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Efficient motor control: how can less be more?

The results of a scientific study sometimes surprise us, and at other times comfort us. Such reactions reflect the framework of expectations we had before the study was carried out. This framework ideally summarizes our shared prior knowledge and makes new results seem more or less plausible in light of that knowledge. In the present issue of The Journal of Physiology, Wu et al. (2008) report a comforting result. When a new motor task is practiced to the point that subjects can perform it automatically, that is, without degradation of performance when a second task is concurrently carried out, a group of motor areas in the brain become more strongly connected. 
 
The same authors had previously shown that when a movement is practiced until it becomes automatic, a major change in brain activation pattern is a reduction of neural activity in task-related areas (Wu et al. 2004). That is, as movement guidance changes from a ‘novice’ mode, requiring attention, to an automatic mode, there is no qualitative change in the pattern of brain activation: the same areas are activated by the task before and after practice. However, these areas become able to control the same movements with a reduced amount of activation. This was (or at least should have been) a surprising result. It might be tempting to interpret reduction of activation as the neural equivalent of subtraction of task components: activity before practice = attention + motor control; reduced activity after practice = motor control alone. However, there is a paradox in this interpretation. If the difference in brain activity reflected only a difference in attention, this would imply that the portion of activity responsible for motor control is the same before and after practice. But then how can motor control after practice be resistant to distraction by a concurrent task? In order for automaticity to have arisen, the neural substrate responsible for controlling the movement must have changed: if the same movements are now resistant to interference by a simultaneous second task, they must be guided by a different motor control strategy, with a presumably different neural code. How could a reduction of activation in motor areas be responsible for the radical change in the nature of motor control associated with automatic execution? 
 
The answer provided by Wu et al. in the present study resolves this paradox, and is reassuring at three levels. First, the authors identified a change in functional connectivity among motor areas associated with the development of automaticity. This finding reassures us that the neural substrate responsible for automatic motor control is indeed different from that responsible for novice performance. Second, the change identified is of the type we have come to expect, ever since Donald Hebb's postulate (Hebb, 1949), to be associated with learning: if a movement must be practiced in order to become automatic, then learning must occur, and thus synaptic strengths must change. Third, the combined findings of Wu et al.'s original (Wu et al. 2004) and present studies, are evidence of an increase in neural efficiency: the combination of reduction of activation and strengthening of functional connectivity suggests a more efficient neural code for controlling a given task. Neural efficiency has long been investigated in relationship to human intelligence (Vernon, 1993), but these studies have been fraught with the long list of potential confounding factors (task complexity, use of strategy, past experience) associated with the study of such a complex measure of nervous system function. A task as simple as tapping fingers in a sequence, when combined with analysis not only of brain activation patterns, but also of functional connectivity, offers a more easily controlled system for investigating concepts like neural efficiency. By presenting evidence of increased neural efficiency in the automatic performance of movement sequences, Wu et al. have brought an exciting new level of detail to our understanding of the neural code underlying movement.