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Abstract

Recognising speech in background noise is a strenuous daily activity, yet most humans can master it. A mechanistic explanation of how the human brain deals with such sensory uncertainty is the Bayesian Brain Hypothesis. In this view, the brain uses a dynamic generative model to simulate the most likely trajectory of the speech signal. Such simulation account can explain why there is a task-dependent modulation of sensory pathway structures (i.e., the sensory thalami) for recognition tasks that require tracking of fast-varying stimulus properties (i.e., speech) in contrast to relatively constant stimulus properties (e.g., speaker identity) despite the same stimulus input. Here we test the specific hypothesis that this task-dependent modulation for speech recognition increases in parallel with the sensory uncertainty in the speech signal. In accordance with this hypothesis, we show—by using ultra-high-resolution functional magnetic resonance imaging in human participants—that the task-dependent modulation of the left primary sensory thalamus (ventral medial geniculate body, vMGB) for speech is particularly strong when recognizing speech in noisy listening conditions in contrast to situations where the speech signal is clear. Exploratory analyses showed that this finding was specific to the left vMGB; it was not present in the midbrain structure of the auditory pathway (left inferior colliculus, IC). The results imply that speech in noise recognition is supported by modifications at the level of the subcortical sensory pathway providing driving input to the auditory cortex.