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Mednick S, Nakayama K, Stickgold R. Sleep-dependent learning: a nap is as good as a night. Nat Neurosci 6: 697-698

DOI:10.1038/nn1078
Authors:
Sara Mednick at University of California, Irvine
Sara Mednick
Ken Nakayama at Harvard University
Ken Nakayama
Robert Stickgold at Harvard Medical School
Robert Stickgold
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The learning of perceptual skills has been shown in some cases to depend on the plasticity of the visual cortex and to require post-training nocturnal sleep. We now report that sleep-dependent learning of a texture discrimination task can be accomplished in humans by brief (60- 90 min) naps containing both slow-wave sleep (SWS) and rapid eye movement (REM) sleep. This nap-dependent learning closely resembled that previously reported for an 8-h night of sleep in terms of magnitude, sleep-stage dependency and retinotopic specificity, and it was additive to subsequent sleep-dependent improvement, such that performance over 24 h showed as much learning as is normally seen after twice that length of time. Thus, from the perspective of behavioral improvement, a nap is as good as a night of sleep for learning on this perceptual task.
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the morning on day 1, tested at 19:00 that evening, and then retested at
9:00 the next morning. In the task, a target screen is briefly flashed,
consisting of a 19 × 19 grid of horizontal bars containing three diago-
nal bars and a central fixation target. Then, after a variable-length
interstimulus interval (ISI), a mask consisting of both horizontal and
diagonal bars appears. The diagonal bars, located in the lower-left
visual quadrant, form either a horizontal row or vertical column. After
each trial, subjects report whether the diagonal bar array was in a row
or column arrangement, as well as whether the central fixation target
was an "L" or "T." Performance is defined as the threshold ISI required
for a subject to be 80% accurate in the row/column discrimination.
Two experimental groups took naps at 14:00 (group 1: total sleep time,
59.3 ± 6.4 (mean ±s.d.); SWS, 20.2 ± 2.0; REM, 4.2 ± 2.2 min; group 2:
total, 96.3 ± 6.3; SWS, 47.2 ± 5.8; REM, 25.6 ± 4.1 min), while ‘no-nap’
control subjects went about their normal day without midday sleep.
All subjects reported sleeping an average of 7.6 ± 2.1 h on the night
before day 1 and 7.5 ± 1.2 h on the night before day 2. Improvement
was measured as a decrease in threshold ISI from each subject’s base-
line threshold (at 9:00 on day 1)
9
.
No-nap control subjects showed the expected deterioration in per-
formance at 19:00 (13.7 ms longer ISI threshold, P = 0.06, Fig. 1), and
performed significantly worse than the nap groups (P = 0.02). The
deterioration in performance from training to first retest in this group,
measured over an 8-h interval, was similar to that seen in our prior
–15
–10
–5
0
5
10
15
Threshold improvement (ms)
No-nap
–SWS
–REM
(
n
= 28)
60-min nap
90-min nap
(
n
= 13)
(
n
= 2)
(
n
= 13) (
n
= 17)
+SWS
–REM
+SWS
+REM
Figure 1 Same-day improvement in no-nap, 60-min nap and 90-min nap
groups, with and without REM and SWS. Left, no-nap group shows
deterioration at 19:00 from baseline test at 9:00. Center, performance
after naps with SWS but without REM shows neither deterioration nor
improvement. Right, naps with SWS and REM led to significant
improvement. Only two subjects in the 90-min nap group showed no REM.
Experiments were approved by the Committee on the Use of Human
Subjects of Harvard University. Informed written consent was obtained from
all subjects.
BRIEF COMMUNICATIONS
Sleep-dependent learning: a nap
is as good as a night
Sara Mednick
1
,Ken Nakayama
1
& Robert Stickgold
2
The learning of perceptual skills has been shown in some cases
to depend on the plasticity of the visual cortex
1
and to require
post-training nocturnal sleep
2
. We now report that sleep-
dependent learning of a texture discrimination task can be
accomplished in humans by brief (60– 90 min) naps containing
both slow-wave sleep (SWS) and rapid eye movement (REM)
sleep. This nap-dependent learning closely resembled that
previously reported for an 8-h night of sleep in terms of
magnitude, sleep-stage dependency and retinotopic specificity,
and it was additive to subsequent sleep-dependent
improvement, such that performance over 24 h showed as much
learning as is normally seen after twice that length of time. Thus,
from the perspective of behavioral improvement, a nap is as
good as a night of sleep for learning on this perceptual task.
A wide variety of learning processes in both humans and animals
requires extended, post-training sleep (for review, see ref. 3).
Depending on the nature of the task, differing stages of sleep con-
tribute to this sleep-dependent consolidation process. For the most
part, studies in humans have investigated the benefits of nocturnal
sleep
3
.Research on behavioral effects of napping has found improve-
ment in alertness, productivity and mood
4,5
, as well as restoration of
perceptual deterioration
6
.But whether relatively brief daytime naps
can produce learning is not known.
Improvement on visual perceptual tasks can occur over periods of
minutes to hours
7
, as well as over extended periods of days
2
, and
remain stable for months
8
.For a visual texture-discrimination task in
which subjects must rapidly discriminate the orientation of a target
embedded in distractors
9
, initial improvement is seen over the first few
minutes of training
10
, and slower improvement is seen over subse-
quent nights of sleep
2,11
.
Both SWS
12
and REM
13
are implicated in a two-stage model of noc-
turnal consolidation, with overnight improvement being retinotopi-
cally specific and highly correlated with the product of the amount of
early-night SWS and late-night REM sleep
11
.Repeated testing within a
day on this task leads to a retinotopically specific deterioration in per-
formance
6
,which is reversed by 60-min midday naps rich in SWS. But
these naps have relatively little REM sleep, and fail to produce signifi-
cant improvement over baseline performance. Similar deterioration
without sleep is also seen at night
12
.
We now report that 60- and 90-min naps containing both SWS and
REM facilitate learning on this texture-discrimination task, in a man-
ner similar to that seen after nocturnal sleep.
Subjects were trained on the texture discrimination task at 9:00 in
1
Psychology Department, Harvard University, 33 Kirkland Street, Cambridge, Massachusetts 02138, USA.
2
Department of Psychiatry at Massachusetts Mental Health
Center, Harvard Medical School, Boston, Massachusetts 02115, USA. Correspondence should be addressed to S.M. (smednick@post.harvard.edu).
NATURE NEUROSCIENCE VOLUME 6
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NUMBER 7
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JULY 2003 697
© 2003 Nature Publishing Group http://www.nature.com/natureneuroscience
BRIEF COMMUNICATIONS
study, which showed 13.8 ms deterioration over a 2-h interval
6
.This
suggests that stimulus exposure rather than inter-test interval pro-
duces deterioration in texture discrimination, and that sleep, rather
than time, is required to reverse this perceptual deterioration
6
.
When tested at 19:00, the 90-min nap group showed significant
improvement (8.4 ms, P = 0.008), whereas the 60-min nap group
showed marginal improvement (4.4 ms, P = 0.07). Mindful of our
hypothesis that both SWS and REM may be necessary for learning in
naps, we divided the 60-min nap group, all of whom had SWS, into
subjects with and without REM and found that 60-min naps with both
SWS and REM produced significant improvement (10.0 ms, P = 0.004,
Fig. 1 right). In contrast, 60-min naps with SWS but not REM showed
no improvement (–1.1 ms, P = 0.72, Fig. 1 center) and significantly less
than seen in the SWS + REM group (P = 0.01). The 90-min nap group
showed similar results (Fig. 1,white bars). When subjects with SWS +
REM naps from the 60- and 90-min groups were combined, improve-
ment correlated significantly with the product of the amount of SWS
and REM sleep (stepwise regression, r = 0.37, P = 0.01), similar to find-
ings for nocturnal sleep
11
.The additive effects of SWS and REM seen
in these naps are also similar to those for early and late night sleep
reported previously
12
.In addition, the amount of improvement did
not differ significantly from that seen in our previous study of
overnight improvement
2
(9.7 vs. 11.9 ms; P = 0.5). Thus, nap-
dependent improvement showed the same magnitude and sleep-stage
dependency as did overnight improvement
2,11
(although of lower
magnitude than that reported for overnight improvement by oth-
ers
12,13
). Naps with SWS but not REM reversed the deterioration but
did not produce actual improvement, whereas naps with both SWS
and REM did both, suggesting that SWS may serve to stabilize per-
formance, and REM may actually facilitate performance improve-
ment.
We t ested the retinotopic specificity of nap-dependent learning by
training subjects at 9:00 in one visual quadrant (lower left or lower
right, counterbalanced) and then retesting them in the contralateral
quadrant at 19:00, with a 90-min nap at 14:00. Training plus a nap had
no significant effect on the untrained quadrant (P > 0.2), indicating
that nap-dependent learning has a retinotopic specificity similar to
that reported for overnight improvement
9
, as well as for same-day
deterioration
6
, and suggesting that the beneficial effects of a 90-min
midday nap are localized to primary visual cortex
9
.
Nap-dependent improvement was not at the expense of subsequent
nocturnal improvement. On the contrary, when subjects were retested
the next morning, the 90-min nap group showed an additional 9.7 ms
of improvement (24-h total, 18.1 ms, P < 0.0001), and greater
improvement than the no-nap group (Fig. 2, P = 0.03).
The no-nap group showed deterioration at 19:00, but normal levels
of improvement on day 2 (7.8 ms; Fig. 2, no-nap). This improvement
did not differ significantly from a second (24-h) control group trained
at 9:00 and retested 24 h later without an intervening test on the
evening of day 1 (P > 0.4; Fig. 2, 24-h control).
The nap group actually showed 50% more improvement over a
period of 24 h than the 24-h control group (18.1 vs. 11.8 ms, P = 0.07).
Indeed, 24-h improvement in the nap group was as great as that previ-
ously reported
2
after two nights of sleep (Fig. 2, 48-h control; 18.1 ms
vs. 17.5 ms; P > 0.99). Taken together, these findings indicate that a 90-
min nap can produce as much improvement as a night of sleep, and a
nap followed by a night of sleep provides as much benefit as two nights
of sleep.
These findings show that naps can lead to improved performance
on a texture discrimination task similar to previously reported learn-
ing after a full night of sleep
2
, in terms of magnitude, retinotopic
specificity and dependence on both SWS and REM. Similar improve-
ment has been reported after 192 min of early-night sleep
12
,where
subjects averaged 74 min of SWS and 24 min of REM—an amount of
REM similar to the 25.6 min found in our 90-min nap group.
Finally, napping can significantly enhance the improvement that
develops over 24 h. Thus, a nap can not only ameliorate experience-
dependent perceptual deterioration, but can also facilitate the learning
process that results from an hour spent training on a visual texture dis-
crimination task.
ACKNOWLEDGMENTS
This research was supported by grants from the US National Institutes of Health
(MH 48832, DA 11744 and NS 26985) and the Air Force Office of Scientific
Research (83-0320).
COMPETING INTERESTS STATEMENT
The authors declare that they have no competing financial interests.
Received 18 March; accepted 16 May 2003
Published online 22 June 2003; doi:10.1038/nn1078
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698 VOLUME 6
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JULY 2003 NATURE NEUROSCIENCE
Day 3
48-h
Control
Threshold improvement (ms)
25
20
15
10
5
0
Day 2
Nap
No-nap
24-h
Control
Figure 2 Improvement for nap and no-nap groups. Left, improvements 24 h
after training for the no-nap group’s second retest, the 24-h control group’s
first retest, and the 90-min nap group’s second retest, all at 9:00 on day 2.
Dotted line shows nap group’s improvement on day 1. Right, improvement
48 h post-training with no nap. Data for 48-h controls are from ref. 2. Data
for 24-h controls were combined with previously published data
2
, which
were not significantly different from the present sample.
© 2003 Nature Publishing Group http://www.nature.com/natureneuroscience
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Full-text available
  • Dec 2022
Background and Objective The role of night sleep in learning and consolidating memory has been mentioned and researched in many studies. Different tools have been used to determine the effect of sleep. However, this kind of research in the Iranian population is limited. Thus, the objective of this study was to determine the role of sleep in immediate and late learning of new words of second language in a number of English language learners.Methods Forty Persian males aged 18–35 years old participated in our study in two groups. The first group completed learning steps at 8–10 AM and repeated the test after 12 hours. The second group performed the initial stages of learning at 8–10 PM and repeated the test next morning. Everyone completed the Pittsburgh Sleep Quality Index Persian version for evaluating their sleep habits during last month and the effect of their sleep habits on achieved task scores.Results Comparison of the number of recalled words (NRW) between the two groups showed a significant difference (p-value < 0.001) with better performance in night group. The NRW during the second stage was positively influenced by better subjective sleep quality, lower sleep latency, higher sleep efficiency, and more sleep duration significantly (p-value < 0.05). There was no significant relationship of NRW with sleep disorders, sleep medications, or daytime dysfunction.Conclusions Adequate night sleep could improve late learning of second language in our research subjects. Sleep quality, latency in falling asleep, and subjective sleep quality might play a role in this learning process.
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Anodal online transcranial direct current stimulation (tDCS) facilitates visual motion perceptual learning
Article
  • Dec 2022
Visual perceptual learning (VPL) has great potential implications for clinical populations, but adequate improvement often takes weeks to months to obtain; therefore, practical applications of VPL are limited. Strategies that enhance visual performance acquisition make great practical sense. Transcranial direct current stimulation (tDCS) could be beneficial to VPL, but thus far, the results are inconsistent. The current study had two objectives: (1) investigate the effect of anodal tDCS on VPL and (2) determine whether the timing sequence of anodal tDCS and training influences VPL. Anodal tDCS was applied on the left human middle temporal (hMT+) during training on a coherent motion discrimination task (online), anodal tDCS was also applied before training (offline), and sham tDCS was applied during training (sham). The coherent thresholds were measured without stimulation before, 2 days after and one month after training. All participants trained for 5 consecutive days. Anodal tDCS resulted in more performance improvement when applied during daily training but not when applied before training. Additionally, neither within‐session improvement nor between‐session improvement differed among the online, offline and sham tDCS conditions. These findings contribute to the development of efficient stimulation protocols and a deep understanding of the mechanisms underlying the effect of tDCS on VPL.
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Dependence on REM sleep of overnight perceptual skill
Article
Full-text available
  • Aug 1994
Several paradigms of perceptual learning suggest that practice can trigger long-term, experience-dependent changes in the adult visual system of humans. As shown here, performance of a basic visual discrimination task improved after a normal night's sleep. Selective disruption of rapid eye movement (REM) sleep resulted in no performance gain during a comparable sleep interval, although non-REM slow-wave sleep disruption did not affect improvement. On the other hand, deprivation of REM sleep had no detrimental effects on the performance of a similar, but previously learned, task. These results indicate that a process of human memory consolidation, active during sleep, is strongly dependent on REM sleep.
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Maintenance of alertness and performance by a brief nap after lunch under prior sleep deficit
Article
Full-text available
  • Oct 2000
We examined the effects of a 15-min nap after lunch on subsequent alertness, performance, and autonomic function following a short sleep the preceding night. Subjects were 12 healthy students who had slept for only 4 hours the night before being tested. They experienced both nap and no-nap conditions in a counterbalanced order, at least a week apart. The nap condition included a 15-min nap opportunity (12:30-12:45) in bed with polygraphic monitoring. We measured the P300 event-related potential, subjective sleepiness (Visual Analog Scale), and electrocardiogram (ECG) at 10:00, 13:15, and 16:15, and task performance (logical reasoning and digit span) at 10:00, 11:30, 13:15, 14:45, 16:15, and 17:45. Mean home sleep measured by actigraphy was 3.5 hours under both conditions. At 13:15, the P300 latency after the nap was significantly shorter than after no nap, but its amplitude was not affected by napping. Subjective sleepiness at 13:15 and 14:45 was significantly lower, and accuracy of logical reasoning at 13:15 was significantly higher after the nap than after no nap. No other performance measures or the ECG R-R interval variability parameters differed significantly between the nap and no-nap conditions. Mean total sleep time during the nap was 10.2 min, and no stage 3 and 4 sleep was observed. The above results suggest that under prior sleep deficit, a 15-min nap during post-lunch rest maintains subsequent alertness and performance, particularly in the mid-afternoon.
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Where Practice Makes Perfect in Texture Discrimination: Evidence for Primary Visual Cortex Plasticity
Article
  • Jul 1991
In terms of functional anatomy, where does learning occur when, for a basic visual discrimination task, performance improves with practice (perceptual learning)? We report remarkable long-term learning in a simple texture discrimination task where learning is specific for retinal input. This learning is (i) local (in a retinotopic sense), (ii) orientation specific but asymmetric (it is specific for background but not for target-element orientation), and (iii) strongly monocular (there is little interocular transfer of learning). Our results suggest that learning involves experience-dependent changes at a level of the visual system where monocularity and the retinotopic organization of the visual input are still retained and where different orientations are processed separately. These results can be interpreted in terms of local plasticity induced by retinal input in early visual processing in human adults, presumably at the level of orientation-gradient sensitive cells in primary visual cortex.
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Neuronal Plasticity That Underlies Improvement in Perceptual Performance
Article
  • Apr 1994
The electrophysiological properties of sensory neurons in the adult cortex are not immutable but can change in response to alterations of sensory input caused by manipulation of afferent pathways in the nervous system or by manipulation of the sensory environment. Such plasticity creates great potential for flexible processing of sensory information, but the actual effects of neuronal plasticity on perceptual performance are poorly understood. The link between neuronal plasticity and performance was explored here by recording the responses of directionally selective neurons in the visual cortex while rhesus monkeys practiced a familiar task involving discrimination of motion direction. Each animal experienced a short-term improvement in perceptual sensitivity during daily experiments; sensitivity increased by an average of 19 percent over a few hundred trials. The increase in perceptual sensitivity was accompanied by a short-term improvement in neuronal sensitivity that mirrored the perceptual effect both in magnitude and in time course, which suggests that improved psychophysical performance can result directly from increased neuronal sensitivity within a sensory pathway.
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Karni, A. & Sagi, D. The time course of learning a visual skill. Nature 365, 250-252
Article
  • Oct 1993
Several examples of experience-dependent perceptual improvement (perceptual learning) suggest that plasticity in specific neuronal loci could underlie the learning process. For a basic visual discrimination task (using an optimal stimulus for 'automatic' pre-attentive texture segregation), discrete retinal input-dependent changes within a very early stage in the stream of visual processing were indicated as the locus of a large and consistent learning effect. When do these changes occur? Here we report that except for a fast, rapidly saturating improvement early in the first practice session, performance was very stable within sessions. Indeed, observers showed little or no improvement until up to 8 hours after their last training session (latent phase). But large improvements occurred thereafter. Finally, there was almost no forgetting; what was gained was retained for at least 2-3 years. We conjecture that some types of perceptual experience trigger permanent neural changes in early processing stages of the adult visual system. These may take many hours to become functional.
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Fast perceptual learning in hyperacuity
Article
We investigated fast improvement of visual performance in several hyperacuity tasks such as vernier acuity and stereoscopic depth perception in almost 100 observers. Results indicate that the fast phase of perceptual learning, occurring within less than 1 hr of training, is specific for the visual field position and for the particular hyperacuity task, but is only partly specific for the eye trained and for the offset tested. Learning occurs without feedback. We conjecture that the site of learning may be quite early in the visual pathway.
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Visual Discrimination Task Improvement: A Multi-Step Process Occurring During Sleep
Article
  • Apr 2000
Performance on a visual discrimination task shows long-term improvement after a single training session. When tested within 24 hr of training, improvement was not observed unless subjects obtained at least 6 hr of posttraining sleep prior to retesting, in which case improvement was proportional to the amount of sleep in excess of 6 hr. For subjects averaging 8 hr of sleep, overnight improvement was proportional to the amount of slow wave sleep (SWS) in the first quarter of the night, as well as the amount of rapid eye movement sleep (REM) in the last quarter. REM during the intervening 4 hr did not appear to contribute to improvement. A two-step process, modeling throughput as the product of the amount of early SWS and late REM, accounts for 80 percent of intersubject variance. These results suggest that, in the case of this visual discrimination task, both SWS and REM are required to consolidate experience-dependent neuronal changes into a form that supports improved task performance.
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Gais, S., Plihal, W., Wagner, U. & Born, J. Early sleep triggers memory for early visual discrimination skills. Nat. Neurosci. 3, 1335−1339
Article
  • Jan 2001
Improvement after practicing visual texture discrimination does not occur until several hours after practice has ended. We show that this improvement strongly depends on sleep. To specify the process responsible for sleep-related improvement, we compared the effects of 'early' and 'late' sleep, dominated respectively by slow-wave and rapid eye movement (REM) sleep. Discrimination skills significantly improved over early sleep, improved even more over a whole night's sleep, but did not improve after late sleep alone. These findings suggest that procedural memory formation is prompted by slow-wave sleep-related processes. Late REM sleep may promote memory formation at a second stage, only after periods of early sleep have occurred.
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Visual discrimination learning requires post-training sleep
Article
  • Jan 2001
Performance on a visual discrimination task showed maximal improvement 48−96 hours after initial training, even without intervening practice. When subjects were deprived of sleep for 30 hours after training and then tested after two full nights of recovery sleep, they showed no significant improvement, despite normal levels of alertness. Together with previous findings11 that subjects show no improvement when retested the same day as training, this demonstrates that sleep within 30 hours of training is absolutely required for improved performance.
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