Abstract
Training-induced performance gains in a visual perceptual learning (VPL) task that take place during sleep are termed “offline performance gains.” Offline performance gains of VPL so far have been reported in the texture discrimination task and other discrimination tasks. This raises the question as to whether offline performance gains on VPL occur exclusively in discrimination tasks. The present study examined whether offline performance gains occur in detection tasks. In Experiment 1, subjects were trained on a Gabor orientation detection task. They were retested after a 12-hr interval, which included either nightly sleep or only wakefulness. Offline performance gains occurred only after sleep on the trained orientation, not on an untrained orientation. In Experiment 2, we tested whether offline performance gains in the detection task occur over a nap using polysomnography. Moreover, we tested whether sigma activity during non-rapid eye movement (NREM) sleep recorded from occipital electrodes, previously implicated in offline performance gains of the texture discrimination task, was associated with the degree of offline performance gains of the Gabor orientation detection task. We replicated offline performance gains on the trained orientation in the detection task over the nap. Sigma activity during NREM sleep was significantly larger in the occipital electrodes relative to control electrodes in correlation with offline performance gains. The results suggest that offline performance gains occur over the sleep period generally in VPL. Moreover, sigma activity in the occipital region during NREM sleep may play an important role in offline performance gains of VPL.
Highlights
We hypothesized that offline sleep-dependent performance gains would occur in the Gabor orientation detection task
Because the Gabor orientation detection task has trained-feature specificity (Shibata et al, 2017), we hypothesized that sleep-dependent offline performance gains occur with the trained orientation, not with the untrained orientation
The present study focused on the role of sleep—in particular, sigma activity during non-rapid eye movement (NREM) sleep—on performance in a detection task
Summary
After the initial acquisition of a skill, a learning state goes through an offline process, through which further improvements in performance are obtained without actual training (Karni et al, 1998; Walker, 2005), and these have been termed ‘‘offline performance gains.’’ It has been suggested that sleep plays an essential role in offline performance gains in various types of learning (Bang, Khalilzadeh, Hamalainen, Watanabe, & Sasaki, 2014; Born & Wilhelm, 2012; Diekelmann & Born, 2010; Gais, Molle, Helms, & Born, 2002; Gais, Plihal, Wagner, & Born, 2000; Huber, Ghilardi, Massimini, & Tononi, 2004; Laureys et al, 2001; Maquet et al, 2000; Mascetti et al, 2013; Matarazzo, Franko, Maquet, & Vogels, 2008; McDevitt, Rokem, Silver, & Mednick, 2014; Mednick, Nakayama, & Stickgold, 2003; Rasch & Born, 2013; Stickgold, 2005; Stickgold, James, & Hobson, 2000; Stickgold, Whidbee, Schirmer, Patel, & Hobson, 2000; Tamaki et al, 2013; Tamaki, Matsuoka, Nittono, & Hori, 2008; Tononi & Cirelli, 2014; Walker, 2005; Walker, Brakefield, Morgan, Hobson, & Stickgold, 2002; Yotsumoto, Sasaki, et al, 2009). One type of learning for which sleep is beneficial is visual perceptual learning (VPL; Bang et al, 2014; Censor, Karni, & Sagi, 2006; Gais et al, 2000; Karni, Tanne, Rubenstein, Askenasy, & Sagi, 1994; Mednick et al, 2003; Stickgold, James, & Hobson, 2000; Yotsumoto, Sasaki, et al, 2009). The amount of improvement after sleep surpasses that after passage of the same amount of time without sleep (Bang et al, 2014; Censor et al, 2006; Gais et al, 2000; Karni et al, 1994; Mednick et al, 2003; Stickgold, James, & Hobson, 2000; Yotsumoto, Sasaki, et al, 2009)
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