Consolidating skill learning through sleep

Nusbaum, Howard C.; Uddin, Sophia; Hedger, Stephen C. Van; Heald, Shannon L. M.

doi:10.1016/j.cobeha.2018.01.013

Cited by 12 publications

(11 citation statements)

References 82 publications

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“…Research has found that sleep deprivation is associated with cognitive lapses and significantly undermines tasks that require flexible thinking (Rossa et al, 2014;Pallesen et al, 2017) as well as mental state and psychological functioning (Scott et al, 2006). The importance of sleep is mainly grounded in the role sleep has in recovery and adaptive processes in the mind and body (Nédélec et al, 2015;Kellmann et al, 2018).…”

Section: The Importance Of Sleepmentioning

confidence: 99%

“…Sleep is thought to impact both physical development, quality of life and health in a positive way ( Watson, 2017 ). In addition, sleep has been found to be especially crucial for cognitive functioning ( Jones and Harrison, 2001 ; Walker et al, 2002 ; Belenky et al, 2003 ; Jarraya et al, 2014 ; Nusbaum et al, 2018 ) and emotional regulation ( Sinnerton and Reilly, 1992 ; Baum et al, 2014 ; Goldstein and Walker, 2014 ; Watson, 2017 ). Research has found that sleep deprivation is associated with cognitive lapses and significantly undermines tasks that require flexible thinking ( Rossa et al, 2014 ; Pallesen et al, 2017 ) as well as mental state and psychological functioning ( Scott et al, 2006 ).…”

Section: Introductionmentioning

confidence: 99%

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Associations Between Sleep Patterns and Performance Development Among Norwegian Chess Players

2020

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An inherent part of elite-level chess are high emotional and cognitive stress loads related to performance development. Sleep is a crucial recovery strategy, previously implicated in athletic performance. The main purpose of the current study was to investigate the associations between performance development and objectively measured sleep in a sample of 14 Norwegian chess players over a period of 120 consecutive days. Seven of the chess players in the current sample had negative development in their International Chess Federation (FIDE) ranking score in the period of sleep monitoring, while 7 had positive development. The sleep patterns of the chess players with positive performance development were different from the players with negative development-with higher amounts of deep sleep, less rapid eye movement (REM) sleep and lower respiration rate in the positive performance development group compared to the negative performance development group. The findings are discussed in terms of existing knowledge on the importance of sleep stage distribution and sleep durations for athlete functioning, and in light of applied implications and possible future research.

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Section: The Importance Of Sleepmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Associations Between Sleep Patterns and Performance Development Among Norwegian Chess Players

2020

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“…Given the reliance on sleep to consolidate rapidly acquired learning into long-term representations (c.f. Nusbaum et al, 2018) previous studies have argued that the sleep-dependent P2 change marks the consolidation of a feature-based representation in long-term memory (Ross and Tremblay, 2009; Tremblay et al, 2014). Here, we point out an implication of this interpretation: if the P2 component is sensitive to the formation of an additional featural representation in long-term memory, it indicates that the auditory P2 evoked response may be sensitive to the number of active featural representations serving current recognition.…”

Section: Discussionmentioning

confidence: 97%

“…Given the reliance on sleep to consolidate rapidly acquired learning into long-term representations (c.f. Nusbaum et al, 2018) previous studies have argued that the sleep-dependent P2 change marks the consolidation of a feature-based representation in long-term memory (Ross and Tremblay, 2009;Tremblay et al, 2014 estimates that this window of topographic change was driven by a decrease in activity in the right superior temporal gyrus, left superior parietal and the anterior cingulate gyrus bilaterally. Both superior parietal cortex as well as the anterior cingulate have been argued to be responsible for attentional resource allocation for ongoing processing (Myers and Theodore, 2017;Piai et al, 2013;Wong et al, 2004).…”

Section: Neural Patterns Of Generalized Learning 30mentioning

confidence: 97%

Going beyond rote auditory learning: Neural patterns of generalized auditory learning

Heald

Hedger

Veillette

et al. 2021

Preprint

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The ability to generalize rapidly across specific experiences is vital for robust recognition of new patterns, especially in speech perception considering acoustic-phonetic pattern variability. Behavioral research has demonstrated that listeners are rapidly able to generalize their experience with a talker’s speech and quickly improve understanding of a difficult-to-understand talker without prolonged practice, e.g., even after a single training session. Here, we examine the differences in neural responses to generalized versus rote learning in auditory cortical processing by training listeners to understand a novel synthetic talker using a Pretest-Posttest design with electroencephalography (EEG). Participants were trained using either (1) a large inventory of words where no words repeated across the experiment (generalized learning) or (2) a small inventory of words where words repeated (rote learning). Analysis of long-latency auditory evoked potentials at Pretest and Posttest revealed that while rote and generalized learning both produce rapid changes in auditory processing, the nature of these changes differed. In the context of adapting to a talker, generalized learning is marked by an amplitude reduction in the N1-P2 complex and by the presence of a late-negative (LN) wave in the auditory evoked potential following training. Rote learning, however, is marked only by temporally later source configuration changes. The early N1-P2 change, found only for generalized learning, suggests that generalized learning relies on the attentional system to reorganize the way acoustic features are selectively processed. This change in relatively early sensory processing (i.e. during the first 250ms) is consistent with an active processing account of speech perception, which proposes that the ability to rapidly adjust to the specific vocal characteristics of a new talker (for which rote learning is rare) relies on attentional mechanisms to adaptively tune early auditory processing sensitivity.Statement of SignificancePrevious research on perceptual learning has typically examined neural responses during rote learning: training and testing is carried out with the same stimuli. As a result, it is not clear that findings from these studies can explain learning that generalizes to novel patterns, which is critical in speech perception. Are neural responses to generalized learning in auditory processing different from neural responses to rote learning? Results indicate rote learning of a particular talker’s speech involves brain regions focused on the memory encoding and retrieving of specific learned patterns, whereas generalized learning involves brain regions involved in reorganizing attention during early sensory processing. In learning speech from a novel talker, only generalized learning is marked by changes in the N1-P2 complex (reflective of secondary auditory cortical processing). The results are consistent with the view that robust speech perception relies on the fast adjustment of attention mechanisms to adaptively tune auditory sensitivity to cope with acoustic variability.

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“…We continuously collected data online, in real time, thus enabling measures of progressive learning and of processes involved in habit formation such as "caching" (Haith and Krakauer, 2018) and "chunking" (Graybiel, 1998). Previous studies have shown that practice in itself is insufficient for habit development as it requires off-line consolidation computations, through longer periods of time (de Wit et al, 2018) and sleep (Walker et al, 2003;Nusbaum et al, 2018). This article presents the method in detail and preliminary data, acquired with 29 healthy human volunteers.…”

Section: Introductionmentioning

confidence: 99%