This paper addresses a common challenge with computational cognitive models: identifying
parameter values that are both theoretically plausible and generate predictions that match …

Mechanistic models of single-neuron dynamics have been extensively studied in
computational neuroscience. However, identifying which models can quantitatively …

Here we investigate the crucial role of trials in task-based neuroimaging from the
perspectives of statistical efficiency and condition-level generalizability. Big data initiatives …

Perception emerges from unconscious probabilistic inference, which guides behaviour in
our ubiquitously uncertain environment. Bayesian decision theory is a prominent …

Machine learning is a powerful set of techniques that has enhanced the abilities of
neuroscientists to interpret information collected through EEG, fMRI, and MEG data. With …

In this work, we address the neuronal encoding problem from a Bayesian perspective.
Specifically, we ask whether neuronal responses in an in vitro neuronal network are …

We study methods for simultaneous analysis of many noisy experiments in the presence of
rich covariate information. The goal of the analyst is to optimally estimate the true effect …

Highlights•Computational approaches provide novel insights into psychiatric
conditions.•There exist several challenges for translating the approaches into clinical …

Cognitive modeling shares many features with statistical modeling, making it seem trivial to
borrow from the practices of robust Bayesian statistics to protect the practice of robust …

According to Bayesian theories in psychology and neuroscience, minds and brains are
(near) optimal in solving a wide range of tasks. We challenge this view and argue that more …