TY  - JOUR
A1  - Weisswange, Thomas H.
A1  - Rothkopf, Constantin
A1  - Rodemann, Tobias
A1  - Triesch, Jochen
T1  - Bayesian cue integration as a developmental outcome of reward mediated learning
T2  - PLoS One
N2  - Average human behavior in cue combination tasks is well predicted by Bayesian inference models. As this capability is acquired over developmental timescales, the question arises, how it is learned. Here we investigated whether reward dependent learning, that is well established at the computational, behavioral, and neuronal levels, could contribute to this development. It is shown that a model free reinforcement learning algorithm can indeed learn to do cue integration, i.e. weight uncertain cues according to their respective reliabilities and even do so if reliabilities are changing. We also consider the case of causal inference where multimodal signals can originate from one or multiple separate objects and should not always be integrated. In this case, the learner is shown to develop a behavior that is closest to Bayesian model averaging. We conclude that reward mediated learning could be a driving force for the development of cue integration and causal inference.
Y1  - 2011
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/22628
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30-114736
SN  - 1932-6203
N1  - Copyright: © 2011 Weisswange et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
VL  - 6
IS  - (7): e21575
ER  -