Overlearning hyperstabilizes a skill by rapidly making neurochemical processing inhibitory-dominant

Abstract

Overlearning refers to the continued training of a skill after performance improvement has plateaued. Whether overlearning is beneficial is a question in our daily lives that has never been clearly answered. Here, we report a new important role: Overlearning abruptly changes neurochemical processing to hyper-stabilize and protect trained perceptual learning from subsequent new learning. Usually, learning immediately after training is so unstable that it can be disrupted by subsequent new learning, unless waiting for passive stabilization, which takes hours. However, overlearning so rapidly and strongly stabilizes the learning state that it not only becomes resilient against, but disrupts, subsequent new learning. Such hyper-stabilization is associated with an abrupt shift from glutamate-dominant excitatory to gamma-aminobutyric-acid-dominant inhibitory processing in early visual areas. Hyper-stabilization contrasts with passive and slower stabilization, which is associated with a mere reduction of an excitatory dominance to baseline levels. Utilizing hyper-stabilization may lead to efficient learning paradigms.

Big GABA: Edited MR spectroscopy at 24 research sites

“…Healthy brain function relies on GABAergic inhibitory processes, and understanding GABAergic mechanisms in both healthy and pathological brain function has been one core focus of neuroscience. MRS measurements of GABA have been associated with individual differences in hemodynamic and electrophysiological signals (Donahue et al, 2010; Hu et al, 2013; Kapogiannis et al, 2013; Muthukumaraswamy et al, 2009) and a number of measures of cognition (Fujihara et al, 2015; Shibata et al, 2017; Yoon et al, 2016) and behavior (Boy et al, 2011; Greenhouse et al, 2017; Puts et al, 2011; Silveri et al, 2013). Differential levels of GABA have been observed in a number of neuropsychiatric disorders, such as schizophrenia (Kegeles et al, 2012; Öngür et al, 2010; Rowland et al, 2016; Yoon et al, 2010) and depression (Bhagwagar et al, 2008; Hasler et al, 2007; Price et al, 2009), neurodevelopmental disorders such as autism spectrum disorder (Drenthen et al, 2016; Gaetz et al, 2014; Puts et al, 2016) and attention deficit hyperactivity disorder (Bollmann et al, 2015; Edden et al, 2012a), and neurological diseases, such as Parkinson’s disease (Emir et al, 2012), amyotrophic lateral sclerosis (Foerster et al, 2012; Foerster et al, 2013) and diabetic neuropathy (Petrou et al, 2012).…”

Causal evidence for LTP-based interference in visual learning

“…Training two related skills in close succession is known to result in interference (Cantarero et al 2013;Leow et al 2013;Wigmore, Tong, and Flanagan 2002;Sing and Smith 2010;Brashers-Krug, Shadmehr, and Bizzi 1996;Shadmehr and Brashers-Krug 1997;Seitz et al 2005;Yotsumoto, Chang, et al 2009;Shibata et al 2017). It is thought that such interference is due to competition in LTP processes associated with each task (Cantarero et al 2013;Ziemann et al 2004;Stefan et al 2006) but direct evidence for this mechanism has been lacking.…”

“…Two types of interference have been observed: anterograde interference (i.e., new learning disrupting future learning) and retrograde interference (i.e., new learning disrupting a previous learning). Both types of interference are commonly observed across motor, perceptual, and cognitive learning (Cantarero et al 2013;Leow et al 2013;Wigmore, Tong, and Flanagan 2002;Sing and Smith 2010;Brashers-Krug, Shadmehr, and Bizzi 1996;Shadmehr and Brashers-Krug 1997;Seitz et al 2005;Yotsumoto, Chang, et al 2009;Shibata et al 2017).…”

“…cTBS is known to act via mechanisms akin to long-term depression (Huang et al 2005;Huang et al 2007;Di Lazzaro et al 2005;Hsieh et al 2014), thus allowing us to use it to interfere with LTP. We chose the early visual cortex as a target brain region because this area shows changes after visual training (Schoups et al 2001;Schwartz, Maquet, and Frith 2002;Li, Piech, and Gilbert 2004;Hua et al 2010;Sasaki, Nanez, and Watanabe 2010;Bang et al 2014;Bang et al 2018;Shibata et al 2017;Rosenthal et al 2016), suggesting that the early visual cortex is involved in visual perceptual learning. We designed a perceptual learning task that resulted in substantial anterograde interference such that the first learning suppressed the second learning.…”

Non-native phonetic learning is destabilized by exposure to phonological variability before and after training

“…Indeed, perceptual learning studies in the visual domain support this notion. One such study by Shibata et al (2017) found that participants who achieved more stability in their learning of a visual skill by overlearning (continuing to train on a skill after mastery) were not only protected against interference from a second task, but they also retained learning of the first task when assessed the following day as compared to a group that received the typical amount of training. A similar study found that participants who trained on a perceptual task until performance reached asymptote improved after a period of sleep, but those who stopped training prior to that point did not, which suggests a role for stability in sleep-mediated consolidation of newly learned skills (Hauptmann et al, 2005).…”