Identifying Informative Environments for Cognition Parameter Inference via Bayesian Experimental Design
Computational cognitive modeling seeks to infer latent cognitive mechanisms underlying observed behavior. Bayesian inverse planning provides a principled framework for such inference, but its success depends critically on the experimental environment. Existing approaches typically treat environments as fixed, leaving open the question of which cognitive experiments are most informative for cognition parameter inference. We formulate the design of cognitive planning experiments as a Bayesian Experimental Design (BED) problem, treating the experimental environment as the design variable. We establish an exact Monte Carlo BED benchmark and introduce an amortized Bayesian experimental design ...
| Authors | Manisha Dubey, Rimvydas Rubavicius, N. Siddharth et al. |
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| Primary category | cs.AI |
| Published | 30 July 2026 |
| Last updated at source | 3 August 2026 |
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