Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.29.361469v1?rss=1 Authors: Cortese, A., Yamamoto, A., Hashemzadeh, M., Sepulveda, P., Kawato, M., De Martino, B. Abstract: 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. Copy rights belong to original authors. Visit the link for more info