Plat Rika, Lowie Wander, de Bot Kees
Department of Applied Linguistics, University of Groningen, Groningen, Netherlands.
Unit for Language Facilitation and Empowerment, University of the Free State, Bloemfontein, South Africa.
Front Psychol. 2018 Jan 17;8:2256. doi: 10.3389/fpsyg.2017.02256. eCollection 2017.
Reaction time data have long been collected in order to gain insight into the underlying mechanisms involved in language processing. Means analyses often attempt to break down what factors relate to what portion of the total reaction time. From a dynamic systems theory perspective or an interaction dominant view of language processing, it is impossible to isolate discrete factors contributing to language processing, since these continually and interactively play a role. Non-linear analyses offer the tools to investigate the underlying process of language use in time, without having to isolate discrete factors. Patterns of variability in reaction time data may disclose the relative contribution of automatic (grapheme-to-phoneme conversion) processing and attention-demanding (semantic) processing. The presence of a fractal structure in the variability of a reaction time series indicates automaticity in the mental structures contributing to a task. A decorrelated pattern of variability will indicate a higher degree of attention-demanding processing. A focus on variability patterns allows us to examine the relative contribution of automatic and attention-demanding processing when a speaker is using the mother tongue (L1) or a second language (L2). A word naming task conducted in the L1 (Dutch) and L2 (English) shows L1 word processing to rely more on automatic spelling-to-sound conversion than L2 word processing. A word naming task with a semantic categorization subtask showed more reliance on attention-demanding semantic processing when using the L2. A comparison to L1 English data shows this was not only due to the amount of language use or language dominance, but also to the difference in orthographic depth between Dutch and English. An important implication of this finding is that when the same task is used to test and compare different languages, one cannot straightforwardly assume the same cognitive sub processes are involved to an equal degree using the same task in different languages.
长期以来,人们一直在收集反应时间数据,以便深入了解语言处理所涉及的潜在机制。均值分析通常试图剖析哪些因素与总反应时间的哪一部分相关。从动态系统理论的角度或语言处理的交互主导观点来看,不可能分离出促成语言处理的离散因素,因为这些因素持续且交互地发挥作用。非线性分析提供了工具,可及时研究语言使用的潜在过程,而无需分离离散因素。反应时间数据的变异性模式可能揭示自动(音素到音位转换)处理和需要注意力的(语义)处理的相对贡献。反应时间序列变异性中存在分形结构表明对任务有贡献的心理结构具有自动性。不相关的变异性模式将表明需要更高程度的注意力处理。关注变异性模式使我们能够在说话者使用母语(L1)或第二语言(L2)时,检查自动处理和需要注意力的处理的相对贡献。一项用L1(荷兰语)和L2(英语)进行的单词命名任务表明,与L2单词处理相比,L1单词处理更多地依赖于自动的拼写发音转换。一项带有语义分类子任务的单词命名任务表明,在使用L2时更多地依赖于需要注意力的语义处理。与L1英语数据的比较表明,这不仅是由于语言使用量或语言优势,还由于荷兰语和英语之间正字法深度的差异。这一发现的一个重要启示是,当使用相同任务测试和比较不同语言时,不能直接假设在不同语言中使用相同任务时涉及相同程度的相同认知子过程。