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GENERATING NATURAL LANGUAGE REPRESENTATIONS OF MENTAL CONTENT FROM FUNCTIONAL BRAIN IMAGES

机译:从功能性大脑图像生成心理内容的自然语言表示

摘要

By way of introduction, the present embodiments described below include apparatuses and methods for generating natural language representations of mental content from functional brain images. Given functional imaging data acquired while a subject reads a text passage, a reconstruction of the text passage is produced. Linguistic semantic vector representations are assigned (1301) to words, phrases or sentences to be used as training stimuli. Basis learning is performed (1305), using brain imaging data acquired (1303) when a subject is exposed to the training stimuli and the corresponding semantic vectors for training stimuli, to learn an image basis directly. Semantic vector decoding (1309) is performed with functional brain imaging data for test stimuli and using the image basis to generate a semantic vector representing the test imaging stimuli. Text generation (1311) is then performed using the decoded semantic vector representing the test imaging stimuli.
机译:通过介绍的方式,下面描述的本实施例包括用于从功能性大脑图像生成心理内容的自然语言表示的设备和方法。给定在对象阅读文本段落时获取的功能成像数据,将生成文本段落的重构。语言语义向量表示( 1301 )被分配给单词,短语或句子,以用作训练刺激。使用对象暴露于训练刺激时获得的脑成像数据( 1303 )进行基础学习( 1305 ),以进行学习直接基于图像。语义矢量解码( 1309 )是通过用于测试刺激的功能性脑成像数据执行的,并使用图像基础生成代表测试成像刺激的语义矢量。然后使用代表测试成像刺激的解码语义向量执行文本生成( 1311 )。

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