AbstractHuman observers automatically extract temporal contingencies from the environment and predict the onset of future events. Temporal predictions are modelled by the hazard function, which describes the instantaneous probability for an event to occur given it has not occurred yet. Here, we tackle the question of whether and how the human brain tracks continuous temporal hazard on a moment-to-moment basis, and how flexibly it adjusts to strictly implicit variations in the hazard function. We applied an encoding-model approach to human electroencephalographic (EEG) data recorded during a pitch-discrimination task, in which we implicitly manipulated temporal predictability of the target tones by varying the interval between cue and target tone (the foreperiod). Critically, temporal predictability was either solely driven by the passage of time (resulting in a monotonic hazard function), or was modulated to increase at intermediate foreperiods (resulting in a modulated hazard function with a peak at the intermediate foreperiod). Forward encoding models trained to predict the recorded EEG signal from different temporal hazard functions were able to distinguish between experimental conditions, showing that implicit variations of temporal hazard bear tractable signatures in the human electroencephalogram. Notably, this tracking signal was reconstructed best from the supplementary motor area (SMA), underlining this area’s link to cognitive processing of time. Our results underline the relevance of temporal hazard to cognitive processing, and show that the predictive accuracy of the encoding-model approach can be utilised to track abstract time-resolved stimuli.Significance StatementExtracting temporal predictions from sensory input allows to process future input more efficiently and to prepare responses in time. In mathematical terms, temporal predictions can be described by the hazard function, modelling the probability of an event to occur over time. Here, we show that the human EEG tracks temporal hazard in an implicit foreperiod paradigm. Forward encoding models trained to predict the recorded EEG signal from different temporal-hazard functions were able to distinguish between experimental conditions that differed in their build-up of hazard over time. These neural signatures of tracking temporal hazard converge with the extant literature on temporal processing and provide new evidence that the supplementary motor area tracks hazard under strictly implicit timing conditions.
Tracking temporal hazard in the human electroencephalogram using a forward encoding model