Advantage Conferred By Overnight Sleep On Schemarelated Memory May Last Only A Day Part 2

Study protocol

Participants were randomly allocated to either the sleep or active wake groups. Participants completed phase 1 to learn the schema then following a 10-minute break they completed learning blocks of phase 2 to learn the schema-related as well as the novel hierarchy. 

So, what is active awakening? Active awakening refers to people's ability to perceive changes in the surrounding environment, initiative, and ability to understand and respond to things. When a person is in a highly awakened state, his facial features will be sharper, his thinking will be more agile, and his ability to perceive and understand things will be stronger.

And this high degree of arousal can also help us better improve our memory. In a state of high arousal, a person can focus more on the knowledge he has learned and better retain the content he has learned in his brain. At the same time, a state of high arousal can also stimulate people's thinking vitality and improve the depth and effect of memory.

Therefore, we need to improve our awareness and memory through a series of methods. First of all, we need to pay attention to maintaining adequate sleep time so as not to affect our perception and understanding due to excessive fatigue. Secondly, we also need to regularly activate our brains and promote our thinking vitality through reading, thinking, etc. Finally, we also need to pay more attention to changes in the surrounding environment in our lives, and learn and be exposed to as many new things as possible to improve our awakening and memory abilities.

In short, the relationship between active arousal and memory is close and indispensable. We must continue to be positive, maintain good living habits, and constantly exercise and challenge ourselves to improve our arousal and memory abilities, to better respond to life's challenges and opportunities. It can be seen that we need to improve memory, and Cistanche deserticola can significantly improve memory, because Cistanche deserticola can also regulate the balance of neurotransmitters, such as increasing the levels of acetylcholine and growth factors. These substances are very important for memory and learning. In addition, Meat can also improve blood flow and promote oxygen delivery, which can ensure that the brain receives sufficient nutrients and energy, thereby improving brain vitality and endurance.

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They were tested after a 12-hour (sleep–wake) interval, followed by a second test 24 hours after the initial encoding session. Sleep was monitored using both polysomnography (PSG) and actigraphy (Figure 1).

Schema-learning paradigm

Despite decades of research and the recent renewed interest in the neuroscience community to study frameworks of organized information, there seems to be no consensus on what qualifies a schema. This has contributed to heterogeneity in the literature. 

It has been proposed that a working definition of schemas include "overlapping, organized knowledge representations" that have the following three features: benefiting memory performance; having a dynamic and adaptable nature when challenged by incongruent novel items, and facilitating novel inferences generalizing over what is directly learned [28]. 

A schema-based learning paradigm based on transitive inference was utilized in the current study [26] that included these essential features. This paradigm has been used in prior works [29–31].

The main paradigm consisted of two main phases as illustrated in Figure 2 and described in detail below. We included active feedback as explicit reinforcement across the learning blocks, as some studies have demonstrated that explicit reinforcement during learning is an essential factor in sleep-dependent benefits to transitive inference [15]. The main paradigm consisted of three main phases as shown in Figure 2 and explained in detail below.

Overview of the protocol

Initial schema formation consisted of two parts with a 10-minute break interval: (phase 1) to form the initial schema followed by (phase 2): schema-related memory integration and learning the no-schema condition. After finishing phase 2, participants underwent immediate and delayed test sessions following 12 hours containing sleep–active wake, and a final test 24 hours after the initial encoding (Figure 1).

Phase1: learning the schema to criterion

Participants were required to learn the correct order of items in a 7-item set related to an age hierarchy of galaxies. The learning process involved trial and error along with active feedback, alternating between learning and testing blocks, until participants achieved a criterion of over 85% accuracy. 

During the learning blocks, participants were shown two adjacent galaxies in the hierarchy (B–C, F–E, A–B, D–C, etc.) for 3 seconds and were asked: "Which galaxy is older?." Participants were encouraged to actively engage in learning and provide responses. 

The correct answer was highlighted in green, irrespective of whether they responded or not. Non-adjacent pairs (B–D, C–E, B–E, etc.) were not presented during learning.

Every learning block was succeeded by a corresponding test block where participants were shown a pair of galaxies and indicated which item was older. They were shown learned adjacent pairs as well as non-adjacent items (inference pairs) that had not been encountered during learning (Figure 2A–B). 

No feedback was given. To correctly answer these questions, participants made transitive inferences (i.e. if A > B and B > C then A > C). 

This phase was concluded once 85% accuracy was reached. Based on pilot experiments and prior work we provisioned for a maximum of 20 learning and testing blocks to attain 85% accuracy. On average about 10 learning blocks were needed to reach the criterion (M = 9.6, SD = 3.9).

Phase 2: schema-related memory integration and learning the no-schema condition

This phase involved the learning of a novel hierarchy of galaxies that either leveraged on the acquired schema (schema condition) or contained items entirely not encountered before (no-schema condition). In the schema condition, participants were required to learn a new hierarchy that was intercalated with galaxies from phase 1 while maintaining the same ranking order. In the no-schema condition, participants were presented with a completely novel hierarchy. Tests on both schema and no-schema sets consisted of adjacent and inference pairs similar to phase 1. Critically, the inference test trials consisted of only novel galaxies for both hierarchies.

The procedures were identical to the previous phase and participants underwent six alternating learning and test blocks for both the schema and no-schema sets presented in an interleaved order. All of the items, the right/left presentation on the screen, the interleaved order, and the order of presentation of pairs were randomized and counterbalanced in all phases (Figure 2A–B).

Hierarchy recall test as a measure to test free recall

Finally, participants were provided with two envelopes each containing the galaxy images of the schema/no-schema set mixed in a random arrangement and were tasked with reconstructing the accurate order of galaxies within each hierarchy from youngest to eldest from left to right on the table. 

Similar to the computer tests hierarchy recall test was also counterbalanced with numbered envelopes and instructions on which to open first. 

The performance of participants was assessed based on the total number of errors made in reordering the galaxies, utilizing a method that penalizes the incorrect placement of an item relative to the deviation from its correct position [32].

Software and image source

Galaxy images were obtained from the HubbleSite galleries, which can be accessed at (https://hubblesite.org/images). The stimuli were created using E-Prime 2.0 software (Psychology Software Tools, Inc., Sharpsburg, PA).

PSG data acquisition and analyses

Electroencephalography (EEG) was conducted using a SOMNOtouch recorder (SOMNOmedics GmbH, Randersacker, Germany) on two central channels, C3 and C4, concerning the contralateral mastoids (A1, A2), and Cz and Fpz as common reference and ground electrodes following the international 10–20 system. 

Electrooculography (EOG) and electromyography (EMG) of chin muscles were also recorded for sleep staging. The impedance was maintained below 5 kΩ before initiating the recording.

The PSG data were first visually examined and subsequently auto-scored using the Z3Score algorithm (https://z3score.com), which has been previously validated and demonstrated to be comparable to expert scorers. Additionally, the FASST EEG toolbox (http://www.montefiore.ulg.ac.be/~phillips/FASST.html) was utilized [33]. 

Sleep stages (N1, N2, N3, REM, and WASO) were scored based on 30-second epochs using the criteria outlined in the American Academy of Sleep Medicine Manual (AASM) [34]. Two records containing more than 10% artifacts were excluded from the final analyses. 

The reasons for exclusion included either premature termination of recordings by the device or inadequate data quality for sleep staging, for example, due to electrodes falling off during the night. Slow-wave activity (SWA), and slow-wave energy as integrated power in the delta band (0.5–4 Hz) were computed using methods previously published [35, 36].

Spindle detection

The Wonambi Python package, v5.24 (https://wonambi-python. github.io) was employed for automatic spindle detection using a validated algorithm [35]. Spindles were classified into slow (9–12 Hz), or fast (12–15 Hz) [35], and were detected for both N2 and N3 sleep stages. Spindle count and spindle density (counts per minute) were used as the main spindle metrics [37]. We manually checked 10 participants' samples to verify the fidelity of automatic detection (for further details see Supplementary Materials).

Statistical analyses

Memory performance was assessed both via the computer test by an item-by-item measure (% correct), as well as the number of recall errors in the hierarchy recall test. For each phase in both groups, we assessed memory performance using a mixed ANOVA to investigate consolidation of memories with schema learning (schema, no-schema), and test sessions (immediate, delayed: 12 hours/24 hours) as the within-participants factors, and consolidation condition (sleep, wake) as the between-participants factor. 

Change in memory following a 12-hour interval of (sleep–wake) was also examined using a mixed ANOVA with the within-participant factors of the schema (schema, no-schema) and pair type (adjacent, inference) and between-participants factor of consolidation (sleep–wake). 

The immediate and delayed 12-hour/24-hour test results were compared with post hoc independent samples and paired t-tests. We examined associations of sleep stages and spindle measures with the memory performance, size of the schema-driven memory benefit (Schema-No-schema), and the change in recall errors in tests following nocturnal sessions using Spearman's correlation analyses. 

P-values of less than 0.05 were considered statistically significant. Data preprocessing and statistical analyses were conducted using MATLAB version R2017b (The MathWorks, Inc., Natick, MA) and SPSS 25.0 (IBM Corp., Armonk, New York).

Results

Initial schema learning at phase 1, and performance in phase 2 for sleep and wake groups

Initial learning of the schema, at phase 1 was comparable between sleep and wake groups. Independent sample t-tests revealed no significant differences in the number of trials required to reach the criterion in phase 1 in the sleep (M = 9.3, SD = 4.7), compared to the wake group (M = 9.9, SD = 3.1), t(51) = 1.42, p = 0.158. ]

There was no significant difference in the performance for non-inference and inference pairs in phase 1 (t ≤ 1.59, p ≥ 0.12). For performance across test blocks in phase 2, a repeated measures ANOVA with schema learning (schema, no-schema), and block (six levels) as within-participants factors, and consolidation condition (sleep, wake) as the between-participants factor found significant main effects of schema, F(1,51) = 50.66, p < 0.001, η p 2 = 0.49, and block, F(5,255) = 14.03, p < 0.001, η p 2 = 0.21, but no significant effect of consolidation condition, F(1,51) = 0.27, p = 0.60; indicating enhanced performance in the schema condition and improvements across the learning blocks for both sleep and wake groups. 

Critically, the initial learning of the schema and no-schema set at Phase 2 were comparable between sleep and wake groups, with comparable performance in both schemas sets across blocks in Phase 2, F(1,51) = 0.27, p = 0.60, as well as the final block, t(51) ≤ 0.67, p ≥ 0.51. 

Similarly, both groups performed comparably at the subsequent immediate test, t(51) = 1.207, p = 0.233. For additional information on the learning trajectory during phase 2 and block-level performance in both groups, please refer to Supplementary Figure 4.

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