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时间二分法受刺激集的总体统计影响。

Temporal bisection is influenced by ensemble statistics of the stimulus set.

机构信息

General and Experimental Psychology, Department of Psychology, LMU Munich, 80802, Munich, Germany.

出版信息

Atten Percept Psychophys. 2021 Apr;83(3):1201-1214. doi: 10.3758/s13414-020-02202-z. Epub 2020 Nov 26.

Abstract

Although humans are well capable of precise time measurement, their duration judgments are nevertheless susceptible to temporal context. Previous research on temporal bisection has shown that duration comparisons are influenced by both stimulus spacing and ensemble statistics. However, theories proposed to account for bisection performance lack a plausible justification of how the effects of stimulus spacing and ensemble statistics are actually combined in temporal judgments. To explain the various contextual effects in temporal bisection, we develop a unified ensemble-distribution account (EDA), which assumes that the mean and variance of the duration set serve as a reference, rather than the short and long standards, in duration comparison. To validate this account, we conducted three experiments that varied the stimulus spacing (Experiment 1), the frequency of the probed durations (Experiment 2), and the variability of the probed durations (Experiment 3). The results revealed significant shifts of the bisection point in Experiments 1 and 2, and a change of the sensitivity of temporal judgments in Experiment 3-which were all well predicted by EDA. In fact, comparison of EDA to the extant prior accounts showed that using ensemble statistics can parsimoniously explain various stimulus set-related factors (e.g., spacing, frequency, variance) that influence temporal judgments.

摘要

虽然人类非常擅长精确的时间测量,但他们的时长判断仍然容易受到时间背景的影响。先前关于时间二分法的研究表明,时长比较受到刺激间隔和整体统计的影响。然而,为解释二分法表现而提出的理论缺乏一个合理的解释,即刺激间隔和整体统计的影响实际上是如何在时间判断中结合的。为了解释时间二分法中的各种上下文效应,我们提出了一个统一的整体分布理论(EDA),该理论假设在时长比较中,时长集合的均值和方差而不是短时长和长时长标准作为参考。为了验证这个理论,我们进行了三个实验,分别改变了刺激间隔(实验 1)、被探测时长的频率(实验 2)和被探测时长的可变性(实验 3)。结果表明,在实验 1 和 2 中,二分点发生了显著的变化,在实验 3 中,时间判断的敏感性发生了变化,这些都被 EDA 很好地预测了。事实上,将 EDA 与现有的先前理论进行比较表明,使用整体统计可以简洁地解释影响时间判断的各种与刺激集合相关的因素(例如,间隔、频率、方差)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08a7/8049899/ee8c7161eacb/13414_2020_2202_Fig1_HTML.jpg

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