Value Shapes Abstraction During Learning

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 linking simple visual features. Mixture-of-experts reinforcement learning algorithms revealed that, with learning, high-value abstract representations increasingly guided participants 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 coding scheme predicts a causal role for valuation: 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 tasks feature with rewards evoked abstraction-based decisions. Together, these findings provide a new interpretation of value as a goal-dependent, key factor in forging abstract representations.