Rogers Timothy T, Lambon Ralph Matthew A, Garrard Peter, Bozeat Sasha, McClelland James L, Hodges John R, Patterson Karalyn
Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK.
Psychol Rev. 2004 Jan;111(1):205-35. doi: 10.1037/0033-295X.111.1.205.
Wernicke (1900, as cited in G. H. Eggert, 1977) suggested that semantic knowledge arises from the interaction of perceptual representations of objects and words. The authors present a parallel distributed processing implementation of this theory, in which semantic representations emerge from mechanisms that acquire the mappings between visual representations of objects and their verbal descriptions. To test the theory, they trained the model to associate names, verbal descriptions, and visual representations of objects. When its inputs and outputs are constructed to capture aspects of structure apparent in attribute-norming experiments, the model provides an intuitive account of semantic task performance. The authors then used the model to understand the structure of impaired performance in patients with selective and progressive impairments of conceptual knowledge. Data from 4 well-known semantic tasks revealed consistent patterns that find a ready explanation in the model. The relationship between the model and related theories of semantic representation is discussed.
韦尼克(1900年,引自G. H. 埃格特,1977年)提出语义知识源于物体和词语的感知表征之间的相互作用。作者提出了该理论的一种并行分布式处理实现方式,其中语义表征源自获取物体视觉表征与其言语描述之间映射关系的机制。为了检验该理论,他们训练模型将物体的名称、言语描述和视觉表征联系起来。当模型的输入和输出被构建以捕捉属性规范实验中明显的结构方面时,该模型为语义任务表现提供了直观的解释。作者随后使用该模型来理解概念知识存在选择性和渐进性损伤的患者表现受损的结构。来自4个著名语义任务的数据揭示了一致的模式,这些模式在模型中能找到现成的解释。本文还讨论了该模型与相关语义表征理论之间的关系。