White David N, Burge Johannes
Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Department of Electrical Engineering & Computer Science, York University, Toronto, Ontario, Canada.
PLoS Comput Biol. 2025 Apr 15;21(4):e1012945. doi: 10.1371/journal.pcbi.1012945. eCollection 2025 Apr.
Stimulus variability-a form of nuisance variability-is a primary source of perceptual uncertainty in everyday natural tasks. How do different properties of natural images and scenes contribute to this uncertainty? Using binocular disparity as a model system, we report a systematic investigation of how various forms of natural stimulus variability impact performance in a stereo-depth discrimination task. With stimuli sampled from a stereo-image database of real-world scenes having pixel-by-pixel ground-truth distance data, three human observers completed two closely related double-pass psychophysical experiments. In the two experiments, each human observer responded twice to ten thousand unique trials, in which twenty thousand unique stimuli were presented. New analytical methods reveal, from this data, the specific and nearly dissociable effects of two distinct sources of natural stimulus variability-variation in luminance-contrast patterns and variation in local-depth structure-on discrimination performance, as well as the relative importance of stimulus-driven-variability and internal-noise in determining performance limits. Between-observer analyses show that both stimulus-driven sources of uncertainty are responsible for a large proportion of total variance, have strikingly similar effects on different people, and-surprisingly-make stimulus-by-stimulus responses more predictable (not less). The consistency across observers raises the intriguing prospect that image-computable models can make reasonably accurate performance predictions in natural viewing. Overall, the findings provide a rich picture of stimulus factors that contribute to human perceptual performance in natural scenes. The approach should have broad application to other animal models and other sensory-perceptual tasks with natural or naturalistic stimuli.
刺激变异性——一种干扰变异性——是日常自然任务中感知不确定性的主要来源。自然图像和场景的不同属性如何导致这种不确定性？以双眼视差作为模型系统，我们报告了一项系统研究，该研究探讨了各种形式的自然刺激变异性如何影响立体深度辨别任务的表现。利用从具有逐像素地面真值距离数据的真实世界场景立体图像数据库中采样的刺激，三名人类观察者完成了两项密切相关且分两次进行的心理物理学实验。在这两项实验中，每位人类观察者对一万次独特试验做出两次反应，共呈现了两万次独特刺激。新的分析方法从这些数据中揭示了自然刺激变异性的两个不同来源——亮度对比模式变化和局部深度结构变化——对辨别表现的特定且几乎可分离的影响，以及刺激驱动变异性和内部噪声在确定表现极限方面的相对重要性。观察者间分析表明，这两个由刺激驱动的不确定性来源在总方差中占很大比例，对不同的人有惊人相似的影响，而且——令人惊讶的是——使逐刺激反应更具可预测性（而非更低）。观察者之间的一致性提出了一个有趣的前景，即图像可计算模型可以在自然观察中做出合理准确的表现预测。总体而言，这些发现提供了一幅丰富的刺激因素图景，这些因素有助于人类在自然场景中的感知表现。该方法应广泛应用于其他动物模型以及其他使用自然或自然主义刺激的感官感知任务。
