A growing body of evidence suggests that sleep facilitates and is beneficial to perceptual learning. However, the underlying mechanism of this facilitatory action is largely unknown. There are two possible types of processing during sleep that may account for the facilitatory action: use-dependent processing and learning- consolidation processing. The use-dependent processing occurs during sleep in the brain mechanisms that are generally used during wakefulness prior to sleep. This processing leads to general changes of neural processing and does not occur specifically for the sake of learning. On the other hand, the learning- consolidation processing works specifically for learning. It is highly controversial concerning whether the use- dependent processing is sufficient for the facilitatory action or whether learning-consolidation processing is necessary for the facilitatory action. It is fundamentally important to know which type of processing occurs during sleep to clarify the mechanism of sleep facilitating perceptual learning, since it has not been directly tested which model is valid. We address this question in the present proposal. Sleep consists of different dynamics, such as those reflected by a multitude of frequency bands in spontaneous brain oscillations in each rapid eye movement (REM) and non-REM (NREM) sleep. This raises the possibility that only one type of processing does not necessarily occur consistently throughout the whole sleep period: the learning-consolidation processing might occur for some frequency bands in some cortical areas, while use-dependent processing might occur for others. Thus, we will systematically examine whether learning-consolidation processing or use-dependent processing occurs in each band in each REM and NREM sleep, and in different brain area(s). To test whether the learning-consolidation processing is necessary for facilitating perceptual learning during sleep, we will compare the spatio-temporal brain activation patterns during sleep that follows task performance that causes learning (learning paradigm) with those during sleep that follows task performance that does not cause learning (interference paradigm). For this purpose, we must obtain highly localized spatio- temporal information about brain activation during sleep;we will use a cutting-edge neuroimaging technique that combines fine temporal information from magnetoencephalography (MEG) and electroencephalography (EEG) with fine spatial information from magnetic resonance imaging (MRI) as well as individual retinotopic mapping in the early visual areas, to estimate the power and phase information of spontaneous oscillatory activities in the precisely localized cortical regions. Imaging will be conducted with concurrent polysomnography measurement to objectively identify sleep stages. Successful research results would provide significant knowledge to clarify how improvement of perceptual learning of a visual task and visual plasticity occurs during sleep after training of the task.
The primary goal of the proposed research is to investigate how learning is strengthened during sleep in young adults using advanced neuroimaging techniques. Successful research results may be used to improve our vision and enhance our learning ability.
|Tamaki, Masako; Bang, Ji Won; Watanabe, Takeo et al. (2016) Night Watch in One Brain Hemisphere during Sleep Associated with the First-Night Effect in Humans. Curr Biol 26:1190-4|
|Amano, Kaoru; Shibata, Kazuhisa; Kawato, Mitsuo et al. (2016) Learning to Associate Orientation with Color in Early Visual Areas by Associative Decoded fMRI Neurofeedback. Curr Biol 26:1861-6|
|Shibata, Kazuhisa; Sasaki, Yuka; Kawato, Mitsuo et al. (2016) Neuroimaging Evidence for 2 Types of Plasticity in Association with Visual Perceptual Learning. Cereb Cortex 26:3681-9|
|Yahata, Noriaki; Morimoto, Jun; Hashimoto, Ryuichiro et al. (2016) A small number of abnormal brain connections predicts adult autism spectrum disorder. Nat Commun 7:11254|
|Sasaki, Yuka; Watanabe, Takeo (2016) V3A takes over a job of MT+ after training on a visual task. Proc Natl Acad Sci U S A 113:6092-3|
|Shibata, Kazuhisa; Watanabe, Takeo; Kawato, Mitsuo et al. (2016) Differential Activation Patterns in the Same Brain Region Led to Opposite Emotional States. PLoS Biol 14:e1002546|
|Kim, Yong-Hwan; Kang, Dong-Wha; Kim, Dongho et al. (2015) Real-Time Strategy Video Game Experience and Visual Perceptual Learning. J Neurosci 35:10485-92|
|Chang, Li-Hung; Yotsumoto, Yuko; Salat, David H et al. (2015) Reduction in the retinotopic early visual cortex with normal aging and magnitude of perceptual learning. Neurobiol Aging 36:315-22|
|Kim, Dongho; Seitz, Aaron R; Watanabe, Takeo (2015) Visual perceptual learning by operant conditioning training follows rules of contingency. Vis cogn 23:147-160|
|Berard, Aaron V; Cain, Matthew S; Watanabe, Takeo et al. (2015) Frequent video game players resist perceptual interference. PLoS One 10:e0120011|
Showing the most recent 10 out of 28 publications