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A Computational Investigation of Sources of Variability in Sentence Comprehension Difficulty in Aphasia

机译:失语症句子理解困难变异源的计算研究

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We present a computational evaluation of three hypotheses about sources of deficit in sentence comprehension in aphasia: slowed processing, intermittent deficiency, and resource reduction. The ACT-R based Lewis and Vasishth (2005) model is used to implement these three proposals. Slowed processing is implemented as slowed execution time of parse steps; intermittent deficiency as increased random noise in activation of elements in memory; and resource reduction as reduced spreading activation. As data, we considered subject vs. object relative sentences, presented in a self-paced listening modality to 56 individuals with aphasia (IWA) and 46 matched controls. The participants heard the sentences and carried out a picture verification task to decide on an interpretation of the sentence. These response accuracies are used to identify the best parameters (for each participant) that correspond to the three hypotheses mentioned above. We show that controls have more tightly clustered (less variable) parameter values than IWA; specifically, compared to controls, among IWA there are more individuals with slow parsing times, high noise, and low spreading activation. We find that (a) individual IWA show differential amounts of deficit along the three dimensions of slowed processing, intermittent deficiency, and resource reduction, (b) overall, there is evidence for all three sources of deficit playing a role, and (c) IWA have a more variable range of parameter values than controls. An important implication is that it may be meaningless to talk about sources of deficit with respect to an abstract verage IWA; the focus should be on the individual's differential degrees of deficit along different dimensions, and on understanding the causes of variability in deficit between participants.
机译:我们提出了三种关于失语症句子理解不足来源的假说的计算评估:处理缓慢,间歇性不足和资源减少。基于ACT-R的Lewis和Vasishth(2005)模型用于实现这三个建议。慢速处理的实现是因为解析步骤的执行时间变慢。间歇性缺陷,因为激活内存中元素的随机噪声增加;和资源减少,因为传播活动减少。作为数据,我们考虑了主语与宾语的相对句子,以自定进度的听力方式向56名失语症患者(IWA)和46位相匹配的对照组进行了介绍。参与者听到了这些句子,并执行了图片验证任务来决定对句子的解释。这些响应精度用于识别与上述三个假设相对应的最佳参数(针对每个参与者)。我们显示,与IWA相比,控件具有更紧密的聚类(较少的变量)参数值。特别是,与对照组相比,IWA中的分析时间较慢,噪声较高且扩展激活较低的个体更多。我们发现(a)个别IWA在加工速度减慢,间歇性短缺和资源减少这三个维度上显示出不同程度的赤字,(b)总体而言,有证据表明所有三种赤字来源都在起作用,并且(c)与控件相比,IWA具有更多可变的参数值范围。一个重要的含义是,就抽象verage IWA讨论缺陷的来源可能毫无意义。重点应放在个体在不同维度上的赤字差异程度,并应了解参与者之间赤字差异的原因。

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