在语法和语义研究中需要定量方法

E. Gibson, Evelina Fedorenko, Diogo Almeida, Leon Bergen, Joan Bresnan, David Caplan, Nick Chater, Morten H. Christiansen, Mike Frank, Adele Goldberg, Helen Goodluck, Greg Hickok, Ray Jackendoff, N. Kanwisher, R. Levy, Maryellen Macdonald, James Myers, Colin Phillips, Steven Piantadosi, Steve Pinker, D. Poeppel, Omer Preminger, Ian Roberts, Greg Scontras, Jon Sprouse, Carson Schü, Mike Tanenhaus, Vince Walsh, Duane Watson, E. Zweig
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引用次数: 160

摘要

在语法和语义研究中,常用的方法是对句子/意义对的可接受性进行判断,通常由论文的作者进行判断,有时也会得到同事的反馈。这种方法不允许对科学假设进行适当的检验,因为:(a)实验参与者数量少(通常是一个);(b)实验刺激数量少(通常是一个);(c)研究者和参与者的认知偏差;(d)前面语境的影响(例如,研究人员最近可能正在考虑的其他结构)。在本文中,我们对一些支持在语法/语义研究中继续使用传统非定量方法的论点进行了回应。菲利普斯(2009)最近对传统方法进行了辩护,他认为到目前为止,句法研究中的非定量方法还没有带来危害。菲利普斯认为,在文献中,没有一个不正确的直觉判断成为被广泛接受的概括或重要理论主张的基础。因此,他得出结论,没有理由采用更严格的数据收集标准。我们通过从文献中提出三个案例来挑战飞利浦的结论,其中错误的直觉导致了错误的概括和错误的理论化,这似乎是由于研究人员的认知偏见。此外,我们提出了严格的数据收集标准的额外论据。例如,允许宽松的数据收集标准会产生不良影响,即结果和主张往往会被具有更严格的方法标准的研究人员忽略。最后,我们观察到,随着亚马逊(Amazon.com)的土耳其机器人(Mechanical Turk)的出现,用英语进行行为实验比以往任何时候都更容易,这是一个可以用来在互联网上收集行为数据的市场界面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The need for quantitative methods in syntax and semantics research
The prevalent method in syntax and semantics research involves obtaining a judgement of the acceptability of a sentence/meaning pair, typically by just the author of the paper, sometimes with feedback from colleagues. This methodology does not allow proper testing of scientific hypotheses because of (a) the small number of experimental participants (typically one); (b) the small number of experimental stimuli (typically one); (c) cognitive biases on the part of the researcher and participants; and (d) the effect of the preceding context (e.g., other constructions the researcher may have been recently considering). In the current paper we respond to some arguments that have been given in support of continuing to use the traditional nonquantitative method in syntax/semantics research. One recent defence of the traditional method comes from Phillips (2009), who argues that no harm has come from the nonquantitative approach in syntax research thus far. Phillips argues that there are no cases in the literature where an incorrect intuitive judgement has become the basis for a widely accepted generalisation or an important theoretical claim. He therefore concludes that there is no reason to adopt more rigorous data collection standards. We challenge Philips' conclusion by presenting three cases from the literature where a faulty intuition has led to incorrect generalisations and mistaken theorising, plausibly due to cognitive biases on the part of the researchers. Furthermore, we present additional arguments for rigorous data collection standards. For example, allowing lax data collection standards has the undesirable effect that the results and claims will often be ignored by researchers with stronger methodological standards. Finally, we observe that behavioural experiments are easier to conduct in English than ever before, with the advent of Amazon.com's Mechanical Turk, a marketplace interface that can be used for collecting behavioural data over the internet.
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