将 "首要惊奇 "作为评估基于错误的学习理论的工具:系统回顾

IF 0.9 0 LANGUAGE & LINGUISTICS
Languages Pub Date : 2024-04-16 DOI:10.3390/languages9040147
J. Fazekas, Giovanni Sala, Julian Pine
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引用次数: 0

摘要

基于错误的语言习得学习理论在语言发展研究中具有很大的影响力,然而事实证明,这些理论所提出的预测性学习机制却很难在实验中得到验证。质点惊奇--观察到结构性引物在更多令人惊奇的质点之后会变得更强--已成为解决这一问题的一种很有前途的方法,因为它检验了基于错误的学习理论的一个关键预测:令人惊奇的输入会导致结构重复和学习的增加。然而,由于素材惊奇法是一种相对较新的范式,因此值得对这一承诺的实现程度进行评估。我们对 PS 研究进行了系统回顾,以评估现有方法的优势和局限性。我们发现,除了统计能力和意外水平的测量方法不一致之外,目前研究成果的范围有限,这让人怀疑 PS 是否可以作为一种通用工具来评估基于错误的学习。我们为未来的研究提出了两个主要方向:第一,在更多语言和语法结构中,通过可靠的统计能力和适当的惊奇测量,瞄准质点惊奇效应本身的范围;第二,利用质点惊奇法作为工具,在质点惊奇已经得到可靠证实的条件下,评估基于错误的学习机制的范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prime Surprisal as a Tool for Assessing Error-Based Learning Theories: A Systematic Review
Error-based learning theories of language acquisition are highly influential in language development research, yet the predictive learning mechanism they propose has proven difficult to test experimentally. Prime surprisal—the observation that structural priming is stronger following more surprising primes—has emerged as a promising methodology for resolving this issue as it tests a key prediction of error-based learning theories: surprising input leads to increased structure repetition as well as learning. However, as prime surprisal is a relatively new paradigm, it is worth evaluating how far this promise has been fulfilled. We have conducted a systemic review of PS studies to assess the strengths and limitations of existing approaches, with 13 contributions selected out of 66 search results. We found that alongside inconsistency in statistical power and how the level of surprisal is measured, the limited scope of current results cast doubt on whether PS can be used as a general tool to assess error-based learning. We suggest two key directions for future research: firstly, targeting the scope of the prime surprisal effect itself with reliable statistical power and appropriate surprisal measurements across a greater variety of languages and grammatical structures; and secondly, using the prime surprisal method as a tool to assess the scope of an error-based learning mechanism utilising conditions in which prime surprisal has been reliably established.
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来源期刊
Languages
Languages Arts and Humanities-Language and Linguistics
CiteScore
1.40
自引率
22.20%
发文量
282
审稿时长
11 weeks
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