Computational framework for investigating predictive processing in auditory perception.

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  • Author(s): Skerritt-Davis B;Skerritt-Davis B; Elhilali M; Elhilali M
  • Source:
    Journal of neuroscience methods [J Neurosci Methods] 2021 Aug 01; Vol. 360, pp. 109177. Date of Electronic Publication: 2021 Apr 09.
  • Publication Type:
    Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Elsevier/North-Holland Biomedical Press Country of Publication: Netherlands NLM ID: 7905558 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-678X (Electronic) Linking ISSN: 01650270 NLM ISO Abbreviation: J Neurosci Methods Subsets: MEDLINE
    • Publication Information:
      Original Publication: Amsterdam, Elsevier/North-Holland Biomedical Press.
    • Subject Terms:
    • Abstract:
      Background: The brain tracks sound sources as they evolve in time, collecting contextual information to predict future sensory inputs. Previous work in predictive coding typically focuses on the perception of predictable stimuli, leaving the implementation of these same neural processes in more complex, real-world environments containing randomness and uncertainty up for debate.
      New Method: To facilitate investigation into the perception of less tightly-controlled listening scenarios, we present a computational model as a tool to ask targeted questions about the underlying predictive processes that connect complex sensory inputs to listener behavior and neural responses. In the modeling framework, observed sound features (e.g. pitch) are tracked sequentially using Bayesian inference. Sufficient statistics are inferred from past observations at multiple time scales and used to make predictions about future observation while tracking the statistical structure of the sensory input.
      Results: Facets of the model are discussed in terms of their application to perceptual research, and examples taken from real-world audio demonstrate the model's flexibility to capture a variety of statistical structures along various perceptual dimensions.
      Comparison With Existing Methods: Previous models are often targeted toward interpreting a particular experimental paradigm (e.g., oddball paradigm), perceptual dimension (e.g., pitch processing), or task (e.g., speech segregation), thus limiting their ability to generalize to other domains. The presented model is designed as a flexible and practical tool for broad application.
      Conclusion: The model is presented as a general framework for generating new hypotheses and guiding investigation into the neural processes underlying predictive coding of complex scenes.
      (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)
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    • Grant Information:
      F31 DC017629 United States DC NIDCD NIH HHS; R01 HL133043 United States HL NHLBI NIH HHS; U01 AG058532 United States AG NIA NIH HHS
    • Contributed Indexing:
      Keywords: Bayesian inference; Predictive coding; Statistical inference; neural decoding; uncertainty
    • Publication Date:
      Date Created: 20210411 Date Completed: 20210823 Latest Revision: 20220802
    • Publication Date:
      20240105
    • Accession Number:
      PMC9017011
    • Accession Number:
      10.1016/j.jneumeth.2021.109177
    • Accession Number:
      33839191