Oscillatory Entrainment Guides Temporal Prediction in Non-Deterministic Continuous Environments, Independent of Bayesian Sequential Learning

Oscillatory entrainment guides human temporal prediction in non-deterministic continuous stimulus streams with substantial temporal jitter, improving detection at oscillator-predicted times in intermediate-jitter conditions.

• Why it matters: Real-world timing signals often lack deterministic regularities, so it is unclear whether oscillatory timing mechanisms still support prediction when event timing is noisy.
• What they did: The authors used model simulations to contrast oscillatory tracking of local input regularities versus Bayesian learning of global statistics, then tested humans in two speeded detection experiments with mechanism-dissociating streams.
• The result: Human performance was facilitated for targets at oscillator-predicted times in intermediate jitter relative to maximal-jitter streams, and this benefit was decoupled from Bayesian-based prediction and non-specific preparation while adapting to stream rate changes.

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