Mental health crucially depends upon affective states such as emotions, stress responses, impulses and moods. These states shape how we think, feel and behave. Often, they support adaptive functioning. At other times, however, they can become detrimental to mental health via maladaptive affect generation processes and/or maladaptive affect regulation processes. Here, we present an integrative framework for considering the role of affect generation and regulation in mental illness and well-being. Our model views affect generation as an iterative cycle of attending to, appraising and responding to situations. It views affect regulation as an iterative series of decisions aimed at altering affect generation. Affect regulation decisions include identifying what, if anything, should be changed about affect, selecting where to intervene in the affect generation cycle, choosing how to implement this intervention, and monitoring the regulation attempt to decide whether to maintain, switch or stop it. Difficulties with these decisions, often arising from biased inputs to them, can contribute to manifestations of mental illness such as clinical symptoms, syndromes and disorders. The model has a number of implications for clinical assessment and treatment. Specifically, it offers a common set of concepts for characterizing different affective states; it highlights interactions between affect generation and affect regulation; it identifies assessment and treatment targets among the component processes of affect regulation; and it is applicable to prevention and treatment of mental illness as well as to promotion and restoration of psychological well-being.

What psychological mechanisms enable people to reappraise a situation to change its emotional impact? We propose that reappraisal works by shifting appraisal outcomes—abstract representations of how a situational construal compares to goals—either by changing the construal ( reconstrual) or by changing the goal set ( repurposing). Instances of reappraisal can therefore be characterized as change vectors in appraisal dimensional space. Affordances for reappraisal arise from the range of mental models that could explain a situation ( construal malleability) and the range of goals that the situation could serve ( goal set malleability). This framework helps to expand our conception of reappraisal, assess and classify different instances of reappraisal, predict their relative effectiveness, understand their brain mechanisms, and relate them to individual differences.

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In the present study we asked whether it is possible to decode personality traits from resting state EEG data. EEG was recorded from a large sample of subjects (n = 289) who had answered questionnaires measuring personality trait scores of the five dimensions as well as the 10 subordinate aspects of the Big Five. Machine learning algorithms were used to build a classifier to predict each personality trait from power spectra of the resting state EEG data. The results indicate that the five dimensions as well as their subordinate aspects could not be predicted from the resting state EEG data. Finally, to demonstrate that this result is not due to systematic algorithmic or implementation mistakes the same methods were used to successfully classify whether the subject had eyes open or closed. These results indicate that the extraction of personality traits from the power spectra of resting state EEG is extremely noisy, if possible at all.

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