Abstract
Mathematical models are a useful tool for gaining insight into mechanisms of decision making. However, like other scientific methods, its application is not without pitfalls. This chapter demonstrates that it can be difficult to distinguish between alternative models and it illustrates that a model-based approach benefits from the availability of a rich dataset that provides sufficient constraints. Ideally, the dataset is not only comprised of behavioral data, but also contains neural data that provide information about the internal processing. The chapter focuses on two examples taken from perceptual decision making. In one case, information about response time distributions is used to reject a model that is otherwise consistent with accuracy data and mean response times. In the other case, only the availability of neural data allows a distinction between two alternative models that are both consistent with the behavioral data.
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Ditterich, J. (2015). Distinguishing Between Models of Perceptual Decision Making. In: Forstmann, B., Wagenmakers, EJ. (eds) An Introduction to Model-Based Cognitive Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2236-9_13
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DOI: https://doi.org/10.1007/978-1-4939-2236-9_13
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