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Rapid computations of spectrotemporal prediction error support perception of degraded speech
Version 2 2023-06-12, 09:34
Version 1 2023-06-09, 22:00
journal contribution
posted on 2023-06-12, 09:34 authored by Ediz SohogluEdiz Sohoglu, Matthew H DavisHuman speech perception can be described as Bayesian perceptual inference but how are these Bayesian computations instantiated neurally? We used magnetoencephalographic recordings of brain responses to degraded spoken words and experimentally manipulated signal quality and prior knowledge. We first demonstrate that spectrotemporal modulations in speech are more strongly represented in neural responses than alternative speech representations (e.g. spectrogram or articulatory features). Critically, we found an interaction between speech signal quality and expectations from prior written text on the quality of neural representations; increased signal quality enhanced neural representations of speech that mismatched with prior expectations, but led to greater suppression of speech that matched prior expectations. This interaction is a unique neural signature of prediction error computations and is apparent in neural responses within 100 ms of speech input. Our findings contribute to the detailed specification of a computational model of speech perception based on predictive coding frameworks.
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Publication status
- Published
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- Published version
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eLifeISSN
2050-084XPublisher
eLife Sciences Publications LtdExternal DOI
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9Page range
1-25Article number
a58077Department affiliated with
- Psychology Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2020-10-30First Open Access (FOA) Date
2020-11-10First Compliant Deposit (FCD) Date
2020-10-29Usage metrics
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