This is a nice, short, introduction to some key issues at the interface of neural aspects of human motor control and higher cognitive decisions – problems of choice. It concisely explains why people in areas such as robotics are excited about some new developments, but it also brings out a couple of things that should be better known within the machine learning community, e.g.,
Placing motor and cognitive decisions in a common theoretical framework brings into sharp relief one apparent difference between them. Researchers have long argued that humans and animals mostly make choices in sensorimotor tasks that are nearly optimal – in the sense of approaching maximal expected utility – or complying with principles of statistical inference. In contrast, work in traditional economic decision-making tasks often focuses on situations in which participants violate the predictions of expected utility theory, for instance by misrepresenting the frequency of rare events or due to interference with emotional factors (Kirk et al., 2011).