fMTP: A Unifying Computational Framework of Temporal Preparation Across Time Scales

Temporal preparation is the cognitive function that takes place when anticipating future events. This is commonly considered to involve a process that maximizes preparation at time points that yield a high hazard. However, despite their prominence in the literature, hazard-based theories fail to explain the full range of empirical preparation phenomena. Here, we present the formalized multiple trace theory of temporal preparation (fMTP), an integrative model which develops the alternative perspective that temporal preparation results from associative learning. fMTP builds on established computational principles from the domains of interval timing, motor planning, and associative memory. In fMTP, temporal preparation results from associative learning between a representation of time on the one hand and inhibitory and activating motor units on the other hand. Simulations demonstrate that fMTP can explain phenomena across a range of time scales, from sequential effects operating on a time scale of seconds to long-term memory effects occurring over weeks. We contrast fMTP with models that rely on the hazard function and show that fMTP's learning mechanisms are essential to capture the full range of empirical effects. In a critical experiment using a Gaussian distribution of foreperiods, we show the data to be consistent with fMTP's predictions and to deviate from the hazard function. Additionally, we demonstrate how changing fMTP's parameters can account for participant-to-participant variations in preparation. In sum, with fMTP we put forward a unifying computational framework that explains a family of phenomena in temporal preparation that cannot be jointly explained by conventional theoretical frameworks.