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学龄儿童基于分布信息学习新的类别。

School-Aged Children Learn Novel Categories on the Basis of Distributional Information.

作者信息

Broedelet Iris, Boersma Paul, Rispens Judith

机构信息

Amsterdam Center for Language and Communication, University of Amsterdam, Amsterdam, Netherlands.

出版信息

Front Psychol. 2022 Jan 24;12:799241. doi: 10.3389/fpsyg.2021.799241. eCollection 2021.

DOI:10.3389/fpsyg.2021.799241
PMID:35140663
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8818729/
Abstract

Categorization of sensory stimuli is a vital process in understanding the world. In this paper we show that distributional learning plays a role in learning novel object categories in school-aged children. An 11-step continuum was constructed based on two novel animate objects by morphing one object into the other in 11 equal steps. Forty-nine children (7-9 years old) were subjected to one of two familiarization conditions during which they saw tokens from the continuum. The conditions differed in the position of the distributional peaks along the continuum. After familiarization it was tested how the children categorized the stimuli. Results show that, in line with our expectations, familiarization condition influenced categorization during the test phase, indicating that the frequency distribution of tokens in the input had induced novel object category formation. These results suggest that distributional learning could play an important role in categorizing sensory stimuli throughout life.

摘要

对感觉刺激进行分类是理解世界的一个重要过程。在本文中,我们表明分布学习在学龄儿童学习新的物体类别中发挥作用。基于两个新的有生命物体构建了一个11步的连续体,即将一个物体以11个相等的步骤逐渐变形为另一个物体。49名儿童(7至9岁)在两种熟悉条件之一中接受测试,在此期间他们看到了连续体中的样本。这两种条件在连续体上分布峰值的位置有所不同。熟悉之后,测试了儿童对刺激的分类方式。结果表明,与我们的预期一致,熟悉条件在测试阶段影响了分类,这表明输入中样本的频率分布诱导了新的物体类别形成。这些结果表明,分布学习可能在一生中对感觉刺激进行分类方面发挥重要作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3490/8818729/b6a1bc934a1a/fpsyg-12-799241-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3490/8818729/b62c32f3dc88/fpsyg-12-799241-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3490/8818729/3cbceff19a70/fpsyg-12-799241-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3490/8818729/6146a62c8990/fpsyg-12-799241-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3490/8818729/b6a1bc934a1a/fpsyg-12-799241-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3490/8818729/b62c32f3dc88/fpsyg-12-799241-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3490/8818729/3cbceff19a70/fpsyg-12-799241-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3490/8818729/6146a62c8990/fpsyg-12-799241-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3490/8818729/b6a1bc934a1a/fpsyg-12-799241-g004.jpg

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Observed effects of "distributional learning" may not relate to the number of peaks. A test of "dispersion" as a confounding factor.“分布学习”的观察效果可能与峰值数量无关。作为混杂因素的“离散度”检验。
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