会读心的计算机

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 performed well above chance when they were made to rank the 1,001 words according to how well they matched the pattern. The results are reported in Science 1.
The idea is similar to another ‘brain-reading' technique, reported in Nature earlier this year2, that can predict what picture a person is seeing from a selection of more than 100. The new model is different in that it has to look at the meanings of the words, rather than just lower-level visual features of a picture.
活动关联
研究小组给9为志愿者出示了一些名词的图片，并使用机能性磁共振成像技术（fMRI）扫描了他们的大脑。然后研究人员给电脑输入了60个名词中的58个来训练它。计算机会在一个有一万亿单词的文本中进行检索，就为找到每个名词是如何与那25个动词相关联的，并且找到这种关联与哪种大脑活动相关。
训练结束，进入测试阶段。电脑的任务就是找到那2个没有被输入单词所对应的大脑活动类型，并且推算出那2个单词。一般来说，电脑的正确率超过75%。
接下来的实验更深一步，这次给电脑输入59个单词用于训练，然后给它一个新的大脑活动类型，让它在1001个单词中选择一个与之配对。根据这1001个单词与脑活动的配比程度，电脑将它们排序，它的表现依然良好。这项结果已经发表于《科学》杂志。
另一种"读脑"技术与此想法类似，它可以从100多幅图片中找到人看过的那一张，这项技术于今年早些时候发表于《自然》杂志。两种技术不同之处在于，本文介绍的新模型着眼于词语的意思，而不仅仅是较低等级的图片的视觉特征。

Mind readers
It shouldn't be too difficult to get the model to choose accurately between a larger number of words, says John-Dylan Haynes, who also works on models of brain decoding at the Bernstein Center for Computational Neuroscience Berlin in Germany. "This study shows a method that allows one to read out a large number of different thoughts from brain activity, even with only a few calibration measurements," he says.
An average English speaker knows 50,000 words, Mitchell says, so the model could in theory be used to select any word a subject chooses to think of.
Even whole sentences might not be too distant a prospect for the model, says Mitchell. "Now that we can see individual words, it gives the scaffolding for starting to see what the brain does with multiple words as it assembles them," he says. This gives researchers the chance to understand the "mental chemistry" that the brain does when it processes such phrases, Mitchell suggests.
Models such as this one could also be useful in diagnosing disorders of language or helping students pick up a foreign language. In semantic dementia, for example, people lose the ability to remember the meanings of things - shown a picture of a chihuahua, they can only recall 'dog', for example - but little is known about what exactly goes wrong in the brain. "We could look at what the neural encoding is for this," says Mitchell.
读心者
"让电脑从更多的词语中精确挑选出一个并不是非常难的事"，John-Dylan Haynes如是说，他在柏林的计算神经科学Bernstein中心工作，同样致力于研发可以解码人脑的模型。他说，"这项研究指出了一种通过大脑活动阅读大量不同想法的方法，这种方法甚至只需要少量的测量进行校准"。
"一般说英语的人认识50000个单词"，米切尔说道，"所以该模型在理论上可以找到任何一个大脑所想的单词"。
"或许不久的将来就可以实现对于整句话的预测"，米切尔表示，"现在我们可以预测单词，这就为我们了解大脑如何处理组合多个单词打好了基础。这就让研究者有机会理解"精神化学"--即大脑在处理这类词语时是如何工作的。"

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