By Birte U. Forstmann, Eric-Jan Wagenmakers
Two contemporary strategies, the emergence of formal cognitive types and the addition of cognitive neuroscience information to the conventional behavioral information, have ended in the beginning of a brand new, interdisciplinary box of research: model-based cognitive neuroscience. regardless of the expanding clinical curiosity in model-based cognitive neuroscience, few lively researchers or even fewer scholars have an outstanding wisdom of the 2 constituent disciplines. the most aim of this edited assortment is to advertise the combination of cognitive modeling and cognitive neuroscience. specialists within the box will offer tutorial-style chapters that specify specific strategies and spotlight their usefulness via concrete examples and diverse case experiences. The ebook also will contain a radical checklist of references pointing the reader in the direction of extra literature and on-line resources.
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Extra resources for An Introduction to Model-Based Cognitive Neuroscience
Similarly, different criteria may select models that differ in some ways but are consistent with respect to the theoretical issue under investigation. We illustrate the process in the next section. 1 Examples of Model Selection Our examples again use the lexical decision data from Wagenmakers and colleagues , focusing on variant selection assuming a diffusion model . The example is based on maximum-likelihood fits to individual participant data, with 29 = 512 diffusion variants fit with the methods described in .
In general, we recommend that a variety of multi-panel plots such as those in Fig. 5 be examined, each tailored to particular aspects of the data, with standard errors appropriate to the questions of interest. We also highly recommend plotting versions of these plots for individual participants, which is made easy using trellis graphics . 6 Engage in Model Selection Cognitive models typically have several parameters that can sometimes interact in complex ways. How does a modeler decide which experimental design factors affect each parameter?
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