Value signals guide abstraction during learning

  1. Aurelio Cortese  Is a corresponding author
  2. Asuka Yamamoto
  3. Maryam Hashemzadeh
  4. Pradyumna Sepulveda
  5. Mitsuo Kawato
  6. Benedetto De Martino  Is a corresponding author
  1. ATR Institute International, Japan
  2. University of Alberta, Canada
  3. University College London, United Kingdom
  4. Advanced Telecommunications Research Institute International, Japan

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

  1. 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.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4567-0924
  2. Asuka Yamamoto

    Computational Neuroscience Laboratories, ATR Institute International, Soraku-gun, Japan
    Competing interests
    The authors declare that no competing interests exist.
  3. Maryam Hashemzadeh

    Computer Science, University of Alberta, Edmonton, Canada
    Competing interests
    The authors declare that no competing interests exist.
  4. Pradyumna Sepulveda

    Institute of Cognitive Neuroscience, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0159-6777
  5. 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.
  6. 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.

Reviewing Editor

  1. Thorsten Kahnt, Northwestern University, United States

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.

Version history

  1. Preprint posted: October 30, 2020 (view preprint)
  2. Received: March 30, 2021
  3. Accepted: July 12, 2021
  4. Accepted Manuscript published: July 13, 2021 (version 1)
  5. Version of Record published: August 3, 2021 (version 2)

Copyright

© 2021, Cortese et al.

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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  1. Aurelio Cortese
  2. Asuka Yamamoto
  3. Maryam Hashemzadeh
  4. Pradyumna Sepulveda
  5. Mitsuo Kawato
  6. Benedetto De Martino
(2021)
Value signals guide abstraction during learning
eLife 10:e68943.
https://doi.org/10.7554/eLife.68943

Share this article

https://doi.org/10.7554/eLife.68943

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