fMTP: A unifying computational framework of temporal preparation across time scales

In warned reaction time (RT) tasks, a warning stimulus (S1) initiates a process of temporal preparation which promotes a speeded response to the target stimulus (S2). Variations of the S1-S2 interval (foreperiod) have been shown to affect the RT to S2 across a range of time scales: within trials, between consecutive trials, across trials within an experimental block, and across blocks. RT distribution analyses suggest that these effects share a common mechanism, and yet theories on preparation have thus far failed to offer an integrative account of these phenomena. We present a computational framework (fMTP) that formalizes the principles of a previously proposed theory of temporal preparation: Multiple Trace Theory of Temporal Preparation. With fMTP we combine models and theories on time perception, motor planning, and associative learning into a single, computational theory. fMTP assumes that for each trial a unique trace is formed by means of associative Hebbian learning between a layer of time cells and a motor layer with an inhibition and activation node. On each new trial, traces from the past collectively determine the temporal preparatory state. We compared fMTP to existing theories which were not yet formalized until now. This model exploration demonstrated that fMTP best described existing datasets. In addition, in an experiment that was set out to validate fMTP, we show the data to align with our model predictions. In sum, we find that fMTP’s single implicit learning mechanism suffices to explain a range of phenomena that previously have been considered to be the result of distinct processes.