Affects affect affects: A Markov Chain

Unveiling the underlying structure of awe in virtual reality and in autobiographical recall: an exploratory study

Over the last two decades, awe has attracted the attention of an increasing number of researchers. The use of virtual reality has been identified as one of the most effective techniques for eliciting awe, in addition to more personalized methods for inducing emotion, such as autobiographical recall. However, previous measures of awe were unable to uncover the hidden structure of this experience. Awe experience scale (AWE-S) has been validated as a comprehensive measure of contingent awe in English, providing new opportunities for analysis. In this two-phases study, we investigated whether the latent structure of the experience of awe evoked by the autobiographical recall technique (Study 1) overlapped with that induced by exposing participants to a validated virtual reality awe-eliciting training (Study 2). The original English AWE-S structure held both in autobiographical recall induction and virtual reality-based elicitation. Despite evidence of overlap between English and Italian structures, low correlations were found between Italian trait measures used to test the concurrent validity of the AWE-S in the Italian sample and AWE-S state dimensions. This study highlights cultural differences in awe experience, trait, and state variations, and provides new insights into the standardized induction of this emotion through simulated environments.

Outlining a novel psychometric model of mental flexibility and affect dynamics

Theoretically, affective states have always been conceived as complex phenomena enabling individuals to respond flexibly and dynamically to environmental demands. Methodologically, the novel field of Affect Dynamics has started to analyze affective states as inherently dynamic and interdependent phenomena by focusing on how and why they fluctuate over time. Fluctuations of affective states can also be conceived as a function of individuals' ability to flexibly modulate their responses according to environmental demands. However, this ability has been sparsely investigated in different disciplines and domains, thus, engendering a plethora of terms and models. In this conceptual analysis, we first aimed to disentangle the puzzle of flexibility by outlining the distinctive cross-domain features of this concept, thus providing a novel comprehensive operationalization. We termed this novel unitary concept "mental flexibility," the general ability to variably adapt to environmental demands. Then, we outlined the interplay between individuals' mental flexibility and affect dynamics by proposing a novel psychometric model of affect dynamics, using Markovian chain.

Exploring Biomarkers of Mental Flexibility in Healthy Aging: A Computational Psychometric Study

Mental flexibility (MF) has long been defined as cognitive flexibility. Specifically, it has been mainly studied within the executive functions domain. However, there has recently been increased attention towards its affective and physiological aspects. As a result, MF has been described as an ecological and cross-subject skill consisting of responding variably and flexibly to environmental cognitive-affective demands. Cross-sectional studies have mainly focused on samples composed of healthy individual and of patients with chronic conditions such as Mild Cognitive Impairment and Parkinson's, emphasizing their behavioral rigidity. Our study is the first to consider a sample of healthy older subjects and to outline physiological and psychological markers typical of mental flexibility, to identify functional biomarkers associated with successful aging. Our results reveal that biomarkers (respiratory and heart rate variability assessments) distinguished between individuals high vs. low in mental flexibility more reliably than traditional neuropsychological tests. This unveiled the multifaceted nature of mental flexibility composed of both cognitive and affective aspects, which emerged only if non-linear multi-variate analytic approaches, such as Supervised Machine Learning, were used.