How Memory Replay in Sleep Boosts Creative Problem-Solving

Sleep and quiet wakefulness signify an idling brain hub for creative insights

Long-term potentiation of synaptic strength is a fundamental aspect of learning and memory. Memories are believed to be stored within specific populations of neurons known as engram cells, which are subsequently reactivated during sleep, facilitating the consolidation of stored information. However, sleep and offline reactivations are associated not only with past experiences but also with anticipation of future events. During periods of offline reactivation, which occur during sleep and quiet wakefulness, the brain exhibits a capability to form novel connections. This process links various past experiences, often leading to the emergence of qualitatively new information that was not initially available. Brain activity during sleep and quiet wakefulness is referred to as the 'idling brain'. Idling brain activity is believed to play a pivotal role in abstracting essential information, comprehending underlying rules, generating creative ideas and fostering insightful thoughts. In this review, we will explore the current state of research and future directions in understanding how sleep and idling brain activity are interconnected with various cognitive functions, especially creative insights. These insights have profound implications for our daily lives, impacting our ability to process information, make decisions and navigate complex situations effectively. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.

Manipulated overlapping reactivation of multiple memories promotes explicit gist abstraction

Sleep is considered to promote gist abstraction on the basis of spontaneous memory reactivation. As speculated in the theory of 'information overlap to abstract (iOtA)', 'overlap' between reactivated memories, beyond reactivation, is crucial to gist abstraction. Yet so far, empirical research has not tested this theory by manipulating the factor of 'overlap'. In the current study, 'overlap' itself was manipulated by targeted memory reactivation (TMR), through simultaneously reactivating multiple memories that either contain or do not contain spatially overlapped gist information, to investigate the effect of overlapping reactivation on gist abstraction. This study had a factorial design of 2 factors with 2 levels respectively (spatial overlap/no spatial overlap, TMR/no-TMR). Accordingly, 82 healthy college students (aged 19 ∼ 25, 57 females) were randomized into four groups. After learning 16 pictures, paired with 4 auditory cues (4 pictures - 1 cue) according to the grouping, participants were given a 90-minute nap opportunity. Then TMR cueing was conducted during N2 and slow wave sleep of the nap. Performance in memory task was used to measure gist abstraction. The results showed a significant main effect of TMR on both implicit and explicit gist abstraction, and a marginally significant interaction effect on explicit gist abstraction. Further analyses showed that explicit gist abstraction in the spatial overlap & TMR group was significantly better than in the control group. Moreover, explicit gist abstraction was positively correlated with spindle density. The current study thus indicates that TMR facilitates gist abstraction, and explicit gist abstraction may benefit more from overlapping reactivation.

Prefrontal coding of learned and inferred knowledge during REM and NREM sleep

Idling brain activity has been proposed to facilitate inference, insight, and innovative problem-solving. However, it remains unclear how and when the idling brain can create novel ideas. Here, we show that cortical offline activity is both necessary and sufficient for building unlearned inferential knowledge from previously acquired information. In a transitive inference paradigm, male C57BL/6J mice gained the inference 1 day after, but not shortly after, complete training. Inhibiting the neuronal computations in the anterior cingulate cortex (ACC) during post-learning either non-rapid eye movement (NREM) or rapid eye movement (REM) sleep, but not wakefulness, disrupted the inference without affecting the learned knowledge. In vivo Ca2+ imaging suggests that NREM sleep organizes the scattered learned knowledge in a complete hierarchy, while REM sleep computes the inferential information from the organized hierarchy. Furthermore, after insufficient learning, artificial activation of medial entorhinal cortex-ACC dialog during only REM sleep created inferential knowledge. Collectively, our study provides a mechanistic insight on NREM and REM coordination in weaving inferential knowledge, thus highlighting the power of idling brain in cognitive flexibility.

Memory Reactivation during Sleep Does Not Act Holistically on Object Memory

Memory reactivation during sleep is thought to facilitate memory consolidation. Most sleep reactivation research has examined how reactivation of specific facts, objects, and associations benefits their overall retention. However, our memories are not unitary, and not all features of a memory persist in tandem over time. Instead, our memories are transformed, with some features strengthened and others weakened. Does sleep reactivation drive memory transformation? We leveraged the Targeted Memory Reactivation technique in an object category learning paradigm to examine this question. Participants (20 female, 14 male) learned three categories of novel objects, where each object had unique, distinguishing features as well as features shared with other members of its category. We used a real-time EEG protocol to cue the reactivation of these objects during sleep at moments optimized to generate reactivation events. We found that reactivation improved memory for distinguishing features while worsening memory for shared features, suggesting a differentiation process. The results indicate that sleep reactivation does not act holistically on object memories, instead supporting a transformation where some features are enhanced over others.

Semantic associative abilities and executive control functions predict novelty and appropriateness of idea generation

Novelty and appropriateness are two fundamental components of creativity. However, the way in which novelty and appropriateness are separated at behavioral and neural levels remains poorly understood. In the present study, we aim to distinguish behavioral and neural bases of novelty and appropriateness of creative idea generation. In alignment with two established theories of creative thinking, which respectively, emphasize semantic association and executive control, behavioral results indicate that novelty relies more on associative abilities, while appropriateness relies more on executive functions. Next, employing a connectome predictive modeling (CPM) approach in resting-state fMRI data, we define two functional network-based models-dominated by interactions within the default network and by interactions within the limbic network-that respectively, predict novelty and appropriateness (i.e., cross-brain prediction). Furthermore, the generalizability and specificity of the two functional connectivity patterns are verified in additional resting-state fMRI and task fMRI. Finally, the two functional connectivity patterns, respectively mediate the relationship between semantic association/executive control and novelty/appropriateness. These findings provide global and predictive distinctions between novelty and appropriateness in creative idea generation.

Can stimulants make you smarter, despite stealing your sleep?

Nonmedical use of psychostimulants for cognitive enhancement is widespread and growing in neurotypical individuals, despite mixed scientific evidence of their effectiveness. Sleep benefits cognition, yet the interaction between stimulants, sleep, and cognition in neurotypical adults has received little attention. We propose that one effect of psychostimulants, namely decreased sleep, may play an important and unconsidered role in the effect of stimulants on cognition. We discuss the role of sleep in cognition, the alerting effects of stimulants in the context of sleep loss, and the conflicting findings of stimulants for complex cognitive processes. Finally, we hypothesize that sleep may be one unconsidered factor in the mythology of stimulants as cognitive enhancers and propose a methodological approach to systematically assess this relation.

Recognizing ideas generated in a creative task: the roles of the hippocampus and medial prefrontal cortex in facilitating self-generated learning

Creative idea generation plays an important role in promoting successful memory formation. Yet, its underlying neural correlates remain unclear. We investigated the self-generated learning of creative ideas motivated by the schema-linked interactions between medial prefrontal and medial temporal regions framework. This was achieved by having participants generate ideas in the alternative uses task, self-evaluating their ideas based on novelty and source (i.e. new or old), and then later being tested on the recognition performance of the generated ideas. At the behavioral level, our results indicated superior performances in discriminating novel ideas, highlighting the novelty effect on memory. At the neural level, the regions-of-interest analyses revealed that successful recognition of novel ideas was associated with greater activations in the hippocampus (HPC) and medial prefrontal cortex (mPFC) during ideation. However, only activation in the right HPC was positively related to the successful recognition of novel ideas. Importantly, the weaker the connection between the right HPC and left mPFC, the higher the recognition accuracy of novel ideas. Moreover, activations in the right HPC and left mPFC were both effective predictors of successful recognition of novel ideas. These findings uniquely highlight the role of novelty in promoting self-generated learning of creative ideas.

The involvement of decomposition and composition processes in restructuring during problem solving

Decomposition of chunks has been widely accepted as a critical proxy of restructuring, but the role of composition in forming new representations has been largely neglected. This study aims to investigate the roles of both decomposition and composition processes in chunk restructuring, as well as their relationships with "aha" experiences during problem-solving. Participants were asked to move a part of a character to another character to create two new characters. Across three experiments, the characters to be decomposed or composed were varied in terms of tight or loose chunks. The results showed that decomposition or composition of tight chunks led to lower success rates, longer response times, and significantly stronger "Aha!" emotional experiences (mainly in terms of surprise and suddenness). This study provides evidence for the contribution of both decomposition and composition processes to restructuring in creative insight.

Recurring memory reactivation: The offline component of learning

One can be aware of the effort needed to memorize a new fact or to recall the name of a new acquaintance. Because of experiences like this, learning can seem to have only two components, encoding information and, after some delay, retrieving information. To the contrary, learning entails additional, intervening steps that sometimes are hidden from the learner. For firmly acquiring fact and event knowledge in particular, learners are generally not cognizant of the necessity of offline consolidation. The memories that persist to be available reliably at a later time, according to the present conceptualization, are the ones we repeatedly rehearse and integrate with other knowledge, whether we do this intentionally or unknowingly, awake or asleep. This article examines the notion that learning is not a function of waking brain activity alone. What happens in the brain while we sleep also impacts memory storage, and consequently is a critical component of learning. The idea that memories can change over time and become enduring has long been present in memory research and is foundational for the concept of memory consolidation. Nevertheless, the notion that memory consolidation happens during sleep faced much resistance before eventually being firmly established. Research is still needed to elucidate the operation and repercussions of repeated reactivation during sleep. Comprehensively understanding how offline memory reactivation contributes to learning is vital for both theoretical and practical considerations.

J, Tandoc M, Antony J, Schapiro A. Memory reactivation during sleep does not act holistically on object memory

Memory reactivation during sleep is thought to facilitate memory consolidation. Most sleep reactivation research has examined how reactivation of specific facts, objects, and associations benefits their overall retention. However, our memories are not unitary, and not all features of a memory persist in tandem over time. Instead, our memories are transformed, with some features strengthened and others weakened. Does sleep reactivation drive memory transformation? We leveraged the Targeted Memory Reactivation technique in an object category learning paradigm to examine this question. Participants (20 female, 14 male) learned three categories of novel objects, where each object had unique, distinguishing features as well as features shared with other members of its category. We used a real-time EEG protocol to cue the reactivation of these objects during sleep at moments optimized to generate reactivation events. We found that reactivation improved memory for distinguishing features while worsening memory for shared features, suggesting a differentiation process. The results indicate that sleep reactivation does not act holistically on object memories, instead supporting a transformation process where some features are enhanced over others.

Learning beyond sensations: How dreams organize neuronal representations

Semantic representations in higher sensory cortices form the basis for robust, yet flexible behavior. These representations are acquired over the course of development in an unsupervised fashion and continuously maintained over an organism's lifespan. Predictive processing theories propose that these representations emerge from predicting or reconstructing sensory inputs. However, brains are known to generate virtual experiences, such as during imagination and dreaming, that go beyond previously experienced inputs. Here, we suggest that virtual experiences may be just as relevant as actual sensory inputs in shaping cortical representations. In particular, we discuss two complementary learning principles that organize representations through the generation of virtual experiences. First, "adversarial dreaming" proposes that creative dreams support a cortical implementation of adversarial learning in which feedback and feedforward pathways engage in a productive game of trying to fool each other. Second, "contrastive dreaming" proposes that the invariance of neuronal representations to irrelevant factors of variation is acquired by trying to map similar virtual experiences together via a contrastive learning process. These principles are compatible with known cortical structure and dynamics and the phenomenology of sleep thus providing promising directions to explain cortical learning beyond the classical predictive processing paradigm.

An online experiment that presents challenges for translating rest-related gains in visual detail memory from the laboratory to naturalistic settings

New memories are labile and consolidate over time. Contemporary findings demonstrate that, like sleep, awake quiescence supports consolidation: people remember more new memories if they experience a brief period of post-encoding quiet rest than sensory processing. Furthermore, it was recently demonstrated that the quality of new memories can also be enhanced significantly by awake quiescence. This phenomenon offers great applied potential, for example, in education and eyewitness testimony settings. However, the translation of rest-related gains from the laboratory to everyday life remains poorly characterised and findings are mixed. Here, we report follow-on evidence demonstrating that rest-related gains in visual detail memory may be more challenging to achieve in naturalistic than laboratory-based settings. In contrast to established laboratory findings, using an online version of an established consolidation paradigm, we observed no memory benefit of post-encoding quiescence, relative to an engaging perceptual task, in the retention of detailed visual memories as measured through a lure discrimination task. This null finding could not be explained by intentional rehearsal in those who rested or between-group differences in participants' demographics or mental state, including fatigue and mood. Crucially, post-experimental reports indicated that those in the rest group experienced challenges in initiating and maintaining a state of quiescence, which may account for our null finding. Based on these findings, we propose three areas of focus for future work should rest-related gains in memory be translated from the lab to field: (1) to establish the specific environmental and individual conditions that are conducive and detrimental to awake consolidation, (2) to understand the barriers to initiating and maintaining a state of quiescence in naturalistic settings, and (3) to examine how knowledge of quiescence and its cognitive benefits can encourage the initiation and maintenance of states that are conductive to awake consolidation.

Neuromodulation via muscarinic acetylcholine pathway can facilitate distinct, complementary, and sequential roles for NREM and REM states during sleep-dependent memory consolidation

Across vertebrate species, sleep consists of repeating cycles of NREM followed by REM. However, the respective functions of NREM, REM, and their stereotypic cycling pattern are not well understood. Using a simplified biophysical network model, we show that NREM and REM sleep can play differential and critical roles in memory consolidation primarily regulated, based on state-specific changes in cholinergic signaling. Within this network, decreasing and increasing muscarinic acetylcholine (ACh) signaling during bouts of NREM and REM, respectively, differentially alters neuronal excitability and excitatory/inhibitory balance. During NREM, deactivation of inhibitory neurons leads to network-wide disinhibition and bursts of synchronized activity led by firing in engram neurons. These features strengthen connections from the original engram neurons to less-active network neurons. In contrast, during REM, an increase in network inhibition suppresses firing in all but the most-active excitatory neurons, leading to competitive strengthening/pruning of the memory trace. We tested the predictions of the model against in vivo recordings from mouse hippocampus during active sleep-dependent memory storage. Consistent with modeling results, we find that functional connectivity between CA1 neurons changes differentially at transition from NREM to REM sleep during learning. Returning to the model, we find that an iterative sequence of state-specific activations during NREM/REM cycling is essential for memory storage in the network, serving a critical role during simultaneous consolidation of multiple memories. Together these results provide a testable mechanistic hypothesis for the respective roles of NREM and REM sleep, and their universal relative timing, in memory consolidation.

Significance statement

Using a simplified computational model and in vivo recordings from mouse hippocampus, we show that NREM and REM sleep can play differential roles in memory consolidation. The specific neurophysiological features of the two sleep states allow for expansion of memory traces (during NREM) and prevention of overlap between different memory traces (during REM). These features are likely essential in the context of storing more than one new memory simultaneously within a brain network.

Computational role of sleep in memory reorganization

Sleep is considered to play an essential role in memory reorganization. Despite its importance, classical theoretical models did not focus on some sleep characteristics. Here, we review recent theoretical approaches investigating their roles in learning and discuss the possibility that non-rapid eye movement (NREM) sleep selectively consolidates memory, and rapid eye movement (REM) sleep reorganizes the representations of memories. We first review the possibility that slow waves during NREM sleep contribute to memory selection by using sequential firing patterns and the existence of up and down states. Second, we discuss the role of dreaming during REM sleep in developing neuronal representations. We finally discuss how to develop these points further, emphasizing the connections to experimental neuroscience and machine learning.

Sleep disturbance and suicidality in psychiatrically hospitalized adolescents: The role of specific emotion regulation domains

BACKGROUND

Sleep disturbance is associated with broadband measurements of emotion regulation (ER). The linkage between ER, a multidimensional process, and suicidal ideation and suicide attempt is also documented in theoretical and empirical work. Recent research indicates that distinct profiles of ER are associated with psychiatric outcomes, including adolescent suicidal ideation and attempt. The present study examined whether specific domains of ER would explain the association between sleep disturbance, and ideation and attempt among psychiatrically hospitalized adolescents.

METHOD

The sample included 284 adolescents who completed self-report measures on sleep disturbance, ER, suicidal ideation, suicide attempt, and psychiatric symptoms upon inpatient hospitalization.

RESULTS

Findings indicated that sleep disturbance was associated with suicidal ideation. Further, a single ER domain (perceived limited access to ER strategies) fully accounted for the significant association between sleep disturbance and suicidal ideation. A reported suicide attempt in the past week was correlated with the nonacceptance of emotional responses, perceived limited access to ER strategies, and emotional clarity, but was not associated with sleep disturbance.

DISCUSSION

The current findings highlight the importance of examining narrowband ER and indicate the presence of differential associations between sleep disturbance, ER, and suicide-related outcomes. Findings further elucidate the possible role of impaired cognitive responses to emotional experiences in the co-occurrence of sleep disturbance and youth psychiatric outcomes.

Individual differences in information processing during sleep and wake predict sleep-based memory consolidation of complex rules

Memory is critical for many cognitive functions, from remembering facts, to learning complex environmental rules. While memory encoding occurs during wake, memory consolidation is associated with sleep-related neural activity. Further, research suggests that individual differences in alpha frequency during wake (∼7 - 13 Hz) modulate memory processes, with higher individual alpha frequency (IAF) associated with greater memory performance. However, the relationship between wake-related EEG individual differences, such as IAF, and sleep-related neural correlates of memory consolidation has been largely unexplored, particularly in a complex rule-based memory context. Here, we aimed to investigate whether wake-derived IAF and sleep neurophysiology interact to influence rule learning in a sample of 35 healthy adults (16 males; mean age = 25.4, range: 18 - 40). Participants learned rules of a modified miniature language prior to either 8hrs of sleep or wake, after which they were tested on their knowledge of the rules in a grammaticality judgement task. Results indicate that sleep neurophysiology and wake-derived IAF do not interact but modulate memory for complex linguistic rules separately. Phase-amplitude coupling between slow oscillations and spindles during non-rapid eye-movement (NREM) sleep also promoted memory for rules that were analogous to the canonical English word order. As an exploratory analysis, we found that rapid eye-movement (REM) sleep theta power at posterior regions interacts with IAF to predict rule learning and proportion of time in REM sleep predicts rule learning differentially depending on grammatical rule type. Taken together, the current study provides behavioural and electrophysiological evidence for a complex role of NREM and REM sleep neurophysiology and wake-derived IAF in the consolidation of rule-based information.

Sleep Differentially and Profoundly Impairs Recall Memory in a Patient with Fornix Damage

In March 2020, C.T., a kind, bright, and friendly young woman underwent surgery for a midline tumor involving her septum pellucidum and extending down into her fornices bilaterally. Following tumor diagnosis and surgery, C.T. experienced significant memory deficits: C.T.'s family reported that she could remember things throughout the day, but when she woke up in the morning or following a nap, she would expect to be in the hospital, forgetting all the information that she had learned before sleep. The current study aimed to empirically validate C.T.'s pattern of memory loss and explore its neurological underpinnings. On two successive days, C.T. and age-matched controls watched an episode of a TV show and took a nap or stayed awake before completing a memory test. Although C.T. performed numerically worse than controls in both conditions, sleep profoundly exacerbated her memory impairment, such that she could not recall any details following a nap. This effect was replicated in a second testing session. High-resolution MRI scans showed evidence of the trans-callosal surgical approach's impact on the mid-anterior corpus callosum, indicated that C.T. had perturbed white matter particularly in the right fornix column, and demonstrated that C.T.'s hippocampal volumes did not differ from controls. These findings suggest that the fornix is important for processing episodic memories during sleep. As a key output pathway of the hippocampus, the fornix may ensure that specific memories are replayed during sleep, maintain the balance of sleep stages, or allow for the retrieval of memories following sleep.

The function of REM and NREM sleep on memory distortion and consolidation

During rapid eye movement (REM) sleep, newly consolidated memories can be distorted to adjust the existing memory base in memory integration. However, only a few studies have demonstrated the role of REM sleep in memory distortion. The present study aims to clarify the role of REM sleep in the facilitation of memory distortion, that is, hindsight bias, compared to non-rapid eye movement (NREM) sleep and wake states. The split-night paradigm was used to segregate REM and NREM sleep. The hypotheses are (1) hindsight bias-memory distortion-is more substantial during REM-rich sleep (late-night sleep) than during NREM-rich sleep (early-night sleep); (2) memory stabilization is more substantial during NREM-rich sleep (early-night sleep) than during REM-rich sleep (late-night sleep); and (3) memory distortion takes longer time than memory stabilization. The results of the hindsight bias test show that more memory distortions were observed after the REM condition in comparison to the NREM condition. Contrary to the hindsight bias, the correct response in the word-pair association test was observed more in the NREM than in the REM condition. The difference in the hindsight bias index between the REM and NREM conditions was identified only one week later. Comparatively, the difference in correct responses in the word-pair association task between the conditions appeared three hours later and one week later. The present study found that (1) memory distortion occurs more during REM-rich sleep than during NREM-rich sleep, while memory stabilization occurs more during NREM-rich sleep than during REM-rich sleep. Moreover, (2) the newly encoded memory could be stabilized immediately after encoding, but memory distortion occurs over several days. These results suggest that the roles of NREM and REM sleep in memory processes could be different.

Association between patterns of nutrient intake and circulating vitamin D with sleep status among Iranian adults

Nutrient pattern analysis is an easy way to compare nutrient intakes across different nations due to the universality of nutrients nature. The purpose of this study was to examine the relationship between dietary nutrient patterns (NPs) and circulating 25(OH)D concentrations with sleep duration and sleep quality among Iranian adults. We used a multistage cluster random sampling method to enroll 535 adults in this cross-sectional investigation. A validated food frequency questionnaire was applied to evaluate typical dietary intakes. Fasting blood samples were obtained to determine levels of circulating 25(OH)D. Sleep characteristics were assessed using the Pittsburgh Sleep Quality Index (PSQI). Participants had a mean age of 42.57 years and 51.2% of them had insufficient or deficient levels of serum vitamin D. Three NPs were identified: "high animal protein", "high vegetable" and "high carbohydrate". After adjustments for potential confounders, no significant associations were observed between "high animal protein" pattern and short sleeping or sleep quality. Greater adherence to "high vegetable" NP was associated with lower odds of short sleeping (OR 0.24; 95% CI 0.10, 0.54) and poor sleep quality (OR 0.45; 95% CI 0.20, 1.05). Stratified analysis revealed that these associations were stronger in normal-weight participants. Greater adherence to "high carbohydrate" NP, on the other hand, was connected to higher odds of short sleeping (OR 2.83; 95% CI 1.20, 6.72). Low adherence to "high vegetable" pattern and vitamin D insufficiency/deficiency were jointly associated with increased odds of short sleeping (OR 3.42, 95% CI 1.42, 6.64). High adherence to pattern comprising mainly of vegetable nutrients was associated with a reduced likelihood of being short sleepers and having poor sleep quality in Iranian adults, especially among those with a normal weight. Lower adherence to vegetable NP and insufficient/deficient vitamin D levels were synergistically associated with greater likelihood of being short sleepers. Greater adherence to carbohydrate NP was associated with an increased likelihood of short sleeping.

Sleep-related benefits to transitive inference are modulated by encoding strength and joint rank

Transitive inference is a measure of relational learning that has been shown to improve across sleep. Here, we examine this phenomenon further by studying the impact of encoding strength and joint rank. In experiment 1, participants learned adjacent premise pairs and were then tested on inferential problems derived from those pairs. In line with prior work, we found improved transitive inference performance after retention across a night of sleep compared with wake alone. Experiment 2 extended these findings using a within-subject design and found superior transitive inference performance on a hierarchy, consolidated across 27 h including sleep compared with just 3 h of wake. In both experiments, consolidation-related improvement was enhanced when presleep learning (i.e., encoding strength) was stronger. We also explored the interaction of these effects with the joint rank effect, in which items were scored according to their rank in the hierarchy, with more dominant item pairs having the lowest scores. Interestingly, the consolidation-related benefit was greatest for more dominant inference pairs (i.e., those with low joint rank scores). Overall, our findings provide further support for the improvement of transitive inference across a consolidation period that includes sleep. We additionally show that encoding strength and joint rank strongly modulate this effect.

Aversive experience drives offline ensemble reactivation to link memories across days

Memories are encoded in neural ensembles during learning and stabilized by post-learning reactivation. Integrating recent experiences into existing memories ensures that memories contain the most recently available information, but how the brain accomplishes this critical process remains unknown. Here we show that in mice, a strong aversive experience drives the offline ensemble reactivation of not only the recent aversive memory but also a neutral memory formed two days prior, linking the fear from the recent aversive memory to the previous neutral memory. We find that fear specifically links retrospectively, but not prospectively, to neutral memories across days. Consistent with prior studies, we find reactivation of the recent aversive memory ensemble during the offline period following learning. However, a strong aversive experience also increases co-reactivation of the aversive and neutral memory ensembles during the offline period. Finally, the expression of fear in the neutral context is associated with reactivation of the shared ensemble between the aversive and neutral memories. Taken together, these results demonstrate that strong aversive experience can drive retrospective memory-linking through the offline co-reactivation of recent memory ensembles with memory ensembles formed days prior, providing a neural mechanism by which memories can be integrated across days.

Eye movements during phasic versus tonic rapid eye movement sleep are biomarkers of dissociable electroencephalogram processes for the consolidation of novel problem-solving skills

The hallmark eye movement (EM) bursts that occur during rapid eye movement (REM) sleep are markers of consolidation for procedural memory involving novel cognitive strategies and problem-solving skills. Examination of the brain activity associated with EMs during REM sleep might elucidate the processes involved in memory consolidation, and may uncover the functional significance of REM sleep and EMs themselves. Participants performed a REM-dependent, novel procedural problem-solving task (i.e. the Tower of Hanoi; ToH) before and after intervals of either overnight sleep (n = 20) or a daytime 8-hour wake period (n = 20). In addition, event-related spectral perturbation of the electroencephalogram (EEG) time-locked to EMs occurring either in bursts (i.e. phasic REM), or in isolation (i.e. tonic REM), were compared to sleep on a non-learning control night. ToH improvement was greater following sleep compared to wakefulness. During sleep, prefrontal theta (~2-8 Hz) and central-parietal-occipital sensorimotor rhythm (SMR) activity (~8-16 Hz) time-locked to EMs, were greater on the ToH night versus control night, and during phasic REM sleep, were both positively correlated with overnight memory improvements. Furthermore, SMR power during tonic REM increased significantly from the control night to ToH night, but was relatively stable from night to night during phasic REM. These results suggest that EMs are markers of learning-related increases in theta and SMR during phasic and tonic REM sleep. Phasic and tonic REM sleep may be functionally distinct in terms of their contribution to procedural memory consolidation.

A shared novelty-seeking basis for creativity and curiosity

Curiosity and creativity are central pillars of human growth and invention. Although they have been studied extensively in isolation, the relationship between them has not yet been established. We propose that both curiosity and creativity emanate from the same mechanism of novelty seeking. We first present a synthesis showing that curiosity and creativity are affected similarly by a number of key cognitive faculties such as memory, cognitive control, attention, and reward. We then review empirical evidence from neuroscience research, indicating that the same brain regions are involved in both curiosity and creativity, focusing on the interplay between three major brain networks: the default mode network, the salience network, and the executive control network. After substantiating the link between curiosity and creativity, we propose a novelty-seeking model (NSM) that underlies them and suggests that the manifestation of the NSM is governed by one's state of mind.

Rule Abstraction Is Facilitated by Auditory Cuing in REM Sleep

Sleep facilitates abstraction, but the exact mechanisms underpinning this are unknown. Here, we aimed to determine whether triggering reactivation in sleep could facilitate this process. We paired abstraction problems with sounds, then replayed these during either slow-wave sleep (SWS) or rapid eye movement (REM) sleep to trigger memory reactivation in 27 human participants (19 female). This revealed performance improvements on abstraction problems that were cued in REM, but not problems cued in SWS. Interestingly, the cue-related improvement was not significant until a follow-up retest 1 week after the manipulation, suggesting that REM may initiate a sequence of plasticity events that requires more time to be implemented. Furthermore, memory-linked trigger sounds evoked distinct neural responses in REM, but not SWS. Overall, our findings suggest that targeted memory reactivation in REM can facilitate visual rule abstraction, although this effect takes time to unfold.SIGNIFICANCE STATEMENT The ability to abstract rules from a corpus of experiences is a building block of human reasoning. Sleep is known to facilitate rule abstraction, but it remains unclear whether we can manipulate this process actively and which stage of sleep is most important. Targeted memory reactivation (TMR) is a technique that uses re-exposure to learning-related sensory cues during sleep to enhance memory consolidation. Here, we show that TMR, when applied during REM sleep, can facilitate the complex recombining of information needed for rule abstraction. Furthermore, we show that this qualitative REM-related benefit emerges over the course of a week after learning, suggesting that memory integration may require a slower form of plasticity.

The memory sources of dreams: serial awakenings across sleep stages and time of night

Memories of waking-life events are incorporated into dreams, but their incorporation is not uniform across a night of sleep. This study aimed to elucidate ways in which such memory sources vary by sleep stage and time of night. Twenty healthy participants (11 F; 24.1 ± 5.7 years) spent a night in the laboratory and were awakened for dream collection approximately 12 times spread across early, middle, and late periods of sleep, while covering all stages of sleep (N1, N2, N3, REM). In the morning, participants identified and dated associated memories of waking-life events for each dream report, when possible. The incorporation of recent memory sources in dreams was more frequent in N1 and REM than in other sleep stages. The incorporation of distant memories from over a week ago, semantic memories not traceable to a single event, and anticipated future events remained stable throughout sleep. In contrast, the relative proportions of recent versus distant memory sources changed across the night, independently of sleep stage, with late-night dreams in all stages having relatively less recent and more remote memory sources than dreams earlier in the night. Qualitatively, dreams tended to repeat similar themes across the night and in different sleep stages. The present findings clarify the temporal course of memory incorporations in dreams, highlighting a specific connection between time of night and the temporal remoteness of memories. We discuss how dream content may, at least in part, reflect the mechanisms of sleep-dependent memory consolidation.

Sleep-A brain-state serving systems memory consolidation

Although long-term memory consolidation is supported by sleep, it is unclear how it differs from that during wakefulness. Our review, focusing on recent advances in the field, identifies the repeated replay of neuronal firing patterns as a basic mechanism triggering consolidation during sleep and wakefulness. During sleep, memory replay occurs during slow-wave sleep (SWS) in hippocampal assemblies together with ripples, thalamic spindles, neocortical slow oscillations, and noradrenergic activity. Here, hippocampal replay likely favors the transformation of hippocampus-dependent episodic memory into schema-like neocortical memory. REM sleep following SWS might balance local synaptic rescaling accompanying memory transformation with a sleep-dependent homeostatic process of global synaptic renormalization. Sleep-dependent memory transformation is intensified during early development despite the immaturity of the hippocampus. Overall, beyond its greater efficacy, sleep consolidation differs from wake consolidation mainly in that it is supported, rather than impaired, by spontaneous hippocampal replay activity possibly gating memory formation in neocortex.

Restructuring insight: An integrative review of insight in problem-solving, meditation, psychotherapy, delusions and psychedelics

Occasionally, a solution or idea arrives as a sudden understanding - an insight. Insight has been considered an "extra" ingredient of creative thinking and problem-solving. Here we propose that insight is central in seemingly distinct areas of research. Drawing on literature from a variety of fields, we show that besides being commonly studied in problem-solving literature, insight is also a core component in psychotherapy and meditation, a key process underlying the emergence of delusions in schizophrenia, and a factor in the therapeutic effects of psychedelics. In each case, we discuss the event of insight and its prerequisites and consequences. We review evidence for the commonalities and differences between the fields and discuss their relevance for capturing the essence of the insight phenomenon. The goal of this integrative review is to bridge the gap between the different views and inspire interdisciplinary research efforts for understanding this central process of human cognition.

How our understanding of memory replay evolves

Memory reactivations and replay, widely reported in the hippocampus and cortex across species, have been implicated in memory consolidation, planning, and spatial and skill learning. Technological advances in electrophysiology, calcium imaging, and human neuroimaging techniques have enabled neuroscientists to measure large-scale neural activity with increasing spatiotemporal resolution and have provided opportunities for developing robust analytic methods to identify memory replay. In this article, we first review a large body of historically important and representative memory replay studies from the animal and human literature. We then discuss our current understanding of memory replay functions in learning, planning, and memory consolidation and further discuss the progress in computational modeling that has contributed to these improvements. Next, we review past and present analytic methods for replay analyses and discuss their limitations and challenges. Finally, looking ahead, we discuss some promising analytic methods for detecting nonstereotypical, behaviorally nondecodable structures from large-scale neural recordings. We argue that seamless integration of multisite recordings, real-time replay decoding, and closed-loop manipulation experiments will be essential for delineating the role of memory replay in a wide range of cognitive and motor functions.

Replay and compositional computation

Replay in the brain has been viewed as rehearsal or, more recently, as sampling from a transition model. Here, we propose a new hypothesis: that replay is able to implement a form of compositional computation where entities are assembled into relationally bound structures to derive qualitatively new knowledge. This idea builds on recent advances in neuroscience, which indicate that the hippocampus flexibly binds objects to generalizable roles and that replay strings these role-bound objects into compound statements. We suggest experiments to test our hypothesis, and we end by noting the implications for AI systems which lack the human ability to radically generalize past experience to solve new problems.

A rubric for human-like agents and NeuroAI

Researchers across cognitive, neuro- and computer sciences increasingly reference 'human-like' artificial intelligence and 'neuroAI'. However, the scope and use of the terms are often inconsistent. Contributed research ranges widely from mimicking behaviour, to testing machine learning methods as neurally plausible hypotheses at the cellular or functional levels, or solving engineering problems. However, it cannot be assumed nor expected that progress on one of these three goals will automatically translate to progress in others. Here, a simple rubric is proposed to clarify the scope of individual contributions, grounded in their commitments to human-like behaviour, neural plausibility or benchmark/engineering/computer science goals. This is clarified using examples of weak and strong neuroAI and human-like agents, and discussing the generative, corroborate and corrective ways in which the three dimensions interact with one another. The author maintains that future progress in artificial intelligence will need strong interactions across the disciplines, with iterative feedback loops and meticulous validity tests-leading to both known and yet-unknown advances that may span decades to come. This article is part of a discussion meeting issue 'New approaches to 3D vision'.

Sleep-mediated regulation of reward circuits: implications in substance use disorders

Our modern society suffers from both pervasive sleep loss and substance abuse-what may be the indications for sleep on substance use disorders (SUDs), and could sleep contribute to the individual variations in SUDs? Decades of research in sleep as well as in motivated behaviors have laid the foundation for us to begin to answer these questions. This review is intended to critically summarize the circuit, cellular, and molecular mechanisms by which sleep influences reward function, and to reveal critical challenges for future studies. The review also suggests that improving sleep quality may serve as complementary therapeutics for treating SUDs, and that formulating sleep metrics may be useful for predicting individual susceptibility to SUDs and other reward-associated psychiatric diseases.

Memory reactivations during sleep

Neuronal activities that occur during awake periods are often reactivated again during sleep, to consolidate recently encoded memories, a process known as consolidation. In recent years, advanced tools, specially optical techniques and in-vivo live Ca²⁺ imaging, have revealed a deeper understanding to the offline periods' neuronal activities and their correspondence to later awake behavioral outputs. Recently, there is a growing consensus that sleep is more of an active process. Sleep has been associated with various functions, memory updating, future imaginations of possible familiar scenarios, decision making and planning by replaying past memories. Also, boosting insightful thoughts, creative thinking and problem solving by forming new associations and connections that were not present in awake states. Sleep activities have been directly associated with many "EUREKA" or "AHA" moments. Here, we describe recent views on memory reactivations during sleep and their implications on learning and memory.

Sleep-like unsupervised replay reduces catastrophic forgetting in artificial neural networks

Artificial neural networks are known to suffer from catastrophic forgetting: when learning multiple tasks sequentially, they perform well on the most recent task at the expense of previously learned tasks. In the brain, sleep is known to play an important role in incremental learning by replaying recent and old conflicting memory traces. Here we tested the hypothesis that implementing a sleep-like phase in artificial neural networks can protect old memories during new training and alleviate catastrophic forgetting. Sleep was implemented as off-line training with local unsupervised Hebbian plasticity rules and noisy input. In an incremental learning framework, sleep was able to recover old tasks that were otherwise forgotten. Previously learned memories were replayed spontaneously during sleep, forming unique representations for each class of inputs. Representational sparseness and neuronal activity corresponding to the old tasks increased while new task related activity decreased. The study suggests that spontaneous replay simulating sleep-like dynamics can alleviate catastrophic forgetting in artificial neural networks.

No evidence for a preferential role of sleep in episodic memory abstraction

Substantial evidence suggests that sleep has a role in declarative memory consolidation. An influential notion holds that such sleep-related memory consolidation is associated with a process of abstraction. The neural underpinnings of this putative process are thought to involve a hippocampo-neocortical dialogue. Specifically, the idea is that, during sleep, the statistical contingencies across episodes are re-coded to a less hippocampus-dependent format, while at the same time losing configural information. Two previous studies from our lab, however, failed to show a preferential role of sleep in either episodic memory decontextualisation or the formation of abstract knowledge across episodic exemplars. Rather these processes occurred over sleep and wake time alike. Here, we present two experiments that replicate and extend these previous studies and exclude some alternative interpretations. The combined data show that sleep has no preferential function in this respect. Rather, hippocampus-dependent memories are generalised to an equal extent across both wake and sleep time. The one point on which sleep outperforms wake is actually the preservation of episodic detail of memories stored prior to sleep.

Sleep prevents catastrophic forgetting in spiking neural networks by forming a joint synaptic weight representation

Artificial neural networks overwrite previously learned tasks when trained sequentially, a phenomenon known as catastrophic forgetting. In contrast, the brain learns continuously, and typically learns best when new training is interleaved with periods of sleep for memory consolidation. Here we used spiking network to study mechanisms behind catastrophic forgetting and the role of sleep in preventing it. The network could be trained to learn a complex foraging task but exhibited catastrophic forgetting when trained sequentially on different tasks. In synaptic weight space, new task training moved the synaptic weight configuration away from the manifold representing old task leading to forgetting. Interleaving new task training with periods of off-line reactivation, mimicking biological sleep, mitigated catastrophic forgetting by constraining the network synaptic weight state to the previously learned manifold, while allowing the weight configuration to converge towards the intersection of the manifolds representing old and new tasks. The study reveals a possible strategy of synaptic weights dynamics the brain applies during sleep to prevent forgetting and optimize learning.

Sleep medicine: Practice, challenges and new frontiers

Sleep medicine is an ambitious cross-disciplinary challenge, requiring the mutual integration between complementary specialists in order to build a solid framework. Although knowledge in the sleep field is growing impressively thanks to technical and brain imaging support and through detailed clinic-epidemiologic observations, several topics are still dominated by outdated paradigms. In this review we explore the main novelties and gaps in the field of sleep medicine, assess the commonest sleep disturbances, provide advices for routine clinical practice and offer alternative insights and perspectives on the future of sleep research.

The time course of creativity: multivariate classification of default and executive network contributions to creative cognition over time

Research indicates that creative cognition depends on both associative and controlled processes, corresponding to the brain's default mode network (DMN) and executive control network (ECN) networks. However, outstanding questions include how the DMN and ECN operate over time during creative task performance, and whether creative cognition involves distinct generative and evaluative stages. To address these questions, we used multivariate pattern analysis (MVPA) to assess how the DMN and ECN contribute to creative cognition over three successive time phases during the production of a single creative idea. Training classifiers to predict trial condition (creative vs non-creative), we used classification accuracy as a measure of the extent of creative activity in each brain network and time phase. Across both networks, classification accuracy was highest in early phases, decreased in mid phases, and increased again in later phases, following a U-shaped curve. Notably, classification accuracy was significantly greater in the ECN than the DMN during early phases, while differences between networks at later time phases were non-significant. We also computed correlations between classification accuracy and human-rated creative performance, to assess how relevant the creative activity in each network was to the creative quality of ideas. In line with expectations, classification accuracy in the DMN was most related to creative quality in early phases, decreasing in later phases, while classification accuracy in the ECN was least related to creative quality in early phases, increasing in later phases. Given the theorized roles of the DMN in generation and the ECN in evaluation, we interpret these results as tentative evidence for the existence of separate generative and evaluative stages in creative cognition that depend on distinct neural substrates.

Flexibility of memory for future-oriented cognition

Memories of daily experiences contain incidental details unique to each experience as well as common latent patterns shared with others. Neural representations focusing on the latter aspect can be reinstated by similar new experiences even though their perceptual features do not match the original experiences perfectly. Such flexible memory use allows for faster learning and better decision-making in novel situations. Here, I review evidence from rodent and primate electrophysiological studies to discuss how memory flexibility is implemented in the spiking activity of neuronal ensembles. These findings uncovered innate and learned coding properties and their potential refinement during sleep that support flexible integration and application of memories for better future adaptation.

Selective engram coreactivation in idling brain inspires implicit learning

Passive priming of prior knowledge to assimilate ongoing experiences underlies advanced cognitive processing. However, the necessary neural dynamics of memory assimilation remains elusive. Uninstructed brain could also show boosted creativity, particularly after idling states, yet it remains unclear whether the idling brain can spontaneously spark relevant knowledge assimilations. We established a paradigm that links/separates context-dependent memories according to geometrical similarities. Mice exploring one of four contexts 1 d before undergoing contextual fear conditioning in a square context showed a gradual fear transfer to preexposed geometrically relevant contexts the next day, but not after 15 min. Anterior cingulate cortex neurons representing relevant, rather than distinct, memories were significantly coreactivated during postconditioning sleep only, before their selective integration the next day during testing. Disrupting sleep coreactivations prevented assimilation while preserving recent memory consolidation. Thus, assimilating pertinent memories during sleep through coreactivation of their respective engrams represents the neural underpinnings of sleep-triggered implicit cortical learning.

Learning cortical representations through perturbed and adversarial dreaming

Humans and other animals learn to extract general concepts from sensory experience without extensive teaching. This ability is thought to be facilitated by offline states like sleep where previous experiences are systemically replayed. However, the characteristic creative nature of dreams suggests that learning semantic representations may go beyond merely replaying previous experiences. We support this hypothesis by implementing a cortical architecture inspired by generative adversarial networks (GANs). Learning in our model is organized across three different global brain states mimicking wakefulness, non-rapid eye movement (NREM), and REM sleep, optimizing different, but complementary, objective functions. We train the model on standard datasets of natural images and evaluate the quality of the learned representations. Our results suggest that generating new, virtual sensory inputs via adversarial dreaming during REM sleep is essential for extracting semantic concepts, while replaying episodic memories via perturbed dreaming during NREM sleep improves the robustness of latent representations. The model provides a new computational perspective on sleep states, memory replay, and dreams, and suggests a cortical implementation of GANs.

Constructive episodic simulation in dreams

Memories of the past help us adaptively respond to similar situations in the future. Originally described by Schacter & Addis in 2007, the "constructive episodic simulation" hypothesis proposes that waking thought combines fragments of various past episodes into imagined simulations of events that may occur in the future. This same framework may be useful for understanding the function of dreaming. N = 48 college students were asked to identify waking life sources for a total of N = 469 dreams. Participants frequently traced dreams to at least one past or future episodic source (53.5% and 25.7% of dreams, respectively). Individual dreams were very often traced to multiple waking sources (43.9% of all dreams with content), with fragments of past memory incorporated into scenarios that anticipated future events. Waking-life dream sources are described in terms of their phenomenology and distribution across time and sleep stage, providing new evidence that dreams not only reflect the past, but also utilize memory in simulating potential futures.

Cueing emotional memories during slow wave sleep modulates next-day activity in the orbitofrontal cortex and the amygdala

Emotional memories are preferentially consolidated during sleep, through the process of memory reactivation. Targeted memory reactivation (TMR) has been shown to boost memory consolidation during sleep, but its neural correlates remain unclear, particularly for emotional memories. Here, we aimed to examine how TMR of emotional material during slow wave sleep (SWS) impacts upon neural processing during a subsequent arousal rating task. Participants were trained on a spatial memory task including negative and neutral pictures paired with semantically matching sounds. The picture-sound pairs were rated for emotional arousal before and after the spatial memory task. Then, half of the sounds from each emotional category (negative and neutral) were cued during SWS. The next day, participants were retested on both the arousal rating and the spatial memory task inside an MRI scanner, followed by another retest session a week later. Memory consolidation and arousal processing did not differ between cued and non-cued items of either emotional category. We found increased responses to emotional stimuli in the amygdala and orbitofrontal cortex (OFC), and a cueing versus emotion interaction in the OFC, whereby cueing neutral stimuli led to an increase in OFC activity, while cueing negative stimuli led to decreased OFC activation. Interestingly, the effect of cueing on amygdala activation was modulated by time spent in REM sleep. We conclude that SWS TMR impacts OFC activity, while REM sleep plays a role in mediating the effect of such cueing on amygdala.

Decoding cognition from spontaneous neural activity

In human neuroscience, studies of cognition are rarely grounded in non-task-evoked, 'spontaneous' neural activity. Indeed, studies of spontaneous activity tend to focus predominantly on intrinsic neural patterns (for example, resting-state networks). Taking a 'representation-rich' approach bridges the gap between cognition and resting-state communities: this approach relies on decoding task-related representations from spontaneous neural activity, allowing quantification of the representational content and rich dynamics of such activity. For example, if we know the neural representation of an episodic memory, we can decode its subsequent replay during rest. We argue that such an approach advances cognitive research beyond a focus on immediate task demand and provides insight into the functional relevance of the intrinsic neural pattern (for example, the default mode network). This in turn enables a greater integration between human and animal neuroscience, facilitating experimental testing of theoretical accounts of intrinsic activity, and opening new avenues of research in psychiatry.

The Algorithms of Mindfulness

This paper analyzes notions and models of optimized cognition emerging at the intersections of psychology, neuroscience, and computing. What I somewhat polemically call the algorithms of mindfulness describes an ideal that determines algorithmic techniques of the self, geared at emotional resilience and creative cognition. A reframing of rest, exemplified in corporate mindfulness programs and the design of experimental artificial neural networks sits at the heart of this process. Mindfulness trainings provide cues as to this reframing, for they detail each in their own way how intermittent periods of rest are to be recruited to augment our cognitive capacities and combat the effects of stress and information overload. They typically rely on and co-opt neuroscience knowledge about what the brains of North Americans and Europeans do when we rest. Current designs for artificial neural networks draw on the same neuroscience research and incorporate coarse principles of cognition in brains to make machine learning systems more resilient and creative. These algorithmic techniques are primarily conceived to prevent psychopathologies where stress is considered the driving force of success. Against this backdrop, I ask how machine learning systems could be employed to unsettle the concept of pathological cognition itself.

Age-related changes in sleep-dependent novel word consolidation

Learning new words is a vital, life-long process that benefits from memory consolidation during sleep in young adults. In aging populations, promoting vocabulary learning is an attractive strategy to improve quality of life and workplace longevity by improving the integration of new technology and the associated terminology. Decreases in sleep quality and quantity with aging may diminish sleep-dependent memory consolidation for word learning. Alternatively, given that older adults outperform young adults on vocabulary-based tasks, and that strength of memory encoding (how well older adults learn) predicts sleep-dependent memory consolidation, word learning may uniquely benefit from sleep in older adults. We assessed age-related changes in memory for novel English word-definition pairs recalled following intervals spent asleep and awake. While sleep was shown to fully preserve memory for word/definition pairs in young adults (N = 53, asleep = 32, awake = 21, 18-30 years), older adults (N = 45, asleep = 21, awake = 24, 58-75 years) forgot items equally over wake and sleep intervals but preserved the accuracy of typed responses better following sleep. However, this was modulated by the strength of encoded memories: the proportion of high strength items consolidated increased for older adults following sleep compared to wake. Older adults consolidated a lower proportion of medium strength items across both sleep and wake intervals compared to young adults. Our results contribute to growing evidence that encoding strength is crucially important to understand the expression of sleep-dependent benefits in older adults and assert the need for sufficiently sensitive performance metrics in aging research.

Sleep onset is a creative sweet spot

The ability to think creatively is paramount to facing new challenges, but how creativity arises remains mysterious. Here, we show that the brain activity common to the twilight zone between sleep and wakefulness (nonrapid eye movement sleep stage 1 or N1) ignites creative sparks. Participants (N = 103) were exposed to mathematical problems without knowing that a hidden rule allowed solving them almost instantly. We found that spending at least 15 s in N1 during a resting period tripled the chance to discover the hidden rule (83% versus 30% when participants remained awake), and this effect vanished if subjects reached deeper sleep. Our findings suggest that there is a creative sweet spot within the sleep-onset period, and hitting it requires individuals balancing falling asleep easily against falling asleep too deeply.

Sleep and incubation: Using problem reactivation during sleep to study forgetting fixation and unconscious processing during sleep incubation

When people are stuck on a problem, they sometimes benefit from an incubation period -a break from working on the problem. Anecdotes and empirical evidence suggest that sleeping during incubation is useful, but the mechanisms remain poorly understood. We examined how targeted memory reactivation during sleep, which boosts next-day solving, relates to forgetting fixation, a well-supported explanation of awake incubation. In evening sessions, participants attempted puzzles, while a unique sound cue played during each puzzle. Half the time, puzzles included fixating information reinforcing an incorrect representation. Later, during deep sleep, sounds associated with half of participants' previously unsolved puzzles were presented. The sounds should strengthen puzzle memories and reduce forgetting of the fixating information. In morning solving, overnight cueing reliably interacted with fixating information: participants solved numerically more cued than uncued puzzles, but only when puzzles included fixating information. These results suggest that additional processing occurred beyond simple fixation forgetting.