Center for Cognitive Brain Imaging, Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania.
Hum Brain Mapp. 2013 Oct;34(10):2624-34. doi: 10.1002/hbm.22087. Epub 2012 Apr 16.
Human neuroimaging studies have increasingly converged on the possibility that the neural representation of specific numbers may be decodable from brain activity, particularly in parietal cortex. Multivariate machine learning techniques have recently demonstrated that the neural representation of individual concrete nouns can be decoded from fMRI patterns, and that some patterns are general over people. Here we use these techniques to investigate whether the neural codes for quantities of objects can be accurately decoded. The pictorial mode (nonsymbolic) depicted a set of objects pictorially (e.g., a picture of three tomatoes), whereas the digit-object mode depicted quantities as combination of a digit (e.g., 3) with a picture of a single object. The study demonstrated that quantities of objects were decodable from neural activation patterns, in parietal regions. These brain activation patterns corresponding to a given quantity were common across objects and across participants in the pictorial mode. Other important findings included better identification of individual numbers in the pictorial mode, partial commonality of neural patterns across the two modes, and hemispheric asymmetry with pictorially-depicted numbers represented bilaterally and numbers in the digit-object mode represented primarily in the left parietal regions. The findings demonstrate the ability to identify individual quantities of objects based on neural patterns, indicating the presence of stable neural representations of numbers. Additionally, they indicate a predominance of neural representation of pictorially depicted numbers over the digit-object mode.
人类神经影像学研究越来越多地得出这样的可能性，即特定数字的神经表示可能可以从大脑活动中解码出来，特别是在顶叶皮层。最近的多元机器学习技术已经证明，个体具体名词的神经表示可以从 fMRI 模式中解码出来，并且某些模式在人群中是通用的。在这里，我们使用这些技术来研究物体数量的神经编码是否可以被准确解码。图形模式（非符号）以图形方式描绘了一组物体（例如，三张西红柿的图片），而数字-物体模式则将数量表示为数字（例如 3）与单个物体的图片的组合。研究表明，可以从顶叶区域的神经激活模式中解码物体的数量。这些与给定数量相对应的大脑激活模式在图形模式中是常见的，并且在对象和参与者之间是共同的。其他重要发现包括在图形模式中更好地识别单个数字，两种模式之间的神经模式部分共同性，以及双侧表示图形化数字，而数字-物体模式主要在左顶叶区域表示的半球不对称性。这些发现表明可以根据神经模式识别物体的个体数量，表明存在稳定的数字神经表示。此外，它们表明图形化表示的数字在神经表示中占主导地位，而数字-物体模式则次之。
