By Birte U. Forstmann, Eric-Jan Wagenmakers
Two contemporary concepts, the emergence of formal cognitive versions and the addition of cognitive neuroscience info to the conventional behavioral information, have led to the start of a brand new, interdisciplinary box of research: model-based cognitive neuroscience. regardless of the expanding medical curiosity in model-based cognitive neuroscience, few energetic researchers or even fewer scholars have a very good wisdom of the 2 constituent disciplines. the most objective of this edited assortment is to advertise the mixing of cognitive modeling and cognitive neuroscience. specialists within the box will supply tutorial-style chapters that designate specific concepts and spotlight their usefulness via concrete examples and diverse case reviews. The publication 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 info for An Introduction to Model-Based Cognitive Neuroscience
The BART task was designed to measure propensity for risk-taking. However, as pointed out by Wallsten and colleagues, performance on the BART task can be influenced by multiple psychological processes . To decompose observed behavior into psychological processes and obtain a separate estimate for the propensity to take risk, Wallsten and colleagues proposed a series of process models. , “Model 3” from , their Table 2) has four parameters: α, β, γ + , and μ. For the present purposes, the precise specification of the model and the meaning of the parameters is irrelevant (for a detailed description see [15, 14]).
However, a number of recent studies have rejected the selective influence of cautious vs. speedy decision-making on decision boundaries [25, 26, 27]. In a later section we show how model-selection was used in this context. 4 A. Heathcote et al. Quantify Uncertainty in Parameter Estimates In many modeling approaches, the focus is on model prediction and model fit for a single “best” set of parameter estimates. For example, suppose we wish to estimate the probability θ that Don correctly discriminates regular beer from alcohol-free beer.
The spread between the 90th and 10th percentiles summarises variability in RT and a larger difference between the 90th and 50th percentiles compared to the 50th to 10th percentile summarises the typically positive skew in RT distribution. Further complication arises when data are multivariate. For example, cognitive process models are usually fit to data from choice tasks. Where one of two choices is classified as correct, the rate of accurate responding provides a sufficient summary. However, participants can trade accuracy for speed , so in many cases it is important to also take RT into account.