A Bayesian Foundation for Individual Learning Under Uncertainty

@inproceedings{Mathys2011ABF,
  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.},
  year={2011}
}
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
Highly Influential
This paper has highly influenced 11 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Recent Discussions
This paper has been referenced on Twitter 3 times over the past 90 days. VIEW TWEETS
123 Citations
57 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 123 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 57 references

foundation for individual learning under uncertainty

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

Similar Papers

Loading similar papers…