Semantic complexity and treatment for lexical access

描述（由申请人提供）：在所提出的项目中，我们研究了基于模型的语义治疗对改善失语症中词汇通达缺陷的影响。我们计划建立在以前的工作突出的作用，词汇加工理论在提高词汇通达。本研究拟建立一个词汇通达缺陷的治疗方法数据集，包括口语对抗命名和类别流利性。我们所有的处理方法都集中在最大限度地泛化到语义类别中未经训练的语义相关项。我们的词汇通达缺陷的治疗方法，利用语义复杂性假设。我们定义的语义复杂性在一个类别中：a）较小程度的重叠项目和原型之间; B）更大的变化的语义特征;和c）层次更大的距离原型和项目之间的多维空间。我们以前的工作已经证明，训练更复杂的非典型的例子，动画类别的结果概括为不太复杂的典型例子，失语症患者命名缺陷。在这个项目中，我们计划进一步建立这个假设，并证明复杂性对各种类别的典型性的影响，如无生命类别，明确定义的类别和特设类别。我们还扩展了我们的调查，以了解正常和脑损伤个体的典型性和复杂性之间的关系的机制。我们将通过跨不同类型语义类别的在线类别验证任务来实现这一点。

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.