By Birte U. Forstmann, Eric-Jan Wagenmakers
Two contemporary techniques, the emergence of formal cognitive versions and the addition of cognitive neuroscience information to the normal behavioral facts, have led to the delivery of a brand new, interdisciplinary box of analysis: 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 options and spotlight their usefulness via concrete examples and various case reports. The ebook also will contain a radical record of references pointing the reader in the direction of extra literature and on-line resources.
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Additional resources for An Introduction to Model-Based Cognitive Neuroscience
9. Quantile-probability (QP) plots  generalize this idea to also display a summary of RT distribution by plotting quantiles on the y-axis (usually the five-quantile summary) instead of the mean. Although the QP plot provides a very compact representation of choice RT data that can be appropriate in some circumstances, we do not recommend it as a general method of investigating model fit for reasons we illustrate in the following example. Rather, we recommend looking at separate plots of accuracy and correct and error RT distributions (or in the n>2 alternative case, RT distributions for each type of choice).
If the researcher’s goal is not just to estimate parameters, but in addition to discriminate between two or more competing theoretical accounts, a similar approach can be used to determine the accuracy of discrimination, called a “model recovery simulation”. Synthetic data are generated from each model, fit using both models, and the results of the fits used to decide which model generated each synthetic data set. The accuracy of these decisions shows the reliability with which the models can be discriminated.
Models of simple perceptual decision making, particularly Ratcliff’s diffusion model ([5, 21, 10]), have around six basic parameters. Their apparent complexity can be justified, however, through tests of selective influence. In seminal work, Ratcliff and Rouder orthogonally manipulated the difficulty of decisions and instructions about cautious vs. speedy decision-making, and demonstrated that manipulations of difficulty selectively influenced a stimulus-related model parameter (drift rate) while changes to instructions influenced a caution-related model parameter (decision boundaries).
An Introduction to Model-Based Cognitive Neuroscience by Birte U. Forstmann, Eric-Jan Wagenmakers