Abstract：
To interpret our surroundings, the brain uses a visual categorization process. Current theories and models suggest that this process comprises a hierarchy of different computations that transforms complex, high-dimensional inputs into lower-dimensional representations (i.e., manifolds) in support of multiple categorization behaviors. Here, we tested this hypothesis by analyzing these transformations reflected in dynamic MEG source activity while individual participants actively categorized the same stimuli according to different tasks: face expression, face gender, pedestrian gender, and vehicle type. Results reveal three transformation stages guided by the pre-frontal cortex. At stage 1 (high-dimensional, 50-120 ms), occipital sources represent both task-relevant and task-irrelevant stimulus features; task-relevant features advance into higher ventral/dorsal regions, whereas task-irrelevant features halt at the occipital-temporal junction. At stage 2 (121-150 ms), stimulus feature representations reduce to lower-dimensional manifolds, which then transform into the task-relevant features underlying categorization behavior over stage 3 (161-350 ms). Our findings shed light on how the brain\'s network mechanisms transform high-dimensional inputs into specific feature manifolds that support multiple categorization behaviors.
摘要：
为了解释我们周围的环境，大脑使用视觉分类过程。当前的理论和模型表明，此过程包含不同计算的层次结构，这些计算将复杂的，高维输入到低维表示(即，流形)支持多种分类行为。这里,我们通过分析动态MEG源活动中反映的这些转换来测试这一假设，而个体参与者根据不同的任务积极地将相同的刺激分类：面部表情，面对性别,行人性别,和车辆类型。结果揭示了前额叶皮层引导的三个转化阶段。在阶段1(高维，50-120ms),枕骨来源代表任务相关和任务无关的刺激特征；任务相关的特征进入更高的腹侧/背侧区域，而与任务无关的特征在枕骨-颞部停止。在阶段2（121-150ms），刺激特征表示简化为低维流形，然后在第3阶段（161-350毫秒）内将其转换为与任务相关的特征作为分类行为的基础。我们的发现揭示了大脑的网络机制如何将高维输入转化为支持多种分类行为的特定特征流形。
