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Value Shapes Abstraction During Learning

By Aurelio Cortese, Asuka Yamamoto, Maryam Hashemzadeh, Pradyumna Sepulveda, Mitsuo Kawato, Benedetto De Martino

Posted 30 Oct 2020
bioRxiv DOI: 10.1101/2020.10.29.361469

Abstractions are critical for flexible behaviours and efficient learning. However, how the brain forgoes the sensory dimension to forge abstract entities remains elusive. Here, in two fMRI experiments, we demonstrate a mechanism of abstraction built upon valuation of task-relevant sensory features. Human volunteers learned hidden association rules between visual features. Computational modelling of participants' choice data with mixture-of-experts reinforcement learning algorithms revealed that, with learning, emerging high-value abstract representations increasingly guided behaviour. Moreover, the brain area encoding value signals - the ventromedial prefrontal cortex - also prioritized and selected latent task elements, both locally and through its connection to visual cortex. In a second experiment, we used multivoxel neural reinforcement to show how reward-tagging the neural sensory representation of a task's feature evoked abstraction-based decisions. Our findings redefine the logic of valuation as a goal-dependent, key factor in constructing the abstract representations that govern intelligent behaviour. ### Competing Interest Statement The authors have declared no competing interest.

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