Department of Mathematics and Computer Science, Rutgers University - Newark, 101 Warren Street, Rm 216, Newark, NJ 07102, USA.
Department of Mathematics and Computer Science, Rutgers University - Newark, 101 Warren Street, Rm 216, Newark, NJ 07102, USA; School of Mathematics, Institute for Advanced Study, Princeton, NJ, USA.
Vision Res. 2022 Aug;197:108056. doi: 10.1016/j.visres.2022.108056. Epub 2022 Apr 27.
Scenes contain many statistical regularities that could benefit visual processing if accounted for by the visual system. One such statistic is the orientation-averaged slope (α) of the amplitude spectrum of natural scenes. Human observers show different discrimination sensitivity to α: sensitivity is highest for α values between 1.0 and 1.2 and decreases as α is steepened or shallowed. The range of α for peak discrimination sensitivity is concordant with the average α of natural scenes, which may indicate that visual mechanisms are optimized to process information at α values commonly encountered in the environment. Here we explore the association between peak discrimination sensitivity and the most viewed αs in natural environments. Specifically, we verified whether discrimination sensitivity depends on the recently viewed environments. Observers were immersed, using a Head-Mounted Display, in an environment that was either unaltered or had its average α steepened or shallowed by 0.4. Discrimination thresholds were affected by the average shift in α, but this effect was most prominent following adaptation to a shallowed environment. We modeled these data with a Bayesian observer and explored whether a change in the prior or a change in the likelihood best explained the psychophysical effects. Change in discrimination thresholds following adaptation could be explained by a shift in the central tendency of the prior concordant with the shift of the environment, in addition to a change in the likelihood. Our findings suggest that expectations on the occurrence of α that result from a lifetime of exposure remain plastic and able to accommodate for the statistical structure of recently viewed environments.
场景包含许多统计规律，如果视觉系统能够利用这些规律，将有助于视觉处理。其中一个统计量是自然场景幅度谱的平均方向斜率 (α)。人类观察者对 α 表现出不同的辨别敏感：对于 1.0 到 1.2 之间的 α 值，敏感性最高，随着 α 变陡或变浅，敏感性降低。峰值辨别敏感性的 α 范围与自然场景的平均 α 一致，这可能表明视觉机制是优化的，用于处理环境中常见的 α 值的信息。在这里，我们探讨了峰值辨别敏感性与自然环境中最常见的 α 值之间的关联。具体来说，我们验证了辨别敏感性是否取决于最近观看的环境。观察者使用头戴式显示器沉浸在环境中，环境要么保持不变，要么其平均 α 值通过 0.4 变陡或变浅。辨别阈值受到 α 平均变化的影响，但这种影响在适应变浅的环境后最为明显。我们使用贝叶斯观察者对这些数据进行建模，并探讨了先验的变化或似然的变化哪个更能解释心理物理效应。适应后辨别阈值的变化可以通过与环境变化一致的先验中心趋势的变化来解释，此外还有似然的变化。我们的研究结果表明，由于一生的暴露而产生的对 α 发生的期望仍然具有可塑性，并能够适应最近观看的环境的统计结构。
