Value signals guide abstraction during learning

Abstract
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Abstract

The human brain excels at constructing and using abstractions, such as rules, or concepts. Here, in two fMRI experiments, we demonstrate a mechanism of abstraction built upon the valuation of sensory features. Human volunteers learned novel association rules based on simple visual features. Reinforcement-learning algorithms revealed that, with learning, high-value abstract representations increasingly guided participant behaviour, resulting in better choices and higher subjective confidence. We also found that the brain area computing value signals - the ventromedial prefrontal cortex - prioritized and selected latent task elements during abstraction, both locally and through its connection to the visual cortex. Such a coding scheme predicts a causal role for valuation. Hence, in a second experiment, we used multivoxel neural reinforcement to test for the causality of feature valuation in the sensory cortex, as a mechanism of abstraction. Tagging the neural representation of a task feature with rewards evoked abstraction-based decisions. Together, these findings provide a novel interpretation of value as a goal-dependent, key factor in forging abstract representations.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 1-6.

Article and author information

Author details

Aurelio Cortese

Computational Neuroscience Laboratories, ATR Institute International, Soraku-gun, Japan

For correspondence
cortese.aurelio@gmail.com

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-4567-0924
Asuka Yamamoto

Computational Neuroscience Laboratories, ATR Institute International, Soraku-gun, Japan

Competing interests
The authors declare that no competing interests exist.
Maryam Hashemzadeh

Computer Science, University of Alberta, Edmonton, Canada

Competing interests
The authors declare that no competing interests exist.
Pradyumna Sepulveda

Institute of Cognitive Neuroscience, University College London, London, United Kingdom

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-0159-6777
Mitsuo Kawato

Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan

Competing interests
The authors declare that no competing interests exist.
Benedetto De Martino

Institute of Cognitive Neuroscience, University College London, London, United Kingdom

For correspondence
benedettodemartino@gmail.com

Competing interests
The authors declare that no competing interests exist.

Funding

Japan Science and Technology Agency (JPMJER1801)

Aurelio Cortese
Mitsuo Kawato

Japan Agency for Medical Research and Development (JP18dm0307008)

Aurelio Cortese
Mitsuo Kawato

Chilean National Agency for Research and Development (72180193)

Pradyumna Sepulveda

Wellcome Trust (102612/A/13/Z)

Benedetto De Martino

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

Ethics

Human subjects: All experiments and data analyses were conducted at the Advanced Telecommunications Research Institute International (ATR). The study was approved by the Institutional Review Board of ATR with ethics protocol numbers 18-122, 19-122, 20-122. All participants gave written informed consent.

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.