Accumulating evidence indicates that the human brain copes with sensory uncertainty in accordance with Bayes' rule. However, it is unknown how humans make predictions when the generative model of the task at hand is described by uncertain parameters. Here, we tested whether and how humans take parameter uncertainty into account in a regression task. Participants extrapolated a parabola from a limited number of noisy points, shown on a computer screen. The quadratic parameter was drawn from a bimodal prior distribution. We tested whether human observers take full advantage of the given information, including the likelihood of the quadratic parameter value given the observed points and the quadratic parameter's prior distribution. We compared human performance with Bayesian regression, which is the (Bayes) optimal solution to this problem, and three sub-optimal models, which are simpler to compute. Our results show that, under our specific experimental conditions, humans behave in a way that is consistent with Bayesian regression. Moreover, our results support the hypothesis that humans generate responses in a manner consistent with probability matching rather than Bayesian decision theory.

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Despite more than a century of investigation into the cortical organization of motor function, the existence of motor somatotopy is still debated. We review functional magnetic resonance imaging (fMRI) studies examining motor somatotopy in the cerebral cortex. In spite of a substantial overlap of representations corresponding to different body parts, especially in non-primary motor cortices, geographic approaches are capable of revealing somatotopic ordering. From the iconic homunculus in the contralateral primary cortex to the subtleties of ipsilateral somatotopy and its relations with lateralization, we outline potential reasons for the lack of segregation between motor representations. Among these are the difficulties in distinguishing activity that arises from multiple muscular effectors, the need for flexible motor control and coordination of complex movements through functional integration and artefacts in fMRI. Methodological advances with regard to the optimization of experimental design and fMRI acquisition protocols as well as improvements in spatial registration of images and indices aiming at the quantification of the degree of segregation between different functional representations are inspected. Additionally, we give some hints as to how the functional organization of motor function might be related to various anatomical landmarks in brain morphometry.