Denison Rachel N, Sheynin Jacob, Silver Michael A
Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, US,
College of Letters and Science, University of California, Berkeley, Berkeley, CA,
J Vis. 2016 Oct 1;16(13):6. doi: 10.1167/16.13.6.
Perception is shaped not only by current sensory inputs but also by expectations generated from past sensory experience. Humans viewing ambiguous stimuli in a stable visual environment are generally more likely to see the perceptual interpretation that matches their expectations, but it is less clear how expectations affect perception when the environment is changing predictably. We used statistical learning to teach observers arbitrary sequences of natural images and employed binocular rivalry to measure perceptual selection as a function of predictive context. In contrast to previous demonstrations of preferential selection of predicted images for conscious awareness, we found that recently acquired sequence predictions biased perceptual selection toward unexpected natural images and image categories. These perceptual biases were not associated with explicit recall of the learned image sequences. Our results show that exposure to arbitrary sequential structure in the environment impacts subsequent visual perceptual selection and awareness. Specifically, for natural image sequences, the visual system prioritizes what is surprising, or statistically informative, over what is expected, or statistically likely.
感知不仅受当前感官输入的影响,还受过去感官经验所产生的预期的影响。在稳定的视觉环境中观看模糊刺激的人类通常更有可能看到与其预期相符的感知解释,但当环境以可预测的方式变化时,预期如何影响感知则不太清楚。我们使用统计学习来教导观察者自然图像的任意序列,并利用双眼竞争来测量作为预测背景函数的感知选择。与之前关于有意识觉知对预测图像的优先选择的演示相反,我们发现最近获得的序列预测会使感知选择偏向于意外的自然图像和图像类别。这些感知偏差与对所学图像序列的明确回忆无关。我们的结果表明,接触环境中的任意序列结构会影响随后的视觉感知选择和觉知。具体而言,对于自然图像序列,视觉系统会优先处理令人惊讶的或具有统计信息的内容,而不是预期的或具有统计可能性的内容。