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Neuropsychologia 49 (2011) 1322–1331

Contents lists available at ScienceDirect

Neuropsychologia

journa l homepage: www.e lsev ier .com/ locate /neuropsychologia

leep-dependent consolidation of statistical learning

imon J. Durranta,∗, Charlotte Taylora, Scott Cairneya, Penelope A. Lewisa,b

School of Psychological Sciences, Zochonis, Building, University of Manchester, Brunswick Street, Manchester M13 9PL, UKInstitute of Cognitive Neuroscience, Alexandra House, University College London, 17 Queen Square, London WC1N 3AR, UK

r t i c l e i n f o

rticle history:eceived 10 November 2010eceived in revised form 8 February 2011ccepted 9 February 2011vailable online 16 February 2011

eywords:leeponsolidationtatistical learning

a b s t r a c t

The importance of sleep for memory consolidation has been firmly established over the past decade.Recent work has extended this by suggesting that sleep is also critical for the integration of disparatefragments of information into a unified schema, and for the abstraction of underlying rules. The ques-tion of which aspects of sleep play a significant role in integration and abstraction is, however, currentlyunresolved. Here, we examined the role of sleep in abstraction of the implicit probabilistic structure insequential stimuli using a statistical learning paradigm, and tested for its role in such abstraction bysearching for a predictive relationship between the type of sleep obtained and subsequent performanceimprovements using polysomnography. In our experiments, participants were exposed to a series oftones in a probabilistically determined sequential structure, and subsequently tested for recognition ofnovel short sequences adhering to this same statistical pattern in both immediate- and delayed-recallsessions. Participants who consolidated over a night of sleep improved significantly more than those whoconsolidated over an equivalent period of daytime wakefulness. Similarly, participants who consolidated

across a 4-h afternoon delay containing a nap improved significantly more than those who consolidatedacross an equivalent period without a nap. Importantly, polysomnography revealed a significant corre-lation between the level of improvement and the amount of slow-wave sleep obtained. We also foundevidence of a time-based consolidation process which operates alongside sleep-specific consolidation.These results demonstrate that abstraction of statistical patterns benefits from sleep, and provide the

role

first clear support for the

. Introduction

The beneficial role of sleep in memory consolidation has beenrmly established over the past decade (Born, Rasch, & Gais, 2006;tickgold & Walker, 2007; Walker, 2008; Walker & Stickgold,006). Studies of procedural memory consolidation have focusedn sequencing tasks (Press, Casement, Pascual-Leone, & Robertson,005; Robertson, Press, & Pascual-Leone, 2005; Walker, Brakefield,obson, & Stickgold, 2003; Walker, Brakefield, Morgan, Hobson, &tickgold, 2002; Walker & Stickgold, 2005) or visuo-motor tasksGais et al., 2008a; Gais, Plihal, Wagner, & Born, 2000; Gais, Rasch,

agner, & Born, 2008b; Stickgold, James, & Hobson, 2000). Declara-ive memory consolidation during sleep has received less attention,ut an increasing literature with tasks such as word pair learning

Backhaus, Hoeckesfeld, Born, Hohagen, & Junghanns, 2008; Gais,ucas, & Born, 2006), face learning (Mograss, Guillem, Brazzini-oisson, & Godbout, 2009; Wagner, Kashyap, Diekelmann, & Born,007), picture recognition (Hu, Stylos-Allan, & Walker, 2006), and

∗ Corresponding author. Tel.: +44 161 275 2581; fax: +44 161 275 2873.E-mail addresses: simon.durrant@manchester.ac.uk,

imon durrant@hotmail.com (S.J. Durrant).

028-3932/$ – see front matter © 2011 Elsevier Ltd. All rights reserved.oi:10.1016/j.neuropsychologia.2011.02.015

of slow-wave sleep in this consolidation.© 2011 Elsevier Ltd. All rights reserved.

card-location association (Benedict, Scheller, Rose-John, Born, &Marshall, 2009; Rasch, Buchel, Gais, & Born, 2007) demonstratesa role for sleep in consolidating these types of memories.

In addition to the direct retention benefit resulting from con-solidation across sleep, there is also increasing evidence that, inkeeping with the standard model of consolidation, sleep plays a rolein the reorganisation of memories (Frankland & Bontempi, 2005).Specifically, sleep has been shown to be important for the integra-tion of disparate elements of newly learned material (Ellenbogen,Hu, Payne, Titone, & Walker, 2007) and the incorporation of suchmaterial into an existing body of knowledge, or schema (Dumay& Gaskell, 2007; Tamminen, Payne, Stickgold, Wamsley, & Gaskell,2010; Tse et al., 2007), which may lead to insightful behaviour (Cai,Mednick, Harrison, Kanady, & Mednick, 2009; Wagner, Gais, Haider,Verleger, & Born, 2004). Abstracting underlying rules or structurefrom a set of examples is a crucial component of skill learning, andit has been hypothesised that this is dependent upon sleep. Theevidence to date, however, is conflicting, with some studies find-

ing improved performance for sleep groups (Djonlagic et al., 2009;Fischer, Drosopoulos, Tsen, & Born, 2006; Fischer, Wilhelm, & Born,2007; Gomez, Bootzin, & Nadel, 2006) and others finding no evi-dence of an improvement on similar tasks (Nemeth et al., 2010;Song, Howard, & Howard, 2007). A recent study reported evidence

S.J. Durrant et al. / Neuropsychologia 49 (2011) 1322–1331 1323

Fig. 1. Structured and unstructured sequences. Left: Transition matrix for the exposure stream and structured test sequences. Values are color coded probabilities, withblack = 0.025 and white = 0.90. The row indexes the last two tones that have occurred, the column indexes the next tone that could occur, and the grayscale value gives thep der trai m), sht enceu abilit

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robability of this transition. The matrix is setup in such a way that zero and first-ornformation. Right: A structured sequence (top) and an unstructured sequence (bottoransitions are in gray, low probability transitions are in black. The structured sequnstructured sequence is generated randomly, and happens to produce 5 high prob

f an active role for sleep, and in particular slow wave sleep, inbstraction as defined by a predictive relationship between sleeparameters and subsequent improvement on a relational memoryask (Lau, Tucker, & Fishbein, 2010). These authors used pairedssociate learning, and tested declarative relational memory oftems common to different pairs which were not directly asso-iated during learning after a retention interval containing sleep.heir observation of a significant relationship between the amountf slow wave sleep obtained and relational memory performanceighlights the possibility that slow wave sleep could be involved inbstraction more broadly. However, to our knowledge no study hasdentified the role of specific aspects of sleep in regard to abstrac-ion on a more procedural task or abstraction of underlying patternsr structure common to all of the stimuli.

Our present study sought to address these issues by using atatistical learning paradigm in a sleep-dependent consolidationetting. This paradigm (Pelucchi, Hay, & Saffran, 2009; Saffran,slin, & Newport, 1996; Saffran, Johnson, Aslin, & Newport, 1999;affran & Thiessen, 2006) involves learning and recognising sta-istical regularities in sequential stimuli. In our first experimente compared the results of consolidation across a 12 h reten-

ion interval including either overnight sleep or daytime wake, inrder to determine whether or not a sleep-dependent improve-ent was present. After finding behavioural evidence for such an

ffect we conducted a second experiment to address the question of

hether or not a nap was a good as a night in this regard (Mednick,akayama, & Stickgold, 2003), and establish which sleep stagesay be involved in the consolidation. This experiment therefore

ompared the impact of napping for 90 min during the early after-oon to that of wakeful consolidation across the same period, and

nsitions are fully balanced, ensuring that they cannot provide additional structuralowing the set of 2nd-order transitions that make up the sequence. High probability

is constrained to have 14 high probability transitions, while each transition in they transitions in this particular case.

examined the relationship between the physiological characteris-tics of the sleep obtained during this nap and the extent of theperformance benefit.

2. Materials and methods

2.1. Stimuli

The stimuli were made up of sequences of pure tones with five different frequen-cies taken from the Bohlen-Pierce scale (261.63 Hz, 300.53 Hz, 345.22 Hz, 396.55 Hz,455.52 Hz). This scale, which consists of intervals that are not heard in Western tonalmusic, was used in order to avoid creating familiar melodic fragments to Westernlisteners. Each tone lasted 200 ms, with a 20 ms gap between tones. Tones weresampled with a frequency of 44,100 Hz, had a fixed amplitude and were Gaussianmodulated to avoid aliasing edge effects. The stimuli consisted of a single long expo-sure stream of 1818 tones (lasting 6 min and 40 s), and 168 short test streams eachcontaining 18 tones (lasting 3.96 s). Half of the test sequences (the unstructuredcondition) were generated randomly (with an equal probability for each possiblesubsequent tone at every position in the sequence), while the exposure stream andthe other half of the test sequences (the structured condition) were determined by atransition matrix containing the probabilities for each potential transition betweena pair of tones and the subsequent tone, forming a second-order Markov chain. Thisis shown in Fig. 1, where each row corresponds to one possible pairing of the previ-ous two tones and each column corresponds to a possible identity of the next tone.Rows are ordered 11, 12, 13 . . . 55 where xy gives the previous tone x and the tonebefore that y.

Each row–column combination in the matrix defines an entry that gives theprobability that the two tones associated with that row will be followed by the toneassociated with that column. In the transition matrix used in these experiments,each row contained one high probability (which we term a likely transition) (p = 0.9;

shown in white in the figure), and four equal low probabilities (unlikely transitions)(p = 0.025; shown in black in the figure); this ensured that a given pair of tones wouldbe followed by a particular third tone 90% of the time, but 10% of the time would befollowed by any of the other four possible tones, making the sequences probabilistic.Importantly, this transition matrix was constructed to give equal probabilities foreach of the five tones when considering only a single previous tone (i.e. uniform

1324 S.J. Durrant et al. / Neuropsychologia 49 (2011) 1322–1331

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ig. 2. Experiment design. Top: Experiment 1, using a day/night design with a 12-xperiment 2, using the same task and session layout, but here testing both groupsSG recorded.

rst-order transitions) or no previous tones (uniform zero-order transitions). Thiseans that any discernible structure in the sequences is second-order or higher,

equiring participants to be aware of not just a single previous tone (either one- orwo-back), but both previous tones.

Structured sequences were generated by randomly sampling the transitionatrix, but under an additional constraint. Three levels of difficulty were defined

easy, medium, hard), which corresponded to different levels of structure within theequence. One way to achieve this is to vary the probability of the likely transitionn the transition matrix prior to sampling, with a harder difficulty level having aower value; this has the effect of reducing the number of likely transitions within aequence, and correspondingly increasing the number of unlikely transitions. Suchandom sampling would not be guaranteed to provide a proportional number ofikely and unlikely transitions within short tone sequences such as those used here16 second-order transitions), e.g. a hard sequence might have 15 likely transitionsy chance, while an easy sequence may come out with only 12. In order to guaranteehat all easy sequences are easier than medium sequences, all of which in turn areasier than hard sequences, we constrained the number of likely transitions to be 14n easy sequences, 11 in medium sequences and 8 in hard sequences. This is equiva-ent to setting the likely transition probability to 0.875, 0.6875 and 0.5, respectively,ut with any sampling error in the creation of the sequences now removed.

.2. Experimental task and design

The timeline of both experiments can be seen in Fig. 2. Experiment 1 consistedf two sessions, the first of which was subdivided into a learning session and anmmediate-recall session and the second of which was just a delayed-recall session,

hich were either at 8.30 am and 8.30 pm on the same (wake group) or 8.30 pm and.30 am the following day (sleep group), ±1.5 h in both cases. The retention intervaletween the sessions was 12 h (±1 h) for both conditions, with the night intervalontaining mostly sleep, and the day interval containing no sleep. Experiment 2 usedhe same sessions structure, but with the first session at 12.30 pm and the secondession at 4.30 pm. In Experiment 2, participants in the nap condition were invitedo take a nap in a bedroom in the Sleep Research Laboratory at the University of

anchester after the first session, where they were monitored with polysomnog-aphy (PSG) while they slept. Participants in the no-nap condition were allowed toeave the testing area between the two sessions, but were asked not to sleep, eat orrink, exercise, or engage in any study or learning activity before returning for theecond session.

The structure of the sessions and trials is shown in Fig. 3. Both experimentstarted with a learning session which involved presentation of the structuredxposure stream for just under 7 min (400 s, 1818 tones in total), in order to famil-

arise the participant with the transition probabilities. This was followed by anmmediate-recall session containing 84 two-alternative forced choice (2afc) trials,ach consisting of two short sequences of 18 tones each – one structured (and shar-ng the transition probabilities with the exposure stream) and one unstructured.articipants were instructed to indicate which sequence sounded more famil-ar by pressing the appropriate response button as soon as they were sure, and

solidation period for each group filled with wake and sleep, respectively. Bottom:same time of day, with the sleep group now taking a nap instead, and also having

always within a response window of 5 s from the end of the auditory presenta-tion. The delayed-recall session consisted of a further 84 2afc trials analogous to theimmediate-recall session. The structured sequences in this session were novel butagain shared the transition probabilities with the exposure stream from the learn-ing session (in other words they had the same statistical structure as the exposurestream). The unpredictable sequences were also novel and generated randomly. Theorder of the sequences was randomised for each participant.

2.3. Participants

All participants were right-handed (a score of 80% or higher on the EdinburghHandedness Inventory) and healthy volunteers, with no history of neurological orsleep disorders (evaluated by a screening questionnaire and short interview). InExperiment 1, 24 participants were randomly divided between the two experimen-tal groups (sleep and wake). 12 participants in the sleep group (10 F, 2 M) wereaged 19–45 (M = 22.42; SE = 2.101), and 12 participants in the wake group (9 F, 3M) were aged 19–35 (M = 21.83; SE = 1.254). In Experiment 2, 28 different partici-pants were recruited, 4 of whom were excluded for not sleeping or not meeting theeligibility criteria. The remaining 24 were randomly divided between the two exper-imental groups (nap and no-nap). 12 participants in the nap group (8 F, 4 M) wereaged 19–24 (M = 20.83; SE = 0.474), and 12 participants in the no-nap group (8 F, 4 M)were aged 19–21 (M = 20.17; SE = 0.207). The age of participants did not significantlydiffer between the two groups of the first experiment (t(22) = 0.238, p = 0.814), thetwo groups of the second experiment (t(22) = 1.289, p = 0.217), or between the firstand second experiments (t(46) = 1.325, p = 0.197). Each subject gave informed con-sent for the experiment, which was approved by the Research Ethics Committee ofthe School of Psychological Sciences at the University of Manchester. Throughoutthe entire period of the experiment, participants were asked to abstain from alcohol,caffeine and other drugs. In addition, participants in the daytime condition of Exper-iment 1, and the no-nap condition of Experiment 2, were asked not to sleep betweenthe sessions, and participants in the night condition of Experiment 1 were asked tosleep normally and report the number of hours they slept. All participants werealso measured on the Stanford Sleepiness Scale to check for differences in alertnessbetween the groups. No participants had taken part in any previous version of theexperiment.

2.4. Equipment

These experiments were realised using Cogent 2000 developed by the Cogent2000 team at the FIL and the ICN and Cogent Graphics developed by John Romaya at

the LON at the Wellcome Department of Imaging Neuroscience. It was written andexecuted using MATLAB© 6.5 running on a desktop PC equipped with a dual-coreXeon processor. Sound was generated using the onboard SoundMAX© digital audiochip, and heard through a pair of Sennheiser© HD207 noise-cancelling headphones.Responses were recorded using a serial multi-button box attached to a Domino 2microcontroller from Micromint©, with a time resolution of approximately 1 ms.

S.J. Durrant et al. / Neuropsychologia 49 (2011) 1322–1331 1325

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ig. 3. Session and trial structure. (A) The first session is subdivided into a learninmmediate test session in which participants have 84 test trials. After a retention4 test trials. (B) Each trial consists of a structured and an unstructured sequencearticipants are asked to indicate which of the two sequences was the structured s

.5. Polysomnographic monitoring

Polysomnographic monitoring was carried out using an Embla© N7000 sleeponitoring system, with Ng–NgCl electrodes attached using EC2© electrogel after

he scalp was first prepared with NuPrep© exfoliating agent. Scalp electrodes werettached at six standard locations using the 10–20 system, C3, C4, F3, F4, O1 and O2,ach referenced to the contralateral mastoid (A1 and A2). Left and right electrooc-lagram, left, right and upper electromyogram, and a ground electrode were alsottached. All electrodes were verified to have a connection impedance of less thank�. In addition, monitoring of physiological signals including movement, pulseximetry and respiration was also carried out. All signals were digitally sampled atrate of 200 Hz.

.6. Statistical analysis

Behavioural performance was assessed by calculating the number of trials onhich the structural sequence was correctly identified; the response time on each

rial was also taken as a secondary measure. Data was analysed in SPSS 15.0 bycombination of t-tests, ANOVAs with post hoc t-tests, and Pearson correlation

ests. In all our results we consider p < 0.05 as significant, all post hoc tests wereonferroni-corrected and all tests are two-tailed unless otherwise stated.

Sleep structure was analysed using RemLogic© 1.1 software. Sleep data wererganised into 30 s epochs, bandpass filtered between 0.3 Hz and 40 Hz to removeow frequency drift and high frequency noise, and visually scored independently bywo trained sleep researchers on the referenced central electrodes (C3-A2 and C4-1) according to the standardised sleep scoring criteria of Rechtschaffen and Kales

1968). The proportion of time in each sleep stage and the overall sleep durationere calculated from the hypnogram.

. Results

.1. Experiment 1

.1.1. Alertness and response times

Participants in the overnight sleep group reported between

and 10 h sleep (M = 7.91, SE = 1.14). Alertness in the first ses-ion, measured by the Stanford Sleepiness Scale, showed thatoth the sleep group (M = 1.92, SE = 0.229) and the wake groupM = 2.25, SE = 0.218) were alert when performing the task, and

ion, in which participants hear a single, continuous tone stream for 400 s, and anal, the second session consists of just a delayed test session containing a furtherseudo-randomised counterbalanced order), and a fixed response period in which

ce.

did not differ in this regard (t(22) = 1.055, p = 0.303). A similar pat-tern was obtained in the second session (sleep group: M = 2.00,SE = 0.246; wake group: M = 2.08, SE = 0.260; comparison of groups:t(22) = 0.233, p = 0.818).

Response times (in seconds) were faster on correctthan incorrect trials for both the immediate-recall (correct:M = 1.244 SE = 0.080; incorrect: M = 1.393, SE = 0.075; compar-ison: F(1,22) = 13.185, p = 0.0015) and delayed-recall (correct:M = 1.231, SE = 0.084; incorrect: M = 1.503, SE = 0.090; comparison:F(1,22) = 20.213, p = 0.0002) sessions as expected. These resultsconfirm that response time is a sensitive measure in our statis-tical learning paradigm. There was no significant main effect ofsleep group (immediate-recall: F(1,22) = 0.749, p = 0.396; delayed-recall: F(1,22) = 0.0001, p = 0.990) and no significant interactionbetween sleep group and trial correctness (immediate-recall:F(1,22) = 0.002, p = 0.961; delayed-recall: F(1,22) = 0.425, p = 0.521)in either session, confirming that both groups had a similar patternof response times.

Taken together, these different measures suggest that the sub-sequent results were not due to any differences in alertness.

3.1.2. BehaviouralBehavioural performance was assessed by calculating the num-

ber of trials on which the structured sequence was correctlyidentified.

The behavioural results for Experiment 1 are summarised inTable 1. Overall performance in each recall session for each groupwas significantly greater than chance (all p < 0.01), demonstratingthat participants in all conditions were able to do the task success-

fully. As expected, there was no significant difference between theperformance of the two groups (sleep and wake) in the immediate-recall session (prior to the consolidation period) (t(22) = 0.021,p = 0.984). In addition, there was a parametric effect of levels of dif-ficulty in the immediate-recall session (repeated measures ANOVA

1326 S.J. Durrant et al. / Neuropsychologia 49 (2011) 1322–1331

Table 1Experiment 1: behavioural results.

Parameter Wake group Sleep group p

Session 1 Correct Trials 55.67 ± 3.16 55.58 ± 2.51 0.984Session 1 Easy Level Correct Trials 18.42 ± 1.47 19.50 ± 1.08 0.559Session 1 Medium Level Correct Trials 19.58 ± 1.19 19.17 ± 1.12 0.801Session 1 Hard Level Correct Trials 17.67 ± 1.14 16.92 ± 1.07 0.636

Session 2 Correct Trials 55.50 ± 3.50 59.75 ± 2.60 0.340Session 2 Easy Level Correct Trials 18.75 ± 1.34 19.92 ± 1.18 0.519Session 2 Medium Level Correct Trials 19.00 ± 1.36 20.50 ± 0.97 0.378Session 2 Hard Level Correct Trials 17.75 ± 1.10 19.33 ± 1.13 0.327

Session 2 – Session 1 Correct Trials −0.17 ± 1.31 4.17 ± 1.07 0.018*

Session 2 – Session 1 Easy Level Correct Trials 0.33 ± 0.94 0.42 ± 1.003 0.952Session 2 – Session 1 Medium Level Correct Trials −0.58 ± 1.04 1.33 ± 1.29 0.261Session 2 – Session 1 Hard Level Correct Trials 0.08 ± 1.18 2.42 ± 1.20 0.178

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* Significance at p = 0.05 level.

ith a single within-subjects factor) (F(2,46) = 3.280, p = 0.047),ith post hoc tests showing that performance on the easiest two

evels was not significantly different (p = 0.586), but performancen the hardest level was significantly lower than both (p = 0.049nd p = 0.050, respectively). In the second session, no effect of levelsf difficulty was found (F(2,46) = 1.489, p = 0.236).

As the consolidation hypothesis is specifically concerned withhe improvement of performance from the immediate-recall tohe delayed-recall session, we used the difference between theseessions as our dependent variable in subsequent analyses (seeig. 4). A 2 × 3 mixed ANOVA (containing a within-subjects factoror levels of difficulty and a between-subjects factor for the differ-nt sleep groups) was used to analyse the effect of sleep and anyossible interaction with levels of difficulty. Performance improve-ent across the sessions was clearly superior for the group that

lept (M = 4.17, SE = 1.313) compared to the group that remainedwake (M = −0.17, SE = 1.072) (F(1,22) = 6.534, p = 0.018). Post hoc

-tests showed that the sleep group improvement was signifi-ant (t = 3.887, p = 0.006) while the wake group showed no changet = −0.127, p = 1.000). There was no main effect of levels of dif-culty (in terms of improvement across sessions) (F(2,44) = 0.314,= 0.723), and no significant interaction between levels of difficulty

ig. 4. Memory consolidation. The difference in the number of correct itemsetween the delayed-recall session and the immediate-recall session is shown foroth sleep and wake groups in each experiment. The sleep group shows significantlyreater consolidation than the wake group in both experiments. *p < 0.05.

ct trials for a session is out of a total of 84; correct trials at each individual level of

and sleep group (F(2,44) = 0.441, p = 0.646). The results do suggestthat there may be a linear trend of difficulty in this interaction,with the greatest improvement on the hardest items (see Table 1),but a linear contrast within a 2 × 3 mixed ANOVA did not show asignificant result here (F(1,22) = 0.761, p = 0.392). This trend seemsto be primarily driven by the sleep group, although a linear con-trast within a separate repeated measures ANOVA for this groupalone also failed to reach significance (F(1,11) = 1.056, p = 0.326),possibly due to a lack of power with the relatively small samplesize. The wake group did not show any such trend (F(1,11) = 0.022,p = 0.885).

3.2. Experiment 2

3.2.1. Alertness and response timesSubjects in the nap group experienced between 69 and 114 min

sleep (M = 83.08, SE = 5.57). Alertness in the first session, measuredby the Stanford Sleepiness Scale, showed that both the sleep group(M = 2.25, SE = 0.279) and the wake group (M = 1.83, SE = 0.297)were alert when performing the task, and did not differ in thisregard (t(22) = −1.025, p = 0.318). A similar pattern was obtained inthe second session (sleep group: M = 2.08, SE = 0.313; wake group:M = 2.17, SE = 0.241; comparison of groups: t(22) = 0.211, p = 0.835).

Response times (in seconds) were faster on correctthan incorrect trials for both the immediate-recall (correct:M = 1.178, SE = 0.083; incorrect: M = 1.352, SE = 0.077; compar-ison: F(1,22) = 16.776, p = 0.0005) and delayed-recall (correct:M = 1.201, SE = 0.086; incorrect: M = 1.452, SE = 0.089; comparison:F(1,22) = 20.740, p = 0.0002) sessions as expected. These resultsagain confirm that response time is a sensitive measure. Therewas no significant main effect of sleep group (immediate-recall:F(1,22) = 0.018, p = 0.894; delayed-recall: F(1,22) = 0.649, p = 0.429)and no significant interaction between sleep group and trialcorrectness (immediate-recall: F(1,22) = 0.060, p = 0.808; delayed-recall: F(1,22) = 0.313, p = 0.581) in either session, confirming thatboth groups had a similar pattern of response times. Finally, nodeficit in alertness related to homeostatic sleep pressure wasdetected in the second experiment, with the amount of SWS notcorrelated with response times in the immediate recall sessionfor correct (r = 0.249, p = 0.435) or incorrect (r = 0.284, p = 0.372)items, in the delayed recall session for correct (r = 0.084, p = 0.795)or incorrect (r = −0.075; p = 0.816) items, or in the change from

the immediate to the delayed recall session for correct (r = −0.340,p = 0.279) or incorrect (r = −0.164, p = 0.443) items.

Similarly to the first experiment, these different measures col-lectively suggest that the subsequent results were not due to anydifferences in alertness.

S.J. Durrant et al. / Neuropsychologia 49 (2011) 1322–1331 1327

Table 2Experiment 2: behavioural results.

Parameter Wake group Sleep group p

Session 1 Correct Trials 53.58 ± 2.65 47.42 ± 2.44 0.101Session 1 Easy Level Correct Trials 19.33 ± 1.42 15.50 ± 0.97 0.036*

Session 1 Medium Level Correct Trials 18.58 ± 1.20 16.42 ± 1.06 0.188Session 1 Hard Level Correct Trials 15.67 ± 0.58 15.50 ± 0.82 0.870

Session 2 Correct Trials 49.08 ± 3.19 49.92 ± 3.24 0.856Session 2 Easy Level Correct Trials 17.50 ± 1.19 18.67 ± 1.21 0.500Session 2 Medium Level Correct Trials 15.42 ± 1.26 16.25 ± 1.36 0.658Session 2 Hard Level Correct Trials 16.17 ± 1.28 15.00 ± 1.03 0.486

Session 2 – Session 1 Correct Trials −4.50 ± 1.72 2.50 ± 1.89 0.012*

Session 2 – Session 1Easy Level Correct Trials −1.83 ± 1.24 3.17 ± 0.99 0.005*

Session 2 – Session 1Medium Level Correct Trials −3.58 ± 1.09 −0.17 ± 0.68 0.014*

Session 2 – Session 1Hard Level Correct Trials 0.50 ± 1.18 −0.50 ± 0.95 0.517

Data are means ± SE, p values are from an independent samples t-test in each case. Correct trials for a session is out of a total of 84; correct trials at each individual level ofd

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see Fig. 5. This correlation was specific to the improvementand not present in either individual session (immediate-recall:r = −0.72, p = 0.824; delayed-recall: r = 0.310, p = 0.327). The corre-lations between performance improvement and stages 1, 2 andREM were not significant (S1: r = −0.037, p = 0.909; S2: r = −0.276,

Table 3Experiment 2: polysomnography results.

Parameter Sleep group

Total sleep time (min) 83.08 ± 5.57Stage 1 (%) 17.15 ± 3.28

ifficulty are out of a total of 28.* Significance at p = 0.05 level.

.2.2. BehaviouralThe behavioural results for Experiment 2 are summarised in

able 2. As all of the task-related variables remained the samecross the two experiments the same analysis was applied to theecond experiment in order to facilitate comparison between thewo.

Overall performance in each session for each group was againignificantly greater than chance (all p < 0.01), and there was noignificant difference between the two sleep groups (sleep andake) in the immediate-recall session (t(22) = 1.710, p = 0.101). In

his experiment, the overall parametric effect of levels of diffi-ulty (repeated measures ANOVA with a single within-subjectsactor) was present not only for the immediate-recall sessionF(2,46) = 3.954, p = 0.026) but also the delayed-recall sessionF(2,46) = 7.618, p = 0.001). In the immediate-recall session, postoc tests revealed that, as in the first experiment, there was noifference in performance on the easiest two levels of difficultyp = 0.893) while performance on the hardest level of difficultyas lower than these easier levels (p = 0.052 and p = 0.022, respec-

ively). The delayed-recall session showed a different pattern, witherformance on the easiest level of difficulty now better than per-ormance on the medium level (p = 0.001) and the hardest levelp = 0.004), with no difference between performance on those twoevels (p = 0.955). Again this demonstrates that participants wereble to successfully perform the task, which was difficult enougho allow separation of performance according to different levels ofifficulty.

Consolidation performance was again measured with a 2 × 3ixed ANOVA (using the difference between the delayed-recall

nd immediate-recall sessions as the dependent variable, andontaining a within-subjects factor for levels of difficulty and aetween-subjects factor for the different sleep groups). Perfor-ance improvement across the sessions (shown in Fig. 4) was

learly superior for the group that slept (M = 2.50, SE = 1.893) com-ared to the group that remained awake (M = −4.50, SE = 1.721)F(1,22) = 8.051, p = 0.010), with post hoc t-tests revealing thathe sleep group had a non-significant improvement (t = 1.321,= 0.426) while the wake group deteriorated (t = 2.615, p = 0.048).

There was a main effect of levels of difficulty (in terms ofmprovement across sessions) (F(2,44) = 3.315, p = 0.046), and a sig-ificant interaction between levels of difficulty and sleep groupF(2,44) = 4.614, p = 0.015). This interaction was driven primarily

y the greater improvement in the easiest level seen in the sleeproup (Level 1: M = 3.167, Level 2: M = −0.167, Level 3: M = −0.500)ather than the smaller improvement in the hardest level of diffi-ulty for the wake group (Level 1: M = −1.833, Level 2: M = −3.583,evel 3: M = 0.500). As in the first experiment, this suggested a lin-

ear trend in the interaction of sleep and difficulty, but here with thegreater improvement for the easiest rather than the hardest items.A linear contrast within a 2 × 3 mixed ANOVA confirmed this trend(F(1,22) = 6.874, p = 0.016). Separate ANOVAs for the sleep and wakegroups revealed that the trend is again primarily driven by the sleepgroup (F(1,11) = 9.308, p = 0.011), with the wake group showing nosuch trend (F(1,11) = 1.435, p = 0.256).

The improvement in the sleep group on the easiest trials mayhave been partly driven by the lower initial performance of thisgroup (t = 2.231, p = 0.036), which was later recovered. This lowerinitial performance does not appear to have been due to a globallack of alertness in the sleep group (see Sections 3.1.1 and 3.2.1).Nor does homeostatic sleep pressure, as shown by a negativecorrelation between initial performance and SWS, seem to havebeen involved specifically in the easy trials (r = −0.276, p = 0.384).It therefore appears unlikely that this initial minor differencebetween the groups on the easy trials is responsible for the dif-ferential improvement observed in sleep and wake groups.

3.2.3. PolysomnographyIn addition to the behavioural measures, participants in Exper-

iment 2 were monitored with PSG, which was subsequentlyanalysed in order to produce measures pertaining to the structureof sleep including the proportion of time spent in each sleep stage(S1, S2, SWS, REM) and the total sleep duration. The results of thesleep analysis can be seen in Table 3.

In order to investigate the role of specific sleep stages in the con-solidation process, the correlation of these sleep parameters to theimprovement across sessions of participants was measured. A sig-nificant correlation was found between the duration of SWS andperformance improvement across sessions (r = 0.624, p = 0.030);

Stage 2 (%) 36.04 ± 5.07SWS (%) 26.77 ± 5.64REM (%) 20.04 ± 5.08

Data are means ± SE, for the nap group in Experiment 2. See Section 2.6 for detailsof calculation.

1328 S.J. Durrant et al. / Neuropsycholo

Fic

pw(

3

lidcbafbbwptnbticpfbsf

ltafictwpssfi

tion from rapidly encoded hippocampal-bound memories can be

ig. 5. Relationship between SWS and behavioural performance. The improvementn task performance from the immediate- to the delayed-recall session was signifi-antly correlated with the amount of SWS obtained (r = 0.624, p = 0.030).

= 0.385; REM: r = −0.394, p = 0.205). Similarly, total sleep timeas not a significant predictor of performance improvement

r = −0.285, p = 0.369).

.3. Both experiments

As both experiments used the same task, and differed only in theength of retention interval and the times of day at which the encod-ng and testing took place, it is instructive to look for any significantifferences between them. In order to determine whether or notonsolidation time was a significant factor, we conducted a 2 × 2etween-subjects ANOVA with factors sleep group (sleep or wake)nd retention interval (4 h or 12 h), measuring the improvementrom the immediate-recall to the delayed-recall test session asefore. Sleep group was, unsurprisingly, a significant factor acrossoth experiments (F(1,44) = 13.636, p = 0.001). Interestingly, thereas also a greater overall improvement for the longer consolidationeriod (F(1,44) = 4.322, p = 0.045); this was driven by the combina-ion of sleep and wake conditions, with neither the sleep groupor the wake group separately showing any significant differenceetween the two experiments in post hoc t-tests (sleep group:(22) = 0.766, p = 0.908; wake group: t(22) = 2.002, p = 0.118). Thenteraction of sleep and retention interval was not at all signifi-ant (F(1,44) = 0.755, p = 0.390), suggesting that these are separaterocesses. Altogether these results show that sleep is a significantactor in both experiments, and the overall consolidation appears toe stronger with a longer retention interval, but the effect of sleeppecifically does not seem to alter between the brief nap and theull night of sleep.

We also examined the apparently different direction of theinear trends of difficulty within the sleep groups across thewo different consolidation delays using linear contrasts within

2 × 3 mixed ANOVA with factors delay (4 h or 12 h) and dif-culty (easy, medium, hard). The different linear trends wereonfirmed in the interaction between difficulty and consolida-ion delay (F(1,22) = 6.137, p = 0.021). The same analysis for theake group revealed no clear difference in trends (F(1,22) = 10.02,= 0.328). These results confirm that participants who slept for a

hort period improved mainly on the easy items, actually gettinglightly worse on the more difficult items, while those who sleptor a whole night improved in all categories, but showed particularmprovement on the more difficult items.

gia 49 (2011) 1322–1331

4. Discussion

We conducted two experiments to test for sleep-relatedenhancements in the abstraction of statistical structure. Our resultsshowed improved detection of statistical patterns after retentionacross both a night of sleep and a brief daytime nap when comparedto equivalent periods of wakefulness. We also provided evidencethat sleep plays an important role in this abstraction of statisticalstructure by showing a predictive relationship between the amountof slow wave sleep obtained and the extent of the post-sleep advan-tage. Finally, we showed that a weaker time-based consolidationprocess operates alongside the sleep-specific process, and is inde-pendent from it. These findings are important because they suggestthat sleep is not only beneficial for memory, but also for the cogni-tive processing of previously learned material.

Some authors have speculated that elements of sleep physiol-ogy, such as slow waves and sleep spindles play an active role inconsolidation (Born et al., 2006; Diekelmann & Born, 2010; Walker,2009), while others maintain that reduced forgetting observedacross periods of sleep is due to the relative absence of interfer-ing stimuli (Vertes & Siegel, 2005; see also Ellenbogen, Payne, &Stickgold, 2006 for a discussion of this issue). Our observation of asignificant correlation between the amount of SWS obtained andthe sleep-related improvement in performance supports an activerole for sleep in the consolidation process. It is also in keepingwith the findings of Lau et al. (2010) who found a similar rela-tionship between SWS and relational memory. These findings areconsistent with the standard model of consolidation (Frankland &Bontempi, 2005) which proposes that the hippocampus initiallylinks disparate neocortical areas, whose direct connections to eachother are gradually strengthened during consolidation, and thatlinks to the hippocampus gradually fade until the memory becomesentirely hippocampal-independent (Takashima et al., 2009; seealso Durrant & Lewis, 2009). It has been argued that this transfer ofconnectivity occurs specifically during sleep (Walker, 2009), and aprominent theory proposes that this process takes place throughlong-term potentiation of recently active synapses during slowoscillations in SWS (Born et al., 2006; Diekelmann & Born, 2010).In combination with a global depression of synaptic strength pro-posed by the synaptic homeostasis theory (Tononi, 2009; Tononi &Cirelli, 2006), this may lead to an improved signal-to-noise ratio asthe background noise is reduced more than the reinforced mem-ory traces, and consequently to a greater abstraction of underlyingstatistical structure or patterns, as found in our data.

The abstraction of underlying regularities, along with the inte-gration of disparate stimuli (Ellenbogen et al., 2007), is an importantstep in the formation of a schema (Tse et al., 2007). A schemaallows newly encoded memories to be consolidated more rapidly,becoming independent of the hippocampus in a shorter space oftime due to integration with existing semantic knowledge. Giventhe connection between abstraction, integration, and schemata, itmay not only be true that systems-level consolidation occurs dur-ing sleep, but also that the formation of schemata which speed upsuch consolidation itself depends on sleep. This suggests a scenariowhereby sleep-dependent consolidation includes a boot-strappingiterative process in which newly encoded information is combinedinto new mental models during sleep, and which are then usedto speed up the integration of further information. The Comple-mentary Learning Systems hypothesis (McClelland, McNaughton,& O’Reilly, 1995) argues that this process of structure abstractionfrom experience is likely to take place during sleep, where informa-

more slowly absorbed into existing neocortical networks throughreactivation, without being disrupted by waking experience.

It is noteworthy that both of our experiments, even withrelatively modest participant numbers (24 in each), showed sig-

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ificantly greater consolidation for the sleep group, regardless ofhether they enjoyed a full night of sleep or just a brief nap. This

ollows the trend set by other napping studies which have alsoound a comparable benefit to consolidation from naps and wholeights (Mednick et al., 2003). Nevertheless, there were also impor-ant differences between the two experiments. The shorter sleepnterval was associated with a stronger consolidation effect for theasiest level of difficulty, whereas the longer sleep intervals weressociated with a relatively stronger effect for the hardest level ofifficulty. Taken together these data could indicate that the easiest

evel of difficulty consolidates earliest, with the more difficult lev-ls only showing the consolidation effect after a longer period ofleep. Alternatively, it is possible that quite different consolidationrocesses are taking place in a full night of sleep and a nap, which

s why the linear trend of improvement in terms of trial difficultyas opposite in the two conditions. In addition to these difficulty-

elated differences, overall consolidation was greater over 12 h thanver 4 h, suggesting that separate sleep- and time-dependent con-olidation processes may be in operation. The results also appearo show a decrease in performance for the 4-h wake group whichs not present in the 12-h wake group. This may be due to an initialrop in performance during or after training, which is followed byslower time-based consolidation process that has longer to work

or the 12-h group than the 4-h group; this would also explain theelatively lower performance of the 4-h sleep group. These differ-nces are not significant, however, so alternatively they may simplyeflect sampling error.

There is some debate in both the statistical learning (Fiser, 2009;iser & Aslin, 2002; Saffran, 2001; Saffran & Thiessen, 2006; Turk-rowne, Isola, Scholl, & Treat, 2008; Turk-Browne, Junge, & Scholl,005) and the closely related artificial grammar learning litera-ures (Reber, 1967, 1993; van den Bos & Poletiek, 2008, 2009) aso whether transition statistics (the pattern of collective probabili-ies for transitions which may be remembered implicitly) or chunkshigh-frequency fragments within the larger sequence which cane remembered explicitly) are the primary units of processingJimenez, 2008; Lieberman, Chang, Chiao, Bookheimer, & Knowlton,004; Perruchet & Pacton, 2006; Perruchet, Tyler, Galland, &eereman, 2004; Thiel, Shanks, Henson, & Dolan, 2003). This dis-inction is important for the current study because we specificallyimed to investigate the importance of sleep for the abstractionf underlying statistical regularities. However, two recent stud-es have found evidence for the implicit processing of transitiontatistics. In a visual statistical learning task based on Saffran’s toneord paradigm (Saffran et al., 1996) that is more likely to favour

hunking due to the exposure stream being constructed from con-atenated chunks, Turk-Browne, Scholl, Chun, and Johnson (2009)ound strong activation of the striatum as well as the hippocampusmplying involvement of both procedural and declarative memoryystems. This was corroborated by a behavioural study of visualtatistical learning using the tone word paradigm, which directlyompared implicit reaction times with questions probing explicitnowledge of the stimulus groups. This procedure found evidencef both types of learning, and showed that these occurred indepen-ently of each other (Kim, Seitz, Feenstra, & Shams, 2009).

Interactions between procedural and declarative memory sys-ems (Poldrack et al., 2001; Poldrack & Foerde, 2008; Poldrack &ackard, 2003; Poldrack & Rodriguez, 2004) have been shown tonterfere with off-line consolidation (Brown & Robertson, 2007a,007b). Furthermore, these interacting systems are thought toecome disengaged during sleep (Robertson, 2009) as a result of

breakdown in cortical connectivity (Massimini et al., 2005). Suchisengagement could allow consolidation which would have beenisrupted or inhibited during wakefulness due to interference fromhe other system to occur during sleep (Robertson, 2009). In ourata, which comes from a task that relies on implicit abstraction

gia 49 (2011) 1322–1331 1329

of underlying regularities but which is also open to declarativeinterference, we found evidence for this sleep-specific componentof consolidation, in addition to an offline time-dependent con-solidation continuing in the background regardless of sleep state.A previous study by Cohen, Pascual-Leone, Press, and Robertson(2005) posited the existence of multiple offline learning mech-anisms, related to a double dissociation between goal-orientedbehaviour (consolidated during sleep) and movement-orientedbehaviour (consolidated during wake). In our current study wefound evidence for multiple offline learning mechanisms in theform of separate sleep-dependent and time-dependent contribu-tions to overall consolidation, as well as the fact that easier itemswere consolidated more during a nap while harder items were con-solidated more during a whole night of sleep. Together with thestudy by Cohen et al., our data suggest that even over 24 h consoli-dation is not a unitary phenomenon.

Critics of sleep-dependent consolidation have occasionallypointed out the dangers of circadian confounds in sleep and mem-ory studies (Siegel, 2001). In our first experiment, the sleep andwake groups differed in terms of the diurnal time at which theywere trained and tested on the statistical learning task. Specifi-cally, the sleep group encoded and were initially tested at 8.30 pmand retested at 8.30 am, while the wake group encoded and wereinitially tested at 8.30 am and retested at 8.30 pm. We examinedthe possibility that this circadian difference across the two groupsmight have an effect, in two ways. Firstly performance of thesetwo groups was compared during the immediate-recall session (atopposite times of day), and this revealed no significant difference.Secondly, we performed a napping study in which training andtesting occurred at the same time of day for both groups. Becausethis second experiment, like the first one, showed a sleep-relatedadvantage, it seems unlikely that the results in the overnight studycould have been determined entirely by diurnal influences. This isreinforced by the fact that there was no significant difference onthe Stanford Sleepiness Scale ratings or the response times for ourparticipants in the morning and the evening; all participants wererelatively alert when performing the task. Furthermore, the strongcorrelation which we observed between the amount of slow wavesleep obtained and the amount of sleep-related behavioural advan-tage, alongside the fact that no correlations were found with othersleep stages or total sleep time, argues strongly against a circadianexplanation for our findings.

Another potential confound is that the necessarily differentsleep regimes in the two experimental groups could have givenrise to different levels of attentional alertness, which in turn mayhave led to the superior performance of the sleep group. If thiswere the case we would expect measures of alertness to show dif-ferences between the groups. Similarly, the correlation betweenperformance improvement and SWS could reflect greater relief ofhomeostatic sleep pressure in individuals who subsequently per-form better on the task; in this case we would expect a negativecorrelation between SWS and both alertness and performance inthe immediate recall session. In fact, our data showed no differ-ences in alertness between the groups in either session, or anycorrelation between SWS and alertness, or any correlation betweenSWS and performance, in the immediate recall session. These find-ings suggest that the relationship between SWS and performanceimprovement is unlikely to be due to homeostatic sleep pressureand alertness, and more likely reflects an active role of SWS. How-ever, without direct manipulation of SWS we cannot rule out thepossibility that its correlation with performance improvement is

an epiphenomenon reflecting better learning amongst subsequenthigher performers rather than something necessary to attain thatperformance.

In summary, our data show that abstraction of underlying struc-ture during sleep is predicted by the amount of SWS obtained.

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his is also the first demonstration that statistical learning con-olidates with sleep. Overall, we provide evidence that sleep is notnly beneficial for the retention of previously learned material, buts also involved in the reorganisation of that material to enhanceubsequent processing.

cknowledgements

The authors would like to thank Jakke Tamminen, Patti Adanknd two anonymous reviewers for helpful comments on theanuscript. This work was supported by a Biotechnology and Bio-

ogical Sciences Research Council (BBSRC) New investigator awardBB/F003048/1] to PL.

ppendix A. Supplementary data

Supplementary data associated with this article can be found, inhe online version, at doi:10.1016/j.neuropsychologia.2011.02.015.

eferences

ackhaus, J., Hoeckesfeld, R., Born, J., Hohagen, F., & Junghanns, K. (2008). Immediateas well as delayed post learning sleep but not wakefulness enhances declarativememory consolidation in children. Neurobiology of Learning and Memory, 89(1),76–80.

enedict, C., Scheller, J., Rose-John, S., Born, J., & Marshall, L. (2009). Enhancinginfluence of intranasal interleukin-6 on slow-wave activity and memory con-solidation during sleep. FASEB Journal, 23(10), 3629–3636.

orn, J., Rasch, B., & Gais, S. (2006). Sleep to remember. Neuroscientist, 12(5),410–424.

rown, R. M., & Robertson, E. M. (2007a). Inducing motor skill improvements witha declarative task. Nature Neuroscience, 10(2), 148–149.

rown, R. M., & Robertson, E. M. (2007b). Off-line processing: Reciprocal interac-tions between declarative and procedural memories. The Journal of Neuroscience,27(39), 10468–10475.

ai, D. J., Mednick, S. A., Harrison, E. M., Kanady, J. C., & Mednick, S. C. (2009). REM,not incubation, improves creativity by priming associative networks. Proceed-ings of the National Academy of Sciences of the United States of America, 106(25),10130–10134.

ohen, D. A., Pascual-Leone, A., Press, D. Z., & Robertson, E. M. (2005). Off-linelearning of motor skill memory: A double dissociation of goal and movement.Proceedings of the National Academy of Sciences of the United States of America,102(50), 18237–18241.

iekelmann, S., & Born, J. (2010). The memory function of sleep. Nature Reviews:Neuroscience, 11(2), 114–126.

jonlagic, I., Rosenfeld, A., Shohamy, D., Myers, C., Gluck, M., & Stickgold, R. (2009).Sleep enhances category learning. Learning & Memory, 16(12), 751–755.

umay, N., & Gaskell, M. G. (2007). Sleep-associated changes in the mental repre-sentation of spoken words. Psychological Science, 18(1), 35–39.

urrant, S., & Lewis, P. A. (2009). Memory consolidation: Tracking transfer withfunctional connectivity. Current Biology, 19(18), R860–R862.

llenbogen, J. M., Hu, P. T., Payne, J. D., Titone, D., & Walker, M. P. (2007). Humanrelational memory requires time and sleep. Proceedings of the National Academyof Sciences of the United States of America, 104(18), 7723–7728.

llenbogen, J. M., Payne, J. D., & Stickgold, R. (2006). The role of sleep in declarativememory consolidation: Passive, permissive, active or none? Current Opinion inNeurobiology, 16(6), 716–722.

ischer, S., Drosopoulos, S., Tsen, J., & Born, J. (2006). Implicit learning—Explicitknowing: A role for sleep in memory system interaction. Journal of CognitiveNeuroscience, 18(3), 311–319.

ischer, S., Wilhelm, I., & Born, J. (2007). Developmental differences in sleep’s role forimplicit off-line learning: Comparing children with adults. Journal of CognitiveNeuroscience, 19(2), 214–227.

iser, J. (2009). Perceptual learning and representational learning in humans andanimals. Learning & Behavior, 37(2), 141–153.

iser, J., & Aslin, R. N. (2002). Statistical learning of higher-order temporal struc-ture from visual shape sequences. Journal of Experimental Psychology. Learning,Memory, and Cognition, 28(3), 458–467.

rankland, P. W., & Bontempi, B. (2005). The organization of recent and remotememories. Nature Reviews: Neuroscience, 6(2), 119–130.

ais, S., Koster, S., Sprenger, A., Bethke, J., Heide, W., & Kimmig, H. (2008). Sleep isrequired for improving reaction times after training on a procedural visuo-motortask. Neurobiology of Learning and Memory, 90(4), 610–615.

ais, S., Lucas, B., & Born, J. (2006). Sleep after learning aids memory recall. Learning

& Memory, 13(3), 259–262.

ais, S., Plihal, W., Wagner, U., & Born, J. (2000). Early sleep triggers memory forearly visual discrimination skills. Nature Neuroscience, 3(12), 1335–1339.

ais, S., Rasch, B., Wagner, U., & Born, J. (2008). Visual-procedural memory consoli-dation during sleep blocked by glutamatergic receptor antagonists. The Journalof Neuroscience, 28(21), 5513–5518.

gia 49 (2011) 1322–1331

Gomez, R. L., Bootzin, R. R., & Nadel, L. (2006). Naps promote abstraction in language-learning infants. Psychological Science, 17(8), 670–674.

Hu, P., Stylos-Allan, M., & Walker, M. P. (2006). Sleep facilitates consol-idation of emotional declarative memory. Psychological Science, 17(10),891–898.

Jimenez, L. (2008). Taking patterns for chunks: Is there any evidence of chunklearning in continuous serial reaction-time tasks? Psychological Research, 72(4),387–396.

Kim, R., Seitz, A., Feenstra, H., & Shams, L. (2009). Testing assumptions of sta-tistical learning: Is it long-term and implicit? Neuroscience Letters, 461(2),145–149.

Lau, H., Tucker, M. A., & Fishbein, W. (2010). Daytime napping: Effects on humandirect associative and relational memory. Neurobiology of Learning and Memory,93(4), 554–560.

Lieberman, M. D., Chang, G. Y., Chiao, J., Bookheimer, S. Y., & Knowlton, B. J. (2004).An event-related fMRI study of artificial grammar learning in a balanced chunkstrength design. Journal of Cognitive Neuroscience, 16(3), 427–438.

Massimini, M., Ferrarelli, F., Huber, R., Esser, S. K., Singh, H., & Tononi, G. (2005).Breakdown of cortical effective connectivity during sleep. Science, 309(5744),2228–2232.

McClelland, J. L., McNaughton, B. L., & O’Reilly, R. C. (1995). Why there are com-plementary learning systems in the hippocampus and neocortex: Insights fromthe successes and failures of connectionist models of learning and memory.Psychological Review, 102(3), 419–457.

Mednick, S., Nakayama, K., & Stickgold, R. (2003). Sleep-dependent learning: A napis as good as a night. Nature Neuroscience, 6(7), 697–698.

Mograss, M. A., Guillem, F., Brazzini-Poisson, V., & Godbout, R. (2009). The effectsof total sleep deprivation on recognition memory processes: A study of event-related potential. Neurobiology of Learning and Memory, 91(4), 343–352.

Nemeth, D., Janacsek, K., Londe, Z., Ullman, M. T., Howard, D. V., & Howard, J. H., Jr.(2010). Sleep has no critical role in implicit motor sequence learning in youngand old adults. Experimental Brain Research, 201(2), 351–358.

Pelucchi, B., Hay, J. F., & Saffran, J. R. (2009). Statistical learning in a natural languageby 8-month-old infants. Child Development, 80(3), 674–685.

Perruchet, P., & Pacton, S. (2006). Implicit learning and statistical learning: Onephenomenon, two approaches. Trends in Cognitive Sciences, 10(5), 233–238.

Perruchet, P., Tyler, M. D., Galland, N., & Peereman, R. (2004). Learning nonadjacentdependencies: No need for algebraic-like computations. Journal of ExperimentalPsychology: General, 133(4), 573–583.

Poldrack, R. A., Clark, J., Pare-Blagoev, E. J., Shohamy, D., Creso Moyano, J., Myers, C.,et al. (2001). Interactive memory systems in the human brain. Nature, 414(6863),546–550.

Poldrack, R. A., & Foerde, K. (2008). Category learning and the memory systemsdebate. Neuroscience and Biobehavioral Reviews, 32(2), 197–205.

Poldrack, R. A., & Packard, M. G. (2003). Competition among multiple memorysystems: Converging evidence from animal and human brain studies. Neuropsy-chologia, 41(3), 245–251.

Poldrack, R. A., & Rodriguez, P. (2004). How do memory systems interact? Evidencefrom human classification learning. Neurobiology of Learning and Memory, 82(3),324–332.

Press, D. Z., Casement, M. D., Pascual-Leone, A., & Robertson, E. M. (2005). Thetime course of off-line motor sequence learning. Brain Research: Cognitive BrainResearch, 25(1), 375–378.

Rasch, B., Buchel, C., Gais, S., & Born, J. (2007). Odor cues during slow-wave sleepprompt declarative memory consolidation. Science, 315(5817), 1426–1429.

Reber, A. S. (1967). Implicit learning of artificial grammars. Journal of Verbal Learningand Verbal Behavior, 6, 855–863.

Reber, A. S. (1993). Implicit learning and tacit knowledge: An essay on the cognitiveunconscious. Oxford University Press.

Rechtschaffen, A., & Kales, A. (1968). A manual of standardized terminology, tech-niques and scoring system for sleep stages of human subjects. Bethesda, MD: U.S.Department of Health.

Robertson, E. M. (2009). From creation to consolidation: A novel framework formemory processing. PLoS Biology, 7(1), e19.

Robertson, E. M., Press, D. Z., & Pascual-Leone, A. (2005). Off-line learning and theprimary motor cortex. The Journal of Neuroscience, 25(27), 6372–6378.

Saffran, J. R. (2001). The use of predictive dependencies in language learning. Journalof Memory and Language, 44, 493–515.

Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-oldinfants. Science, 274(5294), 1926–1928.

Saffran, J. R., Johnson, E. K., Aslin, R. N., & Newport, E. L. (1999). Statistical learningof tone sequences by human infants and adults. Cognition, 70(1), 27–52.

Saffran, J. R., & Thiessen, E. D. (2006). Domain-general learning capacities. In E.Hoff, & M. Shatz (Eds.), Blackwell handbook of language development (pp. 68–86).Blackwell Publishing.

Siegel, J. M. (2001). The REM sleep-memory consolidation hypothesis. Science,294(5544), 1058–1063.

Song, S., Howard, J. H., Jr., & Howard, D. V. (2007). Sleep does not benefit probabilisticmotor sequence learning. The Journal of Neuroscience, 27(46), 12475–12483.

Stickgold, R., James, L., & Hobson, J. A. (2000). Visual discrimination learning requires

sleep after training. Nature Neuroscience, 3(12), 1237–1238.

Stickgold, R., & Walker, M. P. (2007). Sleep-dependent memory consolidation andreconsolidation. Sleep Medicine, 8(4), 331–343.

Takashima, A., Nieuwenhuis, I. L., Jensen, O., Talamini, L. M., Rijpkema, M., & Fernan-dez, G. (2009). Shift from hippocampal to neocortical centered retrieval networkwith consolidation. The Journal of Neuroscience, 29(32), 10087–10093.

ycholo

T

T

T

T

T

T

T

T

v

v

205–211.

S.J. Durrant et al. / Neurops

amminen, J., Payne, J. D., Stickgold, R., Wamsley, E. J., & Gaskell, M. G. (2010). Sleepspindle activity is associated with the integration of new memories and existingknowledge. The Journal of Neuroscience, 30(43), 14356–14360.

hiel, C. M., Shanks, D. R., Henson, R. N., & Dolan, R. J. (2003). Neuronal correlates offamiliarity-driven decisions in artificial grammar learning. Neuroreport, 14(1),131–136.

ononi, G. (2009). Slow wave homeostasis and synaptic plasticity. Journal of ClinicalSleep Medicine, 5(2 Suppl.), S16–S19.

ononi, G., & Cirelli, C. (2006). Sleep function and synaptic homeostasis. SleepMedicine Reviews, 10(1), 49–62.

se, D., Langston, R. F., Kakeyama, M., Bethus, I., Spooner, P. A., Wood, E. R., et al.(2007). Schemas and memory consolidation. Science, 316(5821), 76–82.

urk-Browne, N. B., Isola, P. J., Scholl, B. J., & Treat, T. A. (2008). Multidimensionalvisual statistical learning. Journal of Experimental Psychology: Learning, Memory,and Cognition, 34(2), 399–407.

urk-Browne, N. B., Junge, J., & Scholl, B. J. (2005). The automaticity of visual statis-tical learning. Journal of Experimental Psychology: General, 134(4), 552–564.

urk-Browne, N. B., Scholl, B. J., Chun, M. M., & Johnson, M. K. (2009). Neural evi-

dence of statistical learning: Efficient detection of visual regularities withoutawareness. Journal of Cognitive Neuroscience, 21(10), 1934–1945.

an den Bos, E., & Poletiek, F. H. (2008). Effects of grammar complexity on artificialgrammar learning. Memory & Cognition, 36(6), 1122–1131.

an den Bos, E., & Poletiek, F. H. (2009). Structural selection in implicit learning ofartificial grammars. Psychological Research, 74(2), 138–151.

gia 49 (2011) 1322–1331 1331

Vertes, R. P., & Siegel, J. M. (2005). Time for the sleep community to take a critical lookat the purported role of sleep in memory processing. Sleep, 28(10), 1228–1229(discussion 1230–1223)

Wagner, U., Gais, S., Haider, H., Verleger, R., & Born, J. (2004). Sleep inspires insight.Nature, 427(6972), 352–355.

Wagner, U., Kashyap, N., Diekelmann, S., & Born, J. (2007). The impact of post-learning sleep vs. wakefulness on recognition memory for faces with differentfacial expressions. Neurobiology of Learning and Memory, 87(4), 679–687.

Walker, M. P. (2008). Sleep-dependent memory processing. Harvard Review of Psy-chiatry, 16(5), 287–298.

Walker, M. P. (2009). The role of sleep in cognition and emotion. Annals of the NewYork Academy of Sciences, 1156, 168–197.

Walker, M. P., Brakefield, T., Hobson, J. A., & Stickgold, R. (2003). Dissociablestages of human memory consolidation and reconsolidation. Nature, 425(6958),616–620.

Walker, M. P., Brakefield, T., Morgan, A., Hobson, J. A., & Stickgold, R. (2002). Practicewith sleep makes perfect: Sleep-dependent motor skill learning. Neuron, 35(1),

Walker, M. P., & Stickgold, R. (2005). It’s practice, with sleep, that makes perfect:Implications of sleep-dependent learning and plasticity for skill performance.Clinics in Sports Medicine, 24(2), 301–317, ix.

Walker, M. P., & Stickgold, R. (2006). Sleep, memory, and plasticity. Annual Reviewof Psychology, 57, 139–166.