Are we really Bayesian? Probabilistic inference shows sub-optimal knowledge transfer.

Numerous studies have found that the Bayesian framework, which formulates the optimal integration of the knowledge of the world (i.e. prior) and current sensory evidence (i.

Feasibility of Using Source-Level Brain Computer Interface for People with Multiple Sclerosis.

This work evaluates the feasibility of using a source level Brain-Computer Interface (BCI) for people with Multiple Sclerosis (MS). Data used was previously collected EEG of eight participants (one participant with MS and seven neurotypical participants) who performed imagined movement of the right and left hand. Equivalent current dipole cluster fitting was used to assess related brain activity at the source level and assessed using dipole location and power spectrum analysis.

Short duration event related cerebellar TDCS enhances visuomotor adaptation.

Background: Transcranial direct current stimulation (TDCS) is typically applied before or during a task, for periods ranging from 5 to 30 min.

Hypothesis: We hypothesise that briefer stimulation epochs synchronous with individual task actions may be more effective.

Methods: In two separate experiments, we applied brief bursts of event-related anodal stimulation (erTDCS) to the cerebellum during a visuomotor adaptation task.

Towards a cross-level understanding of Bayesian inference in the brain.

Perception emerges from unconscious probabilistic inference, which guides behaviour in our ubiquitously uncertain environment. Bayesian decision theory is a prominent computational model that describes how people make rational decisions using noisy and ambiguous sensory observations. However, critical questions have been raised about the validity of the Bayesian framework in explaining the mental process of inference.