McGill University, Department of Psychology.
Indiana University, Department of Psychological and Brain Sciences.
J Exp Psychol Gen. 2023 Jun;152(6):1814-1823. doi: 10.1037/xge0001407.
Word frequency (WF) is a strong predictor of lexical behavior. However, much research has shown that measures of contextual and semantic diversity offer a better account of lexical behaviors than WF (Adelman et al., 2006; Jones et al., 2012). In contrast to these previous studies, Chapman and Martin (see record 2022-14138-001) recently demonstrated that WF seems to account for distinct and greater levels of variance than measures of contextual and semantic diversity across a variety of datatypes. However, there are two limitations to these findings. The first is that Chapman and Martin (2022) compared variables derived from different corpora, which makes any conclusion about the theoretical advantage of one metric over another confounded, as it could be the construction of one corpus that provides the advantage and not the underlying theoretical construct. Second, they did not consider recent developments in the semantic distinctiveness model (SDM; Johns, 2021a; Johns et al., 2020; Johns & Jones, 2022). The current paper addressed the second limitation. Consistent with Chapman and Martin (2022), our results showed that the earliest versions of the SDM were less predictive of lexical data relative to WF when derived from a different corpus. However, the later versions of the SDM accounted for substantially more unique variance than WF in lexical decision and naming data. The results suggest that context-based accounts provide a better explanation of lexical organization than repetition-based accounts. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
词频（WF）是词汇行为的强有力预测指标。然而，大量研究表明，语境和语义多样性的测量比 WF 能更好地解释词汇行为（Adelman 等人，2006；Jones 等人，2012）。与这些先前的研究不同，Chapman 和 Martin（见记录 2022-14138-001）最近表明，WF 似乎比语境和语义多样性的测量更能解释不同的和更大程度的变化，而 WF 是跨越各种数据类型的。然而，这些发现有两个限制。首先，Chapman 和 Martin（2022）比较了来自不同语料库的变量，这使得关于一种度量相对于另一种度量的理论优势的任何结论都变得复杂，因为可能是一个语料库的构建提供了优势，而不是潜在的理论构建。其次，他们没有考虑语义独特性模型（SDM；Johns，2021a；Johns 等人，2020；Johns 和 Jones，2022）的最新发展。本文解决了第二个限制。与 Chapman 和 Martin（2022）一致，我们的结果表明，当从不同的语料库中得出时，SDM 的早期版本相对于 WF 对词汇数据的预测性较差。然而，SDM 的后期版本在词汇判断和命名数据中比 WF 解释了更多独特的变化。研究结果表明，基于语境的解释比基于重复的解释提供了对词汇组织的更好解释。（PsycInfo 数据库记录（c）2023 APA，保留所有权利）。
