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

Josh Salet*, Wouter Kruijne, Hedderik Rijn, van

*Corresponding author for this work

Research output: Contribution to conferencePosterAcademic

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Abstract

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. Theories on temporal preparation thus far have failed to offer a complete account for these effects across all scales. 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, and show that by integrating them into a single, computational theory they allow us to capture the range of preparatory phenomena across different scales. fMTP assumes that for each timing experience (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 past trials are automatically retrieved and collectively determine the temporal preparatory state throughout the trial. Each trace contributes to preparation proportional to its strength, with strength gradually dissipating with time. For experimental setups where the predictions of existing accounts and fMTP differed, we show that the data aligns 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.
Original languageEnglish
Publication statusPublished - 20-Dec-2019

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