Network theory provides an intuitively appealing framework for studying relationships
among interconnected brain mechanisms and their relevance to behaviour. As the space of …

Active inference is an approach to understanding behaviour that rests upon the idea that the
brain uses an internal generative model to predict incoming sensory data. The fit between …

In this paper, we revisit the problem of Bayesian model selection (BMS) at the group level.
We originally addressed this issue in Stephan et al.(2009), where models are treated as …

If perception corresponds to hypothesis testing (Gregory,); then visual searches might be
construed as experiments that generate sensory data. In this work, we explore the idea that …

This work is in line with an on-going effort tending toward a computational (quantitative and
refutable) understanding of human neuro-cognitive processes. Many sophisticated models …

Three questions have been prominent in the study of visual working memory limitations:(a)
What is the nature of mnemonic precision (eg, quantized or continuous)?(b) How many …

The development of large-scale network models that infer the effective (directed)
connectivity among neuronal populations from neuroimaging data represents a key …

The mismatch negativity (MMN) is a differential brain response to violations of learned
regularities. It has been used to demonstrate that the brain learns the statistical structure of …

Decision confidence reflects our ability to evaluate the quality of decisions and guides
subsequent behavior. Experiments on confidence reports have almost exclusively focused …

Computational modeling of functional brain networks in fMRI data has advanced the
understanding of higher cognitive function. It is hypothesized that functional networks …