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.
While a growing body of work has investigated the neurocognitive basis of EA, the
neurocomputational processes underlying this ability have received limited 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.
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. 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
