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Related Experiment Video

Updated: Nov 8, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Robust and distributed neural representation of action values.

Eun Ju Shin^1,2, Yunsil Jang^1,2, Soyoun Kim³

¹Center for Synaptic Brain Dysfunctions, Institute for Basic Science, Daejeon, Republic of Korea.

|April 20, 2021

Summary

Researchers found that brain signals related to action value, which guide choices toward rewards, are widespread. These signals are not solely due to neural correlations or other decision factors, confirming their broad distribution.

Keywords:

action value chosen value monkey neuroscience rat reinforcement learning rhesus macaque striatum

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Area of Science:

Neuroscience
Decision Science
Computational Neuroscience

Background:

Action-value signals in the brain are thought to bias choices toward higher expected rewards.
Previous studies may have overestimated these signals due to neural activity correlations and other decision variables.

Purpose of the Study:

To rigorously test the validity of previously identified action-value signals in the striatum, frontal cortex, and hippocampus.
To determine if these signals are influenced by serial correlations and other decision variables.

Main Methods:

Analysis of neural activity from rat, monkey, and human studies.
Application of statistical tests using permutation and surrogate data.
Examination of neural signals in the striatum, frontal cortex, and hippocampus.

Main Results:

Action-value signals in key brain areas are not fully explained by serial correlations.
Neural activity related to action value is intermixed with signals for other decision variables.
Evidence supports broadly distributed neural signals for action value across the brain.

Conclusions:

Previously identified action-value signals are robust and not merely artifacts of data analysis.
The brain utilizes distributed neural networks for representing and processing action values.
These findings advance our understanding of the neural basis of decision-making and reward processing.