Age-related Differences in Implicit Sequence Learning and Consolidation across the Human Life Span: Implications for the Functioning of the Fronto-Striatal Circuitry

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Implicit sequence learning occurs when information is acquired from an environment of complex stimuli without conscious access either to what was learned or to the fact that learning occurred. In everyday life, this learning mechanism is crucial for adapting to the environment and for predicting events unconsciously. Despite the growing interest in implicit learning in the past decades, there has been relatively little research on life-long development of implicit sequence learning and on offline processing of implicitly learned information (i.e., consolidation). Here, we present three studies investigating these issues. In Experiment I, we investigated implicit sequence learning from 4 to 85 years of age and found a marked decrease in learning performance - measured by raw reaction time (RT) – around age of 12. This decrement can be explained by a competition between two fundamentally different forms of learning (model-free and model-based) suggesting that after adolescence frontal lobe-mediated model-based learning has larger effect on the expression of implicit sequence knowledge, while before adolescence basal ganglia-dependent model-free learning is more influencing. As a growing body of data has shown that frontal lobe-mediated processes are disrupted in hypnosis, we tested this assumption in Experiment II by comparing implicit sequence learning in hypnosis and in waking alert state. We found that hypnosis boosted sequence learning in young adults. In addition, this boosting effect was mediated by frontal lobe related executive functions. Finally, we investigated consolidation of implicit sequence knowledge in young and elderly adults after 12-, 24-hour or a 1-week delay period in order to determine age-related differences not only in online learning, but also in offline processing of the learned material (Experiment III). We found that consolidation is not a single process, rather there are multiple mechanisms (e.g., sequence-specific, general skill learning) which are differentially affected by aging and the course of time. Our results contribute not only to the better understanding of learning on a behavioral level, but also to understanding the age-related changes in brain plasticity in healthy participants across the human life span. In addition, these findings can help better understand neurodevelopmental (e.g., autism, dyslexia), neurodegenerative (e.g., Parkinson’s Disease, Huntington’s Disease) and age-related disorders where related brain structures are affected. Finally, our findings can lead to the development of more effective diagnostic tools, training methods and rehabilitation programs.

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