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Journal of Experimental Psychology: General
Individual Differences in Working Memory Capacity
Predict Sleep-Dependent Memory Consolidation
Kimberly M. Fenn and David Z. Hambrick
Online First Publication, September 12, 2011. doi: 10.1037/a0025268
Fenn, K. M., & Hambrick, D. Z. (2011, September 12). Individual Differences in Working
Memory Capacity Predict Sleep-Dependent Memory Consolidation. Journal of Experimental
Psychology: General. Advance online publication. doi: 10.1037/a0025268
Journal of Experimental Psychology: General
2011, Vol. ●●, No. ●, 000 – 000
© 2011 American Psychological Association
0096-3445/11/$12.00 DOI: 10.1037/a0025268
Individual Differences in Working Memory Capacity Predict
Sleep-Dependent Memory Consolidation
Kimberly M. Fenn and David Z. Hambrick
Michigan State University
Decades of research have established that “online” cognitive processes, which operate during conscious
encoding and retrieval of information, contribute substantially to individual differences in memory.
Furthermore, it is widely accepted that “offline” processes during sleep also contribute to memory
performance. However, the question of whether individual differences in these two types of processes are
related to one another remains unanswered. We investigated whether working memory capacity (WMC),
a factor believed to contribute substantially to individual differences in online processing, was related to
sleep-dependent declarative memory consolidation. Consistent with previous studies, memory for word
pairs reliably improved after a period of sleep, whereas performance did not improve after an equal
interval of wakefulness. More important, there was a significant, positive correlation between WMC and
increase in memory performance after sleep but not after a period of wakefulness. The correlation
between WMC and performance during initial test was not significant, suggesting that the relationship
is specific to change in memory due to sleep. This suggests a fundamental underlying ability that may
distinguish individuals with high memory capacity.
Keywords: memory, sleep, individual differences, working memory capacity
Supplemental materials:
goliash, 2010; Walker, Brakefield, Morgan, Hobson, & Stickgold,
2002), and perceptual learning (Fenn, Nusbaum, & Margoliash, 2003;
Karni, Tanne, Rubenstein, Askenasy, & Sagi, 1994; see Diekelmann
& Born, 2010; McGaugh, 2000, for reviews) in humans. As a specific
example, recall of paired associates improves after sleep but does not
improve after an equal interval of wakefulness (Plihal & Born, 1997,
1999). Sleep has also been found to increase resistance to interference
in declarative memory (Ellenbogen, Hulbert, Jiang, & Stickgold,
2009), suggesting that offline processing may strengthen memory
Thus, evidence suggests that learning is influenced by both
online and offline processes. However, whether offline processing
contributes to individual differences in memory has been all but
neglected in the literature, as has the question of whether online
and offline processes are related to one another. With this in mind,
we conducted the first large-scale study of individual differences
in sleep-dependent consolidation. The purpose of this study was to
investigate whether individual differences in a measure of sleeprelated consolidation assumed to reflect offline, or unconscious,
memory processing would correlate with individual differences in
a measure assumed to reflect online, or conscious, processing.
Our specific question was whether individual differences in the
change in memory performance after sleep would correlate with a
measure of working memory capacity (WMC). WMC refers to the
ability to maintain and manipulate information during online processing (Engle, 2002) and has been shown to predict success in
many cognitive tasks, including reading comprehension, decision
making, problem solving, and vocabulary learning (Engle & Kane,
2004). Furthermore, WMC correlates strongly with performance
It has been known for several decades that conscious, or online,
cognitive processes, such as elaborative rehearsal during encoding,
promote durable storage of information in long-term memory and
contribute substantially to individual differences in learning (e.g.,
Craik & Lockhart, 1972). However, it has been known for almost as
long that unconscious, or offline, processes that operate during sleep
also play an important role in learning. It was initially proposed that
the benefit afforded by sleep was a passive mechanism of protection
from interference (cf. Jenkins & Dallenbach, 1924), but current theories typically posit an active consolidation process, in which information acquired during the day is processed during subsequent sleep.
Consistent with this view, several studies have shown that information
is reactivated on a cellular level during sleep (Dave & Margoliash,
2000; Ji & Wilson, 2007; Louie & Wilson, 2001; Wilson & McNaughton, 1994). That is, neurons that fire during task activity fire in
a similar manner during sleep, and this reactivation may be an
underlying mechanism of consolidation.
Research has further established that a period of sleep can consolidate declarative memory (Fenn, Gallo, Margoliash, Roediger, &
Nusbaum, 2009; Gais, Molle, Helms, & Born, 2002; Plihal & Born,
1997, 1999), procedural learning (Brawn, Fenn, Nusbaum, & Mar-
Kimberly M. Fenn and David Z. Hambrick, Department of Psychology,
Michigan State University.
Correspondence concerning this article should be addressed to Kimberly
M. Fenn, Department of Psychology, Michigan State University, East
Lansing, MI 38824. E-mail: [email protected]
on standardized tests of cognitive ability such as the SAT and ACT
(Turner & Engle, 1989), and with performance on tests thought to
provide relatively pure measures of general intelligence (Engle,
Tuholski, Laughlin, & Conway, 1999). Based on this evidence,
WMC is regarded as a fundamental component of the human
cognitive system, and it has even been suggested that it is the
major source of individual differences in human intelligence (e.g.,
Engle & Kane, 2004; Kyllonen, 1996).
There is strong evidence to suggest that WMC reflects the
ability to control attention during online task performance (Engle,
2002). However, it has recently been proposed that WMC also
reflects long-term memory (LTM) processes. Specifically, Unsworth and Engle (2007) proposed that individual differences in
WMC arise from processes involved in maintaining information
over a short period, as well as processes involved in LTM search
and retrieval. Consistent with this view, a recent study showed that
measures of LTM and short-term memory (STM) in list recall each
accounted for significant and unique variance in WMC (Unsworth,
Spillers, & Brewer, 2010). We predicted that if WMC is related to
LTM memory processes, then it may also be related to memory
storage, specifically, consolidation during sleep.
The participants were 348 right-handed native English speakers
who reported no history of sleep or memory disorders. Ninety-three
participants were excluded from all analyses because they reported
napping during a waking retention interval. Naps of even very short
duration can improve performance in this task (Lahl, Wispel, Willigens, & Pietrowsky, 2008). The remaining 255 participants (149
women, 106 men) had a mean age of 19.4 ⫾ 2.0 (mean ⫾ SD) years.
Participants were undergraduate students at Michigan State University and were given course credit for participation.
Participants completed two experimental sessions, separated by
a 12-hr retention interval and were assigned to one of two experimental conditions. For one group (wake condition), the first
session occurred at 9:00, and the second session occurred at 21:00
after a waking day. For the other group (sleep condition), the first
session began at 21:00 and the second session was at 9:00 the
following morning after a regular sleep phase. The first session
included a study phase and cued recall test, and the second session
included a second cued recall test followed by the operation span
(OSPAN) task. Participants also reported their sleep patterns for 1
week prior to the study.
The stimulus set used to assess declarative memory consolidation consisted of 48 pairs of semantically related nouns. Participants studied all 48 word pairs, but eight pairs did not appear on
either of the tests (four that appeared at the beginning and four that
appeared at the end of the study phase) to control for primacy and
recency effects on memory performance. Word pairs were adapted
from Gais and Born (2004) and were matched for frequency,
imagery, and concreteness (Francis & Kucera, 1982; see the Appendix for complete stimulus list).
During the training phase, each word pair was presented in a
random order for 4,000 ms, with a 1,500-ms intertrial interval.
After the training phase, participants were given a cued recall test
on 40 word pairs. The first word of the pair was presented on the
computer screen, and participants were asked to type the second
word. No time limit was imposed on their responses. After each
response, participants received two forms of feedback. They were
first told whether their response was correct or incorrect and were
then shown the correct word pair, regardless of response. Words
were presented randomly during the test. Participants were trained
to a criterion of 60% correct to roughly equate recall performance
on Test 1. If criterion was not met on the first test, the entire cued
recall test was repeated, including feedback, until criterion was
achieved. As in previous studies in which this paradigm has been
used (cf. Plihal & Born, 1997, 1999), feedback was given during
the final recall test in Session 1.
During the second session, participants were first given a cued
recall test on the 40 word pairs. As in the first test, they were given the
first word in the pair and were asked to type the second word. No time
limit was imposed on responses. On this test, however, participants
did not receive feedback on any of the trials. After the recall test,
participants completed the OSPAN task (Unsworth, Heitz, Schrock,
& Engle, 2005) to assess WMC. Each trial consisted of an equation,
followed by a letter. The participants’ task was to verify whether an
answer provided for the equation was correct or incorrect, and then to
remember the letter. After between three and six trials, a recall screen
appeared on which participants were shown 12 letters and instructed
to click on those that had been presented.
Consistent with previous claims that consolidation during sleep
improves declarative memory performance, the group that was
tested after sleep showed a significant improvement in recall
performance over the retention interval while the wake group did
not. As can be seen in Figure 1, the wake group showed very little
improvement in recall from the final test at the end of training
(mean ⫾ SEM in Test 1: 29.9 ⫾ 0.37, correctly recalled items) to
the test given after the 12-hr retention interval (Test 2: 30.6 ⫾
0.47), whereas the sleep group showed large improvement from
Test 1 (30 ⫾ 0.4) to Test 2 (34.4 ⫾ 0.37). Using a repeatedmeasures ANOVA with recall test (Test 1, Test 2) as a withinsubject factor and condition (sleep, wake) as a between-subjects
factor, we found a significant main effect of condition, F1, 223 ⫽
14.26, p ⬍ .001, and recall test, F1, 223 ⫽ 111.7, p ⬍ .01, and a
significant interaction between the factors, F1, 223 ⫽ 61.8, p ⬍
.001. Planned comparisons showed that the sleep group reliably
improved from Test 1 to Test 2, t(110) ⫽ 14.7, p ⬍ .001, d ⫽ 1.97,
but that the change in performance in the wake group failed to
reach significance, t(113) ⫽ 1.7, p ⫽ .08, d ⫽ 0.23, although there
was a trend for performance to improve. These results cannot be
attributed to baseline differences in performance because performance did not differ at the end of the first session between the two
groups, t(223) ⫽ 0.15, p ⫽ .89. This suggests that memory for the
paired associates was strengthened more after sleep than after an
equal interval of wakefulness.
Next, we performed analyses to investigate whether individual
differences in OSPAN were related to individual differences in
Figure 1.
Number of word pairs correctly recalled (out of 40) for wake and sleep conditions at Test 1 and Test 2.
consolidation, which we operationalized as change in performance
across sleep or waking (i.e., the number of correctly recalled word
pairs at Test 2 minus the number recalled at Test 1). There was a
large amount of variability in performance change. In fact, expressed as a percentage of final memory performance in Test 1,
amount of change ranged from a loss of 42% to a gain of 37% in
the wake group and from a loss of 3% to a gain of 44% in the sleep
group. Furthermore, there was a positive correlation between
OSPAN and performance change in the sleep condition (r ⫽ .23,
p ⫽ .02), suggesting that participants high in WMC tended to show
greater improvement in recall after sleep than did those low in
WMC (Figure 2b). This correlation was not significant in the
Wake condition (r ⫽ ⫺.11, ns; Figure 2a). The difference in the
correlations across conditions was significant, z ⫽ 2.44, p ⫽ .02.
Finally, there was not a significant correlation between OSPAN
and final performance on Test 1 (r ⫽ ⫺.07) in the sleep group. As
previously mentioned, participants were trained to criterion in this
session. We therefore expected that performance would be roughly
equated across different levels of WMC and did not expect to find
a correlation between WMC and performance on this test. This
pattern of results indicates that WMC did not predict memory
performance during the first session; it only predicted the change
in memory over time.
To further test for differential relations between OSPAN and
change in memory performance across conditions, we conducted a
regression analysis with performance change as the dependent
variable. Following a standard approach for testing for an interaction between a continuous variable and a categorical variable (cf.
Cohen, Cohen, West, & Aiken, 2003), we regressed performance
change onto OSPAN (mean-centered) and condition (dummycoded) and then onto the OSPAN ⫻ Condition interaction. A
significant increment in variance accounted for in the second step
would indicate an OSPAN ⫻ Condition interaction. As can be seen in
Table 1, there was a large effect of condition on performance change,
R2 ⫽ .226, ␤ ⫽ .42, t(201) ⫽ 6.51, p ⬍ .001, reflecting the large
difference in performance change across conditions illustrated in
Figure 1. The effect of OSPAN was not significant, R2 ⫽ .001, ␤ ⫽
.04, t(201) ⫽ 0.63, p ⫽ .53. However, as shown in Figure 3, and
consistent with the pattern of correlations in Figure 2, the OSPAN ⫻
Condition interaction was significant, R2 ⫽ .020, ␤ ⫽ .15, t(201) ⫽
2.34, p ⫽ .02. OSPAN positively predicted performance change in the
Sleep condition, but not in the Wake condition.
To ensure that our results were not affected by diurnal or
circadian effects on performance, we tested performance during
the initial training session and the test session for both groups.
There were no significant differences between the sleep and wake
Change in Recall Performance
r = -.11, p = .27
Change in Recall Performance
r = .23, p = .02
Operation Span
Figure 2. Correlation between operation span score and change in recall performance between Test 1 and Test
2 for (a) the wake condition and (b) the sleep condition.
groups in final recall performance, t(223) ⫽ 0.15, p ⫽ .89, or the
average number of runs to reach criterion, t(223) ⫽ 0.79, p ⫽ 0.43,
in the first session. The groups also did not show significant
differences in OSPAN scores in the second session, t(223) ⫽ 0.12,
p ⫽ .91. Taken together, these results suggest that time of day at
test cannot explain our findings.
Last, we wanted to ensure that our results could not be explained
by individual differences in amount of sleep. It is possible that
high-WMC individuals simply slept more than low-WMC individuals, and with this in mind, we tested for a correlation between
OSPAN score and self-reported amount of sleep on the night of the
study and self-reported average sleep for the week prior to
the study. Correlations between OSPAN and sleep on the night of
the study (r ⫽ ⫺.02) and average amount of sleep (r ⫽ ⫺.15) were
both negative, although neither was significant. Furthermore, there
was no evidence for a correlation between amount of sleep on the
night of the study and the change in recall performance from Test
1 to Test 2 (r ⫽ ⫺.01) in the sleep group. This is not surprising as
short naps have shown consolidation effects in this task (Lahl et
al., 2008).
To summarize, we found that a measure of WMC, which can be
assumed to reflect online memory processing, positively predicted
Table 1
Regression Model for Performance Change
Step 1
Operation span
Step 2
Condition ⫻ Operation Span
Note. Degrees of freedom: Step 1 ⫽ (2, 202); Step 2 ⫽ (3, 201).
increases in declarative memory performance after sleep. Importantly, we also found that WMC did not predict performance
during the initial training session; it only predicted the change in
performance across sleep. This finding suggests that individual
differences in WMC not only relate to online processing of information and conscious memory acquisition but also relate to offline
processing during sleep and nonconscious memory processing.
Consistent with results of previous studies (cf. Plihal & Born,
1997, 1999), we found evidence for sleep-dependent consolidation
in paired-associates learning. There was a significant increase in
memory performance across a period of sleep but not across a
period of wakefulness. We assume that improvement over baseline
in the sleep condition reflects consolidation of the feedback given
in the final test of Session 1. More important, a measure of WMC
predicted performance change for participants in the sleep condition but not for those in the wake condition. We speculate that
WMC-related differences at encoding may affect subsequent offline processing. In particular, although there was no correlation
between WMC and memory performance during training, highWMC individuals may have created stronger associations between
words at encoding. There is some evidence to suggest that stronger
associative connections during waking show greater reactivation
during sleep. One study investigating hippocampal replay in rats
found that cells that fired together more often during spatial
exploration were more likely to fire together during subsequent
sleep (O’Neill, Senior, Allen, Huxter, & Csicsvari, 2008). Furthermore, one recent study has found that sleep preferentially consolidates memories that are associated with stronger hippocampal
activation at encoding (Rauchs et al., 2011). It is possible that
high-WMC individuals create stronger associative traces during
initial acquisition. These traces might then be more likely to be
reactivated during sleep, resulting in a greater increase in memory
after sleep.
It is also possible that people with different levels of WMC
differ in the quality of their sleep. Although there was no evidence
from the current study that longer sleep duration was related to
stronger consolidation, there is evidence from other studies to
suggest that certain sleep features are associated with declarative
memory consolidation. Several recent studies have shown that
improvement in declarative memory is correlated with Stage 2
sleep spindles (Clemens, Fabo, & Halasz, 2005; Genzel, Dresler,
Wehrle, Grozinger, & Steiger, 2009) and that declarative memory
Figure 3. Illustration of Condition ⫻ Operation Span interaction (i.e., predicted change in recall from Test 1
to Test 2 for low- versus high-operation span in wake versus sleep conditions). WMC ⫽ working-memory
acquisition prior to sleep results in an increase in spindle density
(Schabus et al., 2004; Schabus et al., 2008). Furthermore, recent
work with rats has shown that memory reactivation during sleep is
correlated with spindle activity (Johnson, Euston, Tatsuno, &
McNaughton, 2010). This is important to the current study because
related work has shown that individuals high in general intelligence show increased spindle activity during sleep, regardless of
prior learning (Bódizs et al., 2005; Schabus et al., 2006). As
previously mentioned, WMC is highly correlated with general
intelligence (e.g., Kane et al., 2004). Therefore, it is possible that
individuals high in WMC simply have higher baseline spindle
activity during sleep and in turn derive greater benefit from sleep.
Although we speculate that the correlation between WMC and
sleep-dependent consolidation reflects a direct relationship between these variables, we cannot rule out, on the basis of the
present study, the possibility that this correlation reflects other
factors. For example, it is possible that this correlation is due to
long-term memory ability. Long-term memory contributes to
WMC (Unsworth & Engle, 2007), and sleep improves long-term
declarative memory (cf. Plihal & Born, 1997, 1999). Therefore, it
seems possible that LTM ability, reflecting hippocampal functioning, accounts for the correlation between WMC and consolidation.
An important goal in future research would be to measure a
broader range of factors to better understand the relationship
between WMC and sleep-dependent consolidation.
In conclusion, this is the first study to demonstrate that individual differences in WMC relate to changes in memory performance
after sleep. More research is required to clarify the nature of this
relationship, but a provocative possibility is that individual differences in offline processing translate into individual differences in
working memory performance. For example, consolidation processes may facilitate acquisition of verbal knowledge, and in turn,
verbal knowledge (vocabulary) may be beneficial in performing
WMC tasks like operation span that involve remembering words.
Future research aimed at addressing this sort of possibility will
significantly advance scientific understanding of mechanisms underlying both online and offline memory processing.
Bódizs, R., Kis, T., Lazar, A. S., Havran, L., Rigo, P., Clemens, Z., &
Halasz, P. (2005). Prediction of general mental ability based on neural
oscillation measures of sleep. Journal of Sleep Research, 14, 285–292.
Brawn, T. P., Fenn, K. M., Nusbaum, H. C., & Margoliash, D. (2010).
Consolidating the effects of waking and sleep on motor-sequence learning. The Journal of Neuroscience, 30, 13977–13982. doi:10.1523/
Clemens, Z., Fabo, D., & Halasz, P. (2005). Overnight verbal memory
retention correlates with the number of sleep spindles. Neuroscience,
132, 529 –535. doi:10.1016/j.neuroscience.2005.01.011
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple
correlation/regression analysis for the social sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
Craik, F. I. M., & Lockhart, R. (1972). Levels of processing: A framework
for memory research. Journal of Verbal Learning and Verbal Behavior,
11, 671– 684. doi:10.1016/S0022-5371(72)80001-X
Dave, A. S., & Margoliash, D. (2000, October 20). Song replay during
sleep and computational rules for sensorimotor vocal learning. Science,
290, 812– 816. doi:10.1126/science.290.5492.812
Diekelmann, S., & Born, J. (2010). The memory function of sleep. Nature
Reviews Neuroscience, 11, 114 –126.
Ellenbogen, J. M., Hulbert, J. C., Jiang, Y., & Stickgold, R. (2009). The
sleeping brain’s influence on verbal memory: Boosting resistance to
interference. PLoS One, 4(1), e4117. doi:10.1371/journal.pone.0004117
Engle, R. W. (2002). Working memory capacity as executive attention.
Current Directions in Psychological Science, 11, 19 –23. doi:10.1111/
Engle, R. W., & Kane, M. J. (2004). Executive attention, working memory
capacity, and a two-factor theory of cognitive control. In B. H. Ross
(Ed.), The psychology of learning and motivation (Vol. 44, pp. 145–
199). New York, NY: Academic Press.
Engle, R. W., Tuholski, S. W., Laughlin, J. E., & Conway, A. R. A. (1999).
Working memory, short-term memory, and general fluid intelligence: A
latent variable approach. Journal of Experimental Psychology: General,
128, 309 –331. doi:10.1037/0096-3445.128.3.309
Fenn, K. M., Gallo, D. A., Margoliash, D., Roediger, H. L. I., & Nusbaum,
H. C. (2009). Reduced false memory after sleep. Learning & Memory,
16, 509 –513. doi:10.1101/lm.1500808
Fenn, K. M., Nusbaum, H. C., & Margoliash, D. (2003). Consolidation
during sleep of perceptual learning of spoken language. Nature, 425,
614 – 616. doi:10.1038/nature01951
Francis, W. N., & Kucera, H. (1982). Frequency analysis of English usage:
Lexicon and grammar. Journal of English Linguistics, 18, 64 –70.
Gais, S., & Born, J. (2004). Low acetylcholine during slow-wave sleep is
critical for declarative memory consolidation. Proceedings of the National Academy of Science of the United States of America, 101, 2140 –
2144. doi:10.1073/pnas.0305404101
Gais, S., Molle, M., Helms, K., & Born, J. (2002). Learning-dependent
increases in sleep spindle density. The Journal of Neuroscience, 22,
6830 – 6834.
Genzel, L., Dresler, M., Wehrle, R., Grozinger, M., & Steiger, A. (2009).
Slow wave sleep and REM sleep awakenings do not affect sleep dependent memory consolidation. Sleep, 32, 302–310.
Jenkins, J. M., & Dallenbach, K. M. (1924). Obliviscence during sleep and
waking. American Journal of Psychology, 35, 605– 612. doi:10.2307/
Ji, D., & Wilson, M. A. (2007). Coordinated memory replay in the visual
cortex and hippocampus during sleep. Nature Neuroscience, 10, 100 –
107. doi:10.1038/nn1825
Johnson, L. A., Euston, D. R., Tatsuno, M., & McNaughton, B. L. (2010).
Stored-trace reactivation in rat prefrontal cortex is correlated with downto-up state fluctuation density. The Journal of Neuroscience, 30, 2650 –
2661. doi:10.1523/JNEUROSCI.1617-09.2010
Kane, M. J., Hambrick, D. Z., Tuholski, S. W., Wilhelm, O., Payne, T. W.,
& Engle, R. W. (2004). The generality of working-memory capacity: A
latent-variable approach to verbal and visuo-spatial memory span and
reasoning. Journal of Experimental Psychology: General, 133, 189 –
217. doi:10.1037/0096-3445.133.2.189
Karni, A., Tanne, D., Rubenstein, B. S., Askenasy, J. J. M., & Sagi, D.
(1994, July 29). Dependence on REM sleep of overnight perceptual skill.
Science, 265, 679 – 682. doi:10.1126/science.8036518
Kyllonen, P. C. (1996). Is working memory capacity Spearman’s g? In I.
Dennis & P. Tapsfield (Eds.), Human abilities: Their nature and measurement (pp. 49 –75). Mahwah, NJ: Erlbaum.
Lahl, O., Wispel, C., Willigens, B., & Pietrowsky, R. (2008). An ultra short
episode of sleep is sufficient to promote declarative memory performance. Journal of Sleep Research, 17, 3–10. doi:10.1111/j.13652869.2008.00622.x
Louie, K., & Wilson, M. A. (2001). Temporally structured replay of awake
hippocampal ensemble activity during rapid eye movement sleep. Neuron, 29, 145–156. doi:10.1016/S0896-6273(01)00186-6
McGaugh, J. L. (2000, January 14). Memory: A century of consolidation.
Science, 287, 248 –251. doi:10.1126/science.287.5451.248
O’Neill, J., Senior, T. J., Allen, K., Huxter, J. R., & Csicsvari, J. (2008).
Reactivation of experience-dependent cell assembly patterns in the hippocampus. Nature Neuroscience, 11, 209 –215. doi:10.1038/nn2037
Plihal, W., & Born, J. (1997). Effects of early and late nocturnal sleep on
declarative and procedural memory. Journal of Cognitive Neuroscience,
9, 534 –547. doi:10.1162/jocn.1997.9.4.534
Plihal, W., & Born, J. (1999). Effects of early and late nocturnal sleep on
priming and spatial memory. Psychophysiology, 36, 571–582. doi:
Rauchs, G., Feyers, D., Landeau, B., Bastin, C., Luxen, A., Maquet, P., . . .
Collette, F. (2011). Sleep contributes to the strengthening of some
memories over others, depending on hippocampal activity at learning.
The Journal of Neuroscience, 31, 2563–2568. doi:10.1523/
Schabus, M., Gruber, G., Parpatics, S., Sauter, C., Klosch, G., Anderer, P.,
. . . Zeithofer, J. (2004). Sleep spindles and their significance for declarative memory consolidation. Sleep, 27, 1479 –1485.
Schabus, M., Hodlmoser, K., Gruber, G., Sauter, C., Anderer, P., Kloesch,
G., . . . Zeithofer, J. (2006). Sleep spindle-related activity in the human
EEG and its relation to general cognitive and learning abilities. European Journal of Neuroscience, 23, 1738 –1746. doi:10.1111/j.14609568.2006.04694.x
Schabus, M., Hoedlmose, K., Percherstorfer, T., Anderer, P., Gruber, G.,
Parapatics, S., . . . Zeithofer, J. (2008). Interindividual sleep spindle
differences and their relation to learning-related enhancements. Brain
Research, 1191, 127–135. doi:10.1016/j.brainres.2007.10.106
Turner, M. L., & Engle, R. W. (1989). Is working memory capacity task
dependent? Journal of Memory and Language, 28, 127–154. doi:
Unsworth, N., & Engle, R. W. (2007). The nature of individual differences
in working memory capacity: Active maintenance in primary memory
and controlled search from secondary memory. Psychological Review,
114, 104 –132. doi:10.1037/0033-295X.114.1.104
Unsworth, N., Heitz, R. P., Schrock, J. C., & Engle, R. W. (2005). An
automated version of the operation span task. Behavior Research Methods, 37(3), 498 –505. doi:10.3758/BF03192720
Unsworth, N., Spillers, G. J., & Brewer, G. A. (2010). The contributions of
primary and secondary memory to working memory capacity: An individual differences analysis of immediate free recall. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36, 240 –247.
Walker, M. P., Brakefield, T., Morgan, A., Hobson, J. A., & Stickgold,
R. (2002). Practice with sleep makes perfect: Sleep-dependent motor
skill learning. Neuron, 35, 205–211. doi:10.1016/S0896-6273(02)
Wilson, M. A., & McNaughton, B. L. (1994, July 29). Reactivation of
hippocampal ensemble memories during sleep. Science, 265, 676 – 679.
Stimuli Used in This Experiment
The first four and final four word pairs (bold typeface) were
presented during training but did not appear on any of the recall
tests. Word pairs were presented randomly during study and test
(with the exception of the initial and final four pairs).
Received June 4, 2011
Revision received July 26, 2011
Accepted July 26, 2011 䡲

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