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Computational Neuroscience Journal Club

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If you have a question about this talk, please contact Daniel McNamee.

Mohsen Sadeghi will cover:

  • Generalization of prior information for rapid Bayesian time estimation
  • Neil W. Roach, Paul V. McGrawa, David J. Whitaker, James Heron
  • PNAS (January 2017)
  • http://www.pnas.org/content/114/2/412.full (abstract included below)

Abstract: To enable effective interaction with the environment, the brain combines noisy sensory information with expectations based on prior experience. There is ample evidence showing that humans can learn statistical regularities in sensory input and exploit this knowledge to improve perceptual decisions and actions. However, fundamental questions remain regarding how priors are learned and how they generalize to different sensory and behavioral contexts. In principle, maintaining a large set of highly specific priors may be inefficient and restrict the speed at which expectations can be formed and updated in response to changes in the environment. However, priors formed by generalizing across varying contexts may not be accurate. Here, we exploit rapidly induced contextual biases in duration reproduction to reveal how these competing demands are resolved during the early stages of prior acquisition. We show that observers initially form a single prior by generalizing across duration distributions coupled with distinct sensory signals. In contrast, they form multiple priors if distributions are coupled with distinct motor outputs. Together, our findings suggest that rapid prior acquisition is facilitated by generalization across experiences of different sensory inputs but organized according to how that sensory information is acted on.

This talk is part of the Computational Neuroscience series.

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