■如果神经科学家被问及灵长类动物的哪个大脑区域负责物体识别,大多数人可能会回答下颞叶(IT)皮层。虽然IT可能对精细歧视负责,因此,它由中央凹视觉输入主导,对象识别比精细歧视更重要。重要的是,对感兴趣的物体的模糊通常需要识别,有合理的信心,它在外围的存在。可以说,IT在这种外围设备识别中起着次要作用,而其他视觉区域可能更重要。
■为了研究如何将早期视觉处理区域(例如LGN和V1)携带的信号用于外围的物体识别,我们在这里专注于区分面孔和非面孔的任务。我们测试了各种模型对令人讨厌的参数有多敏感,例如图像的比例和方向的变化,以及图像背景的类型。
■我们发现V1简单或复杂细胞的模型可以提供相当可靠的信息,在现实场景中性能优于80%。LGN模型的表现要差得多。
■因为外围识别对于实现精细识别(通过将感兴趣的物体带到中央凹)都至关重要,可能足以解释我们日常识别引导行为的相当一部分,我们认为目前对区域IT和中央凹处理的关注过于狭窄。我们建议,而不是以类似IT的属性为主要目标的分层系统,对象识别应该被看作是一个并行的过程,高精度中央凹模块与可跨视野操作的较低精度和更快的模块并行操作。
UNASSIGNED: If neuroscientists were asked which brain area is responsible for object recognition in primates, most would probably answer infero-temporal (IT) cortex. While IT is likely responsible for fine discriminations, and it is accordingly dominated by foveal visual inputs, there is more to object recognition than fine discrimination. Importantly, foveation of an object of interest usually requires recognizing, with reasonable confidence, its presence in the periphery. Arguably, IT plays a secondary role in such peripheral recognition, and other visual areas might instead be more critical.
UNASSIGNED: To investigate how signals carried by early visual processing areas (such as LGN and V1) could be used for object recognition in the periphery, we focused here on the task of distinguishing faces from non-faces. We tested how sensitive various models were to nuisance parameters, such as changes in scale and orientation of the image, and the type of image background.
UNASSIGNED: We found that a model of V1 simple or complex cells could provide quite reliable information, resulting in performance better than 80% in realistic scenarios. An LGN model performed considerably worse.
UNASSIGNED: Because peripheral recognition is both crucial to enable fine recognition (by bringing an object of interest on the fovea), and probably sufficient to account for a considerable fraction of our daily recognition-guided behavior, we think that the current focus on area IT and foveal processing is too narrow. We propose that rather than a hierarchical system with IT-like properties as its primary aim, object recognition should be seen as a parallel process, with high-accuracy foveal modules operating in parallel with lower-accuracy and faster modules that can operate across the visual field.