Department of Psychology, University of California, Berkeley, Berkeley, CA 94720, USA; Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan; Japan Society for the Promotion of Science.
Department of Psychology, University of California, Berkeley, Berkeley, CA 94720, USA; Vision Science Program, University of California, Berkeley, Berkeley, CA 94720, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA.
Curr Biol. 2021 Jul 26;31(14):3185-3191.e3. doi: 10.1016/j.cub.2021.05.006. Epub 2021 Jun 3.
In any given perceptual task, the visual system selectively weighs or filters incoming information. The particular set of weights or filters form a kind of template, which reveals the regions or types of information that are particularly useful for a given perceptual decision. Unfortunately, sensory input is noisy and ever changing. To compensate for these fluctuations, the visual system could adopt a strategy of biasing the templates such that they reflect a temporal smoothing of input, which would be a form of serial dependence. Here, we demonstrate that perceptual templates are, in fact, altered by serial dependence. Using a simple orientation detection task and classification-image technique, we found that perceptual templates are systematically biased toward previously seen, task-irrelevant orientations. The results of an orientation discrimination task suggest that this shift in perceptual template derives from a change in the perceptual appearance of orientation. Our study reveals how serial dependence biases internal templates of orientation and suggests that the sensitivity of classification-image techniques in general could be improved by taking into account history-dependent fluctuations in templates.
在任何给定的感知任务中,视觉系统都会选择性地加权或过滤输入信息。特定的权重或滤波器集形成了一种模板,揭示了对于给定的感知决策特别有用的区域或类型的信息。不幸的是,感觉输入是嘈杂且不断变化的。为了补偿这些波动,视觉系统可以采用一种策略来偏向模板,使得它们反映输入的时间平滑,这将是一种串行依赖的形式。在这里,我们证明感知模板实际上是被串行依赖改变的。使用简单的方向检测任务和分类图像技术,我们发现感知模板会被系统地偏向于先前看到的、与任务无关的方向。方向辨别任务的结果表明,这种感知模板的偏移来自于方向的感知外观的变化。我们的研究揭示了串行依赖如何偏向方向的内部模板,并表明通过考虑模板中历史相关的波动,一般来说,分类图像技术的灵敏度可以得到提高。