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Poster C114 in Poster Session C - Friday, August 9, 2024, 11:15 am – 1:15 pm, Johnson Ice Rink
Neural Prioritisation of Past Solutions Supports Generalisation
Sam Hall-McMaster1,2 (), Momchil S. Tomov1,3, Samuel J. Gershman1,4,6, Nicolas W. Schuck2,5,6; 1Harvard University, 2Max Planck Institute for Human Development, 3Motional AD LLC, 4Massachusetts Institute of Technology, 5University of Hamburg, 6Equal contribution
How do we decide what to do in new situations? One way to solve this dilemma is to reuse solutions developed for other situations. There is now some evidence that a computational process capturing this idea – called successor features & generalised policy improvement – can account for how humans transfer prior solutions to new situations. Here we asked whether a simple formulation of this idea could explain human brain activity in response to new tasks. Participants completed a multi-task learning experiment during fMRI (n=40). The experiment included training tasks that participants could use to learn about their environment, and test tasks to probe their generalisation strategy. Behavioural results showed that people learned optimal solutions (policies) to the training tasks, and reused them on test tasks in a reward-selective manner. Neural results showed that optimal solutions from the training tasks received prioritised processing during test tasks in occipitotemporal cortex and dorsolateral prefrontal cortex. These findings suggest that humans evaluate and generalise successful past solutions when solving new tasks.
Keywords: Decision-making Generalisation fMRI Decoding