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In environments where orientation is ambiguous, the visual system uses prior knowledge about lighting coming from above to recognize objects, determine which way is up, and reorient the body. Here we investigated the extent with which assumed light from above preferences are affected by body orientation and the orientation of the retina relative to gravity. We tested the ability to extract shape-from-shading with seven human male observers positioned in multiple orientations relative to gravity using a modified KUKA anthropomorphic robot arm. Observers made convex-concave judgments of a central monocularly viewed stimulus with orientations of a shading gradient consistent with being lit from one of 24 simulated illumination directions. By positioning observers in different roll-tilt orientations relative to gravity and when supine, we were able to monitor change in the light-from-above prior (the orientation at which a shaded disk appears maximally convex). The results confirm previous findings that the light-from-above prior changes with body orientation relative to gravity. Interestingly, the results varied also with retinal orientation as well as an additional component that was approximately twice the frequency of retinal orientation. We use a modelling approach to show that the data are well predicted by summing retinal orientation with cross-multiplied utricle and saccule signals of the vestibular system, yielding gravity-dependent biases in the ability to extract shape-from-shading. We conclude that priors such as light coming from above appear to be constantly updated by neural processes that monitor self-orientation to achieve optimal object recognition over moderate deviations from upright posture at the cost of poor recognition when extremely tilted relative to gravity.

The brain efficiently processes multisensory information by selectively combining related signals across the continuous stream of multisensory inputs. To do so, it needs to detect correlation, lag and synchrony across the senses; optimally integrate related information; and dynamically adapt to spatiotemporal conflicts across the senses. Here we show that all these aspects of multisensory perception can be jointly explained by postulating an elementary processing unit akin to the Hassenstein–Reichardt detector—a model originally developed for visual motion perception. This unit, termed the multisensory correlation detector (MCD), integrates related multisensory signals through a set of temporal filters followed by linear combination. Our model can tightly replicate human perception as measured in a series of empirical studies, both novel and previously published. MCDs provide a unified general theory of multisensory processing, which simultaneously explains a wide spectrum of phenomena with a simple, yet physiologically plausible model., The human brain integrates inputs across multiple sensory streams into a unified percept. Here Parise and Ernst present a model that assesses the correlation, lag and synchrony of multisensory stimuli, and predicts psychophysical performance on multisensory temporal and spatial tasks.

Perception can often be described as a statistically optimal inference process whereby noisy and incomplete sensory evidence is combined with prior knowledge about natural scene statistics. Previous evidence has shown that humans tend to underestimate the speed of unreliable moving visual stimuli. This finding has been interpreted in terms of a Bayesian prior favoring low speed, given that in natural visual scenes objects are mostly stationary or slowly-moving. Here we investigated whether an analogous tendency to underestimate speed also occurs in audition: even if the statistics of the visual environment seem to favor low speed, the statistics of the stimuli reaching the individual senses may differ across modalities, hence potentially leading to different priors. Here we observed a systematic bias for underestimating the speed of unreliable moving sounds. This finding suggests the existence of a slow-motion prior in audition, analogous to the one previously found in vision. The nervous system might encode the overall statistics of the world, rather than the specific properties of the signals reaching the individual senses.

To interpret complex and ambiguous input, the human visual system uses prior knowledge or assumptions about the world. We show that the ‘light-from-above’ prior, used to extract information about shape from shading, is modified in response to active experience with the scene. The resultant adaptation is not specific to the learned scene but generalizes to a different task, demonstrating that priors are constantly adapted by interactive experience with the environment.