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Learning Priors for Bayesian Computations in the Nervous System

Author

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  • Max Berniker
  • Martin Voss
  • Konrad Kording

Abstract

Our nervous system continuously combines new information from our senses with information it has acquired throughout life. Numerous studies have found that human subjects manage this by integrating their observations with their previous experience (priors) in a way that is close to the statistical optimum. However, little is known about the way the nervous system acquires or learns priors. Here we present results from experiments where the underlying distribution of target locations in an estimation task was switched, manipulating the prior subjects should use. Our experimental design allowed us to measure a subject's evolving prior while they learned. We confirm that through extensive practice subjects learn the correct prior for the task. We found that subjects can rapidly learn the mean of a new prior while the variance is learned more slowly and with a variable learning rate. In addition, we found that a Bayesian inference model could predict the time course of the observed learning while offering an intuitive explanation for the findings. The evidence suggests the nervous system continuously updates its priors to enable efficient behavior.

Suggested Citation

  • Max Berniker & Martin Voss & Konrad Kording, 2010. "Learning Priors for Bayesian Computations in the Nervous System," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-9, September.
  • Handle: RePEc:plo:pone00:0012686
    DOI: 10.1371/journal.pone.0012686
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    References listed on IDEAS

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    1. Konrad P Körding & Ulrik Beierholm & Wei Ji Ma & Steven Quartz & Joshua B Tenenbaum & Ladan Shams, 2007. "Causal Inference in Multisensory Perception," PLOS ONE, Public Library of Science, vol. 2(9), pages 1-10, September.
    2. Konrad P. Körding & Daniel M. Wolpert, 2004. "Bayesian integration in sensorimotor learning," Nature, Nature, vol. 427(6971), pages 244-247, January.
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    Cited by:

    1. Elyse H Norton & Luigi Acerbi & Wei Ji Ma & Michael S Landy, 2019. "Human online adaptation to changes in prior probability," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-26, July.
    2. Luigi Acerbi & Sethu Vijayakumar & Daniel M Wolpert, 2014. "On the Origins of Suboptimality in Human Probabilistic Inference," PLOS Computational Biology, Public Library of Science, vol. 10(6), pages 1-23, June.
    3. Leopold Zizlsperger & Thomas Sauvigny & Thomas Haarmeier, 2012. "Selective Attention Increases Choice Certainty in Human Decision Making," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    4. Luigi Acerbi & Sethu Vijayakumar & Daniel M Wolpert, 2017. "Target Uncertainty Mediates Sensorimotor Error Correction," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-21, January.
    5. Adam N Sanborn & Ulrik R Beierholm, 2016. "Fast and Accurate Learning When Making Discrete Numerical Estimates," PLOS Computational Biology, Public Library of Science, vol. 12(4), pages 1-28, April.
    6. Jannes Jegminat & Maya A Jastrzębowska & Matthew V Pachai & Michael H Herzog & Jean-Pascal Pfister, 2020. "Bayesian regression explains how human participants handle parameter uncertainty," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-23, May.
    7. Luigi Acerbi & Daniel M Wolpert & Sethu Vijayakumar, 2012. "Internal Representations of Temporal Statistics and Feedback Calibrate Motor-Sensory Interval Timing," PLOS Computational Biology, Public Library of Science, vol. 8(11), pages 1-19, November.

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