Adriano Andrea, Velde Michaël Vande
Department of Psychology, University of Milano-Bicocca, Italy.
Laboratoire Cognition Langage et Développement, Université Libre de Bruxelles, Belgium.
Vision Res. 2025 Mar;228:108547. doi: 10.1016/j.visres.2025.108547. Epub 2025 Jan 28.
Animals and humans possess an adaptive ability to rapidly estimate approximate numerosity, yet the visual mechanisms underlying this process remain poorly understood. Evidence suggests that approximate numerosity relies on segmented perceptual units modulated by grouping cues, with perceived numerosity decreasing when objects are connected by irrelevant lines, independent of low-level features. However, most studies have focused on physical objects. Illusory contours (ICs) are powerful tools for exploring visual segmentation mechanisms, as "illusory" objects exhibit perceptual biases (e.g., tilt aftereffect) similar to real objects, suggesting shared processing mechanisms. To investigate whether approximate numerosity perception of ICs is influenced by connectedness, we conducted a psychophysical forced-choice task. Participants compared Ehrenstein-like ICs ensembles of varying numerosities interspersed with four task-irrelevant lines. We manipulated the number of connected pairs (0, 2, or 4) by aligning lines with the ICs-triggering gaps, while controlling low-level features across conditions. Our results revealed a monotonic underestimation of numerosity as connections increased, with constant precision reflecting Weber-like encoding. Reaction times proportionally increased with connectedness, suggesting an underlying recurrent neural mechanism. These findings demonstrate that ICs ensembles are subject to the same connectedness effect as real objects, supporting a shared visual mechanism for numerosity extraction. This work highlights the parallels between real and illusory object processing and provides insights into segmentation mechanisms relevant to models of artificial intelligence and visual perception.
动物和人类具有一种适应性能力，能够快速估计近似数量，但这一过程背后的视觉机制仍知之甚少。有证据表明，近似数量依赖于由分组线索调制的分段感知单元，当物体由无关线条连接时，感知到的数量会减少，这与低级特征无关。然而，大多数研究都集中在物理对象上。错觉轮廓（ICs）是探索视觉分割机制的有力工具，因为“错觉”物体表现出与真实物体相似的感知偏差（如倾斜后效），这表明存在共享的处理机制。为了研究ICs的近似数量感知是否受连通性影响，我们进行了一项心理物理学强制选择任务。参与者比较了不同数量的类埃伦斯坦错觉轮廓集合，这些集合中穿插着四条与任务无关的线条。我们通过将线条与触发ICs的间隙对齐来操纵连接对的数量（0、2或4），同时在不同条件下控制低级特征。我们的结果显示，随着连接数量的增加，数量被单调低估，精度恒定反映了类似韦伯定律的编码。反应时间与连通性成正比增加，这表明存在一种潜在的循环神经机制。这些发现表明，ICs集合与真实物体一样受到连通性效应的影响，支持了数量提取的共享视觉机制。这项工作突出了真实物体和错觉物体处理之间的相似之处，并为与人工智能和视觉感知模型相关的分割机制提供了见解。
