Computational Model for the Construction of Cognitive Maps

A Computational Model of Cognitive Maps

Journal of Conflict Resolution ◽

10.1177/0022002714540473 ◽

2014 ◽

Vol 60 (2) ◽

pp. 368-399 ◽

Cited By ~ 1

Author(s):

Stephanie Dornschneider ◽

Nick Henderson

Keyword(s):

Computational Model ◽

Cognitive Maps

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Transferring structural knowledge across cognitive maps in humans and models

Nature Communications ◽

10.1038/s41467-020-18254-6 ◽

2020 ◽

Vol 11 (1) ◽

Cited By ~ 1

Author(s):

Shirley Mark ◽

Rani Moran ◽

Thomas Parr ◽

Steve W. Kennerley ◽

Timothy E. J. Behrens

Keyword(s):

Computational Model ◽

Cognitive Maps ◽

Important Task ◽

Basis Set ◽

Structural Knowledge ◽

Basis Sets ◽

Structural Form ◽

Structural Prior ◽

Novel Environments

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.

Download Full-text

Transferring structural knowledge across cognitive maps in humans and models

10.1101/860478 ◽

2019 ◽

Cited By ~ 2

Author(s):

Shirley Mark ◽

Rani Moran ◽

Thomas Parr ◽

Steve Kennerley ◽

Tim Behrens

Keyword(s):

Computational Model ◽

Cognitive Maps ◽

Important Task ◽

Basis Set ◽

Structural Knowledge ◽

Basis Sets ◽

Structural Form ◽

Structural Prior ◽

Novel Environments

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.

Download Full-text