Neural integration underlying naturalistic prediction flexibly adapts to varying sensory input rate

Thomas J. Baumgarten, Brian Maniscalco, Jennifer L. Lee, Matthew W. Flounders, Patrice Abry and Biyu J. He ()
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Thomas J. Baumgarten: New York University School of Medicine
Brian Maniscalco: New York University School of Medicine
Jennifer L. Lee: New York University
Matthew W. Flounders: New York University School of Medicine
Patrice Abry: Université de Lyon, ENS Lyon
Biyu J. He: New York University School of Medicine

Nature Communications, 2021, vol. 12, issue 1, 1-14

Abstract: Abstract Prediction of future sensory input based on past sensory information is essential for organisms to effectively adapt their behavior in dynamic environments. Humans successfully predict future stimuli in various natural settings. Yet, it remains elusive how the brain achieves effective prediction despite enormous variations in sensory input rate, which directly affect how fast sensory information can accumulate. We presented participants with acoustic sequences capturing temporal statistical regularities prevalent in nature and investigated neural mechanisms underlying predictive computation using MEG. By parametrically manipulating sequence presentation speed, we tested two hypotheses: neural prediction relies on integrating past sensory information over fixed time periods or fixed amounts of information. We demonstrate that across halved and doubled presentation speeds, predictive information in neural activity stems from integration over fixed amounts of information. Our findings reveal the neural mechanisms enabling humans to robustly predict dynamic stimuli in natural environments despite large sensory input rate variations.

Date: 2021
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DOI: 10.1038/s41467-021-22632-z

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