Identifying Informative Environments for Cognition Parameter Inference via Bayesian Experimental Design

Computer Science > Artificial Intelligence [Submitted on 30 Jul 2026] Identifying Informative Environments for Cognition Parameter Inference via Bayesian Experimental Design Manisha Dubey, Rimvydas Rubavicius, N. Siddharth, Subramanian Ramamoorthy 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 framework for efficient posterior inference and design evaluation. Experiments on the Mouselab-MDP process-tracing paradigm show that amortized BED closely matches the environment rankings of exact Monte Carlo BED while substantially reducing computational cost. We further show that no single environment is uniformly optimal across cognitive inference objectives, revealing trade-offs between expected information gain, posterior recoverability, and information efficiency. These results provide a principled framework for designing informative cognitive experiments for Bayesian parameter inference. Comments: 9 pages, 4 tables, 2 figures Subjects: Artificial Intelligence (cs.AI); Machine Learning (stat.ML) Cite as: arXiv:2607.28894 [cs.AI]   (or arXiv:2607.28894v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2607.28894 Focus to learn more Submission history From: Manisha Dubey [view email] [v1] Thu, 30 Jul 2026 23:26:52 UTC (1,446 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-07 Change to browse by: cs stat stat.ML References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)