A Bayesian framework for cue integration in multistable grouping: Proximity, collinearity, and orientation priors in zigzag lattices
A Bayesian framework for cue integration in multistable grouping: Proximity, collinearity, and orientation priors in zigzag lattices
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DOI：
10.1167/8.7.33
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发表时间：
2008-01-01
影响因子：
1.8
通讯作者：
Wagemans, Johan
中科院分区：
文献类型：
--
作者：
Claessens, Peter M. E.;Wagemans, Johan
在轮廓分组中，邻近和良好连续线索的集成被分析为概率推理问题。贝叶斯框架进行了测试，在多稳态点阵实验。在矩形网格中，行和列的距离比和全局方向被操纵。通过在一个方向上施加锯齿形，通过固定或随机位移的元素，引入discollinear。结果表明，接近和良好的延续，一般被视为独立的信息来源，添加到先前的方向对数赔率产生分组感知的赔率。距离似然是很好地捕捉的幂律，和discollinearity似然广义拉普拉斯分布，具有较高的峰度随机锯齿。虽然观察者更喜欢垂直于水平方向，但确切的先验分布是特殊的。与沿沿着倾斜方向相比，沿沿着主轴的感知分组受距离的影响较小，但受非共线的影响较大。结果进行了定性和定量比较，生态统计的轮廓（J。H。埃尔德河M. Goldberg，2002）。层次扩展贝叶斯模型的潜力，更好地了解线索整合的原则进行了讨论。
Integration of proximity and good continuation cues is analyzed as a probabilistic inference problem in contour grouping. A Bayesian framework was tested in a multistable dot lattice experiment. In rectangular lattices, distance ratio and global orientation of rows and columns were manipulated. Discollinearity was introduced by imposing zigzag in one orientation, by either fixed or stochastic displacement of elements. Results indicate that proximity and good continuation are generally treated as independent sources of information, added to prior orientation log-odds to produce the odds of grouping percepts. Distance likelihood is well captured by a power law, and discollinearity likelihoods by generalized Laplace distributions, with higher kurtosis for stochastic zigzag. While observers prefer vertical over horizontal orientations, the exact prior distribution is idiosyncratic. Perceptual grouping along cardinal axes is less affected by distance, but more by discollinearity, than along oblique orientations. Results are qualitatively and quantitatively compared to ecological statistics of contours (J. H. Elder & R. M. Goldberg, 2002). The potential of hierarchically extended Bayes models for a better understanding of principles in cue integration is discussed.