Neural substrates of intermanual transfer of a newly acquired motor skill.

In healthy humans, the two cerebral hemispheres show functional specialization to a degree unmatched in other animals, and such strong hemispheric specialization contributes to unimanual skill acquisition [1, 2]. When most humans learn a new motor skill with one hand, this process results in performance improvements in the opposite hand as well [3-6]. Despite the obvious adaptive advantage of such intermanual transfer, there is no direct evidence identifying the neural substrates of this form of skill acquisition [7-9]. Here, we used functional magnetic resonance imaging (fMRI) to study brain regions activated during intermanual transfer of a learned sequence of finger movements. First, we found that the supplementary motor area (SMA) has more activity when a skill has transferred well than when it has transferred poorly. Second, we found that fMRI activity in the ventrolateral posterior thalamic nucleus correlated with successful future intermanual transfer, whereas activity in the ventrolateral anterior thalamic nucleus correlated with past intermanual transfer. Third, we found that repetitive transcranial magnetic stimulation applied over the SMA blocked intermanual transfer without affecting skill acquisition. These findings provide direct evidence for an SMA-based mechanism that supports intermanual transfer of motor-skill learning.

Probability detection mechanisms and motor learning.

The automatic detection of patterns or regularities in the environment is central to certain forms of motor learning, which are largely procedural and implicit. The rules underlying the detection and use of probabilistic information in the perceptual-motor domain are largely unknown. We conducted two experiments involving a motor learning task with direct and crossed mapping of motor responses in which probabilities were present at the stimulus set level, the response set level, and at the level of stimulus-response (S-R) mapping. We manipulated only one level at a time, while controlling for the other two. The results show that probabilities were detected only when present at the S-R mapping and motor levels, but not at the perceptual one (experiment 1), unless the perceptual features have a dimensional overlap with the S-R mapping rule (experiment 2). The effects of probability detection were mostly facilitatory at the S-R mapping, both facilitatory and inhibitory at the perceptual level, and predominantly inhibitory at the response-set level. The facilitatory effects were based on learning the absolute frequencies first and transitional probabilities later (for the S-R mapping rule) or both types of information at the same time (for perceptual level), whereas the inhibitory effects were based on learning first the transitional probabilities. Our data suggest that both absolute frequencies and transitional probabilities are used in motor learning, but in different temporal orders, according to the probabilistic properties of the environment. The results support the idea that separate neural circuits may be involved in detecting absolute frequencies as compared to transitional probabilities.