Walker Grant M, Fridriksson Julius, Hickok Gregory
Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, USA.
Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA.
Cogn Neuropsychol. 2021 Feb;38(1):50-71. doi: 10.1080/02643294.2020.1837092. Epub 2020 Nov 5.
Connectionist simulation models and processing tree mathematical models of picture naming have complementary advantages and disadvantages. These model types were compared in terms of their predictions of independent language measures and their associations between model components and measures that should be related according to their theoretical interpretations. The models were tasked with predicting independent picture naming data, neuropsychological test scores of semantic association and speech production, grammatical categories of formal errors, and lexical properties of target items. In all cases, the processing tree model parameters provided better predictions and stronger associations between parameters and independent language measures than the connectionist simulation model. Given the enhanced generalizability of latent variable measurements afforded by the processing tree model, evidence regarding mechanistic and representational features of the speech production system are re-evaluated. Several areas are indicated as being potentially viable targets for elaboration of the mechanistic descriptions of picture naming errors.
联结主义模拟模型和图片命名的加工树数学模型各有互补的优缺点。根据它们对独立语言指标的预测以及模型组件与根据理论解释应相关的指标之间的关联，对这些模型类型进行了比较。这些模型的任务是预测独立的图片命名数据、语义联想和言语产生的神经心理学测试分数、形式错误的语法类别以及目标项目的词汇属性。在所有情况下，与联结主义模拟模型相比，加工树模型参数能提供更好的预测，且参数与独立语言指标之间的关联更强。鉴于加工树模型提供的潜在变量测量具有更高的通用性，重新评估了有关言语产生系统的机制和表征特征的证据。指出了几个领域可能是详细阐述图片命名错误机制描述的可行目标。
