Online simulator-based experimental design for cognitive model selection
Alexander Aushev, Aini Putkonen, Gregoire Clarte, Suyog Chandramouli,, Luigi Acerbi, Samuel Kaski, Andrew Howes

TL;DR
BOSMOS is a novel simulator-based experimental design method that efficiently selects between complex cognitive models without tractable likelihoods, significantly reducing experimental time.
Contribution
The paper introduces BOSMOS, a new adaptive experimental design approach for model selection in cognitive science with intractable likelihoods, using a novel utility objective and likelihood approximation.
Findings
Accurately selects models in less time than existing methods
Demonstrates effectiveness across three cognitive tasks
Reduces computational time by up to 100 times
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.…
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Taxonomy
TopicsMachine Learning and Data Classification · Data Stream Mining Techniques · Machine Learning and Algorithms
