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我们是否有一个用于测量数百毫秒范围内时间的通用机制?来自多间隔计时任务的证据。

Do we have a common mechanism for measuring time in the hundreds of millisecond range? Evidence from multiple-interval timing tasks.

作者信息

Merchant Hugo, Zarco Wilbert, Prado Luis

机构信息

Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Campus UNAM-UAQ, Juriquilla, Querétaro, Qro 76230, Mexico.

出版信息

J Neurophysiol. 2008 Feb;99(2):939-49. doi: 10.1152/jn.01225.2007. Epub 2007 Dec 19.

DOI:10.1152/jn.01225.2007
PMID:18094101
Abstract

In the present study we examined the performance variability of a group of 13 subjects in eight different tasks that involved the processing of temporal intervals in the subsecond range. These tasks differed in their sensorimotor processing (S; perception vs. production), the modality of the stimuli used to define the intervals (M; auditory vs. visual), and the number of intervals (N; one or four). Different analytical techniques were used to determine the existence of a central or distributed timing mechanism across tasks. The results showed a linear increase in performance variability as a function of the interval duration in all tasks. However, this compliance of the scalar property of interval timing was accompanied by a strong effect of S, N, and M and the interaction between these variables on the subjects' temporal accuracy. Thus the performance variability was larger not only in perceptual tasks than that in motor-timing tasks, but also using visual rather than auditory stimuli, and decreased as a function of the number of intervals. These results suggest the existence of a partially overlapping distributed mechanism underlying the ability to quantify time in different contexts.

摘要

在本研究中,我们考察了13名受试者在八项不同任务中的表现变异性,这些任务涉及亚秒级时间间隔的处理。这些任务在感觉运动处理(S;感知与产生)、用于定义时间间隔的刺激模态(M;听觉与视觉)以及时间间隔数量(N;一个或四个)方面存在差异。我们使用了不同的分析技术来确定跨任务的中央或分布式计时机制的存在。结果表明,在所有任务中,表现变异性均随时间间隔持续时间呈线性增加。然而,这种间隔计时标量属性的一致性伴随着S、N和M以及这些变量之间的相互作用对受试者时间准确性的强烈影响。因此,不仅感知任务中的表现变异性大于运动计时任务,而且使用视觉刺激时的表现变异性大于使用听觉刺激时,并且表现变异性随时间间隔数量的增加而降低。这些结果表明,在不同情境下量化时间的能力背后存在部分重叠的分布式机制。

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