Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139;
Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA 02139.
Proc Natl Acad Sci U S A. 2019 Dec 10;116(50):25355-25364. doi: 10.1073/pnas.1903887116. Epub 2019 Nov 21.
Events and objects in the world must be inferred from sensory signals to support behavior. Because sensory measurements are temporally and spatially local, the estimation of an object or event can be viewed as the grouping of these measurements into representations of their common causes. Perceptual grouping is believed to reflect internalized regularities of the natural environment, yet grouping cues have traditionally been identified using informal observation and investigated using artificial stimuli. The relationship of grouping to natural signal statistics has thus remained unclear, and additional or alternative cues remain possible. Here, we develop a general methodology for relating grouping to natural sensory signals and apply it to derive auditory grouping cues from natural sounds. We first learned local spectrotemporal features from natural sounds and measured their co-occurrence statistics. We then learned a small set of stimulus properties that could predict the measured feature co-occurrences. The resulting cues included established grouping cues, such as harmonic frequency relationships and temporal coincidence, but also revealed previously unappreciated grouping principles. Human perceptual grouping was predicted by natural feature co-occurrence, with humans relying on the derived grouping cues in proportion to their informativity about co-occurrence in natural sounds. The results suggest that auditory grouping is adapted to natural stimulus statistics, show how these statistics can reveal previously unappreciated grouping phenomena, and provide a framework for studying grouping in natural signals.
世界上的事件和物体必须从感觉信号中推断出来，以支持行为。由于感觉测量在时间和空间上是局部的，因此对物体或事件的估计可以看作是将这些测量值分组为它们共同原因的表示形式。感知分组被认为反映了自然环境的内在规律，但传统上使用非正式观察来识别分组线索，并使用人工刺激来研究这些线索。因此，分组与自然信号统计之间的关系仍然不清楚，可能还存在其他或替代的线索。在这里，我们开发了一种将分组与自然感觉信号相关联的一般方法，并将其应用于从自然声音中推导出听觉分组线索。我们首先从自然声音中学习局部频谱时间特征，并测量它们的共同出现统计数据。然后，我们学习了一小部分可以预测测量特征共同出现的刺激属性。由此产生的线索包括已建立的分组线索，例如谐波频率关系和时间一致性，但也揭示了以前未被注意到的分组原则。人类的感知分组由自然特征的共同出现决定，人类根据这些分组线索来预测自然声音中的共同出现，其依赖程度与这些线索对自然声音中共同出现的信息量成正比。研究结果表明，听觉分组适应于自然刺激统计，展示了这些统计数据如何揭示以前未被注意到的分组现象，并为研究自然信号中的分组提供了一个框架。
