NDLI: Can reinforcement learning explain the development of causal inference in multisensory integration?

Content Provider	IEEE Xplore Digital Library
Author	Weisswange, T.H. Rothkopf, C.A. Rodemann, T. Triesch, J.
Copyright Year	2009
Description	Author affiliation: Honda Research Institute Europe GmbH, Offenbach, Germany (Rodemann, T.) \|\| Frankfurt Institute for Advanced Studies, Germany (Weisswange, T.H.; Rothkopf, C.A.; Triesch, J.)
Abstract	Bayesian inference techniques have been used to understand the performance of human subjects on a large number of sensory tasks. Particularly, it has been shown that humans integrate sensory inputs from multiple cues in an optimal way in many conditions. Recently it has also been proposed that causal inference [1] can well describe the way humans select the most plausible model for a given input. It is still unclear how those problems are solved in the brain. Also, considering that infants do not yet behave as ideal observers [2]–[4], it is interesting to ask how the related abilities can develop. We present a reinforcement learning approach to this problem. An orienting task is used in which we reward the model for a correct movement to the origin of noisy audio visual signals. We show that the model learns to do cue-integration and model selection, in this case inferring the number of objects. Its behaviour also includes differences in reliability between the two modalities. All of that comes without any prior knowledge by simple interaction with the environment.
Starting Page	1
Ending Page	7
File Size	691714
Page Count	7
File Format	PDF
ISBN	9781424441174
DOI	10.1109/DEVLRN.2009.5175531
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning Pediatrics Uncertainty Working environment noise Bayesian methods Humans Europe Data mining Background noise Signal mapping
Content Type	Text
Resource Type	Article

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