Data quality over data quantity in computational cognitive neuroscience.

We analyzed factors that may hamper the advancement of computational cognitive neuroscience (CCN). These factors include a particular statistical mindset, which paves the way for the dominance of statistical power theory and a preoccupation with statistical replicability in the behavioral and neural sciences. Exclusive statistical concerns about sampling error occur at the cost of an inadequate representation of the problem of measurement error.

Mind the Noise When Identifying Computational Models of Cognition from Brain Activity.

The aim of this study was to analyze how measurement error affects the validity of modeling studies in computational neuroscience. A synthetic validity test was created using simulated P300 event-related potentials as an example. The model space comprised four computational models of single-trial P300 amplitude fluctuations which differed in terms of complexity and dependency.

P300 amplitude variations, prior probabilities, and likelihoods: A Bayesian ERP study.

The capability of the human brain for Bayesian inference was assessed by manipulating probabilistic contingencies in an urn-ball task. Event-related potentials (ERPs) were recorded in response to stimuli that differed in their relative frequency of occurrence (.18 to .

A computational analysis of the neural bases of Bayesian inference.

Empirical support for the Bayesian brain hypothesis, although of major theoretical importance for cognitive neuroscience, is surprisingly scarce. This hypothesis posits simply that neural activities code and compute Bayesian probabilities. Here, we introduce an urn-ball paradigm to relate event-related potentials (ERPs) such as the P300 wave to Bayesian inference.

A model-based approach to trial-by-trial p300 amplitude fluctuations.

It has long been recognized that the amplitude of the P300 component of event-related brain potentials is sensitive to the degree to which eliciting stimuli are surprising to the observers (Donchin, 1981). While Squires et al. (1976) showed and modeled dependencies of P300 amplitudes from observed stimuli on various time scales, Mars et al.