aiqbench analysis
This research advances computational cognitive modeling by treating experimental environments as design variables within a Bayesian framework, optimizing how we infer latent human cognition parameters. By introducing an amortized approach to Bayesian Experimental Design, the study significantly reduces computational costs while maintaining high accuracy in identifying informative tasks for process-tracing paradigms like Mouselab-MDP. These findings offer a principled methodology for researchers aiming to maximize information gain and posterior recoverability when designing cognitive experiments using inverse planning techniques.
Identifying Informative Environments for Cognition Parameter Inference via Bayesian Experimental Design
arXiv:2607.28894v1 Announce Type: new Abstract: 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.
Abstract: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.
Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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 arXiv-issued DOI via DataCite (pending registration)
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