Cognitive neuroscientists are often interested in broad research questions, yet use overly
narrow experimental designs by considering only a small subset of possible experimental …

Probabilistic models of decision making under various forms of uncertainty have been
applied in recent years to numerous behavioral and model-based fMRI studies. These …

The Big Data era offers multiple sources of data, with measurements that containa variety of
information in large volumes. For example, neuroimaging data froma participant might be …

Computational models in fields such as computational neuroscience are often evaluated via
stochastic simulation or numerical approximation. Fitting these models implies a difficult …

To ensure robust scientific conclusions, cognitive modelers should optimize planned
experimental designs a priori in order to maximize the expected information gain for …

Recent decades have seen dramatic progress in brain research. These advances were
often buttressed by probing single variables to make circumscribed discoveries, typically …

Experiments in research on memory, language, and in other areas of cognitive science are
increasingly being analyzed using Bayesian methods. This has been facilitated by the …

PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC)
algorithm for posterior and model inference for black-box computational models (Acerbi …

Scientists who study cognition infer underlying processes either by observing behavior (eg,
response times, percentage correct) or by observing neural activity (eg, the BOLD …

Computational neuroscience is, to first order, dominated by two approaches: the 'bottom-
up'approach, which searches for statistical patterns in large-scale neural recordings, and the …