Computer model knows what you're thinking会读心的计算机
Researchers can predict which noun a person is visualizing.
Kerri Smith 译者: Gunner
A computer model has been developed that can predict what word you are thinking of. The model may help to resolve questions about how the brain processes words and language, and might even lead to techniques for decoding people's thoughts.
Researchers led by Tom Mitchell of Carnegie Mellon University in Pittsburgh, Pennsylvania, 'trained' a computer model to recognize the patterns of brain activity associated with 60 images, each of which represented a different noun, such as 'celery' or 'aeroplane'.
The team started with the assumption that the brain processes words in terms of how they relate to movement and sensory information. Words such as 'hammer', for example, are known to cause movement-related areas of the brain to light up; on the other hand, the word 'castle' triggers activity in regions that process spatial information.
Mitchell and his colleagues also knew that different nouns are associated more often with some verbs than with others - the verb 'eat', for example, is more likely to be found in conjunction with 'celery' than with 'aeroplane'.
The researchers designed the model to try and use these semantic links to work out how the brain would react to particular nouns. They fed 25 such verbs into the model.
能够窥探你心中所想词语的计算机已经问世。这项发明或许可以帮助人们了解大脑如何处理单词和语句，并且甚至会发展成为解码人类思想的技术。
在宾夕法尼亚州匹兹堡市的卡耐基梅隆大学，汤姆•米切尔领导的研发人员们"训练"出的电脑可以识别出人脑对于60幅图像的不同反应，这些图像则代表了不同的名词，如"芹菜"、"飞机"等。
研究人员基于这样的假设：单词与动作或感观信息相关联，大脑根据这种关联性来处理单词。例如，"锤子"会刺激大脑中与运动相关的区域；而"城堡"则触发了处理空间信息的区域。
米切尔与同事们也发现，不同的名词与某些特定的动词联系密切--比如，与"飞机"相比，"芹菜"更有可能与"吃"相关联。研究人员试图利用这些语义上的关联设计模型，用以了解大脑对于一些名词的反应。他们给模型输入了25个类似的动词。
Active association
The team then used functional magnetic resonance imaging (fMRI) to scan the brains of 9 volunteers as they looked at images of the nouns. The researchers then fed the model 58 of the 60 nouns to train it. For each noun, the model sorted through a trillion-word body of text to find how it was related to the 25 verbs, and how that related to the activation pattern.
After training, the models were put to the test. Their task was to predict the pattern of activity for the two missing words from the group of 60, and then to deduce which word was which. On average, the models came up with the right answer more than three-quarters of the time.
The team then went one step further, this time training the models on 59 of the 60 test words, and then showing them a new brain activity pattern and offering them a choice of 1,001 words to match it. The models per
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