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Dopamine reward prediction-error signalling: a two-component response

Abstract

Environmental stimuli and objects, including rewards, are often processed sequentially in the brain. Recent work suggests that the phasic dopamine reward prediction-error response follows a similar sequential pattern. An initial brief, unselective and highly sensitive increase in activity unspecifically detects a wide range of environmental stimuli, then quickly evolves into the main response component, which reflects subjective reward value and utility. This temporal evolution allows the dopamine reward prediction-error signal to optimally combine speed and accuracy.

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Figure 1: Reward components.
Figure 2: Sequential neuronal processing of stimulus and reward components.
Figure 3: Factors influencing the initial dopamine activation.
Figure 4: Subjective value and utility coding by the main dopamine response component.
Figure 5: Persistent accurate value representation.

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Acknowledgements

The author thanks A. Dickinson, P. Bossaerts, C. R. Plott and C. Harris for discussions about animal learning theory and experimental economics; his collaborators on the cited studies for their ingenuity, work and patience; and three anonymous referees for comments. The author is also indebted to K. Nomoto, M. Sakagami and C. D. Fiorillo, whose recent experiments encouraged the ideas proposed in this article. The author acknowledges grant support from the Wellcome Trust (Principal Research Fellowship, Programme and Project Grants: 058365, 093270 and 095495), the European Research Council (ERC Advanced Grant 293549) and the US National Institutes of Health Caltech Conte Center (P50MH094258).

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Multi-component neuronal responses (PDF 539 kb)

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Aversive dopamine activations? (PDF 620 kb)

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Glossary

Behavioural pseudoconditioning

A situation in which the context (environment) is paired, through Pavlovian conditioning, to a reinforcer that is present in this environment. Any stimulus occurring in this context thus reflects the same association, without being explicitly paired with the reinforcer. Pseudoconditioning endows an unpaired stimulus with motivational value.

Context conditioning

An association between a specific stimulus (for example, a reward or punisher) and a context (for example, an environment, including all stimuli except the specific explicit stimulus).

Down states

Neuronal membrane states that are defined by hyperpolarized membrane potentials and very little firing.

Economic utility

A mathematical, usually nonlinear function that derives the internal subjective reward value u from the objective value x. Utility is the fundamental variable that decision-makers maximize in rational economic choices between differently valued options.

Hebbian learning

A cellular mechanism of learning, proposed by Donald Hebb, according to which the connection between a presynaptic and a postsynaptic cell is strengthened if the presynaptic cell is successful in activating a postsynaptic cell.

Motivational salience

The ability of a stimulus to elicit attention due to its positive (reward) or negative (punishment) motivational value. Motivational salience is common to reward and punishment.

Novelty salience

The ability of a stimulus to elicit attention due to its novelty.

Physical salience

The ability of a stimulus to elicit attention by standing out, due to its physical intensity or conspicuousness.

Rescorla–Wagner model

The prime error-driven reinforcement model for Pavlovian conditioning, in which the prediction error (reward or punishment outcome minus current prediction) is multiplied by a learning factor and added to the current prediction to result in an updated prediction.

Surprise salience

The ability of a stimulus to elicit attention due to its unexpectedness.

Temporal difference reinforcement models

A family of non-trial-based reinforcement learning models in which the difference between the expected and actual values of a particular state (prediction error) in a sequence of behaviours is used as a teaching signal to facilitate the acquisition of associative rules or policies to direct future behaviour. Temporal difference learning extends Rescorla–Wagner-type reinforcement models to real time and higher-order reinforcers.

Up states

Neuronal membrane states that are defined by relatively depolarized membrane potentials and lots of action potential firing.

Visual search task

An experimental paradigm in which subjects are asked to detect a 'target' item (for example, a red dot) among an array of distractor items (for example, many green dots).

Voltammetry

An electrochemical measurement of oxidation-reduction currents across a range of imposed voltages, used in neuroscience for assessing concentrations of specific molecules, such as dopamine.

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Schultz, W. Dopamine reward prediction-error signalling: a two-component response. Nat Rev Neurosci 17, 183–195 (2016). https://doi.org/10.1038/nrn.2015.26

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