Fig. 1: Hypotheses and models of event anticipation.

a Event anticipation facilitates fast responses to stochastic sensory events. b A mapping rule generates a prediction of reaction time (RT) based on the probability density function (PDF) of events. Mapping rule constituents include a representation of event probability, a transformation function between event probability and RTs, and a model of uncertainty of time estimation. c Overview of variables for temporal anticipation derived from mapping rules. The canonical hazard rate (HR) model is based on three assumptions: (1) the brain employs the HR to anticipate events in time. (2) Time estimation contains uncertainty that scales linearly with elapsed time (temporal blurring). (3) RT to events and the HR are linearly anti-correlated (mirror). The three assumptions are investigated using additional, PDF-based variables incorporating probabilistic blurring and a non-linear, reciprocal relationship between RT and model (see Methods).