Connections and selections: Comparing multivariate predictions and parameter associations from latent variable models of picture naming.

Abstract

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.