印刷词汇的众包和人工智能生成的习得年龄(AoA)规范:扩展Kuperman等人(2012)的规范。

IF 3.9 2区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL
Clarence Green, Anthony Pak-Hin Kong, Marc Brysbaert, Kathleen Keogh
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引用次数: 0

摘要

本文回顾了Kuperman et al.(2012)的习得年龄(AoA)规范。进行了三项研究。研究1报告了一项众包“大型研究”,从参与者中获得了790,024个估计,他们第一次阅读和写作的年龄为11,074个早期获得的单词,来自Kuperman等人(2012)。该研究旨在区分口头语言接受性AoA和基于印刷品的AoA。结果与最初的估计有很好的相关性,如假设的那样,提供了更高的读/写aoa。这些是作为原始规范的补充而发布的。研究2探索了大型语言模型(llm)的潜力,特别是gpt - 40,以复制这些众包的AoA估计。研究结果表明,人工智能生成的估计与人类判断之间存在很强的相关性,表明人工智能在估计AoA和为心理语言学和教育研究制定规范方面的效用,而不是众包。研究3利用人工智能将估计扩展到Kuperman等人(2012)和英语众包项目(ECP)中的所有知名单词。研究3还研究了一个经过训练的模型,该模型对Kuperman等人(2012)的2000个评级进行了微调。微调增加了与人类评分的一致性,尽管与未经训练的模型的比较表明,在英语中,微调对于获得有用的AoA估计并不必要。经过训练和未经训练的人工智能生成的规范都与人类评分高度相关,并且在计算文字处理时间和回归准确性方面表现良好。讨论了人工智能估计的用途和局限性。所有资源都在开放科学框架中提供,可以免费用于研究和教育。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Crowdsourced and AI-generated age-of-acquisition (AoA) norms for vocabulary in print: Extending the Kuperman et al. (2012) norms.

This paper revisits the age-of-acquisition (AoA) norms of Kuperman et al. (2012). Three studies were conducted. Study 1 reports a crowdsourcing 'megastudy' obtaining 790,024 estimates from participants with the age they could first read and write 11,074 early acquired words from Kuperman et al. (2012). The study aimed to differentiate between oral language receptive AoA and print-based AoA. The results correlate well with the original estimates, offering, as hypothesized, higher AoAs for reading/writing. These are released as supplements to the original norms. Study 2 explored the potential of large language models (LLMs), specifically GPT-4o, to replicate these crowdsourced AoA estimates. The findings indicated a strong correlation between AI-generated estimates and human judgments, showing the utility of AI in estimating AoA and developing norms for psycholinguistic and educational research in lieu of crowdsourcing. Study 3 leveraged AI to extend estimates to all well-known words in Kuperman et al. (2012) and the English Crowdsourcing Project (ECP). Study 3 also investigated a trained model fine-tuned on 2000 ratings from Kuperman et al. (2012). Fine-tuning increased alignment with human ratings, though comparisons with untrained models suggested that fine-tuning is not essential in English for obtaining useful AoA estimates. Both trained and untrained AI-generated norms correlated highly with human ratings and performed well in accounting for word processing times and accuracy in regressions. Uses and limitations of the AI estimates are discussed. All resources are made available in the Open Science Framework and can be used freely for research and education.

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来源期刊
CiteScore
10.30
自引率
9.30%
发文量
266
期刊介绍: Behavior Research Methods publishes articles concerned with the methods, techniques, and instrumentation of research in experimental psychology. The journal focuses particularly on the use of computer technology in psychological research. An annual special issue is devoted to this field.
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