New Northwestern University research shows people actually might solve a problem better if they “sleep on it.”
In fact, the researchers were able to improve problem solving upon waking by manipulating a critical process during sleep.
The study provides important information about information processing during sleep, as well as incubation for problem solving – why we sometimes solve a problem better after a break.
“We know that people rehearse or ‘consolidate’ memories during sleep, strengthening and reorganizing them,” said Kristin Sanders, first author of the study and a doctoral student in psychology in the Weinberg College of Arts and Sciences at Northwestern.
“It’s also known that this natural process can be boosted by playing sounds associated with the information being rehearsed.”
Because many tricky problems are solved by thinking of them in a new way, Sanders and colleagues hypothesized that rehearsing unsolved problems during sleep would help people refine their memories of the problems, and improve their chance to solve them the next day.
In the study, people attempted several puzzles in the evening while listening to specific sound cues.
While they slept, a program presented sounds associated with half the puzzles they had failed in the evening.
The following morning participants solved the puzzles that had the associated sound cues played overnight better, compared to the puzzles that got no cues.
“This study provides yet more evidence that brain processing during sleep is helpful to daytime cognition,” said Mark Beeman, professor of psychology and a senior author of the study.”
In this case, if you want to solve problems or make the best decisions, better to sleep on it than to be on Twitter at 3 a.m.”
The research is the first demonstration of actually improving problem solving by targeting memories for unsolved problems for extra processing during sleep.
It strengthens the literature suggesting sleep reorganizes memory, and suggests that problem solving may benefit from sleep due to rehearsal and consolidation of problem memory.
“Problem solving is part of everyone’s daily life. While we use tricky puzzles in our study, the underlying cognitive processes could relate to solving any problem on which someone is stuck or blocked by an incorrect approach,” Sanders said.
However, the research may only apply to situations where people have the background information they need to solve the problem and just haven’t found the right configuration yet.
“For example, no matter how much sleep I get, I’m not going to suddenly figure out black holes or find a cure for a rare disease, because I don’t have the necessary background knowledge,” Beeman said.
“However, if you’ve studied a problem thoroughly and are still stuck, thinking about it during a good night’s sleep may be just the trick.”
“Targeted Memory Reactivation During Sleep Improves Next-Day Problem Solving” published online Oct. 11 in Psychological Science.
During creative problem solving, initial solution attempts often fail because of self-imposed constraints that prevent us from thinking out of the box. In order to solve a problem successfully, the problem representation has to be restructured by combining elements of available knowledge in novel and creative ways.
It has been suggested that sleep supports the reorganization of memory representations, ultimately aiding problem solving. In this study, we systematically tested the effect of sleep and time on problem solving, using classical insight tasks and magic tricks. Solving these tasks explicitly requires a restructuring of the problem representation and may be accompanied by a subjective feeling of insight.
In two sessions, 77 participants had to solve classical insight problems and magic tricks.
The two sessions either occurred consecutively or were spaced 3 h apart, with the time in between spent either sleeping or awake.
We found that sleep affected neither general solution rates nor the number of solutions accompanied by sudden subjective insight.
Our study thus adds to accumulating evidence that sleep does not provide an environment that facilitates the qualitative restructuring of memory representations and enables problem solving.
When facing a difficult problem, we often get the advice to “let it rest” or “sleep on it.”
Indeed, some studies support the view that a period of incubation can subserve problem solving, and it has recently been suggested that sleep may particularly contribute to this psychological function.
It is, however, still a matter of debate under which conditions such beneficial effects occur.
When attempting to solve a problem, the individual elements that constitute the task have to be represented in memory and integrated with prior knowledge to arrive at a solution.
This process can either be based on analytic reasoning, which leads to a solution in an incremental step-by-step manner, or the solution may emerge as a result of sudden insight (Bowden and Jung-Beeman, 2003; Gilhooly, 2016; Llewellyn, 2016).
Even though it is difficult to pinpoint the phenomenon of insight and to define exact criteria of insight tasks (Chronicle et al., 2004; Helie and Sun, 2010), it seems clear that insight involves a change in the representation of a cognitive concept (Ohlsson, 1992; Kounios and Beeman, 2014).
Importantly, insight requires recombining memory and knowledge elements in an innovative, non-obvious fashion by flexibly switching between different associations (Kounios and Beeman, 2014). Öllinger et al. (2014) propose a multi-stage model of representational change during insight problem solving that divides the process into distinct, but dependent stages.
First, different solutions are attempted (1) which lead to consistent failure (2).
Accordingly, reaching an impasse increases the likelihood of a later insight experience and subsequent solution of the problem. The better participants could reach the boundaries of their initial solution space, the easier it was for them to overcome them in a second step.
When a first problem representation is created and no solution can be found within the assumed problem space, the original problem representation has to be modified by representational change.
This change requires relaxing self-imposed constraints on applicable knowledge (Ohlsson, 1992; Knoblich et al., 1999). Other conceptualizations of insight problem solving follow a similar logic, but emphasize that the problem space is initially kept as small as possible and will only modified if no satisfactory progress can be made (Ormerod et al., 2013). Together, both theories share the view that constraints on applicable knowledge are modified during insight problem solving.
Phenomenologically, solving a complex problem is often accompanied by an Aha! experience, meaning that the solution arises in an unexpected, sudden manner, is experienced as obviously correct, and elicits a positive emotional response (Kaplan and Simon, 1990; Gick and Lockhart, 1995; Bowden et al., 2005; Subramaniam et al., 2009; Topolinski and Reber, 2010; Danek et al., 2014; Salvi et al., 2016; Danek and Wiley, 2017).
Furthermore, the solution is often preceded by a period of feeling stuck and might therefore be driven by unconscious processing (Bowden et al., 2005). Indeed, qualitative and quantitative reviews have come to the conclusion that a period during which a problem is set aside, termed incubation, is beneficial for insight problem solving (Dodds et al., 2003; Sio and Ormerod, 2009).
Many studies investigate insight problem solving with the remote associates test (RAT) and observe that a period of incubation improves performance (Mednick et al., 1964; Dodds et al., 2002; Vul and Pashler, 2007).
According to Gilhooly (2016), these incubation benefits might be facilitated by automated spreading of activation along associative links, coupled with an active but below-threshold goal representation.
In line with this assumption, it has been shown that a prime can spark later insight (Cai et al., 2009). Alternatively, activation of neural traces representing unsuccessful solution attempts might decay over a period of incubation, making a relaxation of previous constraints and consequent activation of novel networks more likely (Öllinger et al., 2008).
In principle, sleep offers a period of brain isolation that could have similar benefits as an incubation period in wakefulness. Additionally, sleep might even aid the restructuring of a problem representation by qualitatively transforming memories (Stickgold and Walker, 2013).
It has been established that sleep has a positive effect on the stabilization of newly acquired memory content, meaning that it helps to preserve stored information (Gais and Born, 2004; Schabus et al., 2004; Diekelmann and Born, 2010; Rasch and Born, 2013; Schönauer et al., 2014b).
The standard model of memory consolidation assumes that by repetitive reactivation of hippocampo-cortical networks during sleep, initially hippocampal-dependent memories become gradually integrated into cortico-cortical networks, which leads to a reorganization of memory representations on the neural level (Frankland and Bontempi, 2005; Takashima et al., 2006; Gais et al., 2007).
Whether and under which circumstances this process also entails a qualitative reorganization of the memories is currently under debate (Lewis and Durrant, 2011; Inostroza and Born, 2013; Stickgold and Walker, 2013; Ackermann and Rasch, 2014; Landmann et al., 2014).
Arguing in favor of this idea are studies that investigate the integration of newly encoded information into existing knowledge networks in lexical integration tasks (Tamminen and Gaskell, 2013) and the formation of false memories for words representing the gist of actually studied word lists, which are induced by sleep (Payne et al., 2009; Diekelmann et al., 2011).
Additionally, there is ample evidence that sleep can promote the extraction of statistical regularities, for example in probabilistic learning (Durrant et al., 2011) or in transitive inference tasks (Ellenbogen et al., 2007), making access to patterns and rules explicit (Stickgold and Walker, 2013).
It is assumed that repetitive reactivation of overlapping memory representations in sleep helps to extract regularities from multiple memories (Djonlagic et al., 2009; Lewis and Durrant, 2011). Wagner et al. (2004) investigated problem solving in a version of the Number Reduction Task that could either be solved analytically or via a hidden short cut rule. Subjects used the short cut during a delayed test more often when they spent the time after the initial confrontation with the task asleep than when they stayed awake, indicating that knowledge of the hidden rules governing the task improved over sleep.
Creative problem solving, contrary to memory integration, concept formation, or rule learning, requires the ability to disintegrate existing concepts in order to recombine memory representations in a novel fashion (Landmann et al., 2014). So far, a beneficial effect of sleep on this kind of problem solving is still being discussed (Monaghan et al., 2015; Landmann et al., 2016; Debarnot et al., 2017) and the scarce evidence yields mixed results (Cai et al., 2009; Sio et al., 2013; Beijamini et al., 2014).
To our knowledge, the study by Beijamini et al. (2014) is the only one that investigates the effect of sleep on a problem that requires logical reasoning. Most other sleep studies use the RAT, which is described as an insight problem but actually relies more on associative processing than on the restructuring of a problem representation.
In the RAT three seemingly unrelated words (e.g., ‘dust’/‘cereal’/‘fish’) are presented and participants are asked to find the expression that connects those three words (‘bowl’). Apart from a general benefit of incubation, Cai et al. (2009) found that participants with REM sleep between sessions had higher solution rates in the RAT than participants without REM sleep. In contrast, Landmann et al. (2016) did not observe a sleep-related improvement in the closely similar Compound Remote Associates Test, but only better memory retention for previously solved items, supporting a primarily stabilizing role of sleep.
The goal of the present study was to disentangle effects of incubation and sleep on insight problem solving. Four groups of subjects were confronted with a set of classical problem solving tasks consisting of matchstick-algorithms, the nine-dot problem and the eight-coin problem (Danek et al., 2016).
Furthermore, participants had to find out how several magic tricks worked, a novel insight paradigm (Danek et al., 2014). So far, studies on sleep and problem solving have not addressed the nature of the solutions that were reached by the participants (Walker et al., 2002; Cai et al., 2009; Beijamini et al., 2014; Monaghan et al., 2015; Landmann et al., 2016). Therefore, to be able to separate sudden solutions, which are typical for insight problem solving, from analytical step-by-step problem solving, we also collected qualitative data about the participants’ subjective feeling of sudden insight, and compared qualitative and quantitative performance measures (Danek and Wiley, 2017). We hypothesized that an incubation period would increase insight experiences and overall solution rates. Similar to awake incubation, sleep keeps the brain from conscious processing but may additionally support the restructuring of underlying problem representations. Therefore, we further expected that incubation during sleep would have a larger effect than incubation during wakefulness.
More information: Kristin E. G. Sanders et al. Targeted Memory Reactivation During Sleep Improves Next-Day Problem Solving, Psychological Science (2019). DOI: 10.1177/0956797619873344
Journal information: Psychological Science
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