Quaia Christian, Krauzlis Richard J
Laboratory of Sensorimotor Research, National Eye Institute, NIH, Bethesda, MD, USA.
ArXiv. 2024 Jul 5:arXiv:2407.04816v1.
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.
如果问神经科学家灵长类动物中哪个脑区负责物体识别，大多数人可能会回答是颞下（IT）皮质。虽然IT可能负责精细辨别，并且相应地主要由中央凹视觉输入主导，但物体识别不仅仅是精细辨别。重要的是，对感兴趣物体的中央凹注视通常需要在合理程度上确信其在周边的存在。可以说，IT在这种周边识别中起次要作用，而其他视觉区域可能更关键。为了研究早期视觉处理区域（如外侧膝状体和V1）携带的信号如何用于周边物体识别，我们在此聚焦于区分面孔和非面孔的任务。我们测试了各种模型对干扰参数的敏感程度，如图像的尺度和方向变化以及图像背景类型。我们发现V1简单或复杂细胞模型可以提供相当可靠的信息，在现实场景中的表现优于80%。外侧膝状体模型的表现则差得多。由于周边识别对于实现精细识别（通过将感兴趣物体移至中央凹）至关重要，并且可能足以解释我们日常大量由识别引导的行为，我们认为当前对IT区域和中央凹处理的关注过于狭隘。我们提出，物体识别不应被视为以类似IT属性的层级系统为主要目标，而应被视为一个并行过程，高精度的中央凹模块与能够在整个视野中运行的低精度且更快的模块并行运作。
