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Review
. 2023 Jan:144:104977.
doi: 10.1016/j.neubiorev.2022.104977. Epub 2022 Nov 24.

Emotions as computations

Aviv Emanuel  1 , Eran Eldar  2
Affiliations
Review

Emotions as computations

Aviv Emanuel et al. Neurosci Biobehav Rev. 2023 Jan.

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.

Keywords: Computational modeling; Emotion; Mood; Reinforcement learning; Reward.

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Conflict of interest statement

Conflict of interest None.

Figures

Figure 1.
Figure 1.. Determinants of positive and negative prospective emotions.
A. If the distribution of possible outcomes is wider, there are more probable prospects with high deviation from the expected value, and thus both positive and negative emotions are stronger. The estimated distribution can be wider either because outcomes are random (irreducible uncertainty) or due to lack of information (estimation uncertainty). B. If the skewness of the distribution is negative, then there are more probable prospects with a high negative deviation from the expected value, and thus negative emotions predominate. Conversely, if skewness is positive, then there are more prospects with a high positive deviation, and thus positive emotions predominate.
Figure 2.
Figure 2.
Summary of computations we propose are represented by emotional states.
Figure 3.
Figure 3.. Action-evaluation emotions.
Different emotion labels are used for different combinations of culpable agent (‘poor outcome due to’) and the agent suffering as a result (‘poor outcome suffered by’). These labels are paralleled by corresponding labels in the domain of positive outcomes (e.g., ‘anger’ correspond to ‘gratitude’, ‘frustration corresponds to ‘content’)
Figure 4.
Figure 4.. Proposed impact of reward within the emotion circumplex.
The impact differs for changes in expected reward that have already happened (retrospective) and those that may happen in the future (prospective). Note that ‘reward’ in our account refers to a reward function that sums across both rewarding (positive) and punishing (negative) attributes of states and actions.
See this image and copyright information in PMC

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