Evaluating quantitative and conceptual models of speech production: how does SLAM fare?

In a previous publication, we presented a new computational model called SLAM (Walker & Hickok, Psychonomic Bulletin & Review doi: 10.3758/s13423-015-0903 ), based on the hierarchical state feedback control (HSFC) theory (Hickok Nature Reviews Neuroscience, 13(2), 135-145, 2012). In his commentary, Goldrick (Psychonomic Bulletin & Review doi: 10.3758/s13423-015-0946-9 ) claims that SLAM does not represent a theoretical advancement, because it cannot be distinguished from an alternative lexical + postlexical (LPL) theory proposed by Goldrick and Rapp (Cognition, 102(2), 219-260, 2007). First, we point out that SLAM implements a portion of a conceptual model (HSFC) that encompasses LPL. Second, we show that SLAM accounts for a lexical bias present in sound-related errors that LPL does not explain. Third, we show that SLAM's explanatory advantage is not a result of approximating the architectural or computational assumptions of LPL, since an implemented version of LPL fails to provide the same fit improvements as SLAM. Finally, we show that incorporating a mechanism that violates some core theoretical assumptions of LPL-making it more like SLAM in terms of interactivity-allows the model to capture some of the same effects as SLAM. SLAM therefore provides new modeling constraints regarding interactions among processing levels, while also elaborating on the structure of the phonological level. We view this as evidence that an integration of psycholinguistic, neuroscience, and motor control approaches to speech production is feasible and may lead to substantial new insights.