These probabilistic models formalize the problem of cue combination in an elegant way. Over the last decade, many scientists have gone back to a probabilistic interpretation of cue combination as had been proposed by von Helmholtz. Since then, numerous studies have analyzed the way people use and combine cues for perception, , highlighting the rich set of effects that occur in multimodal perception. Von Helmholtz, in the late 19 th century started considering cue combination, formalizing perception as unconscious probabilistic inference of a best guess of the state of the world. The study of multisensory integration has a long and fruitful history in experimental psychology, neurophysiology, and psychophysics. The nervous system is constantly engaged in combining uncertain information from different sensory modalities into an integrated understanding of the causes of sensory stimulation. It also illustrates how cues from multiple sensory modalities can be used to infer underlying causes. This example illustrates that perceptual cues are seldom ecologically relevant by themselves, but rather acquire their significance through their meaning about their causes. Importantly, the way how you will combine pieces of information must depend on the causal relationships you inferred. Combining both pieces of sensory information, you will be better at judging if there is an animal in the bushes and if so, where exactly it is hiding. However, you may also hear an animal vocalization coming from a similar direction. If you are a hungry predator–or a life-loving prey–this estimation may be critical to your survival. You may infer that this movement was caused by a hidden animal, but you may also consider a gust of wind as an alternative and possibly more probable cause. Imagine you are walking in the forest and you see a sudden movement in the bushes. 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. The results show that indeed humans can efficiently infer the causal structure as well as the location of causes. This model accurately predicts the nonlinear integration of cues by human subjects in two auditory-visual localization tasks. We formulate an ideal-observer model that infers whether two sensory cues originate from the same location and that also estimates their location(s). Here we use multisensory cue combination to study causal inference in perception. The brain should thus be able to efficiently infer the causes underlying our sensory events. Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes.
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