Abstract

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 two orders of magnitude less time than existing LFI alternatives for three cognitive science tasks: memory retention, sequential signal detection, and risky choice.

Original languageEnglish
Pages (from-to)719-737
Number of pages19
JournalComputational Brain and Behavior
Volume6
Issue number4
Early online date21 Sept 2023
DOIs
Publication statusPublished - Dec 2023
MoE publication typeA1 Journal article-refereed

Keywords

  • Cognitive models
  • Experimental design
  • Likelihood-free inference
  • Model selection

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