SOLUTION: PSY 3211 FIU Targeted Memory Reactivation Article Example of References in APA

PSSXXX10.1177/0956797619873344Sanders et al.Targeted Memory Reactivation During Sleep Improves Problem Solving
Research Article
Targeted Memory Reactivation During
Sleep Improves Next-Day Problem Solving
Psychological Science
2019, Vol. 30(11) 1616­–1624
© The Author(s) 2019
Article reuse guidelines:
DOI: 10.1177/0956797619873344
Kristin E. G. Sanders , Samuel Osburn, Ken A. Paller ,
and Mark Beeman
Department of Psychology, Northwestern University
Many people have claimed that sleep has helped them solve a difficult problem, but empirical support for this
assertion remains tentative. The current experiment tested whether manipulating information processing during sleep
impacts problem incubation and solving. In memory studies, delivering learning-associated sound cues during sleep
can reactivate memories. We therefore predicted that reactivating previously unsolved problems could help people
solve them. In the evening, we presented 57 participants with puzzles, each arbitrarily associated with a different
sound. While participants slept overnight, half of the sounds associated with the puzzles they had not solved were
surreptitiously presented. The next morning, participants solved 31.7% of cued puzzles, compared with 20.5% of
uncued puzzles (a 55% improvement). Moreover, cued-puzzle solving correlated with cued-puzzle memory. Overall,
these results demonstrate that cuing puzzle information during sleep can facilitate solving, thus supporting sleep’s
role in problem incubation and establishing a new technique to advance understanding of problem solving and sleep
problem solving, incubation, sleep, targeted memory reactivation, restructuring, creative cognition
Received 3/1/19; Revision accepted 8/9/19
Some problems are so difficult that one reaches an
impasse rather than a solution. Remarkably, incubation—
a pause in actively working on a problem—can increase
the chance of finding a solution (Sio & Ormerod, 2009).
After incubation, people often report having an
insight—a solution comes to them suddenly, seemingly
from nowhere. In these cases, some unconscious reorganization or restructuring of the problem may have
transpired during the intervening period (Schooler &
Melcher, 1995).
Although research on incubation has focused on
awake periods, some empirical evidence tentatively
suggests that problem incubation may be especially
beneficial during sleep. In one experiment, participants
who slept were more likely to discover a hidden shortcut to a tedious numerical task compared with participants who spent an equivalent time awake (Wagner,
Gais, Haider, Verleger, & Born, 2004). Similarly, participants in another experiment were more likely to use
solution hints from an allegedly unrelated task to solve
tricky word problems if they took an intervening nap
with REM sleep (Cai, Mednick, Harrison, Kanady, &
Mednick, 2009). However, other recent studies failed
to find sleep benefits for problem solving (Brodt,
Pöhlchen, Täumer, Gais, & Schönauer, 2018; Schönauer
et al., 2018), suggesting that unspecified study parameters are critical, as in the broader literature on incubation (Sio & Ormerod, 2009).
Carefully and robustly characterizing sleep incubation
may help elucidate problem incubation more generally
by targeting processing that is primarily outside conscious
control. Sleep has a demonstrated impact on subsequent
waking performance, even though what occurs during
sleep seems less volitional than what occurs during waking hours. For example, sleep appears to strengthen and
Corresponding Author:
Kristin E. G. Sanders, Northwestern University, Department of
Psychology, Swift Hall 102, 2029 Sheridan Rd., Evanston, IL 60208
Targeted Memory Reactivation During Sleep Improves Problem Solving
potentially transform memory (Paller, Mayes, Antony, &
Norman, in press; Payne, 2011). Indeed, these memoryconsolidation processes could be conducive to problem
incubation (Stickgold & Walker, 2013), and similar paradigms could be adapted to problem solving.
Numerous studies in humans and other animals demonstrate better memory after sleep compared with a
similar time awake (Rasch & Born, 2013). Replay of
recently learned memories during sleep putatively
strengthens memory. Indeed, neurons that fire when
rats initially learn a maze fire again in similar patterns
while the rats sleep (Wilson & McNaughton, 1994). In
humans, hippocampal areas active during daytime route
learning reactivate during sleep, and the degree of
sleep reactivation correlates with route retrieval after
sleep (Peigneux et al., 2004).
Sleep-related memory processes not only strengthen
but also can optimize, organize, and transform information (Ellenbogen, Hu, Payne, Titone, & Walker, 2007;
Stickgold & Walker, 2013). These processes could be
particularly beneficial when solving problems that
require some form of restructuring. Broadly construed,
restructuring implies forming a new problem representation or approach by ignoring compelling but incorrect
ideas, combining information previously viewed as
unrelated, or otherwise reorganizing problem elements.
Therefore, we examined problems thought to require
restructuring in our experiment.
Memory can be selectively modified during sleep
using cues that trigger or bias memory replay. For
example, odor cues can be associated with learning
some spatial information, and subsequently delivering
the cues again during slow-wave sleep (SWS) produces
better learning compared with control conditions
(Rasch, Büchel, Gais, & Born, 2007). Moreover, in a
targeted-memory-reactivation (TMR) paradigm, specific
knowledge can be selectively cued. For example, if
participants learn object locations, each associated with
a unique sound, and then half of those sounds are
quietly presented during SWS without disrupting sleep
(Rudoy, Voss, Westerberg, & Paller, 2009), participants
on awakening can recall locations more accurately for
cued compared with uncued objects. TMR can apparently bias memory reactivation and consolidation,
yielding a disproportionate benefit for cued items.
Indeed, playing task-related sound cues to sleeping rats
influences the firing of task-related neurons (Bendor &
Wilson, 2012). TMR can also reorganize memories, such
as improving explicit recognition of a learned tone
sequence (Cousins, El-Deredy, Parkes, Hennies, & Lewis,
If memories are reorganized through reactivation
during sleep, then sleep replay may also contribute to
problem incubation by facilitating reorganization of the
problem representation. With this possibility in mind,
we adapted the TMR paradigm to test whether playing
puzzle-associated sound cues during sleep would
improve people’s ability to solve cued compared with
uncued puzzles in the morning after they failed to solve
them the night before.
Prior TMR studies demonstrate memory strengthening and reorganization using cues presented during
SWS (Hu, Cheng, Chiu, & Paller, 2019); therefore, we
used the same experimental strategy here. Our procedure does not address whether other sleep stages, such
as REM, contribute to problem solving. Indeed, some
anecdotes link problem solving with dreaming
(Mazzarello, 2000), and REM has been linked with a
broadening of semantic associations (Stickgold, Scott,
Rittenhouse, & Hobson, 1999) and the incorporation of
purportedly unrelated hints into problem solutions (Cai
et al., 2009). Of course, SWS and REM may both be
relevant, if for example, memories for problems are
reactivated during SWS and then reorganized during
REM (Giuditta et al., 1995; Lewis, Knoblich, & Poe,
2018). Although much remains to be learned about
these sleep stages, our intention was not to identify
which sleep stage is relevant but rather to determine
generally whether sleep could be more strongly linked
with problem solving.
In two consecutive evening sessions, participants
attempted to solve puzzles one at a time. Each puzzle
was associated with a unique sound cue (Fig. 1). Evening sessions continued until participants failed to solve
6 puzzles, yielding 12 incubation-ready puzzles across
two evenings. Each night, participants slept in their own
homes, and sounds from 3 of the incubation-ready
puzzles from that evening were played during SWS.
Each subsequent morning, participants returned to the
lab to try to solve the 6 puzzles they did not solve the
night before. To overcome typical methodological challenges of incubation and sleep studies, we employed a
within-subjects contrast, included a large number of
participants, presented a large set of puzzles that were
distinctive from each other (to allow better incubation),
and spread data collection over two nights to avoid
potential confusion between puzzles (see the Supplemental Material available online for further details). We
predicted that participants would solve more cued than
uncued puzzles.
A total of 61 participants (38 female) from the Northwestern University community enrolled in the experiment (age: range: 18–29 years, M = 20.01, SD = 1.92).
Sanders et al.
Evening Session
Morning Session
for Solving
for Solving
4 Min Each
2 Min Each
Until Six
Unsolved Puzzles
Three Rounds
With Feedback
Half of the Sounds
Play During SWS
One Round With
No Feedback
Recall Before Each
Fig. 1. Experimental procedure (repeated twice). Each evening, participants attempted to solve puzzles while a distinct 15-s sound
clip looped for each one. New puzzles were presented until six puzzles remained unsolved. Then participants were tested, with
feedback, on their recall for which puzzle went with each sound. Participants took home a portable electroencephalogram sleep
monitoring and cuing system. When it detected slow-wave sleep (SWS), the system presented sound cues associated with half of
the unsolved puzzles. The following morning, participants returned to the laboratory to complete recall tests on puzzle sounds and
puzzle details, after which they attempted to solve the puzzles that they had failed to solve the previous night. Analyses contrasted
solving rates for cued versus uncued puzzles.
Our research design and predicted effects were novel,
so we decided in advance to use a sample size of 60
(for scheduling reasons, we actually tested 61). Our
goal was to provide robust power and reproducibility
in a within-subjects design, so we more than doubled
the standard number of participants used in prior
research. Participants were fluent English speakers who
reported no history of neurological or sleep disorders.
We excluded data from 3 participants who did not
complete all four sessions (two evening, two morning)
and a fourth participant who did not receive sound
cues because of equipment failure. Thus, data from 57
participants were analyzed. Participants provided
informed consent and were monetarily compensated.
Procedures were approved by the Northwestern University Institutional Review Board.
Materials and procedure
Participants completed four sessions over 3 days—two
evening sessions (mean start time = 6:01 p.m.) and two
morning sessions (mean start time = 8:47 a.m.)—with
overnight sleep monitoring and cuing in participants’
own homes after each evening session.
Evening session. During the first evening session, we
instructed participants on using a sleep-monitoring
device and introduced the procedure and the types of
puzzles. Then, in both evening sessions, participants
attempted puzzles one at a time. Before each puzzle, a
randomly paired sound clip played once (15 s), to familiarize participants with the sound. Participants then read
the puzzle. If it was familiar, the experimenter skipped
the puzzle; otherwise, participants attempted to solve the
puzzle for 2 min while the sound continuously looped.
During the solving period, if participants had an idea,
they pressed the space bar and spoke their solution
aloud. If the offered solution was incorrect, participants
used the remaining time to continue working on the puzzle. If participants correctly solved the puzzle, it was
excluded from the remainder of the experiment and
replaced with a new puzzle. If participants did not solve
the puzzle within 2 min, the sound played one more
time, and participants were instructed to try to memorize
the sound–puzzle pairing. Puzzle order was randomized
for each participant, and participants continued to
attempt puzzles until 6 puzzles remained unsolved. On
average, participants attempted 7.24 puzzles per evening
session (SD = 1.69). This solving rate was by design
Targeted Memory Reactivation During Sleep Improves Problem Solving
(because we wanted to cue unsolved puzzles) and is
typical for puzzles of this genre. For example, when not
given hints, participants’ solving rates for the nine-dot
problem range from 0% (e.g., MacGregor, Ormerod, &
Chronicle, 2001) to 10% when given 10 min for the
attempt (Chein, Weisberg, Streeter, & Kwok, 2010).
After the puzzle-solving phase, memory for the
sound–puzzle pairings was tested to further reinforce
the association between each of the six remaining puzzles and its corresponding sound. In three rounds, participants heard each sound and reported the paired
puzzle. Then, regardless of accuracy, they saw the correct puzzle presented with the sound. By the third
round, participants correctly recalled 88.0% of puzzles
(SD = 12.8%, 95% confidence interval, or CI = [84.6%,
91.4%]). Accuracy was comparable for puzzles that were
later cued (M = 87.9%, SD = 20.0%, 95% CI = [82.6%,
93.2%]) and those that were not cued (M = 87.5%,
SD = 15.1%, 95% CI = [83.5%, 91.5%]), t(56) = 0.15, p =
.882, dz = 0.02, 95% CI = [–0.40, 0.44]. At the end of
each evening session, participants were told to avoid
thinking about or trying to solve the puzzles until the
morning session, when they would attempt each
unsolved puzzle again.
Half of the unsolved puzzles were automatically
selected for the cued condition using a programmed
algorithm, and the corresponding sounds were presented
during sleep. Neither the experimenter nor the participant knew which puzzles were cued. The algorithm was
designed to select puzzles such that, across participants,
each puzzle was presented equally often in the cued and
uncued conditions (puzzles were cued anywhere from
40% to 63% of the times they were used; given the
imperatives of maintaining double-blind conditions and
variations in puzzle solution rates, a fully balanced
design was unobtainable).
Overnight. After the evening session, participants took
a sleep monitoring and cuing system (SMCS) with them
so sound cues could be administered in their own home.
The SMCS includes a transmitter and algorithm (Zeo,
Boston, MA) as well as a laptop computer adapted to
wirelessly receive signals from the transmitter. Before
going to sleep each night, participants placed the laptop
on a level surface near their bed, snapped three singleuse silver/silver-chloride electrodes into the Zeo wireless
transmitter, filled each sensor with a conductive electrolyte gel (signa gel), and affixed the adhesive transmitter
to their forehead.
Every 30 s, the SMCS employed an algorithm to
determine a participant’s sleep stage (see Sleep Monitoring and Cuing System Validation in the Supplemental
Material). When the SMCS first detected sleep, it began
quietly playing pink noise, which reduced the likelihood that the sound cues would provoke arousal. When
the SMCS detected SWS for at least 1.5 min within a
2-min period, it presented a designated sound for 3 min
before switching to the next sound. However, if the
SMCS detected a stage other than SWS, the sound immediately stopped until SWS was again detected. If participants awoke and heard sounds playing, they pressed
a button to stop the sound. Sounds resumed when SWS
was detected again. The pink noise continued throughout the night. We excluded sounds that were reportedly
heard during the night, but this occurred very rarely
(3.5% of the trials), suggesting that the sound level was
low enough to not provoke awakenings.
The SMCS thus allowed us to present sounds in participants’ typical sleeping environments without disrupting their sleep. Allowing participants to sleep in
their own beds rather than in a laboratory setting
increased comfort levels and sleep quality, reduced the
need for an adaptation night, and more closely mimicked real-world problem-solving situations. Previous
validation efforts (e.g., Shambroom, Fábregas, &
Johnstone, 2012) showed that the SMCS sleep-staging
algorithm can reliably classify sleep, in a manner resembling standard polysomnographic staging. We did, however, introduce the modification of wet electrodes,
which generally would improve signal quality compared with the dry electrodes used for validation (see
the Supplemental Material). There was a strong, positive
correlation between the amount of participants’ selfreported and SMCS-detected sleep, r(55) = .82, p
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