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.