Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation

Antti Kangasrääsiö, Jussi P.P. Jokinen*, Antti Oulasvirta, Andrew Howes, Samuel Kaski

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)
115 Downloads (Pure)

Abstract

This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the progress of computational modeling in cognitive science. In this article, we investigate the capability and role of modern fitting methods—including Bayesian optimization and approximate Bayesian computation—and contrast them to some more commonly used methods: grid search and Nelder–Mead optimization. Our investigation consists of a reanalysis of the fitting of two previous computational models: an Adaptive Control of Thought—Rational model of skill acquisition and a computational rationality model of visual search. The results contrast the efficiency and informativeness of the methods. A key advantage of the Bayesian methods is the ability to estimate the uncertainty of fitted parameter values. We conclude that approximate Bayesian computation is (a) efficient, (b) informative, and (c) offers a path to reproducible results.

Original languageEnglish
Article number12738
Number of pages40
JournalCOGNITIVE SCIENCE
Volume43
Issue number6
DOIs
Publication statusPublished - 1 Jun 2019
MoE publication typeA1 Journal article-refereed

Keywords

  • Approximate Bayesian computation
  • Bayesian optimization
  • Cognitive models
  • Computational statistics
  • Inference
  • Machine learning
  • Parameter estimation

Fingerprint Dive into the research topics of 'Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation'. Together they form a unique fingerprint.

  • Projects

    Interactive machine learning from multiple biodata sources

    Jälkö, J., Hegde, P., Kaski, S., Shen, Z., Siren, J., Gadd, C., Jain, A., Tran, A., Hämäläinen, A. & Trinh, Q.

    01/01/201931/12/2020

    Project: Academy of Finland: Other research funding

    Computational Modelling of Emotional Appraisal in HCI

    Jokinen, J.

    01/09/201731/08/2020

    Project: Academy of Finland: Other research funding

    COMPUTED: Computational User Interface Design

    Oulasvirta, A., Kim, S., Dayama, N., Shiripour, M., Todi, K., Liao, Y., Koch, J., Nioche, A., Feit, A., Bruckner, L. & Leiva, L.

    27/03/201531/08/2020

    Project: EU: ERC grants

    Equipment

    Science-IT

    Mikko Hakala (Manager)

    School of Science

    Facility/equipment: Facility

  • Cite this