Lexical Processing Strongly Affects Reading Times But Not Skipping During Natural Reading.

Q1 Social Sciences
Open Mind Pub Date : 2023-10-01 eCollection Date: 2023-01-01 DOI:10.1162/opmi_a_00099
Micha Heilbron, Jorie van Haren, Peter Hagoort, Floris P de Lange
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Abstract

In a typical text, readers look much longer at some words than at others, even skipping many altogether. Historically, researchers explained this variation via low-level visual or oculomotor factors, but today it is primarily explained via factors determining a word's lexical processing ease, such as how well word identity can be predicted from context or discerned from parafoveal preview. While the existence of these effects is well established in controlled experiments, the relative importance of prediction, preview and low-level factors in natural reading remains unclear. Here, we address this question in three large naturalistic reading corpora (n = 104, 1.5 million words), using deep neural networks and Bayesian ideal observers to model linguistic prediction and parafoveal preview from moment to moment in natural reading. Strikingly, neither prediction nor preview was important for explaining word skipping-the vast majority of explained variation was explained by a simple oculomotor model, using just fixation position and word length. For reading times, by contrast, we found strong but independent contributions of prediction and preview, with effect sizes matching those from controlled experiments. Together, these results challenge dominant models of eye movements in reading, and instead support alternative models that describe skipping (but not reading times) as largely autonomous from word identification, and mostly determined by low-level oculomotor information.

Abstract Image

Abstract Image

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自然阅读过程中词汇加工对阅读时间有强烈影响,但不会跳过。
在典型的文本中,读者看某些单词的时间比看其他单词的时间长得多,甚至跳过了许多单词。从历史上看,研究人员通过低水平的视觉或动眼因素来解释这种变化,但今天,它主要通过决定单词词汇处理容易程度的因素来解释,例如从上下文中预测单词身份或从旁凹预览中辨别单词身份的程度。虽然这些影响的存在在对照实验中已经得到了很好的证实,但预测、预览和低水平因素在自然阅读中的相对重要性仍不清楚。在这里,我们在三个大型自然主义阅读语料库(n=104,150万字)中解决了这个问题,使用深度神经网络和贝叶斯理想观测器对自然阅读中的语言预测和旁凹预览进行建模。引人注目的是,预测和预览对于解释单词跳跃都不重要——绝大多数解释的变化都是通过简单的动眼器模型来解释的,只使用固定位置和单词长度。相比之下,在阅读时间方面,我们发现预测和预览有着强大但独立的贡献,效果大小与对照实验的效果大小相匹配。总之,这些结果挑战了阅读中主要的眼动模型,转而支持替代模型,这些模型将跳跃(但不是阅读时间)描述为在很大程度上独立于单词识别,并且主要由低级眼动信息决定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Open Mind
Open Mind Social Sciences-Linguistics and Language
CiteScore
3.20
自引率
0.00%
发文量
15
审稿时长
53 weeks
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