Rowe et al., How background complexity impairs target detection

Camouflage is frequently used by animals for concealment and thereby improves survival. Typically, it is the animal’s own colour and patterning which is expected to affect its detectability; however the complexity of the background can also have an influence. Although there is a growing literature examining this, the underlying exact mechanism is unknown. In this paper we address this issue by using humans as proxy ‘predators’ in a computer-based search task and monitoring their detection times for targets on varying backgrounds. By using artificial greyscale targets and backgrounds, we were able to isolate and manipulate the normally covarying factors which comprise ‘complexity’ in natural habitats. We show that reduced detection is not explained by greater information content (entropy) or higher variance in the background’s features per se, but instead by reduced signal-to-noise ratio in the visual features that potentially distinguish target from background. This raises questions about when the term complexity should be used, and how observers learn the characteristics of a background.伪装（Camouflage）常被动物用于隐蔽自身，进而提升生存几率。通常而言，动物自身的体色与斑纹被认为会影响其可被探测性；但背景的复杂程度同样会对探测结果产生影响。尽管相关研究文献日益增多，但其背后的确切机制仍未明晰。本文以人类作为替代捕食者（proxy 'predators'），借助基于计算机的搜索任务展开研究，并监测不同背景下目标的探测时长。我们采用人工灰度目标与背景，得以分离并操控自然生境中构成“复杂度”的各项共变因子。研究结果显示，检测率降低并非源于背景特征本身更高的信息含量（entropy）或更大的方差，而是源于区分目标与背景的视觉特征的信噪比（signal-to-noise ratio）降低。这一发现对“复杂度”术语的适用场景，以及观察者如何学习背景特征的相关问题提出了质疑。