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The restorative effect of naps on perceptual deterioration



Human performance on visual texture discrimination tasks improves slowly (over days) in the absence of additional training. This 'slow learning' requires nocturnal sleep after training and is limited to the region of visual space in which training occurred. Here, we tested human subjects four times in one day and found that with repeated, within-day testing, perceptual thresholds actually increased progressively across the four test sessions. This performance deterioration was prevented either by shifting the target stimuli to an untrained region of visual space or by having the subjects take a mid-day nap between the second and third sessions.
As life in our culture becomes more demanding, average nightly
sleep is decreasing in all segments of the population (Stein, M.,
National Sleep Foundation Poll, 2001,
PressArchives/lessfun_lesssleep.html). ‘Power naps’—brief periods
of daytime sleep lasting an hour or less—improve alertness, pro-
ductivity and mood
, especially under sleep-deprived conditions
during nightshift work
and during prolonged periods of driving
Although naps have been shown to enhance psychomotor speed as
well as short-term memory acquisition
, the effect of daytime naps
on previously learned information is not known. The finding that
power naps are common among people reporting daily informa-
tion overload indicates that napping supports a previously unknown
mechanism of off-line information processing, perhaps related to
that which normally occurs during nocturnal sleep
We investigated the phenomenon of information overload at
the perceptual level. Typically in visual perception tasks, fast learn-
ing happens in the first minutes to hours of training
. Previous
studies using a visual texture discrimination task (TDT)
that a slower phase of perceptual learning also exists, which
depends on nocturnal sleep after training
. The slow phase
of improvement becomes evident only after at least six hours of
nocturnal sleep
, and sleep deprivation the night after training
eliminates the normal post-sleep improvement, even when mea-
sured after two full nights of recovery sleep
. The improvement
seen in subjects who sleep for eight hours during the night after
training correlates with the proportion of deep, slow wave sleep
(SWS) in the first quarter of the night and with the proportion of
rapid eye movement sleep (REM) in the last quarter
. These results
indicate that a full night of sleep is important for maintenance and
consolidation of experience-dependent learning, and that without
at least six hours of sleep, this potential consolidation is lost. These
studies do not, however, address the question of how power naps
of an hour or less could aid in such information processing.
Here we show that perceptual performance declined on the
TDT with repeated, within-day training. In the context of this
The restorative effect of naps on
perceptual deterioration
Sara C. Mednick
, Ken Nakayama
, Jose L. Cantero
, Mercedes Atienza
, Alicia A. Levin
Neha Pathak
and Robert Stickgold
Department of Psychology, Harvard University, 33 Kirkland Street, Cambridge, Massachusetts 02138, USA
Laboratory of Neurophysiology and Department of Psychiatry, Massachusetts Mental Health Center, Harvard Medical School,
74 Fenwood Road, Boston, Massachusetts 02115, USA
Correspondence should be addressed to S.C.M. (
Published online: 28 May 2002, doi:10.1038/nn864
Human performance on visual texture discrimination tasks improves slowly (over days) in the
absence of additional training. This ‘slow learning’ requires nocturnal sleep after training and is lim-
ited to the region of visual space in which training occurred. Here, we tested human subjects four
times in one day and found that with repeated, within-day testing, perceptual thresholds actually
increased progressively across the four test sessions. This performance deterioration was prevented
either by shifting the target stimuli to an untrained region of visual space or by having the subjects
take a mid-day nap between the second and third sessions.
deterioration, we found that (i) a daytime nap, but not an equiv-
alent period of rest without visual input, reversed the deteriora-
tion, (ii) the deterioration was retinotopically specific and (iii)
neither an increase in subject motivation nor a decrease in task
difficulty improved performance.
Can too much practice be detrimental?
To investigate whether repeated within-day testing on a percep-
tual learning task can impair performance, subjects were tested on
the TDT four times in a single day (at 9 a.m., 12 p.m., 4 p.m. and
7 p.m.). Each session lasted approximately 60 minutes. For each
session, the speed of perceptual processing was calculated as the
threshold target-to-mask interstimulus interval (ISI) needed to
achieve 80% accuracy. Thirty subjects were randomly assigned
to one of three groups: control, long nap or short nap. Control
subjects (n = 10) showed a 52% slowing in perceptual process-
ing across the four test sessions (Fig. 1, filled circles; P = 0.0003,
repeated measures analysis of variance (ANOVA) and post hoc
tests). Thus, with each successive session, subjects needed increas-
ingly longer exposures to the stimuli to reliably identify targets.
Performance deteriorated despite all testing being done within
12 hours of morning awakening, a time when one would not nor-
mally expect to see cognitive impairment, and without prior sleep
deprivation. Subjects averaged 6.92 ± 0.77 (s.d.) hours of sleep
on the night before testing.
Can daytime sleep reverse perceptual deterioration?
As nocturnal sleep is known to enhance alertness and to consol-
idate TDT learning
, we asked whether a daytime nap
might stop or even reverse the process of deterioration seen with
repeated within-day testing. The remaining 20 subjects were ran-
domly assigned to a long (60-minute) or short (30-minute) nap
condition. All subjects, including no-nap controls, performed
the task four times during the day; experimental subjects took a
nature neuroscience • volume 5 no 7 • july 2002 677
© 2002 Nature Publishing Group
nature neuroscience • volume 5 no 7 • july 2002
nap at 2 p.m.—midway between the second and third test ses-
sions. As predicted, napping significantly affected subsequent
performance (P = 0.001, group × session interaction, mixed-
model ANOVA): short naps prevented the normal deterioration
that was seen in test sessions 3 and 4 (Fig. 1, open circles) and
long naps reversed the deterioration seen in the second test ses-
sion (Fig. 1, open triangles). Thus, whereas controls showed a
14.1-ms increase in ISI threshold between the second and third
sessions, the short nap group showed no change (<1-ms increase)
and the long nap group showed a 20.9-ms decrease (P =0.03,
paired t-test). The short nap group showed no significant change
in ISI thresholds across the last three sessions (P = 0.94), but did
show significantly lower thresholds in the fourth session than did
controls (P = 0.01). The long nap group showed significantly bet-
ter performance than controls on both the third and fourth ses-
sions (P = 0.03). We compared the distributions of sleep stages
in long naps versus short naps (Ta ble 1) and found no signifi-
cant differences in the amount of time spent in stage 1 or stage 2
sleep. There was, however, a difference for SWS and REM sleep:
the long naps contained 4.1–4.4 times more SWS and REM sleep
than the short naps did. This matches the SWS and REM depen-
dency reported for overnight improvement on the task
, sug-
gesting that processes occurring during SWS and/or REM
underlie sleep-induced perceptual recovery.
We compared naps taken on the day of TDT testing with base-
line naps taken on a different day (see Methods) and found that
the long nap group spent significantly more time in SWS on the
test day (27.4 versus 20.9 minutes, P < 0.05, one-tailed paired t-
test) at the expense of time in stage 2 sleep (17.9 versus
25.0 minutes, P = 0.06) (Fig. 2). This large increase in SWS in test-
day naps (31% over baseline) suggests that SWS is crucial for post-
nap performance, perhaps through stabilizing and consolidating
plastic neuronal changes from earlier in the day. Such a function
has previously been proposed for nocturnal SWS (refs. 23–25).
The role of REM, however, is less clear.
The increase in time spent in REM during
test-day naps compared with baseline naps
(60% increase) was larger than that seen for
SWS, but was not statistically significant
(Fig. 2). Similar post-training REM sleep
increases have been reported in animal stud-
ies of sleep-dependent learning
. In the
short nap condition, there was no signifi-
cant change in SWS (9.2 minutes on test
day versus 6.7 minutes baseline) or REM
(0.8 minutes on test day versus 2.0 minutes
baseline) duration. Nap duration was controlled in both groups
and neither group showed a significant difference in nap duration
between baseline and test-day naps (P > 0.27).
Sleep versus rest
To t est whether the benefits of naps resulted from the absence of
visual input rather than from sleep itself, subjects (n = 9) repeat-
ed the long nap protocol; but instead of sleeping, they rested qui-
etly while blindfolded. Wake–sleep state was continuously
monitored physiologically to ensure maintained wakefulness.
The hour of rest without visual input did not have a restorative
effect: subjects still showed performance decrements at 4 p.m.
and 7 p.m. ISI thresholds increased by an average of 29.0 ms
between the second and fourth session (P < 0.05), a performance
decrement nearly identical (P = 0.31) to the 32-ms increase seen
in controls (Fig. 1).
Motivation and task difficulty
To t est whether a decrease in motivation contributed to the per-
formance deterioration, we informed subjects (n = 10) after their
second session that their performance had worsened, and that they
would receive a cash bonus if they subsequently returned to their
baseline performance. Even with this incentive, none of the sub-
jects regained baseline performance during the third or fourth ses-
sions, and mean ISI thresholds were 32.2 ms longer on the fourth
session compared to the first session (P = 0.001).
To t est whether the performance decrement resulted specif-
ically from exposure to more difficult trials, we tested ten sub-
jects throughout the day in four sessions, with all blocks in the
first three sessions run at the longest ISI (400 ms). Subjects were
then tested in the fourth session with the standard 25 blocks of
decreasing ISIs, and their performance in the fourth session did
not differ from that of controls (P = 0.50). Thus, despite hav-
ing an easier task in the first three sessions, subsequent perfor-
mance still declined.
What aspect of processing is impaired?
Several mechanisms might underlie this deterioration. A gener-
alized fatigue effect, mediated by a decrease in alertness or atten-
tional resources, is one possibility that is consistent with our data.
Alternatively, we propose that specific neural networks in pri-
mary visual cortex gradually become saturated with information
through repeated testing, preventing further perceptual process-
ing. This would cause a training-specific deterioration in per-
Fig. 1. Beneficial effect of napping. Performance of control and both
short and long nap groups across same-day sessions. ISI thresholds from
the first session defined baseline performance, and were subtracted
from thresholds for subsequent sessions to determine relative change in
Ta b le 1. Characteristics of short and long naps.
S1 (min) S2 (min) SWS (min) REM (min) Threshold (ms)
Short naps 6.2 ± 1.1 14.3 ± 1.5 6.6 ± 2.1 2.0 ± 1.4 4.5 ± 10.7
Long naps 5.5 ± 0.7 17.9 ± 3.0 27.4 ± 3.8 8.9 ± 3.4 20.8 ± 7.6
P value 0.85 0.22 0.0001 0.07
The mean times spent in each sleep stage during short (30-min) and long (60-min) naps are presented as
min ± s.e.m. S1, stage 1; S2, stage 2; SWS, slow wave sleep (stages 3 and 4); REM, rapid eye movement
sleep. P values from unpaired t-test comparing times in each stage for long versus short naps.
Threshold, the difference between ISI thresholds on the second (pre-nap) and third (post-nap) tests,
expressed as ms ± s.e.m.
© 2002 Nature Publishing Group
ceptual processing. Whereas the generalized fatigue hypothesis
predicts that decrements in performance would be widespread
and largely task-independent, our hypothesis of a training-specific
deterioration predicts that the performance decrements would
be restricted to behaviors mediated by the specific neural net-
works previously involved in processing the target stimuli. We
reasoned that because TDT learning does not transfer to
untrained portions of the visual field
, there should be no
training-specific deterioration if stimuli are presented to an
untrained region of the visual cortex.
To test this hypothesis, we trained 24 subjects as before, but
for half of them we switched the target stimuli to the con-
tralateral visual field for the fourth and final same-day test ses-
sion. Performance of the switch group did not differ
significantly from that of the control group across the first three
sessions, but, unlike the control group, the switch group
showed significant recovery in the fourth session (Fig. 3, P =
0.002, ANOVA group × session interaction and post hoc test).
Performance during the switch condition was not significant-
ly worse than it was during the first session, indicating that the
behavioral deterioration observed in the trained visual quad-
rant did not transfer to the untrained contralateral quadrant.
These results strongly support the training-specific deteriora-
tion hypothesis, and are contrary to the predictions of the gen-
eralized fatigue hypothesis.
Further evidence against the generalized fatigue hypothesis
comes from the dissociation between improved performance and
subjective levels of sleepiness. If the steady decrease in perfor-
mance throughout the day in the control group resulted from a
general fatigue effect, then one should see a parallel increase in
reported sleepiness. But no such increase was seen, and mean
levels of subjective sleepiness on the first and last tests were iden-
tical (P = 0.45, repeated measures ANOVA). Similarly, the switch
group showed no significant change in sleepiness across sessions
(P = 0.49). In contrast, sleepiness decreased from the first to the
last session in the nap groups (P < 0.03, ANOVA and post hoc
tests). Thus, the switch group showed the same improvement in
performance as the nap groups did, but without a similar decrease
in subjective sleepiness; compared to controls, the switch group
showed the same maintenance in degree of sleepiness, but no
deterioration in performance.
Data from the switch group also eliminated another possible
explanation, that of a strictly circadian effect. Although the con-
trol data could have been explained as a circadian rather than
repetition effect, the fact that shifting the stimulus to the con-
tralateral visual field for the last session reversed this decrease
eliminates this possibility. Thus, when subjects were tested at
7 p.m. with stimuli in an untrained region of visual space, they
performed as well as they had at 9 a.m. the same morning.
Tw o learning components occur with TDT testing: a fast, within-
session component and a slow, sleep-dependent component
Our study identified a third consequence of TDT training: with
repeated, same-day training, people require progressively longer
ISIs for texture discrimination. Such perceptual deterioration
has not previously been reported for repeated same-day testing
on other visual tasks. Task procedure may contribute to this dif-
ference, such as whether the task measures vernier acuity
, res-
olution acuity
or texture discrimination
, whether stimuli
are presented foveally
, parafoveally
or at more peripher-
al eccentricities
, and whether stimulus presentations are long
(100–150 ms)
or short (17 ms)
. Furthermore, a number
of perceptual learning protocols train subjects over several
rather than within-day, as in the current study,
making it unclear whether fast or slow learning is occurring.
With these caveats in mind, the present study shows that some
forms of neural plasticity that require sleep for subsequent con-
solidation and improvement of perception may actually hinder
performance before sleep.
Our three main findings—that there was a normal decline in
TDT performance across the day with repeated exposure to the
task, that this decline was specific to previously trained regions
of visual space and that performance was restored by daytime
napping—have important implications. First, as circadian influ-
ences have been ruled out, the performance decline must result
from specific neuronal changes induced by the initial testing peri-
od. Second, as brain regions involved in higher levels of visual
nature neuroscience • volume 5 no 7 • july 2002 679
Fig. 2. Comparison of test-day and baseline long naps. The number of
minutes spent in stage 1 (S1), stage 2 (S2), rapid eye movement (REM)
and slow wave sleep (SWS; stage 3 and 4) during baseline (gray bars) and
test-day (solid bars) naps. * P < 0.05; (*) P = 0.06.
Fig. 3. Beneficial effect of shifting stimulus location. Solid bars, deterio-
ration in performance of control subjects during the third (T3) and
fourth (T4) sessions of the day. Gray bars, deterioration in performance
of experimental ‘switch group’ subjects during T3 followed by recovery
during T4 when stimulus location was shifted.
© 2002 Nature Publishing Group
nature neuroscience • volume 5 no 7 • july 2002
processing lack retinotopic specificity, the critically affected neu-
rons are most likely located in early visual processing areas. Final-
ly, these initially affected neural networks must be further altered
during napping to reverse the performance decrement.
We propose that the performance decrement seen here was a
direct consequence of a mechanism for preserving information
that has been processed but has not yet been consolidated into
memory by sleep. As this hypothetical limited-capacity mecha-
nism becomes saturated with task-specific information, the local
neural network’s ability to process on-line information during task
performance worsens, resulting in the performance decrement.
Several findings support this relationship between information
processing and the performance decrement. First, the retinotopic
specificity of the performance decrement is consistent with previ-
ous studies showing that TDT learning is similarly retinotopic
and dependent on early stages of visual processing
. Second,
post-training sleep is known to be critical for stabilization and con-
solidation of TDT learning
and we now have shown that a
60-minute nap reverses the performance decrement.
Although we cannot exclude a function for REM in this
process, it seems most likely that SWS has the central role.
Although both SWS and REM have previously been implicated
in the nocturnal, sleep-dependent consolidation of this task
the REM-dependent period appears four hours after the SWS-
dependent period has ended
—well beyond the timeframe of
these naps. We posit that during SWS, mechanisms of cortical
plasticity lead to secondary changes in the TDT-trained neural
networks. Thus SWS serves as the initial processing stage of expe-
rience-dependent, long-term learning and as the critical stage for
restoring perceptual performance. Roles for SWS in memory con-
solidation have been proposed by others
This example of a training-induced deterioration in perfor-
mance has several additional implications. First, it indicates that
the cognitive benefits of sleep can be studied over a very short
time period and do not require sleep deprivation or overnight
sessions of sleep. This provides a more favorable set of conditions
to study the involvement of sleep in information processing and
performance. Second, it suggests that the psychological sensa-
tion of ‘burnout,’ described anecdotally as increased irritation
and frustration along with decreased effectiveness after prolonged
cognitive effort, may not reflect a general mental fatigue, but
rather the specific need of an overused local neural network to
enjoy the restorative benefits of sleep.
A total of 129 undergraduates gave informed consent to participate in the
study, which was approved by the Harvard University Department of Psy-
chology internal review board. All su
bjects had normal or corrected-to-
normal vision and no history of neurological, mental or physical illness.
Each subject was tested on the TDT four times in one day: at 9 a.m.,
12 p.m., 4 p.m. and 7 p.m. Tests normally lasted 60–75 min and includ-
ed 1,250 trials. Participants were asked to discriminate the shape of a
target in one of the lower quadrants of the display at 2.5–5.0° eccen-
tricity from the center. Either a ‘T’ or an ‘L’ appeared in the center,
where fixation was maintained. (Except where noted, target arrays
always appeared in the lower left quadrant.) The target consisted of a
horizontally or vertically oriented array of three diagonal bars against
a background of horizontal bars. For each trial, the following sequence
was shown: target screen for 16 ms, blank screen for a variable period
of time, and then a mask for 16 ms. After each trial, subjects report-
ed both the letter (T or L) at the central fixation point and the orien-
tation of the diagonal bar array (horizontal or vertical). For each
session, the speed of perceptual processing was calculated as the
threshold target-to-mask ISI needed to achieve 80% accuracy. The ISI
threshold provides a measure of the minimal effective stimulation
needed for perception of the target. This task was carried out as pre-
viously described
. To examine the amount of change from base-
line, difference scores were calculated by subtracting the threshold for
the first session from those of the second, third and fourth. Subjects
in all groups kept sleep logs for the week before testing, and no sig-
nificant between-group differences were found in sleep patterns.
Thirty subjects were randomly assigned to one of the nap conditions
or to the previously described control condition, permitting statistical
comparisons between all three groups. Naps began at 2 p.m. and were
recorded polysomnographically, with standard electroencephalographic
(EEG), electro-oculographic (EOG) and electromyographic (EMG) mea-
sures. Subjects were allowed to sleep until they had completed either a
full half-hour or a full hour of polysomnographically identified
and then were woken by the experimenter. Sleep stages were subsequently
rescored off-line. On re-scoring, one subject in the short nap group was
found to have slept for only 9.5 min, and was excluded from all analy-
ses. Subjects were recorded during naps on two separate days: the TDT
test day and a control day (‘baseline nap’) either one week before or one
week after the test day, with the order balanced across subjects. Subjects
in the short nap group averaged 29.1 ±2.9 (mean ± s.d.) min of sleep on
the test day; subjects in the long nap group averaged 59.6 ± 5.5 min.
For the quadrant switch study (24 switches; 22 controls), peripheral
targets were presented in the lower left or lower right quadrant (balanced
across subjects) for the first three test sessions. Then, for the fourth session,
the targets were switched to the opposite lower quadrant for the switch
group. For the quiet rest condition, nine subjects followed the long nap
protocol but were instructed not to sleep during the nap hour. Subjects
were blindfolded to prevent visual stimulation and listened to an audio
tape of short stories. Each subject’s wake state was monitored with the
(HealthDyne Technologies, Marietta, Georgia, USA) and sub-
jects were alerted at the first indication of impending sleep. At the start
of the third session, subjects (n = 10) in the motivation protocol were
offered a bonus of $25 if they could regain their initial performance level
during the next two sessions. Subjects (n = 12) in the fixed ISI protocol
performed the standard protocol but with the ISI set to 400 ms for all 25
blocks during test sessions 1–3. For the fourth session, subjects followed
the standard protocol with decreasing ISIs across the 25 blocks. All subjects
rated their sleepiness using the Stanford sleepiness scale
, which rates
subjective sleepiness on a seven-point scale.
This research was supported by grants from the National Institutes of Health
(MH 48,832 and NS 26,985) and AFOSR (83-0320) and by fellowships to J.L.C.
and M.A. from the Spanish Ministry of Education and the NATO Scientific
Program, respectively.
Competing interests statement
The authors declare that they have no competing financial interests.
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... Across experimental designs, post-encoding sleep has been consistently found to enhance memory retrieval on non-verbal (e.g., object-location associations) and verbal (e.g., lists of word pairs) tasks assumed to assess hippocampus-dependent episodic types of memories. 3,13,14 Benefits of sleep have likewise been observed for procedural memories for perceptual and motor skills [15][16][17] which, in theory, represent memories that do not require hippocampal function. 18 Here, tasks requiring motor adaptation (such as pursuit rotor task) might represent an exception as they do not seem to benefit from sleep. ...
... 161,172,173 Stimulating somatostatin positive interneurons increased slow-wave amplitudes and their slopes. 174 The causal role for memory consolidation during sleep is supported by a body of findings showing (11)(12)(13)(14)(15)(16) are generated in the thalamus, reach cortical networks via thalamocortical projections, and tend to nest in the depolarizing SO upstate (SO + spindle). SOs nesting spindles appear to particularly fosters synaptic plasticity through releasing inhibition at pyramidal dendrites. ...
Although long-term memory consolidation is supported by sleep, it is unclear how it differs from that during wakefulness. Our review, focusing on recent advances in the field, identifies the repeated replay of neuronal firing patterns as a basic mechanism triggering consolidation during sleep and wakefulness. During sleep, memory replay occurs during slow-wave sleep (SWS) in hippocampal assemblies together with ripples, thalamic spindles, neocortical slow oscillations, and noradrenergic activity. Here, hippocampal replay likely favors the transformation of hippocampus-dependent episodic memory into schema-like neocortical memory. REM sleep following SWS might balance local synaptic rescaling accompanying memory transformation with a sleep-dependent homeostatic process of global synaptic renormalization. Sleep-dependent memory transformation is intensified during early development despite the immaturity of the hippocampus. Overall, beyond its greater efficacy, sleep consolidation differs from wake consolidation mainly in that it is supported, rather than impaired, by spontaneous hippocampal replay activity possibly gating memory formation in neocortex.
... It is well established that chronic sleep restriction and acute sleep deprivation impair cognition and performance [1][2][3] and that sleep is needed to recover from the cognitive impairment caused by sleep loss [4][5][6][7][8]. There is also strong evidence that the impairment caused by sleep deprivation varies among individuals [9][10][11]. ...
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The cellular consequences of sleep loss are poorly characterized. In the pyramidal neurons of mouse frontal cortex, we found that mitochondria and secondary lysosomes occupy a larger proportion of the cytoplasm after chronic sleep restriction compared to sleep, consistent with increased cellular burden due to extended wake. For each morphological parameter, the within-animal variance was high, suggesting that the effects of sleep and sleep loss vary greatly among neurons. However, the analysis was based on 4-5 mice/group and a single section/cell. Here, we applied serial block-face scanning electron microscopy to identify signatures of sleep and sleep loss in the Drosophila brain. Stacks of images were acquired and used to obtain full 3D reconstructions of the cytoplasm and nucleus of 263 Kenyon cells from adult flies collected after a night of sleep (S) or after 11 h (SD11) or 35 h (SD35) of sleep deprivation (9 flies/group). Relative to S flies, SD35 flies showed increased density of dark clusters of chromatin and Golgi apparata and a trend increase in the percent of cell volume occupied by mitochondria, consistent with increased need for energy and protein supply during extended wake. Logistic regression models could assign each neuron to the correct experimental group with good accuracy, but in each cell, nuclear and cytoplasmic changes were poorly correlated, and within-fly variance was substantial in all experimental groups. Together, these results support the presence of ultrastructural signatures of sleep and sleep loss but underscore the complexity of their effects at the single-cell level.
... Accuracy was defined as the number of sequences containing errors per 30 s. Bars indicate the standard error of the mean. * = p < 0.05 included an average of 6.6 min of SWS (slow wave sleep) [20]; therefore, the 30-min group (lying-in-bed time) in our study might contain SWS, which was closely related to declarative memory consolidation and anti-interference performance [5,21]. Moreover, the SWS in all three groups might reduce the group differences in this study. ...
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Purpose This study explored the relationship between naps and memory among habitual nappers in China. Methods Medical college students participated and were divided into 30-min, 60-min, and 90-min time-in-bed groups. To evaluate declarative and procedural memory performance, A–B and A–C interfering word pair and interfering finger tapping tasks were employed. Results Among 60 students, a significant decrease in the correct recall rate in the declarative task after having a nap was found only in the 30-min group (p = 0.005). After learning interference (A-C word pairs), the correct recall rate for the declarative task decreased significantly in all interference tests (ps < 0.001). In the procedural task, the speed of sequence A in the retests increased after having a nap in all three groups (ps < 0.048), with a significant decrease in accuracy only in the 30-min group (p = 0.042). After learning interference (sequence B) in the procedural task, the speed of sequence A increased in the 60-min group after 1 h (p = 0.049), and both the 60-min and 90-min groups showed increased speed after one night (ps < 0.022). No significant improvement in speed was found in the 30-min group (ps > 0.05), and this group showed the lowest accuracy for sequence A (ps < 0.16). Conclusion A habitual nap time-in-bed of 60 or 90 min had better effects on declarative and procedural memory consolidation and better memory resistance against interference in procedural memory.
... Our experiment was inspired by methodology employed in closely related scientific domains, such as perceptual learning 34-38 and perceptual deterioration 11,[39][40][41] . The majority of previous research stemming from these literatures has tended so far to theorize, or find, an involvement of the primary visual cortices in their phenomenon of interest. ...
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Cognitive fatigue is defined by a reduced capacity to perform mental tasks. Despite its pervasiveness, the underlying neural mechanisms remain elusive. Specifically, it is unclear whether prolonged effort affects performance through alterations in overworked task-relevant neuronal assemblies. Our paradigm based on repeated passive visual stimulation discerns fatigue effects from the influence of motivation, skill and boredom. We induced performance loss and observed parallel alterations in the neural blueprint of the task, by mirroring behavioral performance with multivariate neuroimaging techniques (MVPA) that afford a subject-specific approach. Crucially, functional areas that responded the most to repeated stimulation were also the most affected. Finally, univariate analysis revealed clusters displaying significant disruption within the extrastriate visual cortex. In sum, here we show that repeated stimulation impacts the implicated brain areas' activity and causes tangible behavioral repercussions, providing evidence that cognitive fatigue can result from local, functional, disruptions in the neural signal induced by protracted recruitment.
... These changes are indexed by differences in behavior before versus after an offline period (Fig. 1b). For example, after learning a sequence of tasks, a period of sleep can repair memories that have been damaged due to interference in a preceding wakeful period [19,20,1,4] -an adaptive capacity that biologically detailed models of sleep have explored [7,22,31]. ...
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A remarkable capacity of the brain is its ability to autonomously reorganize memories during offline periods. Memory replay, a mechanism hypothesized to underlie biological offline learning, has inspired offline methods for reducing forgetting in artificial neural networks in continual learning settings. A memory-efficient and neurally-plausible method is generative replay, which achieves state of the art performance on continual learning benchmarks. However, unlike the brain, standard generative replay does not self-reorganize memories when trained offline on its own replay samples. We propose a novel architecture that augments generative replay with an adaptive, brain-like capacity to autonomously recover memories. We demonstrate this capacity of the architecture across several continual learning tasks and environments.
Training on one task (task A) can disrupt learning on a subsequently trained task (task B), illustrating anterograde learning interference. We asked whether the induction of anterograde learning interference depends on the learning stage that task A has reached when the training on task B begins. To do so, we drew on previous observations in perceptual learning in which completing all training on one task before beginning training on another task (blocked training) yielded markedly different learning outcomes than alternating training between the same two tasks for the same total number of trials (interleaved training). Those blocked versus interleaved contrasts suggest that there is a transition between two differentially vulnerable learning stages that is related to the number of consecutive training trials on each task, with interleaved training presumably tapping acquisition, and blocked training tapping consolidation. Here, we used the blocked versus interleaved paradigm in auditory perceptual learning in a case in which blocked training generated anterograde—but not its converse, retrograde—learning interference (A→B, not B←A). We report that anterograde learning interference of training on task A (interaural time difference discrimination) on learning on task B (interaural level difference discrimination) occurred with blocked training and diminished with interleaved training, with faster rates of interleaving leading to less interference. This pattern held for across-day, within-session, and offline learning. Thus, anterograde learning interference only occurred when the number of consecutive training trials on task A surpassed some critical value, consistent with other recent evidence that anterograde learning interference only arises when learning on task A has entered the consolidation stage.
Optimal sleep health is a critical component to high-level performance. In populations such as the military, public service (eg, firefighters), and health care, achieving optimal sleep health is difficult and subsequently deficiencies in sleep health may lead to performance decrements. However, advances in sleep monitoring technologies and mitigation strategies for poor sleep health show promise for further ecological scientific investigation within these populations. The current review briefly outlines the relationship between sleep health and performance as well as current advances in behavioral and technological approaches to improving sleep health for performance.
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While principles governing encoding mechanisms in visual perceptual learning (VPL) are well-known, findings regarding posttraining processing are still unrelated in terms of their underlying mechanisms. Here, we examined the effect of repetitive high-frequency visual stimulation (H-RVS) on VPL in an orientation detection task. Application of H-RVS after a single task session led to enhanced orientation detection performance (n = 12), but not in a sham condition (n = 12). If prior training-based VPL had been established by seven sessions in the detection task, H-RVS instead led to a performance impairment (n = 12). Both sham (n = 8) and low-frequency stimulation (L-RVS, n = 12) did not lead to a significant impairment. These findings may suggest reversal dynamics in which conditions of elevated network excitation lead to a decrease in a signal-related activity instead of a further increase. These reversal dynamics may represent a means to link various findings regarding posttraining processing.
Employees spend a considerable amount of their working time enjoying on-the-job leisure. While this demonstrates a management control problem, it can also function as a novel domain for bonuses. In this study, I investigate the effect of an unconditional bonus (gift) in the form of more off-the-job leisure time. In particular, I examine how the gift of an intentional reduction of working time affects employee work behavior compared to a cash gift. A real-effort laboratory experiment shows that a cash gift neither alters employees’ on-the-job leisure time nor performance. A gift of more off-the-job leisure time, however, does reduce the on-the-job leisure time of employees and increases their performance. A follow-up vignette study among human resource (HR) professionals further provides external validity for these results. Moreover, it also displays the other positive influence of leisure time offered as gifts on several different employee outcomes such as satisfaction, commitment, and health.
Recall of paired-associate lists (declarative memory) and mirror-tracing skills (procedural memory) was assessed after retention intervals defined over early and late nocturnal sleep. In addition, effects of sleep on recall were compared with those of early and late retention intervals filled with wakefulness. Twenty healthy men served as subjects. Saliva cortisol concentrations were determined before and after the retention intervals to determine pituitary-adrenal secretory activity. Sleep was determined somnopolygraphically. Sleep generally enhanced recall when compared with the effects of corresponding retention intervals of wakefulness. The benefit from sleep on recall depended on the phase of sleep and on the type of memory: Recall of paired-associate lists improved more during early sleep, and recall of mirror-tracing skills improved more during late sleep. The effects may reflect different influences of slow wave sleep (SWS) and rapid eye movement (REM) sleep since time in SWS was 5 times longer during the early than late sleep retention interval, and time in REM sleep was twice as long during late than early sleep (p < 0.005). Changes in cortisol concentrations, which independently of sleep and wakefulness were lower during early retention intervals than late ones, cannot account for the effects of sleep on memory. The experiments for the first time dissociate specific effects of early and late sleep on two principal types of memory, declarative and procedural, in humans.
Behavioral studies of memory and learning in both humans and animals support a role for sleep in the consolidation and integration of memories. Physiological studies of hippocampal and cortical activity as well as of brainstem neuromodulatory systems demonstrate the state-dependence of communication both between and within the neocortex and hippocampus. These findings are consonant with observed cognition during sleep and immediately following awakening.
Effects of habitual variations in napping on psychomotor performance, short-term memory and subjective states were investigated. The subjects were 32 healthy male university students who napped twice or more weekly in themorning and at night. Sixteen were randomly assigned to a control group and 16 to a nap(treatment) group. The experiment comprised two conditions of electrographically (EEG) recorded sleep for the nap group and two EEG monitored conditions of wakefulness for the controls. These conditions were scheduled from 9:35 to 11:35 a.m. and 12 hr later between 9:35 p.m. and 11:35 p.m. Measurements were obtained from: (a) a continuous 10-min auditory reaction time task, (b) a free recall task of short-term memory, (c) an activation-mood adjective check list, and (d) the Stanford Sleepiness scale. Except for memory the dependent variables of waking function were assessed 20 min before and 20 min after all conditions. Following each sleep condition the nap group as opposed to the controls showed a statistically significant improvement in reaction time performance, higher short-term retention, less reported sleepiness and elevated subjective states reflected by fice factors on the adjective mood-activation check list. Among the correlations computed the largest significant coefficients were of stage 4 and REM with posttreatment Stanford Sleepiness ratings. After naps, increased postdormital sleepiness was correlated with stage 4 and decreased sleepiness with REM sleep. Although few strikingly divergent functional effects were associated with morning and nocturanal naps, these did covary with sleep psychophysiology. It is postulated that the phase, the EEG-sleep stages and possibly the duration of accustomed naps are less salient factors influencing performance when the time since awakening until behavioral assessment can be kept constant.