aalto1 untyped-item.component.html
Online Simulator-Based Experimental Design for Cognitive Model Selection
Loading...
Access rights
openAccess
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Authors
Aushev, Alexander
Putkonen, Aini
Clarté, Grégoire
Chandramouli, Suyog
Acerbi, Luigi
Kaski, Samuel
Howes, Andrew
Date
Major/Subject
Mcode
Degree programme
Language
en
Pages
19
Series
Computational Brain & Behavior, Volume 6, issue 4, pp. 719-737
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.
Description
Funding Information: Open Access funding provided by Aalto University. This work was supported by the Academy of Finland (Flagship programme: Finnish Center for Artificial Intelligence FCAI; grants 328400, 345604, 328400, 320181). AP and SC were funded by the Academy of Finland projects BAD (Project ID: 318559) and Human Automata (Project ID: 328813). AP was additionally funded by Aalto University School of Electrical Engineering. SC was also funded by Interactive Artificial Intelligence for Research and Development (AIRD) grant by Future Makers. SK was supported by the Engineering and Physical Sciences Research Council (EPSRC; Project ID: EP-W002973-1). Computational resources were provided by the Aalto Science-IT Project. Publisher Copyright: © 2023, The Author(s).
Other note
Citation
Aushev, A, Putkonen, A, Clarté, G, Chandramouli, S, Acerbi, L, Kaski, S & Howes, A 2023, 'Online Simulator-Based Experimental Design for Cognitive Model Selection', Computational Brain & Behavior, vol. 6, no. 4, pp. 719-737. https://doi.org/10.1007/s42113-023-00180-7