Value representations in the rodent orbitofrontal cortex drive learning, not choice

Abstract
Data availability
Article and author information
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Abstract

Humans and animals make predictions about the rewards they expect to receive in different situations. In formal models of behavior, these predictions are known as value representations, and they play two very different roles. Firstly, they drive choice: the expected values of available options are compared to one another, and the best option is selected. Secondly, they support learning: expected values are compared to rewards actually received, and future expectations are updated accordingly. Whether these different functions are mediated by different neural representations remains an open question. Here we employ a recently-developed multi-step task for rats that computationally separates learning from choosing. We investigate the role of value representations in the rodent orbitofrontal cortex, a key structure for value-based cognition. Electrophysiological recordings and optogenetic perturbations indicate that these representations do not directly drive choice. Instead, they signal expected reward information to a learning process elsewhere in the brain that updates choice mechanisms.

Data availability

Data collected for the purpose of this paper will be posted on Figshare upon acceptance. Software used to analyze the data will be made available as a Github release. Software used for training rats and design files for constructing behavioral rigs are available on the Brody lab website.

The following data sets were generated

1. Kevin J Miller
(2022) Data from Value Representations in the Rodent Orbitofrontal Cortex Drive Learning, not Choice
Figshare, 10.6084/m9.figshare.20449140.

https://figshare.com/articles/dataset/ofc-learning-choosing-paper-files/20449140
1. Kevin J Miller
(2022) Code from Value Representations in the Rodent Orbitofrontal Cortex Drive Learning, not Choice
10.5281/zenodo.6974148.

https://github.com/kevin-j-miller/MBB2022-orbitofrontal-learning-choosing

Article and author information

Author details

Kevin J Miller

DeepMind, London, United Kingdom

For correspondence
kevinjmiller@deepmind.com

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-3465-2512
Matthew M Botvinick

DeepMind, London, United Kingdom

For correspondence
botvinick@deepmind.com

Competing interests
The authors declare that no competing interests exist.
Carlos D Brody

Princeton Neuroscience Institute, Princeton University, Princeton, United States

For correspondence
brody@princeton.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-4201-561X

Funding

National Institutes of Health (T-32 MH065214)

Kevin J Miller
Matthew M Botvinick
Carlos D Brody

Princeton University (Harold W Dodds Fellowship)

Kevin J Miller

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All experimental procedures were performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health., and were approved by the Princeton University Institutional Animal Care and Use Committee (protocol #1853)

Copyright

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.