Emotions as computations

Aviv Emanuel*, Eran Eldar

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

13 Scopus citations

Abstract

Emotions ubiquitously impact action, learning, and perception, yet their essence and role remain widely debated. Computational accounts of emotion aspire to answer these questions with greater conceptual precision informed by normative principles and neurobiological data. We examine recent progress in this regard and find that emotions may implement three classes of computations, which serve to evaluate states, actions, and uncertain prospects. For each of these, we use the formalism of reinforcement learning to offer a new formulation that better accounts for existing evidence. We then consider how these distinct computations may map onto distinct emotions and moods. Integrating extensive research on the causes and consequences of different emotions suggests a parsimonious one-to-one mapping, according to which emotions are integral to how we evaluate outcomes (pleasure & pain), learn to predict them (happiness & sadness), use them to inform our (frustration & content) and others’ (anger & gratitude) actions, and plan in order to realize (desire & hope) or avoid (fear & anxiety) uncertain outcomes.

Original languageAmerican English
Article number104977
JournalNeuroscience and Biobehavioral Reviews
Volume144
DOIs
StatePublished - Jan 2023

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Ltd

Keywords

  • Computational modeling
  • Emotion
  • Mood
  • Reinforcement learning
  • Reward

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