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. 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. 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. 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.

翻译：如果问神经科学家哪个脑区负责灵长类的物体识别，大多数人可能会回答颞下皮层。虽然颞下皮层很可能负责精细辨别，并因此主要受中央凹视觉输入支配，但物体识别不仅仅包含精细辨别。重要的是，对感兴趣物体的中央凹注视通常需要以合理置信度识别其在周边视野中的存在。可以说，颞下皮层在此类周边识别中扮演次要角色，而其他视觉区域可能更为关键。为探究早期视觉处理区域（如外侧膝状体和初级视皮层）所传递的信号如何用于周边视野的物体识别，本研究聚焦于区分人脸与非人脸的任务。我们测试了不同模型对干扰参数（如图像尺度和方向的变化、图像背景类型）的敏感度。研究发现，初级视皮层的简单细胞或复杂细胞模型能提供相当可靠的信息，在现实场景中实现超过80%的识别性能。外侧膝状体模型的表现则显著较差。由于周边识别既对实现精细识别（通过将感兴趣物体带入中央凹）至关重要，又可能足以解释我们日常识别引导行为中的相当大部分，我们认为当前对颞下皮层和中央凹处理的关注过于局限。我们提出，物体识别不应被视为以颞下皮层特性为主要目标的层级系统，而应被看作并行处理过程——高精度的中央凹模块与可在整个视野运作、精度较低但更快速的模块并行运作。