Neuron
Volume 105, Issue 2, 22 January 2020, Pages 385-393.e9
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Article
Two Distinct Neural Timescales for Predictive Speech Processing

https://doi.org/10.1016/j.neuron.2019.10.019Get rights and content
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Highlights

  • A neural network model was trained to generate contextual speech predictions

  • Contextual uncertainty and surprise of speech were estimated using this model

  • Human neurophysiological responses to speech showed a hierarchy of timescales

  • Theta and delta were modulated by uncertainty and surprise, respectively

Summary

During speech listening, the brain could use contextual predictions to optimize sensory sampling and processing. We asked if such predictive processing is organized dynamically into separate oscillatory timescales. We trained a neural network that uses context to predict speech at the phoneme level. Using this model, we estimated contextual uncertainty and surprise of natural speech as factors to explain neurophysiological activity in human listeners. We show, first, that speech-related activity is hierarchically organized into two timescales: fast responses (theta: 4–10 Hz), restricted to early auditory regions, and slow responses (delta: 0.5–4 Hz), dominating in downstream auditory regions. Neural activity in these bands is selectively modulated by predictions: the gain of early theta responses varies according to the contextual uncertainty of speech, while later delta responses are selective to surprising speech inputs. We conclude that theta sensory sampling is tuned to maximize expected information gain, while delta encodes only non-redundant information.

Keywords

speech processing
predictive coding
theta oscillations
delta oscillations
auditory processing
MEG
temporal response functions
neural networks
uncertainty
surprise

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