| 钱 倩,王 兰,王曼曼,胡瑞萍.词汇产生交互激活模型在脑卒中后失语症患者命名错误分析中的应用[J].中国康复医学杂志,2022,(8):1051~1056 |
| 词汇产生交互激活模型在脑卒中后失语症患者命名错误分析中的应用 点此下载全文 |
| 钱 倩 王 兰 王曼曼 胡瑞萍 |
| 同济大学附属养志康复医院，上海市，201619 |
| 基金项目：上海市2020年度“科技创新行动计划”生物医药科技支撑专项项目(20S31905700) |
| DOI：10.3969/j.issn.1001-1242.2022.08.007 |
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| 摘要： |
| 摘要
目的：应用词汇产生交互激活模型(the interactive activation model of word production， IA模型)分析不同类型失语症命名错误反应的规律及产生机制，探讨针对性治疗方法。
方法：纳入脑卒中后失语症患者41例，对其进行语言能力评估、失语症亚型分类、视图命名能力评估，根据IA模型对命名错误反应进行分类并分析。
结果：①流利性失语症的命名正确率(42.0%)高于非流利性失语症(28.6%)，前者命名错误类型出现率最高的为语义性错误(19.7%)，后者为遗漏错误(21.05%)；②遗漏错误和组词错误是最能够用以区分流利性和非流利性失语的命名错误反应类型；③每位失语症患者语义性错误占错误数的比率与命名正确率显著相关，命名正确率提高，语义性错误比率也升高。
结论：流利性和非流利性失语症命名错误类型存在显著性差异，可应用IA模型进行命名错误产生机制的分析并选择针对性治疗策略；应用IA模型得出了区分流利性和非流利性失语症的典型判别函数，可用于失语症亚型判别。 |
| 关键词：脑卒中 失语症 命名障碍 词汇提取困难 词汇产生交互激活模型 |
| Application of interactive activation model of word production in analyzing naming errors of patients with post stroke aphasia Download Fulltext |
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| Shanghai Yangzhi Rehabilitation Hospital，Tongji University School of Medicine， 201619 |
| Fund Project: |
| Abstract: |
| Abstract
Objective： To analyze the rule and mechanism of naming errors in different types of aphasia by using the interactive activation model (IA model)， and to explore the targeted treatments.
Method： Forty-one patients with post stroke aphasia were included in this study. Their language ability， subtype classification of aphasia and picture naming ability were evaluated， and their naming errors were classified and analyzed according to the IA model.
Result： ①The naming accuracy rate of fluent aphasia (42.0%) was higher than that of non-fluent aphasia (28.6%). The proportion of semantic error (19.7%) was the highest in fluent aphasia， and the proportion of omission error (21.05%) was the highest in non-fluent aphasia. ②Omission errors and word formation errors are the most effective types of naming errors to distinguish fluent aphasia from non-fluent aphasia. ③The ratio of semantic errors to all naming errors per aphasia patient was significantly correlated with the naming accuracy， and the ratio of semantic errors increased with the increase of naming accuracy.
Conclusion： There are significant differences in the types of naming errors between fluent aphasia and non-fluent aphasia. The IA model can be used to analyze the mechanism of naming errors and select targeted treatment strategies. A canonical discriminant function was obtained by using the IA model， which can be used to identify subtypes of aphasia. |
| Keywords:stroke aphasia naming disorder word retrieval difficulty interactive activation model of word production |
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