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Complexity and specificity of experimentally-induced expectations in motion perception

机译:运动诱发的实验预期期望的复杂性和特异性

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Abstract Abstract: Abstract?? Our perceptions are fundamentally altered by our expectations, i.e., priors about the world. In previous statistical learning experiments (Chalk, Seitz, & Seri?¨s, 2010), we investigated how such priors are formed by presenting subjects with white low contrast moving dots on a blank screen and using a bimodal distribution of motion directions such that two directions were more frequently presented than the others. We found that human observers quickly and automatically developed expectations for the most frequently presented directions of motion. Here, we examine the specificity of these expectations. Can one learn simultaneously to expect different motion directions for dots of different colors? We interleaved moving dot displays of two different colors, either red or green, with different motion direction distributions. When one distribution was bimodal while the other was uniform, we found that subjects learned a single bimodal prior for the two stimuli. On the contrary, when both distributions were similarly structured, we found evidence for the formation of two distinct priors, which significantly influenced the subjects' behavior when no stimulus was presented. Our results can be modeled using a Bayesian framework and discussed in terms of a suboptimality of the statistical learning process under some conditions.
机译:摘要摘要:摘要?我们的看法从根本上改变了我们的期望,即对世界的先验。在以前的统计学习实验中(Chalk,Seitz和Seri?s,2010年),我们研究了如何通过在空白屏幕上为对象显示白色低对比度移动点并使用运动方向的双峰分布(例如两个指示比其他指示更常见。我们发现,人类观察者可以快速自动地对最常见的运动方向产生期望。在这里,我们检查这些期望的特殊性。能否同时学习以期望不同颜色点的运动方向不同?我们交错了两种不同颜色(红色或绿色)的移动点显示,它们具有不同的运动方向分布。当一种分布是双峰分布而另一种分布是均匀分布时,我们发现受试者在两个刺激之前先学习了一个双峰分布。相反,当两种分布的结构相似时,我们发现形成两个不同先验的证据,这在没有刺激的情况下会显着影响受试者的行为。我们的结果可以使用贝叶斯框架进行建模,并在某些条件下根据统计学习过程的次优性进行讨论。

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