# Show Reference: "Computational models of sensorimotor integration"

Computational Models of Sensorimotor Integration In Self-Organization, Computational Maps and Motor Control (1997), pp. 117-147 by Zoubin Ghahramani, Daniel M. Wolpert, Michael I. Jordan edited by Pietro G. Morasso, Vittorio Sanguineti
@inbook{ghahramani-et-al-1997,
abstract = {The sensorimotor integration system can be viewed as an observer attempting to estimate its oen state and the state of the environment by integrating multiple sources of information. We describe a computational framework capturing this notion, and some specific models of integration and adaptation that result from it. Psychophysical results from two sensorimotor systems, subserving the integration and adaptation of visuo-auditory maps, and estimation of the state of the hand during arm movements, are presented and analyzed within this framework. These results suggest that: (1) Spatial information from visual and auditory systems is integrated so as to reduce the variance in localization. (2) The effects of a remapping in the relation between visual and auditory space can be predicted from a simple learning rule. (3) The temporal propagation of errors in estimating the hand's state is captured by a linear dynamic observer, providing evidence for the existence of an internal model which
simulates the dynamic behavior of the arm.},
author = {Ghahramani, Zoubin and Wolpert, Daniel M. and Jordan, Michael I.},
booktitle = {Self-organization, Computational Maps, and Motor Control},
citeulike-article-id = {7550544},
doi = {10.1016/s0166-4115(97)80006-4},
editor = {Morasso, Pietro G. and Sanguineti, Vittorio},
isbn = {9780444823236},
issn = {0166-4115},
pages = {117--147},
posted-at = {2011-07-20 11:50:40},
priority = {4},
publisher = {Elsevier},
title = {Computational Models of Sensorimotor Integration},
url = {http://mlg.eng.cam.ac.uk/zoubin/papers.html},
volume = {119},
year = {1997}
}


By combining information from different senses, one can sometimes make inferences that are not possible with information from one modality alone.

Some modalities can yield low-latency, unreliable information and others high-latency, reliable information.

Combining both can produce fast information which improves over time.

Ghahramani et al. infer the cost function presumably guiding natural multisensory integration from behavioral data.

Ghahramani et al. model multisensory integration as a process minimizing uncertainty.

Multisensory input can provide redundant information on the same thing.

Redundancy reduces uncertainty and increases reliability.

When the error distribution in multiple estimates of a world property on the basis of multiple cues is independent between cues, and Gaussian, then the ideal observer model is a simple weighting strategy.

Ghahramani et al. discuss computational models of sensorimotor integration.