Memory reactivation during sleep does not act holistically on object memory

Abstract

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.

Figure 3-1.. Table of average sleep time and cue numbers.

Note. Stages were determined in 30 s epochs for sleep scoring. All cues marked in epochs of N1 or wake were followed up and confirmed to occur during N2 or N3 and just prior to a change in stage to N1 or wake. Analyses on effects of sleep stage were performed with these cueing edge cases excluded.

Figure 3-2.. Sounds were consistently delivered in SO up-states.

Grand-average ERP during presentation of sound cues, averaged across all channels and all participants. Sounds were played at time = 0. Shaded area represents the standard error of the ERP across participants.

Figure 4-1.. Table of model details.

Note. Variance in number of participants can be due to restrictions on accuracy (i.e., shared >= unique accuracy) and restrictions on the number of times items were cued; variance in the number of data points can be due to whether the items had both shared and unique features (novel items and prototypes only have shared features); variance in item numbers can be due to how many categories were included in the analysis as well as restrictions on accuracy.

Figure 1.. Experimental Design.

A, Study timeline and stimuli. Participants completed a novel object category learning task and took a nap where Targeted Memory Reactivation (TMR) was performed. They studied three categories of novel “satellite” objects (alpha, beta, gamma), where a satellite could have parts that were unique to itself and parts shared with other members of its category. Each satellite had its own unique name (ex: nivex). B, Learning phase. First, participants were exposed to the satellites one-by-one: participants heard a satellite’s name out loud and then saw it on screen. Next, participants completed blocks of trials where they first heard a satellite’s name and then were shown a satellite with one feature missing and instructed to select the missing feature. C, Test phase. Participants heard a satellite’s name and then were shown a satellite with one or two features missing. They selected a feature and then rated their confidence in their decision.

Figure 2.. Pre- and post-nap behavior.

A, Overall accuracy across learning blocks. B, Pre-nap and post-nap test performance. Mean accuracy on unique (orange) and shared (purple) feature memory trials. The dotted line indicates chance. C, Assessment of tradeoff in unique and shared feature accuracy. A line was fit, for each participant, predicting shared feature accuracy from unique feature accuracy of the corresponding object, for the pre-nap (left) and post-nap (middle) tests. Mean slope across participants is represented with a thick dotted line. A negative slope indicates a tradeoff in shared and unique feature accuracy. Right: Barplot of slopes in the pre-nap (blue) and post-nap (red) tests. Bars represent mean slopes across participants. Error bars represent +/− 1 SEM. Dots and lines correspond to individual participants. *p < .05, **p < .01, ***p < .001

Figure 3.. Real-time Targeted Memory Reactivation protocol.

A, Overview of Targeted Memory Reactivation (TMR). (left) TMR cues — delivered aloud as individual satellite names — began after a participant entered and remained in N2 sleep for at least three minutes. One category was cued in interleaved order, one in blocked order, and one was left uncued. The presentation of the two cued categories was intermixed across NREM sleep. See Extended Data Figure 3-1 for more details. B, Real-time TMR administration. We developed a novel TMR protocol in which cues were played in the up-states of slow oscillations (up-state = signal goes above threshold of +35 μV; Ngo and Staresina, 2022) and at least 2.5 seconds after a detected spindle (Antony et al., 2018b). C, left: Time-frequency representation of the difference between the neural response when a TMR sound cue was played versus not (sham), with the ERP response to sound cues overlaid (averaged across all channels and all participants). Sounds were played at time = 0. Right: Cluster-based permutation testing identified two significant clusters in the time-frequency response to TMR sound cues. Un-shaded areas represent clusters identified via performing t-tests across participants (α = .01). Clusters that survived subsequent permutation testing are highlighted in white. Topoplots show the spatial representation of the identified clusters. See Extended Data Figure 3-2 for more details.

Figure 4.. TMR cueing improved unique feature memory and impaired shared feature memory.

A, Impacts of TMR. A linear mixed effects model was used to analyze change in unique and shared feature accuracy across the nap as a function of cueing. Model estimates for accuracy change for shared (purple) and unique (orange) features are plotted. B, Replication of analysis from A using only the subset of items whose shared feature accuracy was greater than or equal to their unique feature accuracy. Left: Mean shared and unique feature accuracy from the pre-nap test. Dots represent participants, error bars represent +/− 1 SEM, the dotted grey line represents chance. Right: Model estimates of unique and shared feature accuracy change for uncued and cued items from this subset. C, Impact of cueing on uncued items from cued categories. Plotted are the model estimates for unique and shared feature accuracy change for items in the designated “uncued” category (Uncued Uncued), items uncued in a designated “cued” category (Uncued Cued), and for items who were cued (Cued Cued). *p < .05, **p < .01, ***p < .001. See Extended Data Figure 4-1 for additional model details.

Figure 5.. Blocked cue presentation led to greater memory change than interleaved.

A, Model estimates of accuracy change for items that were cued in interleaved or blocked order. B, Model estimates of accuracy change for fully cued blocked items, as a function of their sequence in the cueing order. Sequence position 4 corresponds to the last item cued before the participant woke up from their nap; 0 corresponds to items in the uncued category. Plotted are model estimates for accuracy change for unique and shared features, with a linear fit to sequence position (shaded area = 95% confidence intervals).

Figure 6.. Cueing as a driver of differentiation.

A, Schematic of representational overlap for four exemplars from the same category. B, If cued objects differentiate, the unique aspects of their representations are enhanced and the shared aspects are diminished. These effects generalize to an uncued item, because the differentiation of the other exemplars reduces overlap for all items, and all unique elements are subject to less interference.