Segmenting visual stimuli into distinct groups of features and visual objects is central to visual function. Classical psychophysical methods have helped uncover many rules of human perceptual segmentation, and recent progress in machine learning has produced successful algorithms. Yet, the computational logic of human segmentation remains unclear, partially because we lack well-controlled paradigms to measure perceptual segmentation maps and compare models quantitatively. Here we propose a new, integrated approach: given an image, we measure multiple pixel-based same-different judgments and perform model--based reconstruction of the underlying segmentation map. The reconstruction is robust to several experimental manipulations and captures the variability of individual participants. We demonstrate the validity of the approach on human segmentation of natural images and composite textures. We show that image uncertainty affects measured human variability, and it influences how participants weigh different visual features. Because any putative segmentation algorithm can be inserted to perform the reconstruction, our paradigm affords quantitative tests of theories of perception as well as new benchmarks for segmentation algorithms.
翻译:将视觉刺激分解为不同的特征和视觉对象组别是视觉功能的核心。古典心理物理方法帮助揭示了人类感知分解的许多规则,而机器学习最近的进展也产生了成功的算法。然而,人类分解的计算逻辑仍然不清楚,部分原因是我们缺乏衡量感知分解图和定量比较模型的严格控制模式。我们在这里提出了一个新的综合方法:给一个图像,我们测量基于像素的多重相同差异判断,并对基础分解图进行基于模型的重建。重建对若干实验性操作十分强大,并捕捉了个别参与者的变异性。我们展示了人类对自然图象和复合质的分解方法的有效性。我们表明,图像不确定性影响测量的人类变异性,影响参与者如何权衡不同的视觉特征。由于任何推定分解算法都可以插入来进行重建,因此我们的范式能够对认知理论进行定量测试,并为分解算法提供新的基准。