TY - JOUR
T1 - Parameter inference for computational cognitive models with approximate bayesian computation
AU - Kangasrääsiö, Antti
AU - Jokinen, Jussi P. P.
AU - Oulasvirta, Antti
AU - Howes, Andrew
AU - Kaski, Samuel
PY - 2019/6/3
Y1 - 2019/6/3
N2 - 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.
AB - 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.
KW - Approximate Bayesian computation
KW - Bayesian optimization
KW - Cognitive models
KW - Computational statistics
KW - Inference
KW - Machine learning
KW - Parameter estimation
UR - http://www.scopus.com/inward/record.url?scp=85067290215&partnerID=8YFLogxK
U2 - 10.1111/cogs.2019.43.issue-6
DO - 10.1111/cogs.2019.43.issue-6
M3 - Article
SN - 0364-0213
VL - 43
JO - Cognitive Science
JF - Cognitive Science
IS - 6
M1 - e12738
ER -