Simultaneously uncovering the patterns of brain regions involved in different story reading Subprocesses

Simultaneously Uncovering the Patterns of Brain Regions Involved in Different  Story Reading Subprocesses

This Story understanding involves many perceptual and cognitive subprocesses, from perceiving individual words, to parsing sentences, to understanding the relationships among the story characters. We present an integrated computational model of reading that incorporates these and additional subprocesses, simultaneously discovering their fMRI signatures. Our model predicts the fMRI activity associated with reading arbitrary text passages, well enough to distinguish which of two story segments is being read with 74% accuracy. This approach is the first to simultaneously track diverse reading subprocesses during complex story processing and predict the detailed neural representation of diverse story features, ranging from visual word properties to the mention of different story characters and different actions they perform. We construct brain representation maps that replicate many results from a wide range of classical studies that focus each on one aspect of language processing and offer new insights on which type of information is processed by different areas involved in language processing. Additionally, this approach is promising for studying individual differences: it can be used to create single subject maps that may potentially be used to measure reading comprehension and diagnose reading disorders. This work was supported by the National Science Foundation (nsf.gov, 0835797, TM); the National Institute of Child Health and human Development (nichd.nih.gov, 5R01HD075328, TM); and the Rothberg Brain Imaging Award (http://www. cmu.edu/news/archive/2011/July/july7- rothbergawards.shtml, LW AF). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

理解故事这个任务包括了许多感知和认知上的流程,例如感知独立的单词,组合成句子以及理解故事中人物的关系。我们提出了一个计算模型,模型可以合并上述流程,同时发现他们的fMRI特征。我们的模型通过让受试者朗读随机的文段所获得的fMRI活动进行预测。并获得了文段分类的74%的准确率。这个方法是第一个可以同时追踪复杂故事阅读流程的方法,它可以预测关于多样的故事内容的细节神经表示,包括当提到不同故事角色时的视觉-语言特征和他们的不同动作。我们根据之前的研究构造脑部的表示图,它专注于语言处理,并且为其他领域能够运用提供了支持。另外,这个方法研究了个体差异,可以用作创建独立的个体样本,这些样本可能可以用作诊断阅读障碍。

发表评论

邮箱地址不会被公开。 必填项已用*标注