Reward is a foundation of behaviour: we move to attain valuable states. However, moving towards those states implies investing some effort and deploying motor strategies that are very much dependent on the person’s motivation. We performed a decision-making task in with human participants had to accumulate reward by selecting one of two reaching movements of opposite motor cost, to be performed precisely. Our results show that performance and social status were taken into consideration by diminishing error as a function of the partner. This also transpired into an increase movement time between the baseline condition and any social condition. We interpret this as an adaptive process of trade-off between precision, reward and time. Other effects on the movement amplitude became significant when the skill of the companion player was clearly unattainable, such as a reduction of the amplitude, thus escaping the traditional context of the speed-accuracy trade-off. As a context for the study of motivation and motor adaptation we developed a model based on movement benefit and costs optimization. Remarkably, its predictions show that this optimization depends on the context where the movements and the choices are performed, incorporating motivation as part of its internal dynamics.

Ignasi Cos (Barcelona, 1973; MEng Electronics 1996 – Politecnico di Torino, MEng Telecomunications 1997 – Universitat Politècnica de Catalunya; PhD in Cognitive Science and Artificial Intelligence 2006 - University of Edinburgh). After PhD graduation, he went to train as a postdoctoral fellow at the University of California, Berkeley, and at the University of Montreal, where he specialized in the neuroscience of motor control and decision-making. He also trained in theoretical neuroscience at the Université Pierre and Marie Curie, at the Brain and Spine Institute of Paris, and at the Universitat Pompeu Fabra. He is currently an Assistant Professor at the Faculty of Mathematics & Informatics, Universitat de Barcelona, and a member of the Institute of Mathematics (IMUB). His research focuses on developing mathematical techniques to characterize the brain operation, as a whole, in the context of how the brain controls movement.