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In daily interactions, emotions are frequently conveyed and triggered through verbal exchanges. Sometimes, we must modulate our emotional reactions to align with societal norms. Among the emotional words, taboo words represent a specific category that has been poorly studied. One intriguing question is whether these word categories can be predicted from EEG responses with the use of machine learning methods. To address this question, Support Vector Machine (SVM) was applied to decode the word categories from Event Related Potential (ERP) in 40 native Italian speakers. 240 neutral, negative and taboo words were used to this aim. Results indicate that the SVM classifier successfully distinguished between the three-word categories, with significant differences in neural activity ascribed to the late positive potential mainly detected in the central-parietal-occipital and anterior right scalp areas in the time windows of 450-649 ms and 650-850 ms. These findings were in line with the established distribution pattern of the late positive potential. Intriguingly, the study also revealed that word categories were still detectable in the regulate condition. This study extends previous results on the domain of the cortical responses of taboo words, and how machine learning methods can be used to predict word categories from EEG responses.

Acceptance and Reappraisal are considered adaptive emotion regulation strategies. While previous studies have explored the neural underpinnings of these strategies using task based fMRI and sMRI, a gap exists in the literature concerning resting-state functional brain networks contributions to these abilities, especially for what concerns Acceptance. Another intriguing question is whether these strategies rely on similar or different neural mechanisms. Building on the well-known improved emotion regulation and increased cognitive flexibility of individuals who rely on acceptance, we expected to find decreased activity inside the Affective network and increased activity inside the Executive and Sensorimotor networks to be predicted of acceptance. We also expect that these networks may be associated at least in part with Reappraisal, indicating a common mechanism behind different strategies. To test these hypotheses, we conducted a functional connectivity analysis of resting-state data from 134 individuals (95 females). To assess acceptance and reappraisal abilities, we used the Cognitive Emotion Regulation Questionnaire (CERQ) and a group-ICA unsupervised machine learning approach to identify resting state networks. Subsequently, we conducted backward regression to predict acceptance and reappraisal abilities. As expected, results indicated that acceptance was predicted by decreased Affective, and increased Executive, and Sensorimotor networks, while reappraisal was predicted by an increase in the Sensorimotor network. Notably, these findings suggest both distinct and overlapping brain contributions to acceptance and reappraisal, with the Sensorimotor network potentially serving as a core common mechanism. These results not only align with previous findings but also expand upon them, demonstrating the complex interplay of cognitive, affective, and sensory abilities in emotion regulation., Comment: 33 pages, 6 figures, 3 tables

Acceptance is an adaptive emotion regulation strategy characterized by an open and non-judgmental attitude toward mental and sensory experiences. While a few studies have investigated the neural correlates of acceptance in task-based fMRI studies, a gap remains in the scientific literature in dispositional use of acceptance, and how this is sedimented at a structural level. Therefore, the aim of the present study is to investigate the neural and psychological differences between infrequent acceptance users (i.e., low accepters) and frequent users (i.e., high accepters). Another question is whether high and low accepters differ in personality traits and emotional intelligence. To this aim, we applied, for the first time, a data fusion unsupervised machine learning approach (mCCA-jICA) to the gray matter (GM) and white matter (WM) of high accepters (N = 50), and low accepters (N = 78) to possibly find joint GM-WM differences in both modalities. Our results show that two covarying GM-WM networks separate high from low accepters. The first network showed decreased GM-WM concentration in a fronto-temporal-parietal circuit largely overlapping with the Default Mode Network, while the second network showed increased GM-WM concentration in portions of the orbito-frontal, temporal, and parietal areas, related to a Central Executive Network. At the psychological level, the high accepters display higher openness to experience compared to low accepters. Overall, our findings suggest that high accepters compared to low accepters differ in neural and psychological mechanisms. These findings confirm and extend previous studies on the relevance of acceptance as a strategy associated with well-being., Comment: 48 pages, 6 figures, 3 tables

In the emotion regulation literature, the amount of neuroimaging studies on cognitive reappraisal led the impression that the same top-down, control-related neural mechanisms characterize all emotion regulation strategies. However, top-down processes may coexist with more bottom-up and emotion-focused processes that partially bypass the recruitment of executive functions. A case in point is acceptance-based strategies. To better understand neural commonalities and differences behind different emotion regulation strategies, in the present study we applied a meta-analytic method to fMRI studies of task-related activity of reappraisal and acceptance. Results showed increased activity in left-inferior frontal gyrus and insula for both strategies, and decreased activity in the basal ganglia for reappraisal, and decreased activity in limbic regions for acceptance. These findings are discussed in the context of a model of common and specific neural mechanisms of emotion regulation that support and expand the previous dual-routes models. We suggest that emotion regulation may rely on a core inhibitory circuit, and on strategy-specific top-down and bottom-up processes distinct for different strategies., Comment: 32 pages, 3 figures