Semantic complexity and treatment for lexical access

描述（由申请人提供）：在拟建项目中，我们研究基于模型的语义治疗对改善失语症词汇获取缺陷的影响。我们计划在先前工作的基础上强调词汇加工理论在提高词汇获取中的作用。在拟议的项目中，我们计划建立一个词汇获取缺陷的治疗方法数据集，包括口头对抗命名和类别流畅性。我们所有的处理方法都集中在最大化对语义范畴内未训练的语义相关项的泛化。我们对词汇获取缺陷的处理方法利用语义复杂性假设。我们将一个类别的语义复杂性定义为：a)项目和原型之间的重叠程度较低；B)语义特征变化较大；c)在多维空间中，原型与道具之间的层级距离更大。我们之前的工作已经证明，在有命名缺陷的失语患者中，训练更复杂的动画类别的非典型例子会导致对不太复杂的典型例子的泛化。在这个项目中，我们计划进一步建立这一假设，并证明复杂性对各种类别（如无生命类别、定义良好的类别和特别类别）的典型性的影响。我们还扩展了我们的研究，以了解正常和脑损伤个体的典型性和复杂性之间关系的机制。我们将通过跨不同类型语义类别的在线类别验证任务来完成这一任务。

DESCRIPTION (provided by applicant): In the proposed project, we examine the effects of model based semantic treatment on improving lexical access defects in aphasia. We plan to build on previous work highlighting the role of theories of lexical processing in improving lexical access. In the proposed project, we plan to establish a data set of treatment methods for lexical access deficits that include oral confrontation naming and category fluency. All our treatment methods are focused on maximizing generalization to untrained semantically related items within the semantic category. Our treatment approach for lexical access deficits that utilizes the semantic complexity hypothesis. We define semantic complexity within a category in terms of: a) lesser degree of overlap between the item and prototype; b) greater variation of semantic features; and c) hierarchically greater distance between the prototype and item in a multidimensional space. Our previous work has demonstrated that training the more complex atypical examples of animate categories results in generalization to the less complex typical examples in aphasic patients with naming deficits. In this project, we plan to further build this hypothesis and demonstrate the effects of complexity on typicality for a variety of categories such as inanimate categories, well defined categories and ad hoc categories. We also extend our investigations to understand the mechanisms underlying the relationship between typicality and complexity in normal and brain damaged individuals. We will accomplish this through online category verification tasks across different types of semantic categories.