Causal Inference for Spatial Constancy across Saccades.
Causal Inference for Spatial Constancy across Saccades.
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DOI：
10.1371/journal.pcbi.1004766
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发表时间：
2016-03
影响因子：
4.3
通讯作者：
Medendorp WP
中科院分区：
文献类型：
--
作者：
Atsma J;Maij F;Koppen M;Irwin DE;Medendorp WP
我们与环境互动的能力取决于创造一个稳定的视觉世界，尽管视网膜输入不断变化。为了实现视觉稳定性，大脑必须区分由眼睛运动引起的视网膜图像移位和由于视觉场景的运动引起的移位。这个过程似乎并不完美：在扫视过程中，我们经常无法检测到视觉对象是保持稳定还是移动，这被称为扫视位移抑制（SSD）。在计算视觉稳定性时，大脑如何评价扫视前场景的记忆信息和扫视后场景的实际视觉反馈？使用SSD任务，我们测试参与者如何本地化的注视目标，扫视目标或周边非中央凹的目标，是平行或正交的水平扫视期间移位的preaccadic位置，随后查看三个不同的持续时间。结果表明，不同的定位误差的三个目标，这取决于观看时间的后扫视刺激和空间分离的前扫视位置。我们通过贝叶斯因果推理机制对数据进行建模，其中在试验水平上应用了两种可能策略的最佳混合，即前扫视记忆和后扫视感觉信号的整合与分离。这种模型的拟合通常优于其他合理的决策策略，以生产SSD。我们的研究结果表明，人类利用贝叶斯推理过程与两个因果结构来调解视觉稳定性。在扫视眼球运动期间，与主观经验相反，我们视网膜上的图像是高度不稳定的。本研究探讨了大脑如何区分扫视引起的图像扰动和视觉场景变化引起的图像扰动。我们首先表明，参与者作出了严重的错误，在判断一个对象，在扫视移动preaccadic位置。然后，我们表明，这些意见可以建模的基础上因果推理原则，评估是否presaccadic和postsaccadic对象感知来自一个单一的稳定的对象或没有。在一个单一的试验水平上，这种评价不是“非此即彼”，而是一种概率，它也确定了在判断扫视对象位置时将扫视前和扫视后信号分离和整合的权重。
Our ability to interact with the environment hinges on creating a stable visual world despite the continuous changes in retinal input. To achieve visual stability, the brain must distinguish the retinal image shifts caused by eye movements and shifts due to movements of the visual scene. This process appears not to be flawless: during saccades, we often fail to detect whether visual objects remain stable or move, which is called saccadic suppression of displacement (SSD). How does the brain evaluate the memorized information of the presaccadic scene and the actual visual feedback of the postsaccadic visual scene in the computations for visual stability? Using a SSD task, we test how participants localize the presaccadic position of the fixation target, the saccade target or a peripheral non-foveated target that was displaced parallel or orthogonal during a horizontal saccade, and subsequently viewed for three different durations. Results showed different localization errors of the three targets, depending on the viewing time of the postsaccadic stimulus and its spatial separation from the presaccadic location. We modeled the data through a Bayesian causal inference mechanism, in which at the trial level an optimal mixing of two possible strategies, integration vs. separation of the presaccadic memory and the postsaccadic sensory signals, is applied. Fits of this model generally outperformed other plausible decision strategies for producing SSD. Our findings suggest that humans exploit a Bayesian inference process with two causal structures to mediate visual stability. During saccadic eye movements, the image on our retinas is, contrary to subjective experience, highly unstable. This study examines how the brain distinguishes the image perturbations caused by saccades and those due to changes in the visual scene. We first show that participants made severe errors in judging the presaccadic location of an object that shifts during a saccade. We then show that these observations can be modeled based on causal inference principles, evaluating whether presaccadic and postsaccadic object percepts derive from a single stable object or not. On a single trial level, this evaluation is not “either/or” but a probability that also determines the weight by which pre- and postsaccadic signals are separated and integrated in judging object locations across saccades.