An Introduction to Model-Based Cognitive Neuroscience by Birte U. Forstmann, Eric-Jan Wagenmakers

By Birte U. Forstmann, Eric-Jan Wagenmakers

Fresh options, the emergence of formal cognitive types and the addition of cognitive neuroscience information to the normal behavioral information, have ended in the start of a brand new, interdisciplinary box of analysis: model-based cognitive neuroscience.  regardless of the expanding medical curiosity in model-based cognitive neuroscience, few lively researchers or even fewer scholars have a great wisdom of the 2 constituent disciplines. the most objective of this edited assortment is to advertise the combination of cognitive modeling and cognitive neuroscience. specialists within the box will supply tutorial-style chapters that designate specific thoughts and spotlight their usefulness via concrete examples and diverse case studies.  The booklet also will contain an intensive record of references pointing the reader in the direction of extra literature and on-line assets.

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5−20 True α − true μ 5−15 Fig. 1 The 4-parameter BART model recovers parameters γ + and β, but fails to recover parameters α and μ (results based on a 300-trial BART). The dots represent the median of 1000 point estimates from 1000 different BARTs performed by a single synthetic agent. The violin shapes around the dots are density estimates for the entire distribution of point estimates, with the extreme 5 % truncated [16]. 1 The results of one of those recovery studies are presented in Fig. 1.

9. Quantile-probability (QP) plots [42] 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).

1 The results of one of those recovery studies are presented in Fig. 1. This figure shows the results of 1000 simulations of a single synthetic participant completing 300 BART trials2 , for each of six sets of data-generating parameter values. For each of the 1000 simulations, van Ravenzwaaij et al. obtained a point estimate for each parameter. In Fig. 1, the dots represent the median of the 1000 point estimates, and the “violins” that surround the dots represent density estimates that represent the entire distribution of point estimates, with the extreme 5 % truncated.

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