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Bayesian cue integration as a developmental outcome of reward mediated learning
Thomas H Weisswange1, Constantin A Rothkopf, Tobias Rodemann
1Frankfurt Institute for Advanced Studies, Frankfurt, Germany. weisswange@fias.uni-frankfurt.de
Plos One
|July 14, 2011
Summary
Reward-driven learning may explain how humans develop cue integration and causal inference. A reinforcement learning model successfully learned to weight sensory information and distinguish object origins, mimicking Bayesian models.
Area of Science:
- Cognitive Science
- Neuroscience
- Machine Learning
Background:
- Human cue combination is accurately modeled by Bayesian inference.
- Understanding the developmental learning mechanisms for this capability is crucial.
Purpose of the Study:
- Investigate if reward-dependent learning contributes to the development of cue integration and causal inference.
- Explore the application of reinforcement learning algorithms in this context.
Main Methods:
- Utilized a model-free reinforcement learning algorithm to simulate cue integration.
- Assessed the algorithm's ability to weight uncertain cues based on reliability, even with changing reliabilities.
- Examined the model's performance in causal inference tasks involving multimodal signals from single or multiple objects.
Main Results:
- The reinforcement learning algorithm successfully learned cue integration, adapting weights based on cue reliability.
- The model demonstrated adaptability to changing cue reliabilities.
- In causal inference scenarios, the algorithm developed behavior approximating Bayesian model averaging.
Conclusions:
- Reward-mediated learning is a plausible driving force for the development of cue integration.
- Reinforcement learning provides a viable mechanism for acquiring causal inference capabilities.
- This research bridges computational modeling, learning theory, and developmental psychology.
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