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Abstract

Neural oscillations reflect fluctuations in excitability, which biases the percept of ambiguous sensory input. Why this bias occurs is still not fully understood. We hypothesized that neural populations representing likely events are more sensitive, and thereby become active on earlier oscillatory phases, when the ensemble itself is less excitable. Perception of ambiguous input presented during less-excitable phases should therefore be biased towards frequent or predictable stimuli that have lower activation thresholds. Here, we show with computational modelling, psychophysics, and magnetoencephalography such a frequency bias in spoken word recognition; a computational model matched the double dissociation found with MEG, where the phase of oscillations in the superior temporal gyrus (STG) and medial temporal gyrus (MTG) biased word-identification behavior based on phoneme and lexical frequencies, respectively. These results demonstrate that oscillations provide a temporal ordering of neural activity based on the sensitivity of separable neural populations.