A Bayesian Reformulation of the Extended Drift-Diffusion Model in Perceptual Decision Making
| Autor: | Pouyan Rafiei Fard, Stefan J. Kiebel, Andrej Warkentin, Hame Park, Sebastian Bitzer |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
parameter fitting
Computer science Bayesian probability Neuroscience (miscellaneous) Bayesian inference Machine learning computer.software_genre single-trial models 050105 experimental psychology lcsh:RC321-571 03 medical and health sciences Cellular and Molecular Neuroscience 0302 clinical medicine Influence diagram Bayesian hierarchical modeling 0501 psychology and cognitive sciences ddc:610 lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Bayesian average Bayesian models Original Research business.industry 05 social sciences Variable-order Bayesian network Bayesian statistics perzeptuelle Entscheidungsfindung Drift-Diffusionsmodell Bayes-Modelle Parameteranpassung exakte Eingangsmodellierung Modellvergleich Single-Trial-Modelle Technische Universität Dresden Publikationsfonds Perceptual decision drift-diffusion model model comparison perceptual decision making drift-diffusion model Bayesian models parameter fitting exact input modeling model comparison single-trial models Technische Universität Dresden Publishing Fund Artificial intelligence exact input modeling business computer 030217 neurology & neurosurgery perceptual decision making Neuroscience |
| Zdroj: | Frontiers in Computational Neuroscience Frontiers in Computational Neuroscience, Vol 11 (2017) |
| Popis: | Perceptual decision making can be described as a process of accumulating evidence to a bound which has been formalized within drift-diffusion models. Recently, an equivalent Bayesian model has been proposed. In contrast to standard drift-diffusion models, this Bayesian model directly links information in the stimulus to the decision process. Here, we extend this Bayesian model further and allow inter-trial variability of two parameters following the extended version of the drift-diffusion model. We derive parameter distributions for the Bayesian model and show that they lead to predictions that are qualitatively equivalent to those made by the extended drift-diffusion model. Further, we demonstrate the usefulness of the extended Bayesian model for the analysis of concrete behavioral data. Specifically, using Bayesian model selection, we find evidence that including additional inter-trial parameter variability provides for a better model, when the model is constrained by trial-wise stimulus features. This result is remarkable because it was derived using just 200 trials per condition, which is typically thought to be insufficient for identifying variability parameters in drift-diffusion models. In sum, we present a Bayesian analysis, which provides for a novel and promising analysis of perceptual decision making experiments. |
| Databáze: | OpenAIRE |
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