  The focus of the efforts for defining and modelling emotion is broadly
shifting from classical definite marker theory to statistically context
situated conceptual theory. However, the role of context processing and its
interaction with the affect is still not comprehensively explored and modelled.
With the help of neural decoding of functional networks, we have decoded
cognitive functions for 12 different basic and complex emotion conditions.
Using transfer learning in deep neural architecture, we arrived at the
conclusion that the core affect is unable to provide varieties of emotions
unless coupled with cortical cognitive functions such as autobiographical
memory, dmn, self-referential, social, tom and salient event detection.
Following our results, in this article, we present a 'cognition-affect
integrated model of emotion' which includes many cortical and subcortical
regions and their interactions. Our model suggests three testable hypotheses.
First, affect and physiological sensations alone are inconsequential in
defining or classifying emotions until integrated with the domain-general
cognitive systems. Second, cognition and affect modulate each other throughout
the generation of meaningful instance which is situated in the current context.
And, finally, the structural and temporal hierarchies in the brain's
organization and anatomical projections play an important role in emotion
responses in terms of hierarchical activities and their durations. The model,
along with the analytical and anatomical support, is presented. The article
concludes with the future research questions.
