Validación de una versión breve de la Escala de Dificultades en la Regulación Emocional con población hispanohablante (DERS-S SF)

Emotion regulation refers to all the processes involved in adapting to relatively strong emotional episodes, and specifically to identifying, differentiating and monitoring intense emotional states in order to cope with stressful situations. Difficulties in regulating emotions are associated with problems such as depression, anxiety and maladaptive behaviors. The DERS (Difficulties in Emotion Regulation Scale) is the most complete tool for measuring difficulties with emotion regulation. Several brief versions of this scale in English are described in the literature, but no a brief Spanish version has been found. The purpose of this study is to validate a brief version of the DERS in Spanish. The DERS tool was used with a Spanish speaking population (n=351, inhabitants of Cartagena, Colombia, 56% were woman, Mage 39 years, SD = 14.98) who responded the 5-point Likert scale. The brief version (18 items) was validated using confirmatory factor analysis (X2 / df = 1.19, CFI= .99, TLI = .99, RMSEA=.02). However, neither the reliability nor the stability of the awareness dimension was confirmed. This point and other results are examined on the light of extant literature.

Comparing supervised and unsupervised approaches to emotion categorization in the human brain, body, and subjective experience

Machine learning methods provide powerful tools to map physical measurements to scientific categories. But are such methods suitable for discovering the ground truth about psychological categories? We use the science of emotion as a test case to explore this question. In studies of emotion, researchers use supervised classifiers, guided by emotion labels, to attempt to discover biomarkers in the brain or body for the corresponding emotion categories. This practice relies on the assumption that the labels refer to objective categories that can be discovered. Here, we critically examine this approach across three distinct datasets collected during emotional episodes—measuring the human brain, body, and subjective experience—and compare supervised classification solutions with those from unsupervised clustering in which no labels are assigned to the data. We conclude with a set of recommendations to guide researchers towards meaningful, data-driven discoveries in the science of emotion and beyond.

Smart Sensing: An Info-Structural Model of Cognition for Non-Interacting Agents

This study explores an info-structural model of cognition for non-interacting agents affected by human sensation, perception, emotion, and affection. We do not analyze the neuroscientific or psychological debate concerning the human mind working, but we underline the importance of modeling the above cognitive levels when designing artificial intelligence agents. Our aim was to start a reflection on the computational reproduction of intelligence, providing a methodological approach through which the aforementioned human factors in autonomous systems are enhanced. The presented model must be intended as part of a larger one, which also includes concepts of attention, awareness, and consciousness. Experiments have been performed by providing visual stimuli to the proposed model, coupling the emotion cognitive level with a supervised learner to produce artificial emotional activity. For this purpose, performances with Random Forest and XGBoost have been compared and, with the latter algorithm, 85% accuracy and 92% coherency over predefined emotional episodes have been achieved. The model has also been tested on emotional episodes that are different from those related to the training phase, and a decrease in accuracy and coherency has been observed. Furthermore, by decreasing the weight related to the emotion cognitive instances, the model reaches the same performances recorded during the evaluation phase. In general, the framework achieves a first emotional generalization responsiveness of 94% and presents an approximately constant relative frequency related to the agent’s displayed emotions.

Mental Perturbance

Understanding intrusive mentation, rumination, obsession, and worry, known also as "repetitive thought" (RT), is important for understanding cognitive and affective processes in general. RT is of transdiagnostic significance—for example obsessive-compulsive disorder, insomnia and addictions involve counterproductive RT. It is also a key but under-acknowledged feature of emotional episodes. We argue that RT cannot be understood in isolation but must rather be considered within models of whole minds and for this purpose we suggest an integrative design-oriented (IDO) approach. This approach involves the design stance of theoretical Artificial Intelligence (the central discipline of cognitive science), augmented by systematic conceptual analysis, aimed at explaining how autonomous agency is possible. This requires developing, exploring and implementing cognitive-affective-conative information-processing architectures. Empirical research on RT and emotions needs to be driven by such theories, and theorizing about RT needs to consider such data. Mental perturbance is an IDO concept that, we argue, can help characterize, explain, and theoretically ground the concept of RT. Briefly, perturbance is a mental state in which motivators tend to disrupt, or otherwise influence, executive processes even if reflective processes were to try to prevent or minimize the motivators’ influence. We draw attention to an IDO architecture of mind, H-CogAff, to illustrate the IDO approach to perturbance. We claim, further, that the intrusive mentation of some affective states— including grief and limerence (the attraction phase of romantic love) — should be conceptualized in terms of perturbance and the IDO architectures that support perturbance. We call for new taxonomies of RT and emotion in terms of IDO architectures such as H-CogAff. We point to areas of research in psychology that would benefit from the concept of perturbance.

More than just an angry face: A critical review and theoretical expansion of research on leader anger expression

Stories about angry bosses in the workplace are relatively common. Have you ever wondered what causes their anger and how the expressed anger impacts the workplace? Our review of 58 studies on leader anger expression provides an overview of research findings on this phenomenon. The review demonstrates significant research progress in understanding leader anger expression, including its causes, consequences, mechanisms, and boundary conditions. However, the review also reveals that the current approaches to leader anger expression are quite static, which creates the need for a dynamic approach to examining leader anger expression. Integrating a range of theories, we suggest three ways of building dynamic models of leader anger expression, considering its temporal dimension, the dynamics between its mechanisms, and the complexity of emotional episodes in which anger is expressed. Our research contributes to the existing literature by being the first to take stock of leader anger expression research to date and propose a dynamic approach to understanding this phenomenon.

Comparing supervised and unsupervised approaches to emotion categorization in the human brain, body, and subjective experience.

Machine learning methods provide powerful tools to map physical measurements to scientific categories. But are such methods suitable for discovering the ground truth about psychological categories? We use the science of emotion as a test case to explore this question. In studies of emotion, researchers use supervised classifiers, guided by emotion labels, to attempt to discover biomarkers in the brain or body for the corresponding emotion categories. This practice relies on the assumption that the labels refer to objective categories that can be discovered. Here, we critically examine this approach across three distinct datasets collected during emotional episodes- measuring the human brain, body, and subjective experience- and compare supervised classification studies with those from unsupervised clustering in which no a priori labels are assigned to the data. We conclude with a set of recommendations to guide researchers towards meaningful, data-driven discoveries in the science of emotion and beyond.

Turning It Off: Emotions in Digital-Free Travel

This article aims to theorize digitally disconnected travel experiences by investigating various emotional responses during the process of withdrawal and regain of technological affordances. The theoretical concepts of affordance and emotional episodes were adopted in this study to create a conceptual framework. Fifteen diaries and 18 interviews were collected from 24 participants’ reflections of their disconnected experiences. This study thus contributes a contextual update of emotional episodes by providing a detailed account of various emotions in the entire disconnecting/reconnecting travel experience. Also, this study contributes to the affordance literature by exploring the fluidity of technology affordances and environmental affordances. This article develops the Disconnected Emotions Model (DEM), a theoretical framework to provide an understanding of the changing relationship between human emotions and material affordances.

Why do bilingual code-switch when emotional? Insights from immigrant parent-child interactions

Previous research has found that bilingual speakers’ first (L1) and second languages (L2) are differentially associated with their emotional experiences. Moreover, bilinguals appear to code-switch (alternate between two or more languages in a single conversation) during emotional episodes. However, prior evidence has been limited to clinical case studies and self-report studies, leaving open the specificity of the link between code-switching (CS) and emotion, and its underlying mechanisms. The present study examined the dynamic associations between CS and facial emotion behavior in a sample of 68 Chinese-American parents and children during a dyadic emotion-inducing puzzle box task. Specifically, bilingual parents’ language use (L1 Chinese or L2 English), CS behavior (L1L2 or L2L1 switches), and facial emotion behavior (positive and negative valence) were coded at each 5-second interval. Multilevel modeling was used to analyze whether facial emotion behavior predicted later CS, and vice versa. We found that negative facial emotion predicted higher subsequent CS in both L1L2 and L2L1 directions, with stronger associations for the L2L1 direction. On the other hand, positive facial emotion was associated with lower contemporaneous L2L1 CS. CS did not predict later facial emotion behavior, suggesting language switching may not have an immediate effect on emotion. The present findings are consistent with the idea that emotional arousal, especially negative arousal, reduces cognitive control and may trigger spontaneous CS. Together, these findings provide insight into why bilingual speakers switch languages during emotional episodes, and hold implications for clinical interventions serving bilingual individuals and families.

Enacting affectivity

The enactive approach to mind and cognition has several important implications for our understanding of affectivity. It entails that cognition is inherently affective and, relatedly, that the process of cognitive appraisal is not “purely brainy” but embodied. It also entails that a dynamical systems approach is more suitable than other conceptual frameworks to account for the variability of emotional episodes across individuals and populations, while acknowledging the important role of evolution in shaping the physiological and behavioral aspects of those episodes. Finally, the enactive approach does not entail that the material vehicles of affective episodes are necessarily only biological processes occurring inside the organism; rather, it allows extraorganismic processes to be part of the physical realizers of affectivity, and to be phenomenologically incorporated into affective experiences.