Motor learning (the ability of the brain to learn and update how an action is executed) is a fundamental process which influences many aspects of our lives such as learning to walk during childhood; the day-to-day behavioural adjustments required as an adult or in healthy ageing; and the rehabilitation process following an illness or injury. Despite the impact to society, it has proved extremely difficult to develop interventions that significantly enhance human motor learning. Therefore, devising protocols which optimise motor learning is a state-of-the-art research question that promises to deliver scientific, clinical and societal impact.
Seeking reward and avoiding punishment are powerful factors in motivating humans to alter behaviour during cognition-based learning (selecting which action to perform), with sensitivity to reward and punishment being biased by the availability of dopamine in the brain. Intriguingly, reward and punishment are also known to affect generic motor learning (deciding how an action is executed) tasks which involve multiple underlying mechanisms. However to establish their potential for optimizing motor learning, we must understand how explicit reward- and punishment-based motivational feedback impact motor learning systems with unique computational and anatomical features.
Using a combination of behavioural analysis, computational modelling and genetics, this PhD project will contribute to the first systems-based account of how reward, punishment and dopamine influence motor learning. Specifically, the successful candidate will collect a large dataset of young/healthy participants and examine how genetic variations influence an individual’s sensitivity to reward and punishment during motor learning. This ERC project will fund 3 post doctoral and 2 PhD positions between 2015-2020.
If interested then please email me your CV: [email protected]
I am looking for an enthusiastic student with experience either in motor control/learning, decision making, computational modelling or engineering (robotics). The ideal candidate will have experience with programming in matlab, collecting data (from participants) on behavioural tasks and some understanding of statistics. A masters and/or research assistant experience would be ideal but not compulsory.
1. Galea J.M, Mallia E, Rothwell JC, Diedrichsen J. The dissociable effects of punishment and reward on motor learning. Nature Neuroscience
2. Galea J.M, Ruge D, Buijink A, Bestmann S & Rothwell J.C. Punishment induced behavioural and neurophysiological variability reveals dopamine-dependent selection of kinematic movement parameters. Journal of Neuroscience
3 Galea J.M, Vazquez A, Pasricha N, Orban de Xivry J.J & Celnik P. Dissociating the roles of the cerebellum and motor cortex during adaptive learning: the motor cortex retains what the cerebellum learns. Cerebral Cortex