Natural scene statistics account for the representation of scene categories in human visual cortex.
Natural scene statistics account for the representation of scene categories in human visual cortex.
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DOI：
10.1016/j.neuron.2013.06.034
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发表时间：
2013-09-04
期刊：
影响因子：
16.2
通讯作者：
Gallant JL
中科院分区：
文献类型：
--
作者：
Stansbury DE;Naselaris T;Gallant JL
在自然视觉过程中，人类会对他们遇到的场景进行分类：办公室、海滩等等，这些分类是通过对自然场景中物体共存方式的了解来确定的。人类大脑是如何聚集关于物体的信息来表示场景类别的？为了探索这个问题，我们使用统计学习方法来学习客观地捕捉大量自然场景中对象的共现统计数据的类别。使用学习的类别，我们模拟了人类受试者在观看场景图像时诱发的fMRI脑信号。我们发现，前视皮层的大部分诱发活动是由学习的类别来解释的。此外，基于这些场景类别的解码器根据由这些场景诱发的大脑活动准确地预测包括新颖场景的类别和对象。这些结果表明，人类的大脑代表场景类别，捕捉世界上物体的共现统计。
During natural vision, humans categorize the scenes they encounter: an office, the beach, and so on. These categories are informed by knowledge of the way that objects co-occur in natural scenes. How does the human brain aggregate information about objects to represent scene categories? To explore this issue we used statistical learning methods to learn categories that objectively capture the co-occurrence statistics of objects in a large collection of natural scenes. Using the learned categories, we modeled fMRI brain signals evoked in human subjects when viewing images of scenes. We find that evoked activity across much of anterior visual cortex is explained by the learned categories. Furthermore, a decoder based on these scene categories accurately predicts the categories and objects comprising novel scenes from brain activity evoked by those scenes. These results suggest that the human brain represents scene categories that capture the co-occurrence statistics of objects in the world.