Geriatric Research Education and Clinical Center, VA Healthcare System, Pittsburgh, PA.
Department of Communication Sciences and Disorders, University of Pittsburgh, PA.
J Speech Lang Hear Res. 2020 Feb 26;63(2):599-614. doi: 10.1044/2019_JSLHR-19-00255. Epub 2020 Feb 5.
Purpose Aphasia is a language disorder caused by acquired brain injury, which generally involves difficulty naming objects. Naming ability is assessed by measuring picture naming, and models of naming performance have mostly focused on accuracy and excluded valuable response time (RT) information. Previous approaches have therefore ignored the issue of processing efficiency, defined here in terms of optimal RT cutoff, that is, the shortest deadline at which individual people with aphasia produce their best possible naming accuracy performance. The goals of this study were therefore to (a) develop a novel model of aphasia picture naming that could accurately account for RT distributions across response types; (b) use this model to estimate the optimal RT cutoff for individual people with aphasia; and (c) explore the relationships between optimal RT cutoff, accuracy, naming ability, and aphasia severity. Method A total of 4,021 naming trials across 10 people with aphasia were scored for accuracy and RT onset. Data were fit using a novel ex-Gaussian multinomial RT model, which was then used to characterize individual optimal RT cutoffs. Results Overall, the model fitted the empirical data well and provided reliable individual estimates of optimal RT cutoff in picture naming. Optimal cutoffs ranged between approximately 5 and 10 s, which has important implications for assessment and treatment. There was no direct relationship between aphasia severity, naming RT, and optimal RT cutoff. Conclusion The multinomial ex-Gaussian modeling approach appears to be a promising and straightforward way to estimate optimal RT cutoffs in picture naming in aphasia. Limitations and future directions are discussed.
失语症是一种由脑损伤引起的语言障碍,通常涉及物体命名困难。命名能力通过测量图片命名来评估,命名表现模型主要集中在准确性上,排除了有价值的反应时间 (RT) 信息。因此,以前的方法忽略了处理效率的问题,这里定义为最佳 RT 截止值,即患有失语症的个体产生最佳命名准确性表现的最短截止时间。本研究的目的是:(a) 开发一种新的失语症图片命名模型,能够准确解释不同反应类型的 RT 分布;(b) 使用该模型估计个体失语症患者的最佳 RT 截止值;(c) 探索最佳 RT 截止值、准确性、命名能力和失语症严重程度之间的关系。
对 10 名失语症患者的 4021 次命名试验进行了准确性和 RT 起始的评分。使用新的 Ex-Gaussian 多项 RT 模型对数据进行拟合,然后使用该模型来描述个体的最佳 RT 截止值。
总体而言,该模型很好地拟合了经验数据,并为图片命名中的个体最佳 RT 截止值提供了可靠的估计。最佳截止值范围在大约 5 到 10 秒之间,这对评估和治疗具有重要意义。失语症严重程度、命名 RT 和最佳 RT 截止值之间没有直接关系。
多项 Ex-Gaussian 建模方法似乎是一种很有前途和直接的方法,可以估计失语症图片命名中的最佳 RT 截止值。讨论了限制和未来方向。