Implicit skill learning underlies obtaining not only motor, but also cognitive and social skills through the life of an individual. Yet, the ontogenetic changes in humans’ implicit learning abilities have not yet been characterized, and, thus, their role in acquiring new knowledge efficiently during development is unknown. We investigated such learning across the life span, between 4–85 years of age with an implicit probabilistic sequence learning task, and we found that the difference in implicitly learning high vs. low probability events - measured by raw reaction time (RT) - exhibited a rapid decrement around age of 12. Accuracy and z-transformed data showed partially different developmental curves suggesting a re-evaluation of analysis methods in developmental research. The decrement in raw RT differences supports an extension of the traditional 2-stage lifespan skill acquisition model: in addition to a decline above the age 60 reported in earlier studies, sensitivity to raw probabilities and, therefore, acquiring new skills is significantly more effective until early adolescence than later in life. These results suggest that due to developmental changes in early adolescence, implicit skill learning processes undergo a marked shift in weighting raw probabilities vs. more complex interpretations of events, which, with appropriate timing, prove to be an optimal strategy for human skill learning.

The influence of sleep on motor skill consolidation has been a research topic of increasing interest. In this study, we distinguished general skill learning from sequence-specific learning in a probabilistic implicit sequence learning task (alternating serial reaction time) in young and old adults before and after a 12-h offline interval which did or did not contain sleep (p.m.-a.m. and a.m.-p.m. groups, respectively). The results showed that general skill learning, as assessed via overall reaction time, improved offline in both the young and older groups, with the young group improving more than the old. However, the improvement was not sleep-dependent, in that there was no difference between the a.m.-p.m. and p.m.-a.m. groups. We did not find sequence-specific offline improvement in either age group for the a.m.-either p.m. or p.m.-a.m. groups, suggesting that consolidation of this kind of implicit motor sequence learning may not be influenced by sleep.

It has been reported recently that while general sequence learning across ages conforms to the typical inverted-U shape pattern, with best performance in early adulthood, surprisingly, the basic ability of picking up in an implicit manner triplets that occur with high vs. low probability in the sequence is best before 12 years of age and it significantly weakens afterwards. Based on these findings, it has been hypothesized that the cognitively controlled processes coming online at around 12 are useful for more targeted explicit learning at the cost of becoming relatively less sensitive to raw probabilities of events. To test this hypothesis, we collected data in a sequence learning task using probabilistic sequences in five age groups from 11 to 39 years of age (N = 288), replicating the original implicit learning paradigm in an explicit task setting where subjects were guided to find repeating sequences. We found that in contrast to the implicit results, performance with the high- vs. low-probability triplets was at the same level in all age groups when subjects sought patterns in the sequence explicitly. Importantly, measurements of explicit knowledge about the identity of the sequences revealed a significant increase in ability to explicitly access the true sequences exactly around the age where the earlier study found the significant drop in ability to learn implicitly raw probabilities. These findings support the conjecture that the gradually increasing involvement of more complex internal models optimizes our skill learning abilities by compensating for the performance loss due to down-weighting the raw probabilities of the sensory input, while expanding our ability to acquire more sophisticated skills.

Procedural learning facilitates the efficient processing of complex environmental stimuli and contributes to the acquisition of automatic behaviors. Although earlier findings suggest different temporal trajectories of the multiple learning processes within procedural learning, this has not been clarified at the level of neurocognitive correlates. Therefore, we investigated whether two prominent learning processes - statistical learning and sequence learning - can be distinguished using event-related brain potentials (ERPs) within the same experimental setting. Healthy young adults (N = 40) performed the Alternating Serial Reaction Time task while RTs and ERPs were measured time-locked to the onset of the task's stimuli. Both RT and N2 effects reflected the rapid acquisition of statistical probabilities. At the same time, these effects reflected the gradual learning of sequential structures. The amplitude change of the P3 reflected only gradual sequence learning. The P1 component was sensitive to both learning processes, which did not change as the task progressed. Our results altogether indicate that statistical learning and sequence learning develop differently at the level of both ERPs and overt responses. These findings could provide insight to the dynamic change of multiple parallel learning processes that occur during procedural memory formation.