Skills constitute one of two distinct, broad categories of memory (12, 13). Although different taxonomies exist, the dichotomy accounts for deficits of fact and event memories (“what,” declarative knowledge) on the one hand, and the preservation of skills and habits (“how to,” procedural knowledge) on the other, in individuals and nonhuman primates with focal lesions to medial temporal lobe structures (12–15). Many instances of skill learning, both perceptual and motor, are specific for basic parameters of the training experience; that is, learning can be strongly dependent on simple physical attributes of the stimulus presented in training a perceptual task (e.g., refs. 16–18) or on factors such as the specific effector organs’ positions, trajectories and sequence of trajectories experienced in motor training (e.g., refs. 6 and 19–21). For example, training to perform an arm movement aimed at a specific target location against a specific perturbation resulted in learning to compensate for the perturbation in the trained part of the workspace but showed little generalization to the rest of the workspace (19). Similarly, training to overcome a specific perturbation did not generalize to overcoming an identical perturbation in the orthogonal direction (20).
There is considerable anatomical and physiological evidence for a hierarchical organization of information processing in sensory and motor systems in the mammalian brain, such that many physical parameters of a sensory input, or a motor output, are selectively represented only in specific processing stages. The specificity of learning for a given parameter of the training experience implies, therefore, that only a discrete part (or subset of neurons) within a processing stream—that wherein the parameter is differentially represented—has undergone learning related changes. At a level of processing in which neurons respond invariantly, one would expect learning to generalize for that particular parameter. Thus, the finding of specificity in the learning of a given skill has been used to generate predictions on the possible neuronal loci and type of representations affected by the training experience (6, 16–19, 21–24). This is not to say that all skills are specific for low-level parameters of the training experience: indeed, one would predict otherwise whenever the relevant aspects of a task are represented at higher levels within the processing stream (25). Nevertheless, in many instances the degree of specificity has indicated discrete changes in low-level representations as an important locus of learning (25). This interpretation of the human behavioral data is supported by experimental animal studies that have revealed that the details of the representation of the sensory input in low-level processing areas engaged in the performance of a given sensory discrimination task change, so as to reflect, by evolving improved and enlarged representations, the specific behavioral experiences of the animals under study (26–29). Similarly, motor representations have been shown to undergo experience-specific reorganization after long-term training (7), whereas cortical representational maps, often on a much shorter time scale, have been found to be altered by manipulations of their sensory inputs (1, 2) or motor outputs (3, 30).
An important difference between declarative and procedural memory is the time course of learning. Declarative learning can be very fast and may take place even after a single event (13, 31). Procedural learning, in contrast, is slow and often requires many repetitions, usually over several training sessions, to evolve (12, 31). Thus, one may remember the contents of a book after a single reading but the skills of reading evolve over multiple practice sessions and require many repetitions to become established.
Several recent studies have examined the time course of experience-dependent perceptual learning (8, 9, 32–34). In these studies, adult individuals were found to gain an increase in perceptual sensitivity when given practice in basic sensory discrimination tasks. These studies indicate that improved perceptual performance often evolves in two distinct stages (8): first, a fast within-session improvement that can be induced by a limited number of trials on a time scale of minutes (“fast learning”), and second, slowly evolving, incremental performance gains, triggered by practice but taking hours to become effective (“slow learning”). In many instances, most gains in performance evolved in a latent manner not during, but rather a minimum of 6–8 hr after training, that is, between sessions (8, 33–35). Improvements in performance continued to develop over the course of 5–10 daily practice sessions, spaced 1 to 3 days apart, before nearing asymptotic performance. The skill then was retained for months and years (8). Because of the long-term retention and by analogy to the time course described in several paradigms of developmental plasticity (36, 37), the latent phase in human skill learning is thought to reflect a process of consolidation of experience-dependent changes in the adult cortex that is triggered by training but continues to evolve after the training session has ended (8). Furthermore, it was proposed that fast learning reflects the setting up of a task-specific processing routine for solving the perceptual problem whereby those representations that are relevant for task performance are selected. Slow learning, on the other hand, is thought to reflect ongoing, perhaps structural, modifications of basic perceptual modules within the selected representations (8, 25, 32, 38).
Recent studies suggest that a similar time course may characterize the acquisition of some motor skills by human adults (6, 20, 39). Studies conducted in the early decades of this century have described a latent consolidation phase in perceptuomotor tasks under the term reminiscence (see ref. 40). In the monkey, fast, within-session learning, as well as large incremental gains in performance over weeks of daily training sessions—“slow” learning—have been described in both perceptual and motor skill learning paradigms (7, 27, 29). The monkey data further suggest that the long-term changes that can be induced in different brain areas by the learning of motor (7, 41) and perceptual skills (29) may be subserved by similar mechanisms of plasticity. Although the data are limited by the small number of studies and the different time windows examined in each of these studies, the results lend support to the idea that although the nature of the practice-dependent cortical representational changes are determined by the specifics of the training experience, the time course of skill learning may be determined by the time constants of basic mechanisms of neuronal plasticity irrespective of the locus of plasticity.
The learning of many motor skills involves the formation of novel sequences of muscle activity and the reconstruction of existing muscle control architectures (3, 41, 42). A hallmark of such learning is improved speed of motor execution without reciprocal deterioration in accuracy (43), which indicates the acquisition of a new capability of the motor system rather than functional adaptation within the limits of a pre-existing motor gain control mechanism (44). In recent years, the learning of sequential finger movements—related to skills such as writing, typing, or playing musical instruments—has become an important paradigm for the study of the acquisition of motor skills by using imaging techniques (45–52). These studies however, have been confined to relatively short time intervals and were not designed to look at the effects of long-term training. Also, many of these studies were concerned not only with the issue of how the performance of a known sequence of