Summary: Learning consolidation that occurs during sleep is a result of the learning process, and is not simply due to neural processes and brain regions associated with learning.
The consolidation of learning that occurs during sleep is a result of the learning process and not merely because certain brain regions get used a lot during learning, a RIKEN researcher and her collaborator have shown.
This finding is published in The Journal of Neuroscience and resolves a long-standing debate among sleep researchers.
In Japan, many school students stay up very late to cram for exams, but that is a self-defeating strategy according to Masako Tamaki of the RIKEN Center for Brain Science. “When you want to learn something, you should go to bed at a regular time,” she recommends. “Students study very late, but a lot of that knowledge will be lost if they don’t get enough sleep.”
That’s because new knowledge and skills that we acquire while awake are consolidated through neural processing that occurs when we sleep.
But there has been much debate about how this consolidation occurs. Is it simply because neurons that get used a lot when learning are downregulated for renormalization during sleep? Or is there something inherent in the learning process that causes this consolidation to occur?
Now, strong evidence for the learning-dependent model for consolidation during sleep has been found by Yuka Sasaki at Brown University in the United States and Tamaki, who first became interested in sleep research after a sleep-paralysis episode during which she thought she was being strangled by a stranger.
Two groups of young volunteers each underwent two sessions of training with a visual exercise. For the first group, the two training sessions were identical and they got better at the exercise. In contrast, the second training session for the second group was designed to nullify the learning achieved in the first session, and consequently they showed very little overall improvement.
The two groups then slept, and their performances on the visual exercise were measured on waking. The results provided strong support for the learning-dependent model.
First, the behavioral results indicated that the first group showed substantial improvements after sleeping, whereas the second group showed almost none despite having been trained for the same amount of time.
Second, the brain-signal monitoring during sleeping revealed that two kinds of activities consistent with that model were involved in processing, namely theta activity during rapid eye movement (REM) sleep and sigma activity during non-REM sleep.
However, the study found no involvement of slow-wave activity during non-REM sleep, which has been shown to be associated with use-dependent processes.
These results confirmed the pair’s suspicions: learning, and not just brain usage, is critical for consolidation during sleep. “Previous studies we had done were more consistent with the learning-dependent model,” notes Tamaki.
About this learning research news
Author: Adam Phillips Source: RIKEN Contact: Adam Phillips – RIKEN Image: The image is in the public domain
Sleep-Dependent Facilitation of Visual Perceptual Learning Is Consistent with a Learning-Dependent Model
How sleep leads to offline performance gains in learning remains controversial. A use-dependent model assumes that sleep processing leading to performance gains occurs based on general cortical usage during wakefulness, whereas a learning-dependent model assumes that this processing is specific to learning.
Here, we found evidence that supports a learning-dependent model in visual perceptual learning (VPL) in humans (both sexes).
First, we measured the strength of spontaneous oscillations during sleep after two training conditions that required the same amount of training or visual cortical usage; one generated VPL (learning condition), while the other did not (interference condition).
During a post-training nap, slow-wave activity (SWA) and sigma activity during non-rapid eye movement (NREM) sleep and theta activity during REM sleep were source localized to the early visual areas using retinotopic mapping. Inconsistent with a use-dependent model, only in the learning condition, sigma and theta activity, not SWA, increased in a trained region-specific manner and correlated with performance gains.
Second, we investigated the roles of occipital sigma and theta activity during sleep. Occipital sigma activity during NREM sleep was significantly correlated with performance gains in presleep learning; however, occipital theta activity during REM sleep was correlated with presleep learning stabilization, shown as resilience to interference from postsleep learning in a trained region-specific manner. Occipital SWA was not associated with offline performance gains or stabilization.
These results demonstrate that sleep processing leading to performance gains is learning dependent in VPL and involves occipital sigma and theta activity during sleep.
The present study shows strong evidence that could help resolve the long-standing controversy surrounding sleep processing that strengthens learning (performance gains). There are two conflicting models.
A use-dependent model assumes that sleep processing leading to performance gains occurs because of general cortical usage during wakefulness, whereas a learning-dependent model assumes that processing occurs specifically for learning.
Using visual perceptual learning and interference paradigms, we found that processing did not take place after general cortical usage.
Moreover, sigma activity during non-rapid eye movement (REM) sleep and theta activity during REM sleep in occipital areas were found to be involved in processing, which is consistent with the learning-dependent model and not the use-dependent model. These results support the learning-dependent model.