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词汇竞争在书面和口头词识别中的发展。

The development of lexical competition in written- and spoken-word recognition.

机构信息

Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, USA.

Department of Communication Sciences and Disorders, University of Maryland, College Park, MD, USA.

出版信息

Q J Exp Psychol (Hove). 2023 Jan;76(1):196-219. doi: 10.1177/17470218221090483. Epub 2022 Apr 27.

Abstract

Efficient word recognition depends on the ability to overcome competition from overlapping words. The nature of the overlap depends on the input modality: spoken words have temporal overlap from other words that share phonemes in the same positions, whereas written words have spatial overlap from other words with letters in the same places. It is unclear how these differences in input format affect the ability to recognise a word and the types of competitors that become active while doing so. This study investigates word recognition in both modalities in children between 7 and 15. Children complete a visual-world paradigm eye-tracking task that measures competition from words with several types of overlap, using identical word lists between modalities. Results showed correlated developmental changes in the speed of target recognition in both modalities. In addition, developmental changes were seen in the efficiency of competitor suppression for some competitor types in the spoken modality. These data reveal some developmental continuity in the process of word recognition independent of modality but also some instances of independence in how competitors are activated. Stimuli, data, and analyses from this project are available at: https://osf.io/eav72.

摘要

高效的单词识别取决于克服来自重叠单词竞争的能力。重叠的性质取决于输入方式:口语单词在同一位置上有共享音素的其他单词的时间重叠,而书面单词在同一位置上有具有相同字母的其他单词的空间重叠。目前尚不清楚这些输入格式的差异如何影响识别单词的能力以及在识别过程中变得活跃的竞争类型。本研究在 7 至 15 岁的儿童中调查了两种模态下的单词识别。儿童使用两种模态之间相同的单词列表,完成视觉世界范式眼动追踪任务,该任务可衡量具有多种重叠类型的单词的竞争。结果表明,在两种模态下,目标识别的速度都呈现出相关的发展变化。此外,在口语模态中,对于某些竞争类型,竞争抑制的效率也发生了变化。这些数据揭示了在不依赖于模态的情况下,单词识别过程中的一些发展连续性,但也有一些竞争如何被激活的独立性实例。本项目的刺激物、数据和分析可在以下网址获得:https://osf.io/eav72。

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