Online simulator-based experimental design for cognitive model selection
This addresses the challenge of efficient experimental design for cognitive scientists working with computationally intensive models, representing a novel method rather than an incremental improvement.
The paper tackles the problem of selecting between complex cognitive models with intractable likelihoods using limited experimental trials, proposing BOSMOS, which achieves accurate model selection in up to 2 orders of magnitude less time than existing alternatives in simulated tasks.
The problem of model selection with a limited number of experimental trials has received considerable attention in cognitive science, where the role of experiments is to discriminate between theories expressed as computational models. Research on this subject has mostly been restricted to optimal experiment design with analytically tractable models. However, cognitive models of increasing complexity, with intractable likelihoods, are becoming more commonplace. In this paper, we propose BOSMOS: an approach to experimental design that can select between computational models without tractable likelihoods. It does so in a data-efficient manner, by sequentially and adaptively generating informative experiments. In contrast to previous approaches, we introduce a novel simulator-based utility objective for design selection, and a new approximation of the model likelihood for model selection. In simulated experiments, we demonstrate that the proposed BOSMOS technique can accurately select models in up to 2 orders of magnitude less time than existing LFI alternatives for three cognitive science tasks: memory retention, sequential signal detection and risky choice.