Susanne Diekelmann

Human self-beliefs hinge on social feedback, but their formation and revision are not solely based on new information. Biases during learning, such as confirming initial expectations, can lead to inaccurate beliefs. This study uses computational modeling to explore how initial expectations about one’s own and others’ abilities and confidence in these beliefs affect processes of belief formation and belief revision in novel behavioral domains. In the first session, participants formed performance beliefs through trial-by-trial feedback. In the second session, feedback contingencies were reversed to promote a revision of beliefs. Results showed that people form and revise beliefs in a confirmatory manner, with lower initial expectations being linked to more negatively biased belief formation and revision, while growing confidence strengthened these beliefs over time. Once formed, these beliefs proved resistant to change even when faced with contradictory feedback. The findings suggest that newly formed beliefs become entrenched and resistant to new, contradictory information in a short period of time. Understanding how self-beliefs are formed, the role that confidence plays in this process, and why established beliefs are difficult to revise can inform the development of interventions aimed at promoting more adaptive learning in educational, clinical, and social contexts.

Self-beliefs hinge on social feedback, but their formation and revision are not solely based on new information. Biases, such as confirming initial expectations, can lead to inaccurate self-beliefs. This study uses computational modeling to explore how initial expectations and confidence affect self-belief formation and revision in novel behavioral domains. In the first session, participants developed performance self-beliefs through trial-by-trial feedback. In the second session, feedback contingencies were reversed, requiring belief revision for accurate self-beliefs. Results showed a confirmation bias in belief updating, with initial expectations being linked to biased learning during both formation and revision. Higher confidence was associated with reduced belief revision and on average, self-beliefs persisted despite the conflicting evidence. This study extends the literature on confirmation bias to learning in uncharged, novel behavioral domains. Further, it demonstrates the importance of initial expectations and associated confidence for biased self-belief formation and subsequent learning.

Sleep facilitates declarative memory consolidation, which is assumed to rely on the reactivation of newly encoded memories orchestrated by the temporal interplay of slow oscillations (SO), fast spindles and ripples. SO as well as the number of spindles coupled to SO are more frequent during slow wave sleep (SWS) compared to lighter sleep stage 2 (S2). But, it is unclear whether memory reactivation is more effective during SWS than during S2. To test this question, we applied Targeted Memory Reactivation (TMR) in a declarative memory design by presenting learning-associated sound cues during SWS vs. S2 in a counterbalanced within-subject design. Contrary to our hypothesis, memory performance was not significantly better when cues were presented during SWS. Event-related potential (ERP) amplitudes were significantly higher for cues presented during SWS than S2, and the density of SO and SO-spindle complexes was generally higher during SWS than during S2. Whereas SO density increased during and after the TMR period, SO-spindle complexes decreased. None of the parameters were associated with memory performance. These findings suggest that the efficacy of TMR does not depend on whether it is administered during SWS or S2, despite differential processing of memory cues in these sleep stages.

Targeted Memory Reactivation (TMR) is a noninvasive tool to manipulate memory consolidation during sleep. TMR builds on the brain’s natural processes of memory reactivation during sleep and aims to facilitate or bias these processes in a certain direction. The basis of this technique is the association of learning content with sensory cues, such as odors or sounds, that are presented during subsequent sleep to promote memory reactivation. Research on TMR has drastically increased over the last decade with rapid developments. The aim of the present review is to highlight the most recent advances of this research. We focus on effects of TMR on the strengthening of memories in the declarative, procedural and emotional memory domain as well as on ways in which TMR can be used to promote forgetting. We then discuss advanced technical approaches to determine the optimal timing of TMR within the ongoing oscillatory activity of the sleeping brain as well as the specificity of TMR for certain memory contents. We further highlight the specific effects of TMR during REM sleep and in influencing dream content. Finally, we discuss recent evidence for potential applications of TMR for mental health, educational purposes and in the home setting. In conclusion, the last years of research have provided substantial advances in TMR that can guide future endeavors in research and application.

Sleep facilitates memory consolidation, which is assumed to rely on the reactivation of newly encoded memories orchestrated by the temporal interplay of slow oscillations (SO), fast spindles and ripples. SO as well as the number of spindles coupled to SO are more frequent during slow wave sleep (SWS) compared to lighter sleep stage 2 (S2). But, it is unclear whether memory reactivation is more effective during SWS than during S2. To test this question, we applied Targeted Memory Reactivation (TMR) by presenting learning-associated sound cues during SWS vs. S2 in a counterbalanced within-subject design. Contrary to our hypothesis, memory performance was not significantly better when cues were presented during SWS. Event-related potential (ERP) amplitudes were significantly higher for cues presented during SWS than S2, and the density of SO and SO-spindle complexes was generally higher during SWS than during S2. Whereas SO density increased during and after the TMR period, SO-spindle complexes decreased. None of the parameters were associated with memory performance. These findings suggest that the efficacy of TMR does not depend on whether it is administered during SWS or S2, despite differential processing of memory cues in these sleep stages.

Understanding the complex relationship between sleep and memory is a major challenge in neuroscience. Many studies on memory consolidation in humans suggest that sleep triggers offline memory processes, resulting in less forgetting of declarative memory and performance stabilization in non-declarative memory. However, issues related to non-optimal experimental designs, task characteristics and measurements, and inappropriate data analysis practices can significantly affect the interpretation of the effect of sleep on memory. In this article, we discuss these issues and suggest constructive solutions to address them. We believe that implementing these solutions in future sleep and memory research will significantly advance this field by improving the understanding of the specific role of sleep in memory consolidation.

Introduction: Sleep is assumed to facilitate the consolidation of new memories in an active process of covert reactivation of the underlying memory representations. Recent evidence suggests that this process is selective by favoring memories that are of future relevance, and can be externally triggered by learning-associated sensory cues presented during sleep [i.e., targeted memory reactivation (TMR)]. In the present study, we (1) set out to confirm the preferential sleep effect for relevant information, and then asked whether (2) simultaneous TMR of relevant and irrelevant information facilitates the advantage for relevant information, and (3) whether the preferential benefit of sleep and TMR for relevant information persists over time.Methods: To test these questions, participants explicitly learned two sets of picture-location associations, of which one set was instructed (after encoding) to be relevant and the other to be irrelevant for later testing. In Experiment 1, memory was tested after ~12 h of night sleep (n = 28) or daytime wakefulness (n = 28) as well as again after ~1 week.Results: Results showed overall better memory retention after sleep compared to wakefulness after 12 h as well as after 1 week. The relevant memories were overall retained better than the irrelevant memories. Interestingly, a trend toward a stronger sleep benefit for the relevant memories emerged after 1 week, although this effect failed to reach significance. In Experiment 2, learning of the relevant and irrelevant picture-location associations took place in the presence of an odor. During subsequent sleep, in the first phase of slow wave sleep (SWS), participants were either presented with the odor again (n = 23) or received an odorless vehicle (n = 20). Memory retention was assessed after the first SWS period (following awakening) as well as after ~one week. As in Experiment 1, relevant memories were overall retained better than irrelevant memories. However, TMR did not differentially affect the retention of relevant and irrelevant memories.Discussion: These findings provide tentative evidence that the selective benefit of sleep for relevant memories evolves over time but is not further facilitated by TMR.

Summary: The beneficial effects of sleep for memory consolidation are assumed to rely on the reactivation of memories in conjunction with the coordinated interplay of sleep rhythms like slow oscillations and spindles. Specifically, slow oscillations are assumed to provide the temporal frame for spindles to occur in the slow oscillations up‐states, enabling a redistribution of reactivated information within hippocampal–neocortical networks for long‐term storage. Memory reactivation can also be triggered externally by presenting learning‐associated cues (like odours or sounds) during sleep, but it is presently unclear whether there is an optimal time‐window for the presentation of such cues in relation to the phase of the slow oscillations. In the present within‐subject comparison, participants (n = 16) learnt word‐pairs visually presented with auditory cues of the first syllable. These syllables were subsequently used for real‐time cueing either in the up‐ or down‐state of endogenous slow oscillations. Contrary to our hypothesis, we found differences in memory performance neither between up‐ and down‐state cueing, nor between word‐pairs that were cued versus uncued. In the up‐state cueing condition, higher amounts of rapid eye movement sleep were associated with better memory for cued contents, whereas higher amounts of slow‐wave sleep were associated with better memory for uncued contents. Evoked response analyses revealed signs of cue processing in both conditions. Interestingly, both up‐ and down‐state cueing evoked a similar spindle response with the induced slow oscillations up‐state at ~1000 ms post‐cue. We speculate that our cueing procedure triggered generalised reactivation processes that facilitated the consolidation of both cued and uncued memories irrespective of the slow oscillation phase.

According to the active systems consolidation theory, memories undergo reactivation during sleep that can give rise to qualitative changes of the representations. These changes may generate new knowledge such as gaining insight into solutions for problem solving. targeted memory reactivation (TMR) uses learning-associated cues, such as sounds or odors, which have been shown to improve memory consolidation when re-applied during sleep. Here we tested whether TMR during slow wave sleep (SWS) and/or rapid eye movement (REM) sleep increases problem solving. Young healthy volunteers participated in one of two experiments. Experiment 1 tested the effect of natural sleep on problem solving. Subjects were trained in a video game-based problem solving task until being presented with a non-solved challenge. Followed by a ~10-h incubation interval filled with nocturnal sleep or daytime wakefulness, subjects were tested on the problem solving challenge again. Experiment 2 tested the effect of TMR on problem solving, with subjects receiving auditory TMR either during SWS (SWSstim), REM sleep (REMstim), or wakefulness (Wakestim). In Experiment 1, sleep improved problem solving, with 62% of subjects from the Sleep group solving the problem compared to 24% of the Wake group. Subjects with higher amounts of SWS in the Sleep group had a higher chance to solve the problem. In Experiment 2, TMR did not change the sleep effect on problem solving: 56 and 58% of subjects from the SWSstim and REMstim groups solved the problem compared to 57% from the Wakestim group. These findings indicate that sleep, and particularly SWS, facilitates problem solving, whereas this effect is not further increased by TMR.