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将语言研究从传统桌面环境转变到虚拟环境。

Transforming language research from classic desktops to virtual environments.

作者信息

Rocabado Francisco, Muntini Laís, Jubran Omar Fahmi, Lachmann Thomas, Duñabeitia Jon Andoni

机构信息

Centro de Investigación Nebrija en Cognición, Universidad Nebrija, Madrid, 28043, Spain.

Center for Cognitive Science, RPTU Kaiserslautern, 67663, Kaiserslautern, Germany.

出版信息

Sci Rep. 2025 Jul 2;15(1):23118. doi: 10.1038/s41598-025-08319-1.

Abstract

Virtual Reality (VR) offers novel opportunities for investigating human perception beyond conventional laboratory settings, facilitating the study of naturalistic behavior with controlled virtual environments. To benefit from this technology, the foundational aspects must be compared to traditional personal computer (PC) monitor setups. The validity and reliability of stimuli presentation and response collection must be established to ensure that any findings can be attributed to experimental variables, not the method. To address this, we designed a single-word recognition (lexical decision) task administered in both VR and PC monitor setups. Stimulus presentation was controlled across tasks, visual angles were matched, and responses were gathered via VR controllers in both settings. Results replicated the lexicality effect (i.e., faster word than pseudoword reading) in both setups. Reaction times and error rates showed no significant differences between VR and PC monitor setups, underscoring VR's utility for collecting reliable behavioral data in language studies. These results demonstrate that VR-derived mental chronometry measures yield findings comparable to conventional methods, establishing a benchmark for future immersive research.

摘要

虚拟现实(VR)为在传统实验室环境之外研究人类感知提供了新的机会,有助于利用可控的虚拟环境研究自然行为。为了从这项技术中受益,必须将其基础方面与传统个人计算机(PC)显示器设置进行比较。必须确定刺激呈现和反应收集的有效性和可靠性,以确保任何研究结果都可归因于实验变量,而非方法。为了解决这个问题,我们设计了一个在VR和PC显示器设置中都能进行的单字识别(词汇判断)任务。跨任务控制刺激呈现,匹配视角,并在两种设置中通过VR控制器收集反应。两种设置的结果都重现了词汇性效应(即读单词比读伪词更快)。反应时间和错误率在VR和PC显示器设置之间没有显著差异,这突出了VR在语言研究中收集可靠行为数据的效用。这些结果表明,源自VR的心理计时测量结果与传统方法相当,为未来的沉浸式研究建立了一个基准。

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