Variable rather than extreme slow reaction times distinguish brain states during sustained attention

Abstract A common behavioral marker of optimal attention focus is faster responses or reduced response variability. Our previous study found two dominant brain states during sustained attention, and these states differed in their behavioral accuracy and reaction time (RT) variability. However, RT di...

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Autores principales:	Ayumu Yamashita, David Rothlein, Aaron Kucyi, Eve M. Valera, Laura Germine, Jeremy Wilmer, Joseph DeGutis, Michael Esterman
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0f94b68b7bd948c99368e210b43d7387
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spelling	oai:doaj.org-article:0f94b68b7bd948c99368e210b43d73872021-12-02T16:26:22ZVariable rather than extreme slow reaction times distinguish brain states during sustained attention10.1038/s41598-021-94161-02045-2322https://doaj.org/article/0f94b68b7bd948c99368e210b43d73872021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94161-0https://doaj.org/toc/2045-2322Abstract A common behavioral marker of optimal attention focus is faster responses or reduced response variability. Our previous study found two dominant brain states during sustained attention, and these states differed in their behavioral accuracy and reaction time (RT) variability. However, RT distributions are often positively skewed with a long tail (i.e., reflecting occasional slow responses). Therefore, a larger RT variance could also be explained by this long tail rather than the variance around an assumed normal distribution (i.e., reflecting pervasive response instability based on both faster and slower responses). Resolving this ambiguity is important for better understanding mechanisms of sustained attention. Here, using a large dataset of over 20,000 participants who performed a sustained attention task, we first demonstrated the utility of the exGuassian distribution that can decompose RTs into a strategy factor, a variance factor, and a long tail factor. We then investigated which factor(s) differed between the two brain states using fMRI. Across two independent datasets, results indicate unambiguously that the variance factor differs between the two dominant brain states. These findings indicate that ‘suboptimal’ is different from ‘slow’ at the behavior and neural level, and have implications for theoretically and methodologically guiding future sustained attention research.Ayumu YamashitaDavid RothleinAaron KucyiEve M. ValeraLaura GermineJeremy WilmerJoseph DeGutisMichael EstermanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ayumu Yamashita David Rothlein Aaron Kucyi Eve M. Valera Laura Germine Jeremy Wilmer Joseph DeGutis Michael Esterman Variable rather than extreme slow reaction times distinguish brain states during sustained attention
description	Abstract A common behavioral marker of optimal attention focus is faster responses or reduced response variability. Our previous study found two dominant brain states during sustained attention, and these states differed in their behavioral accuracy and reaction time (RT) variability. However, RT distributions are often positively skewed with a long tail (i.e., reflecting occasional slow responses). Therefore, a larger RT variance could also be explained by this long tail rather than the variance around an assumed normal distribution (i.e., reflecting pervasive response instability based on both faster and slower responses). Resolving this ambiguity is important for better understanding mechanisms of sustained attention. Here, using a large dataset of over 20,000 participants who performed a sustained attention task, we first demonstrated the utility of the exGuassian distribution that can decompose RTs into a strategy factor, a variance factor, and a long tail factor. We then investigated which factor(s) differed between the two brain states using fMRI. Across two independent datasets, results indicate unambiguously that the variance factor differs between the two dominant brain states. These findings indicate that ‘suboptimal’ is different from ‘slow’ at the behavior and neural level, and have implications for theoretically and methodologically guiding future sustained attention research.
format	article
author	Ayumu Yamashita David Rothlein Aaron Kucyi Eve M. Valera Laura Germine Jeremy Wilmer Joseph DeGutis Michael Esterman
author_facet	Ayumu Yamashita David Rothlein Aaron Kucyi Eve M. Valera Laura Germine Jeremy Wilmer Joseph DeGutis Michael Esterman
author_sort	Ayumu Yamashita
title	Variable rather than extreme slow reaction times distinguish brain states during sustained attention
title_short	Variable rather than extreme slow reaction times distinguish brain states during sustained attention
title_full	Variable rather than extreme slow reaction times distinguish brain states during sustained attention
title_fullStr	Variable rather than extreme slow reaction times distinguish brain states during sustained attention
title_full_unstemmed	Variable rather than extreme slow reaction times distinguish brain states during sustained attention
title_sort	variable rather than extreme slow reaction times distinguish brain states during sustained attention
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0f94b68b7bd948c99368e210b43d7387
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Variable rather than extreme slow reaction times distinguish brain states during sustained attention

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