Theories of motor control postulate that the brain uses internal models of the body to control movements accurately. Internal models are neural representations of how, for instance, the arm would respond to a neural command, given its current position and velocity. Previous studies have shown that the cerebellar cortex can acquire internal models through motor learning. Because the human cerebellum is involved in higher cognitive function as well as in motor control, we propose a coherent computational theory in which the phylogenetically newer part of the cerebellum similarly acquires internal models of objects in the external world. While human subjects learned to use a new tool (a computer mouse with a novel rotational transformation), cerebellar activity was measured by functional magnetic resonance imaging. As predicted by our theory, two types of activity were observed. One was spread over wide areas of the cerebellum and was precisely proportional to the error signal that guides the acquisition of internal models during learning. The other was confined to the area near the posterior superior fissure and remained even after learning, when the error levels had been equalized, thus probably reflecting an acquired internal model of the new tool.

We studied the neural correlates of visuomotor sequence learning using functional magnetic resonance imaging (fMRI). In the test condition, subjects learned, by trial and error, the correct order of pressing two buttons consecutively for 10 pairs of buttons (2 x 10 task); in the control condition, they pressed buttons in any order. Comparison between the test condition and the control condition revealed four brain areas specifically related to learning: the dorsolateral prefrontal cortex (DLPFC), the presupplementary motor area (pre-SMA), the precuneus, and the intraparietal sulcus (IPS). We found that the time course of activation during learning was different between these areas. To normalize the individual differences in the speed of learning, we classified the performance of each subject into three learning stages: early, intermediate, and advanced stages. Both the relative increase of signal intensity and the number of activated pixels within the four areas showed significant changes across the learning stages, with different time courses. The two frontal areas, DLPFC and pre-SMA, were activated in the earlier stages of learning, whereas the two parietal areas, precuneus and IPS, were activated in the later stages. Specifically, DLPFC, pre-SMA, precuneus, and IPS were most highly activated in the early stage, in both the early and intermediate stages, in the intermediate stage, and in both the intermediate and advanced stages, respectively. The results suggest that the acquisition of visuomotor sequences requires frontal activation, whereas the retrieval of visuomotor sequences requires parietal activation, which might reflect the transition from the declarative stage to the procedural stage.

Rhythm is determined solely by the relationship between the time intervals of a series of events. Psychological studies have proposed two types of rhythm representation depending on the interval ratio of the rhythm: metrical and nonmetrical representation for rhythms formed with small integer ratios and noninteger ratios, respectively. We used functional magnetic resonance imaging to test whether there are two neural representations of rhythm depending on the interval ratio. The subjects performed a short-term memory task for a seven-tone rhythm sequence, which was formed with 1:2:4, 1:2:3, or 1:2.5:3.5 ratios. The brain activities during the memory delay period were measured and compared with those during the retention of a control tone sequence, which had constant intertone intervals. The results showed two patterns of brain activations; the left premotor and parietal areas and right cerebellar anterior lobe were active for 1:2:4 and 1:2:3 rhythms, whereas the right prefrontal, premotor, and parietal areas together with the bilateral cerebellar posterior lobe were active for 1:2.5:3.5 rhythm. Analysis on individual subjects revealed that these activation patterns depended on the ratio of the rhythms that were produced by the subjects rather than the ratio of the presented rhythms, suggesting that the observed activations reflected the internal representation of rhythm. These results suggested that there are two neural representations for rhythm depending on the interval ratio, which correspond to metrical and nonmetrical representations.

1. Using functional magnetic resonance imaging, we investigated the neural correlates of sequential procedural learning. During the test scans the subjects learned a new sequence (position or color) of button presses; during the control scans they pressed the buttons in any order. The comparison of the test and control scans was expected to reveal the neural activities related to learning, not sensory-motor processes. 2. We found that a localized area in what we regard to be the human homologue of the presupplementary motor area (pre-SMA) was particularly active for learning of new sequential procedures (either position or color sequences), not movements per se. 3. In contrast, the SMA proper (posterior to pre-SMA) was active for the performance of sequential movements, not learning. This was shown in another paradigm in which the subjects pressed the buttons in any order in the test scans and just watched the sequence in the control scans. 4. The learning-related pre-SMA region, which was consistent across different experiments in single subjects, was identified on only one side in each subject.

Successful motor behavior requires making appropriate response (response selection) at the right time (timing adjustment). Earlier psychological studies have suggested that the response selection and timing adjustment processes are performed serially in separate stages. We tested this hypothesis using functional magnetic resonance imaging. The subjects performed a choice reaction time task in four conditions: two (on-line response selection required or not) by two (on-line timing adjustment required or not). We found that the neural correlates for the two processes were indeed separate: the anterior medial premotor cortex (presupplementary motor area) was selectively active in response selection, whereas the cerebellar posterior lobe was selectively active in timing adjustment. However, the functional separation was only partial in that the lateral premotor cortex and the intraparietal sulcus were active equally for response selection and timing adjustment. The lateral premotor cortex was most active when both processes were required, suggesting that it integrates the information on response selection and the information on timing adjustment; alternatively, it might contribute to the allocation of attentional resources during dual information processing. The intraparietal sulcus was equally active when either response selection or timing adjustment was required, suggesting that it modifies, rather than integrates, these processes. Furthermore, our results suggest that these activations related to response selection and timing adjustment were distinct from sensory or motor processes.