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适应会使被知觉到的数量产生并行偏差。

Adaptation biases the parallel perception of subitized numerosities.

机构信息

College of Education, Yunnan Minzu University, Kunming, China.

College of Teacher Education, Dali University, Dali, China.

出版信息

Sci Rep. 2024 Oct 29;14(1):26014. doi: 10.1038/s41598-024-76536-1.

Abstract

Numerosity adaptation is a phenomenon in which prolonged exposure to a stimulus of greater numerosity makes subsequent stimuli appear less numerous, and vice versa. It has been confined to moderated numerosities outside the subitizing range (> 4). This study investigated whether the estimation of small numerosities (1-4), which is performed rapidly and accurately due to the mechanism of subitizing, is susceptible to adaptation. After adapting to a 50-dot stimulus, participants were presented with stimuli consisting of 1-5 color sets. In some trials, participants were informed of the target color-set before the presentation of stimuli, while in others, they were instructed afterwards. When estimating 1-4 dots in the single-color set or superset (the total dots), no adaptation effect was observed. The coefficient of variation (CV) was below 0.05, indicating the effective function of subitizing. However, when enumerating subsets in parallel, adaptation biased the estimation. The CV in estimating subitized numerosities was comparable to and correlated with that of estimating moderate numerosities (5-12), suggesting that subitizing was superseded by numerosity estimation. Greater effects arise when the targets were probed afterwards, with elevated CV. The prior adaptor may be more weighted to optimize detection of number deviations, especially under higher perceptual uncertainty.

摘要

数值适应是一种现象,即长时间暴露于较大数值的刺激会使后续刺激看起来较少,反之亦然。它仅限于亚数量范围(> 4)之外的适度数值。本研究探讨了由于亚数量化机制而快速准确地进行的小数值(1-4)估计是否容易受到适应的影响。在适应了 50 个点的刺激后,参与者会看到由 1-5 个颜色组组成的刺激。在一些试验中,参与者在呈现刺激之前就被告知目标颜色组,而在其他试验中,则在之后才被告知。当估计单个颜色组或超集(总点数)中的 1-4 个点时,没有观察到适应效应。变异系数(CV)低于 0.05,表明亚数量化的有效功能。然而,当并行枚举子集时,适应会使估计产生偏差。估计亚数量化数值的 CV 与估计中等数值(5-12)的 CV 相当且相关,这表明亚数量化被数值估计所取代。当目标在之后被探测时,效果会更大,CV 会升高。先前的适应器可能更注重优化对数值偏差的检测,尤其是在更高的感知不确定性下。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37f9/11522287/951ddf584eac/41598_2024_76536_Fig1_HTML.jpg

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