skip to main content

Title: Targeted memory reactivation during sleep boosts intentional forgetting of spatial locations

Although we experience thousands of distinct events on a daily basis, relatively few are committed to memory. The human capacity to intentionally control which events will be remembered has been demonstrated using learning procedures with instructions to purposely avoid committing specific items to memory. In this study, we used a variant of the item-based directed-forgetting procedure and instructed participants to memorize the location of some images but not others on a grid. These instructions were conveyed using a set of auditory cues. Then, during an afternoon nap, we unobtrusively presented a cue that was used to instruct participant to avoid committing the locations of some images to memory. After sleep, memory was worse for to-be-forgotten image locations associated with the presented sound relative to those associated with a sound that was not presented during sleep. We conclude that memory processing during sleep can serve not only to secure memory storage but also to weaken it. Given that intentional suppression may be used to weaken unpleasant memories, such sleep-based strategies may help accelerate treatments for memory-related disorders such as post-traumatic stress disorder.

; ; ; ;
Award ID(s):
1921678 1461088 1829414
Publication Date:
Journal Name:
Scientific Reports
Nature Publishing Group
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Sleep's role in memory consolidation is widely acknowledged, but its role in weakening memories is still debated. Memory weakening is evolutionary beneficial and makes an integral contribution to cognition. We sought evidence on whether sleep-based memory reactivation can facilitate memory suppression. Participants learned pairs of associable words (e.g., DIET–CREAM) and were then exposed to hint words (e.g., DIET) and instructed to either recall (“think”) or suppress (“no-think”) the corresponding target words (e.g., CREAM). As expected, suppression impaired retention when tested immediately after a 90-min nap. To test if reactivation could selectively enhance memory suppression during sleep, we unobtrusively presented one of two sounds conveying suppression instructions during sleep, followed by hint words. Results showed that targeted memory reactivation did not enhance suppression-induced forgetting. Although not predicted, post-hoc analyses revealed that sleep cues strengthened memory, but only for suppressed pairs that were weakly encoded before sleep. The results leave open the question of whether memory suppression can be augmented during sleep, but suggest strategies for future studies manipulating memory suppression during sleep. Additionally, our findings support the notion that sleep reactivation is particularly beneficial for weakly encoded information, which may be prioritized for consolidation.

  2. Abstract

    The synaptic homeostasis theory of sleep proposes that low neurotransmitter activity in sleep optimizes memory consolidation. We tested this theory by asking whether increasing acetylcholine levels during early sleep would weaken motor memory consolidation. We trained separate groups of adult mice on the rotarod walking task and the single pellet reaching task, and after training, administered physostigmine, an acetylcholinesterase inhibitor, to increase cholinergic tone in subsequent sleep. Post-sleep testing showed that physostigmine impaired motor skill acquisition of both tasks. Home-cage video monitoring and electrophysiology revealed that physostigmine disrupted sleep structure, delayed non-rapid-eye-movement sleep onset, and reduced slow-wave power in the hippocampus and cortex. Additional experiments showed that: (1) the impaired performance associated with physostigmine was not due to its effects on sleep structure, as 1 h of sleep deprivation after training did not impair rotarod performance, (2) a reduction in cholinergic tone by inactivation of cholinergic neurons during early sleep did not affect rotarod performance, and (3) stimulating or blocking muscarinic and nicotinic acetylcholine receptors did not impair rotarod performance. Taken together, the experiments suggest that the increased slow wave activity and inactivation of both muscarinic and nicotinic receptors during early sleep due to reduced acetylcholine contribute to motormore »memory consolidation.

    « less
  3. Abstract

    Face memory, including the ability to recall a person’s name, is of major importance in social contexts. Like many other memory functions, it may rely on sleep. We investigated whether targeted memory reactivation during sleep could improve associative and perceptual aspects of face memory. Participants studied 80 face-name pairs, and then a subset of spoken names with associated background music was presented unobtrusively during a daytime nap. This manipulation preferentially improved name recall and face recognition for those reactivated face-name pairs, as modulated by two factors related to sleep quality; memory benefits were positively correlated with the duration of stage N3 sleep (slow-wave sleep) and negatively correlated with measures of sleep disruption. We conclude that (a) reactivation of specific face-name memories during sleep can strengthen these associations and the constituent memories, and that (b) the effectiveness of this reactivation depends on uninterrupted N3 sleep.

  4. Many people have claimed that sleep has helped them solve a difficult problem, but empirical support for this assertion remains tentative. The current experiment tested whether manipulating information processing during sleep impacts problem incubation and solving. In memory studies, delivering learning-associated sound cues during sleep can reactivate memories. We therefore predicted that reactivating previously unsolved problems could help people solve them. In the evening, we presented 57 participants with puzzles, each arbitrarily associated with a different sound. While participants slept overnight, half of the sounds associated with the puzzles they had not solved were surreptitiously presented. The next morning, participants solved 31.7% of cued puzzles, compared with 20.5% of uncued puzzles (a 55% improvement). Moreover, cued-puzzle solving correlated with cued-puzzle memory. Overall, these results demonstrate that cuing puzzle information during sleep can facilitate solving, thus supporting sleep’s role in problem incubation and establishing a new technique to advance understanding of problem solving and sleep cognition.
  5. Abstract

    Optical coherence tomography (OCT) is a widely used non-invasive biomedical imaging modality that can rapidly provide volumetric images of samples. Here, we present a deep learning-based image reconstruction framework that can generate swept-source OCT (SS-OCT) images using undersampled spectral data, without any spatial aliasing artifacts. This neural network-based image reconstruction does not require any hardware changes to the optical setup and can be easily integrated with existing swept-source or spectral-domain OCT systems to reduce the amount of raw spectral data to be acquired. To show the efficacy of this framework, we trained and blindly tested a deep neural network using mouse embryo samples imaged by an SS-OCT system. Using 2-fold undersampled spectral data (i.e., 640 spectral points per A-line), the trained neural network can blindly reconstruct 512 A-lines in 0.59 ms using multiple graphics-processing units (GPUs), removing spatial aliasing artifacts due to spectral undersampling, also presenting a very good match to the images of the same samples, reconstructed using the full spectral OCT data (i.e., 1280 spectral points per A-line). We also successfully demonstrate that this framework can be further extended to process 3× undersampled spectral data per A-line, with some performance degradation in the reconstructed image quality compared tomore »2× spectral undersampling. Furthermore, an A-line-optimized undersampling method is presented by jointly optimizing the spectral sampling locations and the corresponding image reconstruction network, which improved the overall imaging performance using less spectral data points per A-line compared to 2× or 3× spectral undersampling results. This deep learning-enabled image reconstruction approach can be broadly used in various forms of spectral-domain OCT systems, helping to increase their imaging speed without sacrificing image resolution and signal-to-noise ratio.

    « less