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(2023-04-10 11:47):
#paper doi:/10.1371/journal.pone.0000943.Causal Inference in Multisensory Perception,2007,Plos One（发表在Plos One,但是引用高）
神经系统不断地将来自不同感觉方式的不确定信息组合成对感觉刺激原因的综合理解。这些信息可能有相同的来源，也可能来自不同的来源，因此，线索的组合必须根据线索的因果关系。多模式感知整合的方法之一是线索整合概率模型，线索整合概率模型的基础是假定原因是统一的，但是后来的实验发现，当视觉和听觉刺激差异很大时，这种整合就会失效。信息之间的差异称为disparity（分离度）。当两个线索之间的disparity（分离度）增大，那么线索A对于另一个线索B的影响就会减小，反之亦然。disparity（分离度）的存在说明强制融合（无条件假定原因统一）是不成立的，因此还需要对线索之间的因果关系进行推断，需要在模型中增加一个检验交互性的先验（一个联合先验分布），用来分析两个线索同源的可能性高，还是不同源的可能性高。本研究提出了一个因果推断模型，该模型准确地预测了人类受试者在两个听觉-视觉定位任务中对线索的非线性整合。结果表明，人类确实可以有效地推断因果结构以及线索源的位置。推断因果结构的能力不仅限于有意识的、高层次的认知；它也在感知中不断地、毫不费力地进行。
Abstract:
Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able …
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Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events. Here we use multisensory cue combination to study causal inference in perception. We formulate an ideal-observer model that infers whether two sensory cues originate from the same location and that also estimates their location(s). This model accurately predicts the nonlinear integration of cues by human subjects in two auditory-visual localization tasks. The results show that indeed humans can efficiently infer the causal structure as well as the location of causes. By combining insights from the study of causal inference with the ideal-observer approach to sensory cue combination, we show that the capacity to infer causal structure is not limited to conscious, high-level cognition; it is also performed continually and effortlessly in perception.
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