The best time to acquire new skills: age-related differences in implicit sequence learning across the human lifespan

Abstract

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 lifespan, between 4 and 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 two-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.

Figure 1

Sequence learning in all groups. Reaction time (A) and accuracy (B) for high and low frequency triplets are plotted. Learning measures of RT (C) and accuracy (D) represents the RT/ACC difference between low- and high-frequency triplets. Error bars indicate SEM.

Figure 2

Sequence learning measured the z-transformed RT data in all groups.

Figure 3

Individual data for sequence learning measured by raw RT (A),accuracy (B), and z-scores (C) in all ages.

Figure 4

Competition between model-based and model-free neurocognitive subsystems of skill learning across lifespan. (A) Before adolescence, underdeveloped internal models (dashed boundary) have little influence on interpretations of detected raw statistical probabilities of events in the environment (dashed arrows). Skill learning performance is determined by raw probabilities. (B) From adolescence to late adulthood, well-developed internal models (solid boundary) strongly modulate the interpretations of observed statistics of the input. This helps extracting complex relations but relatively impairs measuring and utilizing raw probabilities in skill learning (dotted arrow). (C) In older ages, skill learning performance decreases. This decline could be caused by the combination of reduced sensitivity to raw statistical probabilities (dashed boundary), increasingly rigid internal models (dashed boundary) and/or weaker connection between these systems (dashed arrows).