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.

中文翻译：

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连接和选择：比较图片命名的潜在变量模型的多元预测和参数关联

图片命名的连接主义仿真模型和处理树数学模型各有优劣。这些模型类型根据它们对独立语言测量的预测以及它们在模型组件和测量之间的关联进行了比较，根据它们的理论解释，它们应该相关。这些模型的任务是预测独立的图片命名数据、语义关联和语音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特性。在所有情况下，处理树模型参数提供了更好的预测以及参数和独立语言度量之间的更强关联，而不是连接主义模拟模型。鉴于处理树模型提供的潜在变量测量的增强的普遍性，有关语音产生系统的机械和表征特征的证据被重新评估。有几个领域被认为是详细说明图片命名错误的机械描述的潜在可行目标。