Likelihood-based parameter estimation and comparison of dynamical cognitive models
- Dynamical models of cognition play an increasingly important role in driving theoretical and experimental research in psychology. Therefore, parameter estimation, model analysis and comparison of dynamical models are of essential importance. In this article, we propose a maximum likelihood approach for model analysis in a fully dynamical framework that includes time-ordered experimental data. Our methods can be applied to dynamical models for the prediction of discrete behavior (e.g., movement onsets); in particular, we use a dynamical model of saccade generation in scene viewing as a case study for our approach. For this model, the likelihood function can be computed directly by numerical simulation, which enables more efficient parameter estimation including Bayesian inference to obtain reliable estimates and corresponding credible intervals. Using hierarchical models inference is even possible for individual observers. Furthermore, our likelihood approach can be used to compare different models. In our example, the dynamicalDynamical models of cognition play an increasingly important role in driving theoretical and experimental research in psychology. Therefore, parameter estimation, model analysis and comparison of dynamical models are of essential importance. In this article, we propose a maximum likelihood approach for model analysis in a fully dynamical framework that includes time-ordered experimental data. Our methods can be applied to dynamical models for the prediction of discrete behavior (e.g., movement onsets); in particular, we use a dynamical model of saccade generation in scene viewing as a case study for our approach. For this model, the likelihood function can be computed directly by numerical simulation, which enables more efficient parameter estimation including Bayesian inference to obtain reliable estimates and corresponding credible intervals. Using hierarchical models inference is even possible for individual observers. Furthermore, our likelihood approach can be used to compare different models. In our example, the dynamical framework is shown to outperform nondynamical statistical models. Additionally, the likelihood based evaluation differentiates model variants, which produced indistinguishable predictions on hitherto used statistics. Our results indicate that the likelihood approach is a promising framework for dynamical cognitive models.…
Author details: | Heiko Herbert SchüttORCiDGND, Lars Oliver Martin RothkegelORCiDGND, Hans Arne TrukenbrodORCiD, Sebastian ReichORCiDGND, Felix A. WichmannORCiD, Ralf EngbertORCiDGND |
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DOI: | https://doi.org/10.1037/rev0000068 |
ISSN: | 0033-295X |
ISSN: | 1939-1471 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/28447811 |
Title of parent work (English): | Psychological Review |
Publisher: | American Psychological Association |
Place of publishing: | Washington |
Publication type: | Article |
Language: | English |
Date of first publication: | 2017/07/01 |
Publication year: | 2017 |
Release date: | 2022/04/13 |
Tag: | dynamical model; eye movements; likelihood; model comparison; model fitting |
Volume: | 124 |
Issue: | 4 |
Number of pages: | 20 |
First page: | 505 |
Last Page: | 524 |
Funding institution: | Deutsche Forschungsgemeinschaft [EN 471/13-1, WI 2103/4-1] |
Organizational units: | Humanwissenschaftliche Fakultät / Strukturbereich Kognitionswissenschaften / Department Psychologie |
DDC classification: | 1 Philosophie und Psychologie / 15 Psychologie / 150 Psychologie |
Peer review: | Referiert |
Publishing method: | Open Access / Green Open-Access |