Huang Jun, Yang Yan, Zhou Ke, Zhao Xudong, Zhou Quan, Zhu Hong, Yang Yingshan, Zhang Chunming, Zhou Yifeng, Zhou Wu
CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of ChinaHefei, China.
Department of Otolaryngology and Communicative Sciences, University of Mississippi Medical CenterJackson, MS, United States.
Front Neurosci. 2017 Aug 25;11:474. doi: 10.3389/fnins.2017.00474. eCollection 2017.
Visual objects are recognized by their features. Whereas, some features are based on simple components (i.e., local features, such as orientation of line segments), some features are based on the whole object (i.e., global features, such as an object having a hole in it). Over the past five decades, behavioral, physiological, anatomical, and computational studies have established a general model of vision, which starts from extracting local features in the lower visual pathways followed by a feature integration process that extracts global features in the higher visual pathways. This local-to-global model is successful in providing a unified account for a vast sets of perception experiments, but it fails to account for a set of experiments showing human visual systems' superior sensitivity to global features. Understanding the neural mechanisms underlying the "global-first" process will offer critical insights into new models of vision. The goal of the present study was to establish a non-human primate model of rapid processing of global features for elucidating the neural mechanisms underlying differential processing of global and local features. Monkeys were trained to make a saccade to a target in the black background, which was different from the distractors (white circle) in color (e.g., red circle target), local features (e.g., white square target), a global feature (e.g., white ring with a hole target) or their combinations (e.g., red square target). Contrary to the predictions of the prevailing local-to-global model, we found that (1) detecting a distinction or a change in the global feature was faster than detecting a distinction or a change in color or local features; (2) detecting a distinction in color was facilitated by a distinction in the global feature, but not in the local features; and (3) detecting the hole was interfered by the local features of the hole (e.g., white ring with a squared hole). These results suggest that monkey ON visual systems have a subsystem that is more sensitive to distinctions in the global feature than local features. They also provide the behavioral constraints for identifying the underlying neural substrates.
视觉对象是通过其特征来识别的。然而，有些特征基于简单的成分（即局部特征，如线段的方向），而有些特征则基于整个对象（即全局特征，如一个有洞的对象）。在过去的五十年里，行为学、生理学、解剖学和计算学研究建立了一个通用的视觉模型，该模型从较低视觉通路中提取局部特征开始，随后是一个在较高视觉通路中提取全局特征的特征整合过程。这种从局部到全局的模型成功地为大量的感知实验提供了统一的解释，但它无法解释一组显示人类视觉系统对全局特征具有更高敏感性的实验。理解“全局优先”过程背后的神经机制将为新的视觉模型提供关键见解。本研究的目的是建立一个用于阐明全局和局部特征差异处理背后神经机制的全局特征快速处理的非人灵长类动物模型。训练猴子向黑色背景中的目标进行扫视，该目标在颜色（如红色圆圈目标）、局部特征（如白色方块目标）、全局特征（如有洞的白色圆环目标）或它们的组合（如红色方块目标）方面与干扰物（白色圆圈）不同。与流行的从局部到全局模型的预测相反，我们发现：（1）检测全局特征中的差异或变化比检测颜色或局部特征中的差异或变化更快；（2）全局特征中的差异促进了颜色差异的检测，但局部特征中的差异则不然；（3）检测孔洞受到孔洞局部特征（如有方形孔洞的白色圆环）的干扰。这些结果表明，猴子的视锥视觉系统有一个对全局特征差异比对局部特征差异更敏感的子系统。它们还为识别潜在的神经基质提供了行为学上的限制。
