The Implicit Learning of Noise: Behavioral Data and Computational Models

  • Trevor R. Agus
  • Marion Beauvais
  • Simon J. Thorpe
  • Daniel Pressnitzer
Conference paper

Abstract

A prerequisite for the recognition of natural sounds is that an auditory signature of the sources should be learnt through experience. Here, we used random waveforms to investigate whether implicit learning occurs with repeated exposure. Listeners were asked to discriminate between 1-s samples of running noise and two seamlessly repeated 0.5-s samples of noise (repeated noise; RN). Unbeknownst to them, one particular exemplar of repeated noise was presented in several trials inter­spersed in a block (reference repeated noise; RefRN). A first experiment showed that RefRN was discriminated more efficiently than RN, even though listeners did not know that memorizing noise samples across trials could be beneficial, nor which trials should be memorized. Most of the learning occurred rapidly within ten presentations. A second experiment confirmed that the effect was a genuine increase in sensitivity. Preliminary investigations of computational models of cochlear and cortical activity failed to distinguish noises that produced large learning effects, so it is unclear which noise aspects were learnt. The findings show neural learning mechanisms of acoustic information that are unsupervised, resilient to interference, and fast-acting.

Keywords

Memory Auditory psychophysics Perceptual learning Plasticity 

Notes

Acknowledgments

This work was supported by grant ANR-06-NEUR-O22-01. We thank Timothée Masquelier for sharing pilot data using a similar paradigm (Masquelier & Thorpe, unpublished observations).

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Trevor R. Agus
  • Marion Beauvais
  • Simon J. Thorpe
  • Daniel Pressnitzer
    • 1
  1. 1.CNRS & Université Paris Descartes & École normale supérieureParisFrance

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