The role of sensory uncertainty in simple contour integration.
The role of sensory uncertainty in simple contour integration.
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DOI：
10.1371/journal.pcbi.1006308
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发表时间：
2020-11
影响因子：
4.3
通讯作者：
Ma WJ
中科院分区：
文献类型：
--
作者：
Zhou Y;Acerbi L;Ma WJ
感知组织是将场景元素分组为整体实体的过程。一个经典的例子是轮廓整合，其中单独的线段被视为连续的轮廓。这种分组的不确定性来自场景模糊性和感官噪声。一些经典的轮廓整合的完形原则，更广泛地说，知觉组织，已经被重新构建在贝叶斯推理方面，观察者计算整个实体存在的概率。以往的研究，提出了一个贝叶斯解释的知觉组织，然而，忽略了感官的不确定性，尽管事实上，占当前水平的知觉不确定性是贝叶斯决策的主要特征之一。至关重要的是，对感觉不确定性的逐个试验操作是一个关键的测试，以检验人类是否在轮廓整合中执行接近最佳的贝叶斯推理，而不是使用一些明显的非贝叶斯启发式。我们区分这些假设之间的简化形式的轮廓整合，即判断是否两条线段分离的遮挡共线。我们通过改变视网膜偏心率来操纵感觉的不确定性。贝叶斯最优观测器将以非常特定的方式考虑感官不确定性的水平，以确定线段之间的测量偏移是由于非共线性还是感官噪声。我们发现，人们稍微偏离，但系统地从贝叶斯最优，同时仍然执行“概率计算”的意义上说，他们考虑到感官的不确定性，通过启发式规则。我们的工作有助于理解感官不确定性在高阶感知中的作用。我们对世界的感知不仅受我们所能获得的感官信息的支配，还受我们解释这些信息的方式的支配。当我们看到一个视觉场景时，我们的视觉系统会经历一个将视觉元素组合在一起以形成连贯实体的过程，以便我们能够更容易和更有意义地解释场景。例如，当观察一堆秋叶时，即使整片叶子部分被另一片叶子覆盖，人们仍然可以感知和识别整片叶子。虽然格式塔心理学家长期以来一直用一套定性的定律来描述知觉组织，但最近的研究提供了一种统计学术语中的最佳贝叶斯解释，即观察者在给定可用的感官输入的情况下选择概率最高的场景配置。然而，这些研究得出的结论并没有考虑到这种物理最优计算中的一个关键因素，即感官不确定性的作用。我们可以很容易地想象，当我们从远距离观察（高感官不确定性）到近距离观察（低感官不确定性）时，我们对两个轮廓是属于同一片叶子还是不同叶子的判断可能会发生变化。我们的研究探讨了人们是否以及如何将不确定性纳入轮廓整合，感知组织的基本形式，通过在一个简单的轮廓整合任务中不断尝试不同的感官不确定性。我们发现，人们确实考虑到了感官的不确定性，但在某种程度上，这微妙地偏离了最佳行为。
Perceptual organization is the process of grouping scene elements into whole entities. A classic example is contour integration, in which separate line segments are perceived as continuous contours. Uncertainty in such grouping arises from scene ambiguity and sensory noise. Some classic Gestalt principles of contour integration, and more broadly, of perceptual organization, have been re-framed in terms of Bayesian inference, whereby the observer computes the probability that the whole entity is present. Previous studies that proposed a Bayesian interpretation of perceptual organization, however, have ignored sensory uncertainty, despite the fact that accounting for the current level of perceptual uncertainty is one of the main signatures of Bayesian decision making. Crucially, trial-by-trial manipulation of sensory uncertainty is a key test to whether humans perform near-optimal Bayesian inference in contour integration, as opposed to using some manifestly non-Bayesian heuristic. We distinguish between these hypotheses in a simplified form of contour integration, namely judging whether two line segments separated by an occluder are collinear. We manipulate sensory uncertainty by varying retinal eccentricity. A Bayes-optimal observer would take the level of sensory uncertainty into account—in a very specific way—in deciding whether a measured offset between the line segments is due to non-collinearity or to sensory noise. We find that people deviate slightly but systematically from Bayesian optimality, while still performing “probabilistic computation” in the sense that they take into account sensory uncertainty via a heuristic rule. Our work contributes to an understanding of the role of sensory uncertainty in higher-order perception. Our percept of the world is governed not only by the sensory information we have access to, but also by the way we interpret this information. When presented with a visual scene, our visual system undergoes a process of grouping visual elements together to form coherent entities so that we can interpret the scene more readily and meaningfully. For example, when looking at a pile of autumn leaves, one can still perceive and identify a whole leaf even when it is partially covered by another leaf. While Gestalt psychologists have long described perceptual organization with a set of qualitative laws, recent studies offered a statistically-optimal—Bayesian, in statistical jargon—interpretation of this process, whereby the observer chooses the scene configuration with the highest probability given the available sensory inputs. However, these studies drew their conclusions without considering a key actor in this kind of statistically-optimal computations, that is the role of sensory uncertainty. One can easily imagine that our decision on whether two contours belong to the same leaf or different leaves is likely going to change when we move from viewing the pile of leaves at a great distance (high sensory uncertainty), to viewing very closely (low sensory uncertainty). Our study examines whether and how people incorporate uncertainty into contour integration, an elementary form of perceptual organization, by varying sensory uncertainty from trial to trial in a simple contour integration task. We found that people indeed take into account sensory uncertainty, however in a way that subtly deviates from optimal behavior.
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