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      Sleep-Related Offline Improvements in Gross Motor Task Performance Occur Under Free Recall Requirements

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          Abstract

          Nocturnal sleep effects on memory consolidation following gross motor sequence learning were examined using a complex arm movement task. This task required participants to produce non-regular spatial patterns in the horizontal plane by successively fitting a small peg into different target-holes on an electronic pegboard. The respective reaching movements typically differed in amplitude and direction. Targets were visualized prior to each transport movement on a computer screen. With this task we tested 18 subjects (22.6 ± 1.9 years; 8 female) using a between-subjects design. Participants initially learned a 10-element arm movement sequence either in the morning or in the evening. Performance was retested under free recall requirements 15 min post training, as well as 12 and 24 h later. Thus, each group was provided with one sleep-filled and one wake retention interval. Dependent variables were error rate (number of Erroneous Sequences, ES) and average sequence execution time (correct sequences only). Performance improved during acquisition. Error rate remained stable across retention. Sequence execution time (inverse to execution speed) significantly decreased again during the sleep-filled retention intervals, but remained stable during the respective wake intervals. These results corroborate recent findings on sleep-related enhancement consolidation in ecological valid, complex gross motor tasks. At the same time, they suggest this effect to be truly memory-based and independent from repeated access to extrinsic sequence information during retests.

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          Most cited references16

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          System consolidation of memory during sleep

          Over the past two decades, research has accumulated compelling evidence that sleep supports the formation of long-term memory. The standard two-stage memory model that has been originally elaborated for declarative memory assumes that new memories are transiently encoded into a temporary store (represented by the hippocampus in the declarative memory system) before they are gradually transferred into a long-term store (mainly represented by the neocortex), or are forgotten. Based on this model, we propose that sleep, as an offline mode of brain processing, serves the ‘active system consolidation’ of memory, i.e. the process in which newly encoded memory representations become redistributed to other neuron networks serving as long-term store. System consolidation takes place during slow-wave sleep (SWS) rather than rapid eye movement (REM) sleep. The concept of active system consolidation during sleep implicates that (a) memories are reactivated during sleep to be consolidated, (b) the consolidation process during sleep is selective inasmuch as it does not enhance every memory, and (c) memories, when transferred to the long-term store undergo qualitative changes. Experimental evidence for these three central implications is provided: It has been shown that reactivation of memories during SWS plays a causal role for consolidation, that sleep and specifically SWS consolidates preferentially memories with relevance for future plans, and that sleep produces qualitative changes in memory representations such that the extraction of explicit and conscious knowledge from implicitly learned materials is facilitated.
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            Parallel neural networks for learning sequential procedures.

            Recent studies have shown that multiple brain areas contribute to different stages and aspects of procedural learning. On the basis of a series of studies using a sequence-learning task with trial-and-error, we propose a hypothetical scheme in which a sequential procedure is acquired independently by two cortical systems, one using spatial coordinates and the other using motor coordinates. They are active preferentially in the early and late stages of learning, respectively. Both of the two systems are supported by loop circuits formed with the basal ganglia and the cerebellum, the former for reward-based evaluation and the latter for processing of timing. The proposed neural architecture would operate in a flexible manner to acquire and execute multiple sequential procedures.
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              Awareness modifies the skill-learning benefits of sleep.

              Behind every skilled movement lies months of practice. However, practice alone is not responsible for the acquisition of all skill; performance can improve between, not just within, practice sessions. An important principle shaping these offline improvements may be an individual's awareness of learning a new skill. New skills, such as a sequence of finger movements, can be learned unintentionally (with little awareness for the sequence, implicit learning) or intentionally (explicit learning). We measured skill in an implicit and explicit sequence-learning task before and after a 12 hr interval. This interval either did (8 p.m. to 8 a.m.) or did not (8 a.m. to 8 p.m.) include a period of sleep. Following explicit sequence learning, offline skill improvements were only observed when the 12 hr interval included sleep. This overnight improvement was correlated with the amount of NREM sleep. The same improvement could also be observed in the evening (with an interval from 8 p.m. to 8 p.m.), so it was not coupled to retesting at a particular time of day and cannot therefore be attributed to circadian factors. In contrast, in the implicit learning task, offline learning was observed regardless of whether the 12 hr interval did or did not contain a period of sleep. However, these improvements were not observed with only a 15 min interval between sessions. Therefore, the practice available within each session cannot account for these skill improvements. Instead, sufficient time is necessary for offline learning to occur. These results show a behavioral dissociation, based upon an individual's awareness for having learned a sequence of finger movements. Offline learning is sleep dependent for explicit skills but time dependent for implicit skills.
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                Author and article information

                Contributors
                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                29 March 2016
                2016
                : 10
                : 134
                Affiliations
                [1]Sport Science, Training Science, Saarland University Saarbruecken, Saarland, Germany
                Author notes

                Edited by: Genevieve Albouy, KU Leuven, Belgium

                Reviewed by: Bjoern Rasch, University of Fribourg, Switzerland; Arnaud Boutin, University of Montreal, Canada

                *Correspondence: Klaus Blischke k.blischke@ 123456mx.uni-saarland.de
                Article
                10.3389/fnhum.2016.00134
                4809884
                27065834
                8a9da211-5bb8-4866-89a5-0071fbec1e0b
                Copyright © 2016 Malangré and Blischke.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 30 October 2015
                : 14 March 2016
                Page count
                Figures: 3, Tables: 1, Equations: 1, References: 23, Pages: 8, Words: 5427
                Categories
                Neuroscience
                Original Research

                Neurosciences
                sleep,enhancement consolidation,gross motor task,sequence learning,free recall
                Neurosciences
                sleep, enhancement consolidation, gross motor task, sequence learning, free recall

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