Likelihood-based parameter estimation and comparison of dynamical cognitive models
| Autor: | Felix A. Wichmann, Sebastian Reich, Ralf Engbert, Heiko H. Schütt, Lars O. M. Rothkegel, Hans A. Trukenbrod |
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| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Department Psychologie Inference Bayesian inference 01 natural sciences 050105 experimental psychology Methodology (stat.ME) 010104 statistics & probability Bayes' theorem Cognition ddc:150 Humans 0501 psychology and cognitive sciences Computer Simulation 0101 mathematics General Psychology Statistics - Methodology Likelihood Functions Models Statistical Computer simulation Estimation theory 05 social sciences Experimental data Statistical model Bayes Theorem Likelihood function Algorithm |
| Zdroj: | Psychological review. 124(4) |
| ISSN: | 1939-1471 |
| Popis: | 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. Here 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 non-dynamical 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. 29 pages, 10 figures, to appear in Psychological Review as a theoretical note |
| Databáze: | OpenAIRE |
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