A Bayesian Foundation for Individual Learning Under Uncertainty

  title={A Bayesian Foundation for Individual Learning Under Uncertainty},
  author={Christoph Mathys and Jean Daunizeau and Karl J. Friston and Klaas Enno Stephan},
  booktitle={Front. Hum. Neurosci.},
Computational learning models are critical for understanding mechanisms of adaptive behavior. However, the two major current frameworks, reinforcement learning (RL) and Bayesian learning, both have certain limitations. For example, many Bayesian models are agnostic of inter-individual variability and involve complicated integrals, making online learning difficult. Here, we introduce a generic hierarchical Bayesian framework for individual learning under multiple forms of uncertainty (e.g… CONTINUE READING
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foundation for individual learning under uncertainty

  • Mathys, Daunizeau, Friston, Stephan
  • Front. Hum. Neurosci. 5:39. doi: 10.3389/fnhum…
  • 2011

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