for this proposal has been found when comparing smiles related to genuine and feigned positive emotion; however, it has not yet been examined when comparing smiles related to genuine positive and negative emotion. The current study addressed this gap in the literature by examining spontaneous smiles from 136 participants during the elicitation of amusement, embarrassment, fear, and pain (from the BP4D+ dataset). Bayesian multilevel regression models were used to quantify the associations between AU6 and self-reported amusement while controlling for smile intensity. Models were estimated to infer amusement from AU6 and to explain the intensity of AU6 using amusement. In both cases, controlling for smile intensity substantially reduced the hypothesized association, whereas the effect of smile intensity itself was quite large and reliable. These results provide further evidence that the Duchenne smile is likely an artifact of smile intensity rather than a reliable and unique indicator of genuine positive emotion.

The affective computing community has invested heavily in building automated tools for the analysis of facial behavior and the expression of emotion. These tools present a valuable, but largely untapped, opportunity for social scientists to perform observational analyses of nonverbal behavior at very large scale. Various tech companies are collecting huge corpora of images and videos from around the world that could be used to study important scientific questions. However, privacy restrictions and intellectual property concerns render these data inaccessible to most academics. Unfortunately, this limits the potential for scientific advancement and leads to the consolidation of data and opportunity into the hands of a few powerful institutions. In this paper, we ask whether similar psychological insights can be gained by analyzing smaller, public datasets that are more within reach for academic researchers. As a proof-of-concept for this idea, we gather, analyze, and release a corpus of public images and metadata and use it to replicate recent psychological findings about smiling, gender, and culture. In so doing, we provide evidence that psychological insights can indeed by democratized through the automated analysis of nonverbal behavior.

Objective While much is known about the quality of social behavior among neurotypical individuals and those with autism spectrum disorder (ASD), little work has evaluated quantity of social interactions. This study used ecological momentary assessment (EMA) to quantify in vivo daily patterns of social interaction in adults as a function of demographic and clinical factors. Method Adults with and without ASD (NASD = 23, NNeurotypical = 52) were trained in an EMA protocol to report their social interactions via smartphone over one week. Participants completed measures of IQ, ASD symptom severity and alexithymia symptom severity. Results Cyclical multilevel models were used to account for nesting of observations. Results suggest a daily cyclical pattern of social interaction that was robust to ASD and alexithymia symptoms. Adults with ASD did not have fewer social interactions than neurotypical peers; however, severity of alexithymia symptoms predicted fewer social interactions regardless of ASD status. Conclusions These findings suggest that alexithymia, not ASD severity, may drive social isolation and highlight the need to reevaluate previously accepted notions regarding differences in social behavior utilizing modern methods.

The present study applied the interpersonal perspective in testing the narcissistic admiration and rivalry concept (NARC) and examining the construct validity of the corresponding Narcissistic Admiration and Rivalry Questionnaire (NARQ). Two undergraduate samples (Sample 1: N = 290; Sample 2: N = 188) completed self-report measures of interpersonal processes based in the interpersonal circumplex (IPC), as well as measures of related constructs. In examining IPC correlates, the authors used a novel bootstrapping approach to determine if admiration and rivalry related to differing interpersonal profiles. Consistent with the authors’ hypotheses, admiration was distinctly related to generally agentic (i.e., dominant) interpersonal processes, whereas rivalry generally reflected (low) communal (i.e., hostile) interpersonal processes. Furthermore, NARQ-admiration and NARQ-rivalry related to generally adaptive and maladaptive aspects of status-related constructs, emotional, personality, and social adjustment, respectively. This research provides further support for the NARC, as well as construct validation for the NARQ.

Automated facial computing (AFC) has made major advances in detecting emotion expression, occurrence and intensity of anatomic facial actions, physical pain, and depression, and revealed dynamics of interpersonal behavior. Commercial and shareware software has become available for general use. AFC systems, however, have yet to be validated for use in domains (e.g., databases) other than those in which they have been trained. To evaluate the generalizability of AFC systems across domains, we reviewed 39 studies that compared within- and between-domain generalizability of AFC. The findings were compromised by lack of comparability across studies. Most studies reported only aggregate findings for disparate tasks and used different performance metrics. Data imbalance, which biases many of the performance metrics, varied across studies. Statistical significance tests between and within studies were not reported. And few if any studies reported variation due to individual differences in participants or other operational factors. With these limitations in mind, the overall finding was that cross-domain generalizability trails that of within-domain performance. To advance cross-domain generalizability, use of transfer learning and especially domain adaptation is proposed. Pending advances in cross-domain generalizability, caution is advised in use of AFC systems in domains other than which they have been trained.

Analysis of observable behavior in depression primarily relies on subjective measures. New computational approaches make possible automated audiovisual measurement of behaviors that humans struggle to quantify (e.g., movement velocity and voice inflection). These tools have the potential to improve screening and diagnosis, identify new behavioral indicators of depression, measure response to clinical intervention, and test clinical theories about underlying mechanisms. Highlights include a study that measured the temporal coordination of vocal tract and facial movements, a study that predicted which adolescents would go on to develop depression based on their voice qualities, and a study that tested the behavioral predictions of clinical theories using automated measures of facial actions and head motion.