2020
DOI: 10.1007/s00221-020-05765-3
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Effects of white noise on word recall performance and brain activity in healthy adolescents with normal and low auditory working memory

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Cited by 4 publications
(4 citation statements)
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“…Certain types of noise can have a calming effect on humans. Othman et al (2020) exemplified that WN can lead to an improvement in auditory working memory. This was associated with significantly increased activity in the auditory system, cingulate, and frontal brain, among others.…”
Section: Discussionmentioning
confidence: 99%
“…Certain types of noise can have a calming effect on humans. Othman et al (2020) exemplified that WN can lead to an improvement in auditory working memory. This was associated with significantly increased activity in the auditory system, cingulate, and frontal brain, among others.…”
Section: Discussionmentioning
confidence: 99%
“…소음이 청 각적 정보처리 및 말지각에 부정적인 영향을 미치지만 높은 청 각적 작업기억을 가진 집단일수록 '칵테일 파티 효과(cocktail party effect)'를 일으켜 소음 환경에서 더 나은 작업기억력 의 결과를 나타낸다는 연구들도 있다 (Angwin et al, 2017;Baijot et al, 2016;Fu & Galvin, 2007;Manan et al, 2012;Pickens et al, 2019;Söderlund & Jobs, 2016;Söderlund et al, 2010). Othman et al(2020) (Windsor & Kohnert, 2004). 작업기억의 차이 는 언어의 이해와 언어발달, 학습, 사고, 등의 과제 수행과 관련 이 있고 언어발달 개인차를 설명해 주는 요인으로 볼 수 있으며 (Kim et al, 2004), 단순언어장애 아동의 낮은 작업기억이 어 휘, 문장 이해, 복잡한 구문의 산출과 같은 언어발달을 저해한 다는 연구들을 뒷받침하고 있다 (Adams & Gathercole, 1995;Cho et al, 2012;Cho, 2015;Just & Carpenter, 1992).…”
Section: Introductionunclassified
“…9 This method is possible as healthy individuals have distinct neuronal activity patterns that reflect their behavioral characteristics. 10 Thus, one's behavior may be predicted based on the displayed neuronal activity patterns. In the present work, the authors in particular attempted to distinguish between binge and non-binge drinkers using fMRI data collected while participants engaged in cognitive tasks and at rest.…”
mentioning
confidence: 99%
“…In this issue of JMRI , Li et al further examined the method to classify binge from non‐binge drinkers in young adults by applying deep learning methods to functional magnetic resonance imaging (fMRI) data obtained from the Human Connectome Project (HCP) 9 . This method is possible as healthy individuals have distinct neuronal activity patterns that reflect their behavioral characteristics 10 . Thus, one's behavior may be predicted based on the displayed neuronal activity patterns.…”
mentioning
confidence: 99%