Computational Neuroscience Labs, ATR Institute International, Kyoto, Japan; Institute of Cognitive Neuroscience, University College London, London, United Kingdom
Asuka Yamamoto
Computational Neuroscience Labs, ATR Institute International, Kyoto, Japan; School of Information Science, Nara Institute of Science and Technology, Nara, Japan
Maryam Hashemzadeh
Department of Computing Science, University of Alberta, Edmonton, Canada
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.
