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Value signals guide abstraction during learning
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. Reinforc...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8331191/ https://www.ncbi.nlm.nih.gov/pubmed/34254586 http://dx.doi.org/10.7554/eLife.68943 |
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author | Cortese, Aurelio Yamamoto, Asuka Hashemzadeh, Maryam Sepulveda, Pradyumna Kawato, Mitsuo De Martino, Benedetto |
author_facet | Cortese, Aurelio Yamamoto, Asuka Hashemzadeh, Maryam Sepulveda, Pradyumna Kawato, Mitsuo De Martino, Benedetto |
author_sort | Cortese, Aurelio |
collection | PubMed |
description | 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 – prioritised 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. |
format | Online Article Text |
id | pubmed-8331191 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-83311912021-08-04 Value signals guide abstraction during learning Cortese, Aurelio Yamamoto, Asuka Hashemzadeh, Maryam Sepulveda, Pradyumna Kawato, Mitsuo De Martino, Benedetto eLife Neuroscience 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 – prioritised 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. eLife Sciences Publications, Ltd 2021-07-13 /pmc/articles/PMC8331191/ /pubmed/34254586 http://dx.doi.org/10.7554/eLife.68943 Text en © 2021, Cortese et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Neuroscience Cortese, Aurelio Yamamoto, Asuka Hashemzadeh, Maryam Sepulveda, Pradyumna Kawato, Mitsuo De Martino, Benedetto Value signals guide abstraction during learning |
title | Value signals guide abstraction during learning |
title_full | Value signals guide abstraction during learning |
title_fullStr | Value signals guide abstraction during learning |
title_full_unstemmed | Value signals guide abstraction during learning |
title_short | Value signals guide abstraction during learning |
title_sort | value signals guide abstraction during learning |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8331191/ https://www.ncbi.nlm.nih.gov/pubmed/34254586 http://dx.doi.org/10.7554/eLife.68943 |
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