Integrating prediction errors at two time scales permits rapid recalibration of speech sound categories

Abstract
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Abstract

Speech perception presumably arises from internal models of how specific sensory features are associated with speech sounds. These features change constantly (e.g. different speakers, articulation modes etc.), and listeners need to recalibrate their internal models by appropriately weighing new versus old evidence. Models of speech recalibration classically ignore this volatility. The effect of volatility in tasks where sensory cues were associated with arbitrary experimenter-defined categories were well described by models that continuously adapt the learning rate while keeping a single representation of the category. Using neurocomputational modelling we show that recalibration of natural speech sound categories is better described by representing the latter at different time scales. We illustrate our proposal by modeling fast recalibration of speech sounds after experiencing the McGurk effect. We propose that working representations of speech categories are driven both by their current environment and their long-term memory representations.

Data availability

The original MATLAB scripts used to run the simulations are available online (https://gitlab.unige.ch/Miren.Olasagasti/recalibration-of-speech-categories).

Article and author information

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Itsaso Olasagasti

Basic Neurosciences, University of Geneva, Geneva, Switzerland

For correspondence
itsaso.olasagasti@gmail.com

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-5172-5373
Anne-Lise Giraud

Department of Neuroscience, University of Geneva, Geneva, Switzerland

Competing interests
The authors declare that no competing interests exist.

Funding

Swiss National Science Foundation (320030B_182855)

Anne-Lise Giraud

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.