A dynamic code for economic object valuation in prefrontal cortex neurons

Abstract Neuronal reward valuations provide the physiological basis for economic behaviour. Yet, how such valuations are converted to economic decisions remains unclear. Here we show that the dorsolateral prefrontal cortex (DLPFC) implements a flexible value code based on object-specific valuations by single neurons. As monkeys perform a reward-based foraging task, individual DLPFC neurons signal the value of specific choice objects derived from recent experience. These neuronal object values satisfy principles of competitive choice mechanisms, track performance fluctuations and follow predictions of a classical behavioural model (Herrnstein’s matching law). Individual neurons dynamically encode both, the updating of object values from recently experienced rewards, and their subsequent conversion to object choices during decision-making. Decoding from unselected populations enables a read-out of motivational and decision variables not emphasized by individual neurons. These findings suggest a dynamic single-neuron and population value code in DLPFC that advances from reward experiences to economic object values and future choices.

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Primate prefrontal neurons signal economic risk derived from the statistics of recent reward experience

eLife ◽

10.7554/elife.44838 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 3

Author(s):

Fabian Grabenhorst ◽

Ken-Ichiro Tsutsui ◽

Shunsuke Kobayashi ◽

Wolfram Schultz

Keyword(s):

Dorsolateral Prefrontal Cortex ◽

Choice Task ◽

Support Vector ◽

Probabilistic Choice ◽

Economic Decisions ◽

Decision Variables ◽

Dorsolateral Prefrontal ◽

Process Risk ◽

The Brain ◽

Statistical Variance

Risk derives from the variation of rewards and governs economic decisions, yet how the brain calculates risk from the frequency of experienced events, rather than from explicit risk-descriptive cues, remains unclear. Here, we investigated whether neurons in dorsolateral prefrontal cortex process risk derived from reward experience. Monkeys performed in a probabilistic choice task in which the statistical variance of experienced rewards evolved continually. During these choices, prefrontal neurons signaled the reward-variance associated with specific objects (‘object risk’) or actions (‘action risk’). Crucially, risk was not derived from explicit, risk-descriptive cues but calculated internally from the variance of recently experienced rewards. Support-vector-machine decoding demonstrated accurate neuronal risk discrimination. Within trials, neuronal signals transitioned from experienced reward to risk (risk updating) and from risk to upcoming choice (choice computation). Thus, prefrontal neurons encode the statistical variance of recently experienced rewards, complying with formal decision variables of object risk and action risk.

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Population coding of strategic variables during foraging in freely-moving macaques

10.1101/811992 ◽

2019 ◽

Cited By ~ 3

Author(s):

Neda Shahidi ◽

Paul Schrater ◽

Tony Wright ◽

Xaq Pitkow ◽

Valentin Dragoi

Keyword(s):

Prefrontal Cortex ◽

Time Course ◽

Internal Representation ◽

Population Coding ◽

Neural Responses ◽

Population Activity ◽

Relevant Task ◽

Dorsolateral Prefrontal ◽

Future Outcomes ◽

Foraging Task

Animals forage within their environment to extract valuable resources at lowest cost. Previous studies have suggested that animals simply maximize the current flow of reward without predicting the future outcomes of their actions and that this recent reward rate is represented in various brain areas. To test this, we devised a foraging task in which the relevant reward dynamics were hidden from the animal, and wirelessly record population activity in dorsolateral prefrontal cortex (dlPFC) while monkeys forage freely in their environment. We discover that their brains indeed contain predictions of future rewards and plans of their next actions. By decoding the dynamic reward probability and the memory of recent outcomes from the dlPFC population response, we show that monkeys create an internal representation of reward dynamics. The decoded variables predicted animal’s subsequent actions better than either the true experimental variables or the raw neural responses. Our results suggest that the relevant task variables and behavioral decisions are dynamically encoded in prefrontal cortex during the time course of foraging.

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