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.e. likelihood), captures human behaviours sufficiently well. However, there are debates regarding whether humans use precise but cognitively demanding Bayesian computations for behaviours. Across two studies, we trained participants to estimate hidden locations of a target drawn from priors with different levels of uncertainty. In each trial, scattered dots provided noisy likelihood information about the target location. Participants showed that they learned the priors and combined prior and likelihood information to infer target locations in a Bayes fashion. We then introduced a transfer condition presenting a trained prior and a likelihood that has never been put together during training. How well participants integrate this novel likelihood with their learned prior is an indicator of whether participants perform Bayesian computations. In one study, participants experienced the newly introduced likelihood, which was paired with a different prior, during training. Participants changed likelihood weighting following expected directions although the degrees of change were significantly lower than Bayes-optimal predictions. In another group, the novel likelihoods were never used during training. We found people integrated a new likelihood within (interpolation) better than the one outside (extrapolation) the range of their previous learning experience and they were quantitatively Bayes-suboptimal in both. We replicated the findings of both studies in a validation dataset. Our results showed that Bayesian behaviours may not always be achieved by a full Bayesian computation. Future studies can apply our approach to different tasks to enhance the understanding of decision-making mechanisms.

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. Dipole clusters were resolved within the motor cortices with some notable spatial difference between the MS and control participants. Neural sources that generate motor imagery originated from similar motor areas in the participant with MS compared to the neurotypical participants. Power spectral analysis indicated a reduced level of alpha power in the participant with MS during imagery tasks compared to neurotypical participants. Power in the beta band may be used to distinguish between left and right imagined movement for users with MS in BCI applications.Clinical Relevance- This paper demonstrates the cortical areas activated during imagined BCI-type tasks in a participant with Multiple Sclerosis (MS), and is a proof of concept for translating BCI research to potential users with MS.

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. RESULTS: The first study demonstrated that 1 s duration erTDCS time-locked to the participants' reaching actions enhanced adaptation significantly better than sham. A close replication in the second study demonstrated 0.5 s erTDCS synchronous with the reaching actions again resulted in better adaptation than standard TDCS, significantly better than sham. Stimulation either during the inter-trial intervals between movements or after movement, during assessment of visual feedback, had no significant effect. Because short duration stimulation with rapid onset and offset is more readily perceived by the participants, we additionally show that a non-electrical vibrotactile stimulation of the scalp, presented with the same timing as the erTDCS, had no significant effect. CONCLUSIONS: We conclude that short duration, event related, anodal TDCS targeting the cerebellum enhances motor adaptation compared to the standard model. We discuss possible mechanisms of action and speculate on neural learning processes that may be involved.

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. Firstly, some natural behaviours deviate from Bayesian optimum. Secondly, the neural mechanisms that support Bayesian computations in the brain are yet to be understood. Taking Marr's cross level approach, we review the recent progress made in addressing these challenges. We first review studies that combined behavioural paradigms and modelling approaches to explain both optimal and suboptimal behaviours. Next, we evaluate the theoretical advances and the current evidence for ecologically feasible algorithms and neural implementations in the brain, which may enable probabilistic inference. We argue that this cross-level approach is necessary for the worthwhile pursuit to uncover mechanistic accounts of human behaviour.