The functional form of value normalization in human reinforcement learning
Abstract
Reinforcement learning research in humans and other species indicates that rewards are represented in a context-dependent manner. More specifically, reward representations seem to be normalized as a function of the value of the alternative options. The dominant view postulates that value context-dependence is achieved via a divisive normalization rule, inspired by perceptual decision-making research. However, behavioral and neural evidence points to another plausible mechanism: range normalization. Critically, previous experimental designs were ill-suited to disentangle the divisive and the range normalization accounts, which generate similar behavioral predictions in many circumstances. To address this question, we designed a new learning task where we manipulated, across learning contexts, the number of options and the value ranges. Behavioral and computational analyses falsify the divisive normalization account and rather provide support for the range normalization rule. Together, these results shed new light on the computational mechanisms underlying context-dependence in learning and decision-making.
Data availability
Data and codes are available here https://github.com/hrl-team/3options
Article and author information
Author details
Funding
European Research Council (101043804)
- Stefano Palminteri
Agence Nationale de la Recherche (ANR-21-CE23-0002-02)
- Stefano Palminteri
Agence Nationale de la Recherche (ANR-21-CE37-0008-01)
- Stefano Palminteri
Agence Nationale de la Recherche (ANR-21-CE28-0024-01)
- Stefano Palminteri
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: The research was carried out following the principles and guidelines for experiments including human participants provided in the declaration of Helsinki (1964, revised in 2013). The INSERM Ethical Review Committee / IRB00003888 approved and participants were provided written informed consent prior to their inclusion
Copyright
© 2023, Bavard & Palminteri
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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