Neurocomputational Understanding of Decision-Making in Novel Environments

This thesis investigates the neural and computational mechanisms underlying human decision-making in unfamiliar environments through three interconnected studies. The first study demonstrates that aesthetic value computation for visual art can be systematically predicted from visual features, which are hierarchically represented along the brain's rostrocaudal axis, as revealed by combining deep neural networks with functional MRI data. The second study examines feature-based transfer learning, highlighting the importance of slow integration mechanisms, akin to glial cell functions, for effective knowledge transfer in humans. The third study explores how action affordance influences decision-making in novel environments, showing that action selection results from a competitive interaction between affordance-based and value-based systems, with meta-control exerted by the pre-supplementary motor area and anterior cingulate cortex. Taken together, these studies provide a comprehensive neuro-computational perspective for understanding how the brain navigates novel environments by doing feature-based value computation, transferring knowledge, and using affordance as a guide for action selection.