Value signals guide abstraction during learning

eLife

Asuka Yamamoto¹  Pradyumna Sepulveda²  Aurelio Cortese³  Maryam Hashemzadeh³  Mitsuo Kawato⁴  Benedetto De Martino⁵ 

[1] Institute of Cognitive Neuroscience, University College London, London, United Kingdom;School of Information Science, Nara Institute of Science and Technology, Nara, Japan;Computational Neuroscience Labs, ATR Institute International, Kyoto, Japan;Department of Computing Science, University of Alberta, Edmonton, Canada;Institute of Cognitive Neuroscience, University College London, London, United Kingdom;

关键词: reinforcement learning;    abstraction;    vmpfc;    confidence;    multivoxel neural reinforcement;    valuation;   

DOI  :  10.7554/eLife.68943

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown