Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA.
Neuron. 2011 Oct 20;72(2):404-16. doi: 10.1016/j.neuron.2011.08.026.
We present a high-dimensional model of the representational space in human ventral temporal (VT) cortex in which dimensions are response-tuning functions that are common across individuals and patterns of response are modeled as weighted sums of basis patterns associated with these response tunings. We map response-pattern vectors, measured with fMRI, from individual subjects' voxel spaces into this common model space using a new method, "hyperalignment." Hyperalignment parameters based on responses during one experiment--movie viewing--identified 35 common response-tuning functions that captured fine-grained distinctions among a wide range of stimuli in the movie and in two category perception experiments. Between-subject classification (BSC, multivariate pattern classification based on other subjects' data) of response-pattern vectors in common model space greatly exceeded BSC of anatomically aligned responses and matched within-subject classification. Results indicate that population codes for complex visual stimuli in VT cortex are based on response-tuning functions that are common across individuals.
我们提出了一个人类腹侧颞（VT）皮层表示空间的高维模型，其中的维度是个体间共有的响应调谐函数，而响应模式则被建模为与这些响应调谐相关的基础模式的加权和。我们使用一种新方法“超对齐”将个体受试者的体素空间中的响应模式向量映射到这个共同的模型空间中。基于一个实验——电影观看——的响应的超对齐参数，确定了 35 个共同的响应调谐函数，这些函数捕获了电影和两个类别感知实验中广泛刺激之间的细微差别。在共同模型空间中，基于其他受试者数据的响应模式向量的受试者间分类（BSC，基于多变量模式分类）大大超过了解剖对齐响应的 BSC，并且与受试者内分类相匹配。结果表明，VT 皮层中复杂视觉刺激的群体代码基于个体间共有的响应调谐函数。
