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Optimal multi-sensory integration is learned (for many tasks).

In many audio-visual localization tasks, humans integrate information optimally.

In some audio-visual time discrimination tasks, humans do not integrate information optimally.

Körding and Wolpert showed that their subjects correctly learned the distribution of displacement of the visual feedback wrt. the actual position of their hand and used it in the task consistent with a Bayesian cue integration model.

Antonelli et al. use Bayesian and Monte Carlo methods to integrate optic flow and proprioceptive cues to estimate distances between a robot and objects in its visual field.

Ernst and Banks show that humans combine visual and haptic information optimally in a height estimation task.

According to Landy et al., humans often combine cues (intra- or cross-sensory) optimally, consistent with MLE.

Beck et al. argue that sub-optimal computations in biological and artificial neural networks can amplify behavioral and perceptual variability caused by internal and external noise.

Beck et al. argue that sub-optimal computations are a greater cause of behavioral and perceptual variability than internal noise.

Optimal operations are often not feasible for complex tasks for two reasons:

• the generative models necessary to do optimal estimation are too complex and require a lot of knowledge to create
• applying these models is much too computationally intensive

An experiment by Burr et al. showed auditory dominance in a temporal bisection task (studying the temporal ventriloquism effect). The results were qualitatively but not quantitatively predicted by an optimal-integration model.

There are two possibilities explaining the latter result:

• audio-visual integration is not optimal in this case, or
• the model is incorrect. Specifically, the assumption of Gaussian noise in timing estimation may not reflect actual noise.

In many instances of multi-sensory perception, humans integrate information optimally.