Psychophysiological responses to appraisal dimensions in a computer game

A computer game was used to study psychophysiological reactions to emotion‐relevant events. Two dimensions proposed by Scherer (1984a, 1984b) in his appraisal theory, the intrinsic pleasantness and goal conduciveness of game events, were studied in a factorial design. The relative level at which a player performed at the moment of an event was also taken into account. A total of 33 participants played the game while cardiac activity, skin conductance, skin temperature, and muscle activity as well as emotion self‐reports were assessed. The self‐reports indicate that game events altered levels of pride, joy, anger, and surprise. Goal conduciveness had little effect on muscle activity but was associated with significant autonomic effects, including changes to interbeat interval, pulse transit time, skin conductance, and finger temperature. The manipulation of intrinsic pleasantness had little impact on physiological responses. The results show the utility of attempting to manipulate emotion‐constituent appraisals and measure their peripheral physiological signatures.     

Studying the emotion-antecedent appraisal process: An expert system approach

Abstract

The surprising convergence between independently developed appraisal theories of emotion elicitation and differentiation is briefly reviewed. It is argued that three problems are responsible for the lack of more widespread acceptance of such theories: (1) the criticism of excessive cognitivism raised by psychologists working on affective phenomena; (2) the lack of process orientation in linking appraisal to the complex unfolding of emotion episodes over time; and (3) the lack of consensus on the number and types of appraisal criteria between theorists in this domain. Although readers are referred to recent theoretical discussions and evidence from the neurosciences with respect to the first two issues, an empirical study using computerised experimentation is reported with respect to the third issue. Data obtained with an expert system based on Scherer's (1984a) “stimulus evaluation check” predictions show the feasibility of this approach in determining the number and types of appraisal criteria needed to explain emotion differentiation. It is suggested to use computer modelling and experimentation as a powerful tool to further theoretical development and collect pertinent data on the emotion-antecedent appraisal process.     

Studying appraisal-driven emotion processes: taking stock and moving to the future

ABSTRACT

Appraisal theories of emotion, and particularly the Component Process Model, have claimed over the past three decades that the different components of the emotion process (action tendencies, physiological reactions, expressions, and feeling experiences) are essentially driven by the results of multi-level cognitive appraisals and that the feeling component constitutes a central integration and representation of these processes. Given the complexity of the proposed architecture of emotion generation, comprehensive experimental tests of these predictions are difficult to perform and thus evidence has been slow to appear. Complementing earlier work on self-reported appraisal, a massive amount of empirical results from studies with experimental designs based on appraisal manipulation, using electroencephalographic and electromyographic measures, now confirms many of the theoretical predictions with respect to the effect of different appraisal checks, their interactions, and their exact timing. A major issue for future research is the nature of the coherence or synchronisation of the appraisal-driven components in the unfolding emotion process. It is suggested that interdisciplinary multi-team research will be needed to face the theoretical and methodological challenges of experimentally investigating the dynamics of the emotion process.     

A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial

Death by suicide demonstrates profound personal suffering and societal failure. While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought markers. In this novel prospective, multimodal, multicenter, mixed demographic study, we used machine learning to measure and fuse two classes of suicidal thought markers: verbal and nonverbal. Machine learning algorithms were used with the subjects’ words and vocal characteristics to classify 379 subjects recruited from two academic medical centers and a rural community hospital into one of three groups: suicidal, mentally ill but not suicidal, or controls. By combining linguistic and acoustic characteristics, subjects could be classified into one of the three groups with up to 85% accuracy. The results provide insight into how advanced technology can be used for suicide assessment and prevention.     

The Relation of Cognitive Reappraisal and Expressive Suppression to Suicidal Ideation and Suicidal Desire

The differential relations between the emotion regulation strategies “cognitive reappraisal” and “expressive suppression” and suicidality in a mixed inpatient sample (N = 232, 69.4% female) of a German psychotherapeutic hospital were examined. Patients filled in the Emotion Regulation Questionnaire and items on suicidal ideation and desire. A structural equation model fitted the data (RMSEA = .044; CFI = .96) and revealed that “expressive suppression” significantly predicted increased suicidal ideation. Moderation analysis showed that results were independent from a current depressive episode. Potential implications for psychotherapeutic treatment of suicidality are discussed.