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虚拟距离再现过程中时间和空间估计的可分离神经指标。

Dissociable neural indices for time and space estimates during virtual distance reproduction.

机构信息

Department of Psychology, University of Arizona, Tuscon, AZ 85721, United States; Department of Psychology, George Mason University, 4400 University Drive, 3F5, Fairfax, VA 22030, United States.

Department of Psychology, George Mason University, 4400 University Drive, 3F5, Fairfax, VA 22030, United States.

出版信息

Neuroimage. 2021 Feb 1;226:117607. doi: 10.1016/j.neuroimage.2020.117607. Epub 2020 Dec 5.

Abstract

The perception and measurement of spatial and temporal dimensions have been widely studied. Yet, whether these two dimensions are processed independently is still being debated. Additionally, whether EEG components are uniquely associated with time or space, or whether they reflect a more general measure of magnitude quantity remains unknown. While undergoing EEG, subjects performed a virtual distance reproduction task, in which they were required to first walk forward for an unknown distance or time, and then reproduce that distance or time. Walking speed was varied between estimation and reproduction phases, to prevent interference between distance or time in each estimate. Behaviorally, subject performance was more variable when reproducing time than when reproducing distance, but with similar patterns of accuracy. During estimation, EEG data revealed the contingent negative variation (CNV), a measure previously associated with timing and expectation, tracked the probability of the upcoming interval, for both time and distance. However, during reproduction, the CNV exclusively oriented to the upcoming temporal interval at the start of reproduction, with no change across spatial distances. Our findings indicate that time and space are neurally separable dimensions, with the CNV both serving a supramodal role in temporal and spatial expectation, yet an exclusive role in preparing duration reproduction.

摘要

空间和时间维度的感知和测量已经得到了广泛的研究。然而,这两个维度是否是独立处理的仍在争论之中。此外,EEG 成分是否与时间或空间独特相关,或者它们是否反映了更一般的数量度量仍然未知。在进行 EEG 时,受试者执行了一个虚拟距离再现任务,要求他们先向前走未知的距离或时间,然后再现那个距离或时间。在估计和再现阶段,行走速度会有所变化,以防止每个估计中的距离或时间之间的干扰。行为上,当再现时间时,受试者的表现比再现距离时更不稳定,但准确性模式相似。在估计过程中,EEG 数据显示了伴随负变(CNV),这是一种以前与时间和预期相关的测量方法,它跟踪了时间和距离的即将到来的间隔的概率。然而,在再现过程中,CNV 仅在再现开始时针对即将到来的时间间隔定向,而在空间距离上没有变化。我们的发现表明,时间和空间是神经可分离的维度,CNV 在时间和空间预期中都起到了超模式的作用,但在准备持续时间再现方面则起到了独特的作用。

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