关于爱沙乌克单人单词命名的实验与计算研究
摘要
本研究探讨了爱沙乌克语中的词汇处理。报告了一项大规模单受试者实验，将单词命名任务与眼动追踪相结合。分析了五个响应变量（首次定瞳持续时间、总定瞳持续时间、定瞳次数、单词命名延迟和口语单词持续时间），采用广义可加模型进行分析。 central research question is whether measures for lexical processing generated by a computational model of the mental lexicon (the Discriminative Lexicon Model, DLM) are predictive for these response variables, and how they compare to classical predictors such as word frequency, neighborhood size, and inflectional paradigm size. Computational models were implemented both with linear and deep mappings. Central findings are, first, that DLM-based measures are powerful predictors for lexical processing, second, that DLM-measures using deep learning are not necessarily more precise predictors of lexical processing than DLM-measures using linear mappings, third, that classical predictors tend to provide somewhat more precise fits compared to DLM-based predictors (except for total fixation duration, where the two provide equivalent goodness of fit), and fourth, that in the naming task lexical variables are not predictive for first fixation duration and the total number of fixations. As the DLM works with mappings from form to meaning, the predictivity of DLM-based measures for total fixation duration, naming latencies, and spoken word duration indicates that meaning is heavily involved in the present word naming task.
引用
@article{arxiv.2509.03143,
title = {An experimental and computational study of an Estonian single-person word naming},
author = {Kaidi Lõo and Arvi Tavast and Maria Heitmeier and Harald Baayen},
journal= {arXiv preprint arXiv:2509.03143},
year = {2025}
}