By Birte U. Forstmann, Eric-Jan Wagenmakers
Two fresh options, the emergence of formal cognitive versions and the addition of cognitive neuroscience info to the normal 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 energetic researchers or even fewer scholars have an exceptional wisdom of the 2 constituent disciplines. the most target of this edited assortment is to advertise the mixing of cognitive modeling and cognitive neuroscience. specialists within the box will offer tutorial-style chapters that designate specific concepts and spotlight their usefulness via concrete examples and various case reviews. The ebook also will comprise a radical record of references pointing the reader in the direction of extra literature and on-line resources.
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Additional info for An Introduction to Model-Based Cognitive Neuroscience
In their first experiment participants were given instructions that emphasised either the accuracy or speed of responding. They fit a relatively simple 12-parameter diffusion model to these data, assuming that instructions selectively influenced response caution and bias, whereas stimulus type selectively influenced the mean drift rate. Rae and colleagues  refit these data, including two extra participants not included in the originally reported data set (17 in total), in order to investigate the selective influence assumption about emphasis.
Brown and Eric-Jan Wagenmakers Abstract Cognitive modeling can provide important insights into the underlying causes of behavior, but the validity of those insights rests on careful model development and checking. We provide guidelines on five important aspects of the practice of cognitive modeling: parameter recovery, testing selective influence of experimental manipulations on model parameters, quantifying uncertainty in parameter estimates, testing and displaying model fit, and selecting among different model parameterizations and types of models.
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