Predicting the future: From implicit learning to consolidation

Abstract

Sequence learning can be differentiated according to phases (rapid and slower), modalities (perceptual and motor), and whether or not it is conscious (implicit and explicit). 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. Implicit sequence learning underlies not only motor, but also cognitive and social skills; it is therefore an important aspect of life from infancy to old age. Moreover, this kind of learning does not occur only during practice, in the so-called online periods, but also between practice periods, during the so-called offline periods. The process that occurs during the offline periods is referred to as consolidation, which denotes the stabilization of a memory trace after the initial acquisition; this can result in increased resistance to interference or even improvement in performance following an offline period. Understanding the multiple aspects and influencing factors of consolidation can help us to reveal the nature of memory and changes in brain plasticity. Our review focuses on how consolidation varies with factors such as awareness, the length of offline periods, the type of information to be learned, and the age of participants. We highlight that consolidation is not a single process; instead, there are multiple mechanisms in the offline period, which are differently influenced by these factors.

Offline Optimization of the Relative Timing of Movements in a Sequence Is Blocked by Retroactive Behavioral Interference

“…Sequences can be learned in different ways: explicitly, e.g., in finger-opposition or finger-tapping paradigms (Korman et al, 2003; Doyon et al, 2009b; Friedman and Korman, 2012) or implicitly, e.g., in serial reaction time paradigms (Destrebecqz and Peigneux, 2005; Janacsek and Nemeth, 2012), depending on the intention or awareness during acquisition (Nissen and Bullemer, 1987; Hikosaka et al, 2002; Robertson, 2007). Both types of sequence learning depend on the amount of practice (Karni, 1996; Schmidt and Lee, 1999; Page and Norris, 2009).…”

“…Numerous studies have examined the time course of learning sequences of finger movements on a basic behavioral level (number of sequences performed, error rate) as well as neural correlates using neuroimaging (Karni et al, 1995; Hikosaka et al, 2002; Keele et al, 2003; Korman et al, 2003; Luft and Buitrago, 2005; Janacsek and Nemeth, 2012). A few hours after practice, skills are processed, changed and strengthened in memory (Karni and Sagi, 1993; Karni et al, 1995), a phenomenon commonly described as procedural memory “consolidation” (Karni and Sagi, 1993; Walker, 2005).…”

Do Environmental Factors (Practice and Feedback) Moderate the Effect of the Val66met BDNF Polymorphism on Motor Learning?

“…The issue could be explained by previous studies in which it is indicated that the lack of practice within hours or days stabilizes memory traces created in the acquisition period and makes neural substrates resistant. [22,23] This concept, called memory consolidation, can lead to increased resistance to interference or even an improvement in performance after a no-practice period. [23] In fact, motor skills are completed by two different processes.…”

“…[22,23] This concept, called memory consolidation, can lead to increased resistance to interference or even an improvement in performance after a no-practice period. [23] In fact, motor skills are completed by two different processes. Skill can develop during practice (acquisition) sessions called on-line learning, and also develop off-line -between practice sessions or during a no-practice period 24 .…”

Prediction and Expectation

“…So, how do we estimate what will be next (and exactly when), given the presence? Depending on the situation or experimental task, either formal transition rules (Janacsek & Nemeth, 2012), naive physics (Zago & Lacquaniti, 2005), or probability distributions (Fiser, Berkes, Orbán, & Lengyel, 2010) can serve to tune expectation for upcoming stimuli. However, a straightforward way to classify prediction is by its memory base.…”