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Humans excel at learning complex tasks, and elite performers such as musicians or athletes develop motor skills that defy biomechanical constraints. All actions require the movement of massive bodies. Of particular interest in the process of sensorimotor learning and control is the impact of gravitational forces on the body. Indeed, efficient control and accurate internal representations of the body configuration in space depend on our ability to feel and anticipate the action of gravity. Here we review studies on perception and sensorimotor control in both normal and altered gravity. Behavioral and modeling studies together suggested that the nervous system develops efficient strategies to take advantage of gravitational forces across a wide variety of tasks. However, when the body was exposed to altered gravity, the rate and amount of adaptation exhibited substantial variation from one experiment to another and sometimes led to partial adjustment only. Overall, these results support the hypothesis that the brain uses a multimodal and flexible representation of the effect of gravity on our body and movements. Future work is necessary to better characterize the nature of this internal representation and the extent to which it can adapt to novel contexts.

Opinion evolution and judgment revision are mediated through social influence. Based on a large crowdsourced in vitro experiment (n=861), it is shown how a consensus model can be used to predict opinion evolution in online collective behaviour. It is the first time the predictive power of a quantitative model of opinion dynamics is tested against a real dataset. Unlike previous research on the topic, the model was validated on data which did not serve to calibrate it. This avoids to favor more complex models over more simple ones and prevents overfitting. The model is parametrized by the influenceability of each individual, a factor representing to what extent individuals incorporate external judgments. The prediction accuracy depends on prior knowledge on the participants' past behaviour. Several situations reflecting data availability are compared. When the data is scarce, the data from previous participants is used to predict how a new participant will behave. Judgment revision includes unpredictable variations which limit the potential for prediction. A first measure of unpredictability is proposed. The measure is based on a specific control experiment. More than two thirds of the prediction errors are found to occur due to unpredictability of the human judgment revision process rather than to model imperfection., Accepted for publication in PLOS ONE (2016)