Abstract:

The prediction of future events enables action preparation. This talk investigates how the probability of when an event will occur shapes human behavior and its neural underpinnings. To predict the timing of an event, neural systems need to estimate elapsed time and how event probability changes over time. A prominent hypothesis suggests that neural systems meet these computational demands by estimating the hazard rate of events. Based on psychophysical experiments, we demonstrate that humans instead compute a simpler variable: the event probability density function. At the neural level, we show that this event probability density function is represented in three distinct cortical signals, each of which predicts reaction times. Finally, I will present ongoing work investigating how time itself influences predictive processes. Our data suggest that the computations underlying the anticipation of imminent events are time-scale invariant. Our results contribute to a mechanistic understanding of anticipation – a fundamental process across many cognitive domains.