Online simulator-based experimental design for cognitive model selection

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. 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.