Transferring structural knowledge across cognitive maps in humans and models

Shirley Mark (), Rani Moran, Thomas Parr, Steve W. Kennerley and Timothy E. J. Behrens
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Shirley Mark: Wellcome Trust Centre for Neuroimaging
Rani Moran: Max Planck UCL Center for Computational Psychiatry and Aging Research
Thomas Parr: Wellcome Trust Centre for Neuroimaging
Steve W. Kennerley: University College London
Timothy E. J. Behrens: University of Oxford, John Radcliffe Hospital

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: Abstract Relations between task elements often follow hidden underlying structural forms such as periodicities or hierarchies, whose inferences fosters performance. However, transferring structural knowledge to novel environments requires flexible representations that are generalizable over particularities of the current environment, such as its stimuli and size. We suggest that humans represent structural forms as abstract basis sets and that in novel tasks, the structural form is inferred and the relevant basis set is transferred. Using a computational model, we show that such representation allows inference of the underlying structural form, important task states, effective behavioural policies and the existence of unobserved state-trajectories. In two experiments, participants learned three abstract graphs during two successive days. We tested how structural knowledge acquired on Day-1 affected Day-2 performance. In line with our model, participants who had a correct structural prior were able to infer the existence of unobserved state-trajectories and appropriate behavioural policies.

Date: 2020
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DOI: 10.1038/s41467-020-18254-6

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