Walker Grant M, Hickok Gregory
Cognitive Sciences, University of California, Irvine, CA, USA.
Psychon Bull Rev. 2016 Apr;23(2):653-60. doi: 10.3758/s13423-015-0962-9.
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.
在之前的一篇论文中，我们提出了一种名为SLAM的新计算模型（沃克和希科克，《心理onomic通报与评论》，doi:10.3758/s13423-015-0903），该模型基于分层状态反馈控制（HSFC）理论（希科克，《自然神经科学评论》，13(2)，135-145，2012）。在他的评论中，戈德里克（《心理onomic通报与评论》，doi:10.3758/s13423-015-0946-9）声称SLAM并不代表理论上的进步，因为它无法与戈德里克和拉普提出的另一种词汇+后词汇（LPL）理论（《认知》，102(2)，219-260，2007）区分开来。首先，我们指出SLAM实现了一个包含LPL的概念模型（HSFC）的一部分。其次，我们表明SLAM解释了与声音相关的错误中存在的词汇偏差，而LPL无法解释这一点。第三，我们表明SLAM的解释优势并非源于近似LPL的架构或计算假设，因为LPL的一个实现版本未能提供与SLAM相同的拟合改进。最后，我们表明纳入一种违反LPL某些核心理论假设的机制——使其在交互性方面更像SLAM——能让该模型捕捉到与SLAM相同的一些效应。因此，SLAM提供了关于处理层次之间相互作用新的建模约束，同时也阐述了语音层次的结构。我们认为这证明了将心理语言学、神经科学和运动控制方法整合到言语产生研究中是可行的，并且可能会带来重大的新见解。
