Department of Cognitive and Brain Sciences
The Hebrew University of Jerusalem
Emotions ubiquitously impact action, learning, and perception, yet their essence and role remain widely debated. The recent emergence of computational cognitive accounts of emotion has the potential to offer greater conceptual precision informed by normative principles and neurobiological data. However, these nascent efforts have so far been mutually inconsistent and limited in scope. In this talk, I will offer an integrative computational account of the human emotional landscape as composed of different emotions, each promoting adaptive behavior by mediating a distinct type of inference concerning oneself and the environment. This account builds on three parsimonious assumptions. First, that the inference each emotion mediates can be deduced from the circumstances that evoke it and the behavior it promotes. Second, emotions that primarily differ in valence reflect similar inferences about positive versus negative experiences. Third, emotional states that primarily differ in scope (timespan and object-specificity) reflect similar inferences about the immediate context versus the general environment. We apply these principles to integrate a large body of empirical research on the causes and consequences of different emotions with the computational and cognitive neuroscience of learning and decision making. The results bring to light an emotional ecosystem composed of multiple interacting elements which together serve to evaluate outcomes (pleasure & pain), learn expected values (happiness & sadness), adjust behavior (content & anger), and plan in order to realize (desire & hope) or avoid (fear & anxiety) uncertain future prospects.
View a recording of this session here.