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    Automatic speech engages a widespread brain network, challenging traditional views of the speech motor cortex. This study reveals complex feedforward and feedback dynamics in neural control during overlearned speech tasks.

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    Area of Science:

    • Neuroscience
    • Speech Motor Control
    • Computational Neuroscience

    Background:

    • The speech motor cortex is crucial for human speech.
    • Traditional views limit its role to articulation and somatosensation.
    • Automatic speech processing remains poorly understood.

    Purpose of the Study:

    • Investigate neural dynamics during automatic speech production.
    • Examine cortical recruitment and timing in overlearned speech.
    • Clarify the role of speech motor cortex in automatic speech.

    Main Methods:

    • Intracranial electrocorticography (ECoG) recordings from the left perisylvian cortex.
    • Encoding models (multivariate temporal response function).
    • Decoding models (deep neural network speech synthesis).

    Main Results:

    • Automatic speech involves a distributed network (superior temporal, precentral, post-central cortices).
    • Attenuated pre-articulatory activity and weaker frontal encoding observed.
    • Speech motor cortex shows mixed feedforward/feedback signals, with some exclusively feed-forward sites.

    Conclusions:

    • Automatic speech utilizes a broader cortical network than previously thought.
    • Speech motor cortex exhibits complex dynamics beyond purely feedforward control.
    • Findings redefine the spatiotemporal organization of automatic speech processing.