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Algorithms for survival: a comparative perspective on emotions

Nature Reviews Neuroscience volume 18, pages 311319 (2017) | Download Citation

Abstract

The nature and neural implementation of emotions is the subject of vigorous debate. Here, we use Bayesian decision theory to address key complexities in this field and conceptualize emotions in terms of their relationship to survival-relevant behavioural choices. Decision theory indicates which behaviours are optimal in a given situation; however, the calculations required are radically intractable. We therefore conjecture that the brain uses a range of pre-programmed algorithms that provide approximate solutions. These solutions seem to produce specific behavioural manifestations of emotions and can also be associated with core affective dimensions. We identify principles according to which these algorithms are implemented in the brain and illustrate our approach by considering decision making in the face of proximal threat.

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Acknowledgements

The authors thank G. Castegnetti, L. Harris, C. Korn, M. Mendl, H. Nakahara, L. Paul, the reviewers and many others for inspiring discussions during the writing of this article. This work was supported by the University of Zurich (to D.R.B.), the Gatsby Charitable Foundation (to P.D.) and a grant from the UK National Centre for the Replacement Refinement and Reduction of Animals in Research (K/00008X/1; to M. Mendl, E. Paul and P.D.). The Wellcome Trust Centre for Neuroimaging is supported by core funding from the Wellcome Trust (091593/Z/10/Z).

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Affiliations

  1. Division for Clinical Psychiatry Research, Psychiatric Hospital, University of Zurich; at the Neuroscience Centre Zurich, University of Zurich, 8032 Zurich, Switzerland; and at the Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, UK.

    • Dominik R. Bach
  2. Gatsby Computational Neuroscience Unit, University College London, London W1T 4JG, UK.

    • Peter Dayan

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Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Dominik R. Bach.

Glossary

Decision theory

A computational-level theory for making choices given information about states and resulting utilities. Bayesian decision theory is a formally optimal (normative) decision theory.

Algorithms

In this article, an algorithm denotes an abstract, self-contained set of operations or effective procedures that maps sensory input and internal state to external and internal actions.

Controllers

In this article, a controller corresponds to a realized neural circuit that is capable of implementing one or several algorithms for choosing or emitting actions.

Constructionist approaches

A family of theoretical approaches that view subjectively experienced mental categories (such as feelings) as constructed representations of more-basic psychological operations, which are not consciously accessible.

Utility functions

A real utility function quantifies how useful or dangerous certain outcomes are to an agent, in a given situation, and is realized in the output of actual neural circuits. A virtual utility function is an as-if construct that provides quantifications that are consistent with behavioural choices, but without necessarily underlying those choices.

Consistency

Choice consistency, or independence, denotes that if A is preferred over B, then A + C is preferred over B + C, irrespective of what C is. This is a fundamental component of expected utility theory and of revealed choice theory.

Transitivity

Assuming that A is preferred over B and that B is preferred over C, these preferences are said to be transitive if A is also preferred over C. This is a fundamental component of expected utility theory and of revealed choice theory.

Pre-programming

In this article, pre-programming refers to any restriction on the workings of a controller that can be cast in Bayesian decision theory terms as an immutable, prior mapping of state or prediction to action, or utility function.

Action contingency

The causal relationship between the execution of actions and the outcomes that result.

Pavlovian

In this article, the term Pavlovian is used to denote an algorithm or a controller making a choice of actions that is insensitive to the actual consequences of those actions. Here, the term is not used to denote design characteristics of experiments (as is sometimes the case).

Instrumental

In this article, the term instrumental refers to an algorithm or a controller making choices that are contingent on their past or predicted future consequences. Here, the term does not refer to design characteristics of experiments.

Model-based

In this article, the term model-based is used to characterize algorithms that exploit a model of the structure of the environment and the outcomes that it affords to make long-run predictions about the future. Predictions need not be action contingent and thus can support either Pavlovian or instrumental controllers.

Model-free

In this article, the term model-free is used to describe algorithms that learn to make long-run predictions by caching or saving experiences from the past, generally by enforcing self-consistency in successive outputs. Predictions are typically scalar, for instance, of summed future value and consequently do not encode the specific outcomes underpinning those values. Model-free predictions need not be action contingent and thus can support either Pavlovian or instrumental controllers.

Appraisal theory

A family of emotion theories, all of which posit that manifestations of emotions (feelings, motivational processes, bodily reactions, and so on) are the output of a set of cognitive appraisals or encompass such appraisals. Theories differ widely according to the appraisals that they consider part of the set.

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https://doi.org/10.1038/nrn.2017.35

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