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Updated: Jul 24, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

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Published on: August 15, 2020

The functional form of value normalization in human reinforcement learning.

Sophie Bavard^1,2,3, Stefano Palminteri^1,2

¹Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et Recherche Médicale, Paris, France.

|July 10, 2023

Summary

This summary is machine-generated.

Reward value is context-dependent, with range normalization, not divisive normalization, explaining this phenomenon in learning and decision-making. This finding offers new insights into cognitive processes.

Keywords:

cognitive science computational biology computational model decision-making efficient coding human reinforcement learning relative value systems biology

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Area of Science:

Neuroscience
Cognitive Science
Computational Neuroscience

Background:

Reward processing in reinforcement learning is context-dependent.
This context-dependence is often explained by divisive normalization, but range normalization is an alternative mechanism.
Previous studies lacked designs to differentiate between these normalization models.

Purpose of the Study:

To investigate whether divisive or range normalization better explains context-dependent reward representation.
To design a novel learning task capable of distinguishing between these two normalization theories.

Main Methods:

A new learning task was developed manipulating the number of options and value ranges across contexts.
Behavioral data was collected and analyzed.
Computational modeling was employed to test normalization accounts.

Main Results:

Behavioral and computational analyses falsified the divisive normalization account.
Evidence strongly supported the range normalization rule for reward representation.
The study successfully distinguished between the two normalization mechanisms.

Conclusions:

Range normalization, not divisive normalization, underlies context-dependent reward representation in learning.
These findings advance our understanding of the computational mechanisms governing learning and decision-making context-dependence.