sec. Both speed (the number of sequences performed within the test interval) and accuracy (the number of times an out-of-sequence finger opposition movement was executed within the test interval) were scored independently, from the video recordings. In the training session, one of the sequences, randomly chosen, was tapped at a rate of 2 Hz, paced by a metronome, in six short training intervals of 40 sec each, separated by 2–3 min of rest.
Motor performance for the two sequences, before, immediately after, and on the day after training is shown in Fig. 3 B and C. Initial performance of the two sequences, in terms of speed and accuracy, did not differ. Training, however, induced a significant gain in both speed and accuracy for the trained sequence. Moreover, on the next day, with no additional training, a significant gain in both speed and accuracy, compared with the immediate post-training performance level, was found for the trained sequence only (Fig. 3B and C).
Our results show that not all learning in a sequential finger opposition task is concurrent with practice. A limited amount of paced opposition movements was sufficient practice not only to improve performance during the session but also to initiate significant additional gains that affect performance by the next day; apparently, some gains require time to become effective and continue to develop after motor practice has ended. The concurrent gain in speed and accuracy, is characteristic of the acquisition of a new skill (44).
Delayed neuronal plasticity, which evolves hours after the inducing experience, has been demonstrated in several studies of the developing visual cortex in kittens (36, 37). These studies showed that the changes in neuronal properties induced by brief visual experience became effective, that is, consolidated, only after time, several hours to several days, was allowed to elapse. The notion of consolidation in these studies is consistent with the distinction between the “induction” and the “expression” and maintenance of plasticity suggested by studies of synaptic plasticity at the cellular and biochemical level (10, 11). It is also consistent with the kinetics of memory consolidation, in terms of its resistance to disruption, in animal and cellular models of learning (39). Karni and Sagi (8) have described similar delayed gains in the performance of adults emerging a minimum of 6–8 hr after training in a simple visual detection task. The term consolidation was suggested for the process, presumably initiated during the practice session, which underlies the improvement of performance hours after the training experience was terminated, and results in an enduring memory of the skill. Recently, while training subjects on moving a manipulandum against a force-field, Brashers-Krug et al. (20, 39) found evidence for an ongoing process of consolidation after training for one task condition was terminated. The introduction of a second task condition within a time window of several hours after the initial training disrupted long-term (overnight) improvement on the first task. Moreover, their data show that training not only results in within session (fast) gains, but also, provided enough time was allowed for the consolidation phase, in additional gains that are only apparent by the next day. Similar delayed gains in performance after a latent consolidation phase also have been described for a rotor pursuit task (J.Travis quoted in ref. 40). Altogether, these results indicate that human motor memory continues to evolve after the training session, and with the passage of time is transformed into a long-term trace. Furthermore, the data establish an important parallel between the time course of motor skill learning and perceptual learning and suggest the idea that the time course of skill learning may reflect the time constants of basic neuronal mechanisms of memory storage that are shared by different cortical representations in the adult brain.
Although the fractionation of skill learning into only two discrete phases is most likely an oversimplification (11). it provides an important conceptual framework for describing and accounting for the human skill learning data (6, 8, 22, 39). Our imaging data suggest that the acquisition of skilled motor performance occurs in two distinct phases in M1. First, a within-first-session switch in the representation of the repeatedly performed sequences of movements from a habituation-like decrease to an increase in the extent of motor cortex activated by a given sequence of repeated movements; and second, after about 3 weeks of training, the emergence of an enlarged, differential representation of the trained as compared with the untrained sequence of movements. Both stages of sequence learning are experience specific. The switch in ordering effects, or fast learning, occurs only for those sequences that have been repeated a critical number of times in the session, and it is correlated with a specific, significant gain in performance occurring within the session. The emerging, more extensive representation of the trained sequence of movements in M1 was a correlate of highly specific gains in performance that were incrementally acquired over a few weeks of daily practice (slow learning).
The switch in M1 processing mode may constitute an important step in initiating subsequent experience-dependent changes in M1. The imaging data show that the switched ordering effect that occurred in M1 late in the first imaging session was maintained, for the designated control sequence, for at least 1 week during which the sequence was not performed. This is not to say that the switch in M1 processing mode, and much of the behavioral effects that constitute fast learning, are products of major changes principally occurring in M1 within the time frame of a single session. The switch in ordering effects may reflect neural changes occurring in other parts of the distributed motor system (45, 47–52, 59–60). Psychophysical data from perceptual (8) and motor (39) learning tasks suggest that fast learning is mediated, at least in part, by brain regions distinct from those that subserve slow learning. It has been argued, based on electrophysiological data from monkeys, that brain regions active during the acquisition of a motor skill do not necessarily correspond to the regions that eventually will store the memory (4, 61). In humans, there is evidence from functional brain imaging studies that distinct brain areas are differentially activated during initial, naive performance compared with subsequent performance as learning proceeds both within a session [see for example Buckner (66) and Petersen (67) in this issue of the Proceedings] and across consecutive sessions (60, 62).
One should note, that in our study (6), the extent of activation in M1 for either sequence did not increase significantly during the initial scanning session. The learning related changes in M1 that occurred during the first session were related to the ordering effects within a time window of 40 sec. A number of positron emission tomography (PET) studies have examined changes in brain activations occurring within a single session as a consequence of practice in motor and sensory-motor tasks (45, 47–52). Although some studies have suggested that, as learning proceeded within the session, blood flow in M1 increased (47, 49), no significant changes in blood flow have been found in M1 when the rate of movements in the trained and untrained conditions were kept the same (50, 52). A recent PET study in which movement rate was controlled (45), as well as a transcranial magnetic stimulation study (46). found increased activity in M1 as learning progressed but only when subjects had no previous implicit knowledge of the sequence of finger movements. When explicit knowledge of the sequence was allowed to develop no significant learning-related M1 changes were found. However, in contrast to the M1 findings, several PET studies have found a consistent