Interaction and threshold effects of appraisal on componential patterns of emotion : a study using cross-cultural semantic data

Studies that investigated the relation between appraisal and emotion have largely focused on the linear effect of appraisal criteria on subjective feelings (e.g., the effect of appraised goal obstruction on anger). Emotional responding can be extended to include more than just feelings, however. Componential definitions of emotion also add motivation, physiology, and expression. Moreover, a linear model is not compatible with the idea held by many appraisal theorists that appraisal criteria interact to produce emotional responding. In the present study, we modeled adaptive nonlinear interaction effects of appraisal criteria on motivation, expression, and physiology simultaneously. We applied a combination of principal component analysis for data reduction and multivariate adaptive regression splines (MARS) for automatic interaction identification. Data were obtained from a large-scale cross-cultural study on emotion concepts conducted in 27 countries, which represented semantic profiles of component information in 24 common emotion words. Results of modeling indicated that (a) appraisal of relevance, familiarity, goal compatibility, coping potential, and suddenness showed main effects on component responses; (b) appraisals of agency and norm compatibility uniquely showed interaction effects on component responses; (c) interaction effects explained significant variance only in some component responses but not all; and (d) the emotion patterns simulated by the fitted MARS model could be clustered according to qualitative emotion categories

Anxiety in families of individuals with neurodevelopmental conditions in the early months of the COVID-19 pandemic in Switzerland

In the spring of 2020, the COVID-19 pandemic generated a health, social, political, and economic crisis that dramatically reduced the institutional support for families of individuals with neurodevelopmental conditions (NDCs). To understand how these families may have experienced and coped with the pandemic, we created an online questionnaire that reached more than 10,000 families in 78 countries. The current manuscript - framed within the International Classification of Functioning, Disability and Health (ICF-10) model - investigates the impact of specific health conditions and personal or environmental factors on the anxiety of families living in Switzerland during the early months of the pandemic. To assess how differences in anxiety over time were predicted by specific health conditions or personal and environmental factors, two separate multilevel analyses were conducted for parents and their children with NDCs (N = 256). First, results showed that only parents reported an increase in anxiety when the pandemic started. Second, concerns related to loss of institutional support and financial and economic problems were the most anxiety-provoking factors for parents, whereas parents reported that the most anxiety-provoking factor for children was their concern about becoming bored. Many parents may have struggled with economic problems and managed multiple extra roles and tasks in their daily lives because institutional support was no longer available. As reported by their parents, although individuals with NDCs did not show an increase in anxiety, they may have struggled with boredom. This result may represent the inability to engage in satisfactory activities in daily life associated with a partial unawareness of the pandemic and the respective protective measures. Further research should more thoroughly investigate the potential effects of the individual’s primary condition, presence and severity of intellectual disability and awareness of the pandemic on the anxiety of individuals with NDCs. Ultimately, we present a series of reflections and practical suggestions that could help guide policymakers in potential future periods of crisis, social estrangement, and distance learning

Anxiety, concerns and COVID-19: Cross-country perspectives from families and individuals with neurodevelopmental conditions

BACKGROUND: The COVID-19 pandemic had a major impact on the mental health and well-being of children with neurodevelopmental conditions (NDCs) and of their families worldwide. However, there is insufficient evidence to understand how different factors (e.g., individual, family, country, children) have impacted on anxiety levels of families and their children with NDCs developed over time. METHODS: We used data from a global survey assessing the experience of 8043 families and their children with NDCs (mean of age (m) = 13.18 years, 37% female) and their typically developing siblings (m = 12.9 years, 45% female) in combination with data from the European Centre for Disease Prevention and Control, the University of Oxford, and the Central Intelligence Agency (CIA) World Factbook, to create a multilevel data set. Using stepwise multilevel modelling, we generated child-, family- and country-related factors that may have contributed to the anxiety levels of children with NDCs, their siblings if they had any, and their parents. All data were reported by parents. RESULTS: Our results suggest that parental anxiety was best explained by family-related factors such as concerns about COVID-19 and illness. Children’s anxiety was best explained by child-related factors such as children’s concerns about loss of routine, family conflict, and safety in general, as well as concerns about COVID-19. In addition, anxiety levels were linked to the presence of pre-existing anxiety conditions for both children with NDCs and their parents. CONCLUSIONS: The present study shows that across the globe there was a raise in anxiety levels for both parents and their children with NDCs because of COVID-19 and that country-level factors had little or no impact on explaining differences in this increase, once family and child factors were considered. Our findings also highlight that certain groups of children with NDCs were at higher risk for anxiety than others and had specific concerns. Together, these results show that anxiety of families and their children with NDCs during the COVID-19 pandemic were predicted by very specific concerns and worries which inform the development of future toolkits and policy. Future studies should investigate how country factors can play a protective role during future crises

Computational modeling of appraisal theory of emotion

Appraisal theories of emotion have proposed detailed—and causal—hypotheses about the connection between situations and emotional responding, and between the components that constitute emotional responding. Many of these hypotheses present computational challenges to scientific research, in that they require the analysis of numerous mental and bodily changes simultaneously over time. In this thesis, I applied statistical models of machine learning to address these challenges, and to investigate hypotheses concerning interaction effects, curvilinear associations, feedback among emotion components, synchronization of components, and the felt experience of synchronized changes (e.g., feeling angry). Results of the four studies generally supported the algorithmic complexity that underlies emotion unfolding, and that modelling these complexities is necessary to differentiate patterns of emotional responding quantitatively and qualitatively. Using a novel measure for emotional synchronization, I showed experimentally that changes in motivation, physiology, and expression responses synchronized following a manipulation of the appraised importance of a situation

Induction and profiling of strong multi-componential emotions in virtual reality

Psychological theories of emotion have often defined an emotion as simultaneous changes in several mental and bodily components. In addition, appraisal theories assume that an appraisal component elicits changes in the other emotion components (e.g., motivational, behavioural, experiential). Neither the componential definition of emotion nor appraisal theory have been systematically translated to paradigms for emotion induction, many of which rely on passive emotion induction without a clear theoretical framework. As a result, the observed emotions are often weak. This study explored the potential of virtual reality (VR) to evoke strong emotions in ecologically valid scenarios that fully engaged the mental and bodily components of the participant. Participants played several VR games and reported on their emotions. Multivariate analyses using hierarchical clustering and multilevel linear modelling showed that participants experienced intense, multi-componential emotions in VR. We identified joy and fear clusters of responses, each involving changes in appraisal, motivation, physiology, feeling, and regulation. Appraisal variables were found to be the most predictive for fear and joy intensities, compared to other emotion components, and were found to explain individual differences in VR scenarios, as predicted by appraisal theory. The results advocate upgraded methodologies for the induction and analysis of emotion processes

Human emotion experiences can be predicted on theoretical grounds: evidence from verbal labeling.

In an effort to demonstrate that the verbal labeling of emotional experiences obeys lawful principles, we tested the feasibility of using an expert system called the Geneva Emotion Analyst (GEA), which generates predictions based on an appraisal theory of emotion. Several thousand respondents participated in an Internet survey that applied GEA to self-reported emotion experiences. Users recalled appraisals of emotion-eliciting events and labeled the experienced emotion with one or two words, generating a massive data set on realistic, intense emotions in everyday life. For a final sample of 5969 respondents we show that GEA achieves a high degree of predictive accuracy by matching a user's appraisal input to one of 13 theoretically predefined emotion prototypes. The first prediction was correct in 51% of the cases and the overall diagnosis was considered as at least partially correct or appropriate in more than 90% of all cases. These results support a component process model that encourages focused, hypothesis-guided research on elicitation and differentiation, memory storage and retrieval, and categorization and labeling of emotion episodes. We discuss the implications of these results for the study of emotion terms in natural language semantics

Nonlinear Appraisal Modeling: An Application of Machine Learning to the Study of Emotion Production

Appraisal theory of emotion claims that emotions are not caused by "raw" stimuli, as such, but by the subjective evaluation (appraisal) of those stimuli. Studies that analyzed this relation have been dominated by linear models of analysis. These methods are not ideally suited to examine a basic assumption of many appraisal theories, which is that appraisal criteria interact to differentiate emotions, and hence show nonlinear effects. Studies that did model interactions were either limited in scope or exclusively theory-driven simulation attempts. In the present study, we improve on these approaches using data-driven methods from the field of machine learning. We modeled a categorical emotion response as a function of 25 appraisal predictors, using a large dataset on recalled emotion experiences (5901 cases). A systematic comparison of machine learning models on these data supported the interactive nature of the appraisal–emotion relationship, with the best nonlinear model significantly outperforming the best linear model. The interaction structure was found to be moderately hierarchical. Strong main effects of intrinsic valence and goal compatibility appraisal differentiated positive from negative emotions, while more specific emotions (e.g., pride, irritation, despair) were differentiated by interactions involving agency appraisal and norm appraisal

Beyond 'mythbusting': how to respond to myths and perceived undeservingness in the British benefits system

status: publishe

Automated Recognition of Emotion Appraisals

Most computer models for the automatic recognition of emotion from nonverbal signals (e.g., facial or vocal expression) have adopted a discrete emotion perspective, i.e., they output a categorical emotion from a limited pool of candidate labels. The discrete perspective suffers from practical and theoretical drawbacks that limit the generalizability of such systems. The authors of this chapter propose instead to adopt an appraisal perspective in modeling emotion recognition, i.e., to infer the subjective cognitive evaluations that underlie both the nonverbal cues and the overall emotion states. In a first step, expressive features would be used to infer appraisals; in a second step, the inferred appraisals would be used to predict an emotion label. The first step is practically unexplored in emotion literature. Such a system would allow to (a) link models of emotion recognition and production, (b) add contextual information to the inference algorithm, and (c) allow detection of subtle emotion states