The ability to conceptualize and understand one’s affective responses has become the topic of a growing body of empirical work ( McRae et al., 2008 Smith et al., 2015, 2017b, c, 2018c, d, e, 2019a, c Wright et al., 2017). These results offer a proof of principle that cognitive-emotional processes can be modeled formally, and highlight the potential for both theoretical and empirical extensions of this line of research on emotion and emotional disorders. We validate the model and inherent constructs by showing (i) it can successfully acquire a repertoire of emotion concepts in its “childhood”, as well as (ii) acquire new emotion concepts in synthetic “adulthood,” and (iii) that these learning processes depend on early experiences, environmental stability, and habitual patterns of selective attention. Here, we present a formal Active Inference (AI) model of emotion conceptualization that can simulate the neurocomputational (Bayesian) processes associated with learning about emotion concepts and inferring the emotions one is feeling in a given moment. While a growing body of work has investigated the neurocognitive basis of EA, the neurocomputational processes underlying this ability have received limited attention. The ability to conceptualize and understand one’s own affective states and responses – or “Emotional awareness” (EA) – is reduced in multiple psychiatric populations it is also positively correlated with a range of adaptive cognitive and emotional traits.
2Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.1Laureate Institute for Brain Research, Tulsa, OK, United States.
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