Rapid visual categorization is not guided by early salience-based selection

The current dominant visual processing paradigm in both human and machine research is the feedforward, layered hierarchy of neural-like processing elements. Within this paradigm, visual saliency is seen by many to have a specific role, namely that of early selection. Early selection is thought to enable very fast visual performance by limiting processing to only the most salient candidate portions of an image. This strategy has led to a plethora of saliency algorithms that have indeed improved processing time efficiency in machine algorithms, which in turn have strengthened the suggestion that human vision also employs a similar early selection strategy. However, at least one set of critical tests of this idea has never been performed with respect to the role of early selection in human vision. How would the best of the current saliency models perform on the stimuli used by experimentalists who first provided evidence for this visual processing paradigm? Would the algorithms really provide correct candidate sub-images to enable fast categorization on those same images? Do humans really need this early selection for their impressive performance? Here, we report on a new series of tests of these questions whose results suggest that it is quite unlikely that such an early selection process has any role in human rapid visual categorization.

当前人类与机器视觉研究领域的主流视觉处理范式，是基于类神经处理单元的前馈式分层层级结构。在此范式框架下，诸多研究者认为视觉显著性（visual saliency）承担着特定功能，即早期选择。早期选择被认为可通过将处理范围限定于图像中最具显著性的候选区域，实现极快的视觉处理效率。该策略催生了大量显著性检测算法，这些算法确实提升了机器视觉系统的处理时间效率，这也进一步佐证了人类视觉或许也采用了类似的早期选择策略。然而，针对人类视觉中早期选择的这一作用，至少有一组关键检验从未被开展过。那些首次为该视觉处理范式提供实验证据的研究者所使用的视觉刺激素材，当前最优的显著性模型在其上的表现会如何？算法是否真能生成准确的候选子图像，以支持对这些图像进行快速分类？人类真的需要借助这类早期选择，才能达成其出色的视觉表现吗？本文针对上述问题展开了一系列全新的测试，测试结果表明，在人类快速视觉分类任务中，这类早期选择过程几乎不可能发挥任何作用。