Barthélemy Frédéric V, Perrinet Laurent U, Castet Eric, Masson Guillaume S
Team DyVA, Institut de Neurosciences Cognitives de la Méditerranée, UMR6193 CNRS-Aix Marseille Université, 13402 Marseille, France.
Vision Res. 2008 Feb;48(4):501-22. doi: 10.1016/j.visres.2007.10.020. Epub 2008 Jan 25.
Integrating information is essential to measure the physical 2D motion of a surface from both ambiguous local 1D motion of its elongated edges and non-ambiguous 2D motion of its features such as corners or texture elements. The dynamics of this motion integration shows a complex time course as read from tracking eye movements: first, local 1D motion signals are extracted and pooled to initiate ocular responses, then 2D motion signals are integrated to adjust the tracking direction until it matches the surface motion direction. The nature of these 1D and 2D motion computations are still unclear. One hypothesis is that their different dynamics may be explained from different contrast sensitivities. To test this, we measured contrast-response functions of early, 1D-driven and late, 2D-driven components of ocular following responses to different motion stimuli: gratings, plaids and barberpoles. We found that contrast dynamics of 1D-driven responses are nearly identical across the different stimuli. On the contrary, late 2D-driven components with either plaids or barberpoles have similar latencies but different contrast dynamics. Temporal dynamics of both 1D- and 2D-driven responses demonstrates that the different contrast gains are set very early during the response time course. Running a Bayesian model of motion integration, we show that a large family of contrast-response functions can be predicted from the probability distributions of 1D and 2D motion signals for each stimulus and by the shape of the prior distribution. However, the pure delay (i.e. largely independent upon contrast) observed between 1D- and 2D-motion supports the fact that 1D and 2D probability distributions are computed independently. This two-pathway Bayesian model supports the idea that 1D and 2D mechanisms represent edges and features motion in parallel.
整合信息对于从物体细长边缘模糊的局部一维运动以及角点或纹理元素等特征的明确二维运动来测量物体表面的二维物理运动至关重要。从追踪眼动中读取，这种运动整合的动力学呈现出复杂的时间进程：首先，提取并汇总局部一维运动信号以引发眼部反应，然后整合二维运动信号以调整追踪方向，直至其与物体表面运动方向匹配。这些一维和二维运动计算的本质仍不清楚。一种假设是，它们不同的动力学可能由不同的对比度敏感度来解释。为了验证这一点，我们测量了眼部跟随反应中早期的一维驱动成分和晚期的二维驱动成分对不同运动刺激（光栅、格子图案和条纹图案）的对比度响应函数。我们发现，一维驱动反应的对比度动力学在不同刺激下几乎相同。相反，格子图案或条纹图案的晚期二维驱动成分具有相似的潜伏期，但对比度动力学不同。一维和二维驱动反应的时间动力学表明，在反应时间进程的早期就设定了不同的对比度增益。通过运行运动整合的贝叶斯模型，我们表明，根据每种刺激的一维和二维运动信号的概率分布以及先验分布的形状，可以预测出一大类对比度响应函数。然而，一维和二维运动之间观察到的纯延迟（即很大程度上与对比度无关）支持了一维和二维概率分布是独立计算的这一事实。这种双通路贝叶斯模型支持了一维和二维机制并行代表边缘和特征运动的观点。
