Kruijne, Wouter

Rijn, van, Hedderik

Salet, Josh

English

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

Proceedings - University of Groningen

   University of GroningenfMTPSalet, Josh; Kruijne, Wouter; Rijn, van, HedderikIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Publication date:2019Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Salet, J., Kruijne, W., & Rijn, van, H. (2019). fMTP: A Unifying Computational Framework of TemporalPreparation Across Time Scales.CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 11-12-2022Josh M. Salet1*, Wouter Kruijne1, Sander A. Los2*j.m.salet@rug.nl1University of Groningen2Vrije Universiteit AmsterdamTime MotorFPTime Cells500 1000 1500 t(ms)500 1000 1500 t(ms)The core of fMTP is an implicit associative learning process between time cells and activation- and inhibition motor unitsTemporal preparation is modulated across a vast range of time scalesTrace Formation Trace RetrievalDistributionPrevious  trial Across blocksThis project was partially supported by the research programme "Interval Timing in the Real World: A functional, computational and neuroscience approach",  project number 453-16-005, financed by the NWOWe pursue the view that these phenomena are driven by implicitly learned processesHazard accounts: Preparation is controlled by a process that follows the conditional probability fMTP integratesinsights on the coding of time, episodic memory, and motor planningPrevious experiences drive preparation according their recency Strong influence of recent trials gives rise to short-term dynamicsLong-lasting influence of each trial gives rise to long-term dynamicsWe show the data to be consistent with fMTP and to deviate from the Hazard functionPrevious trial? What is the unifying  mechanism giving rise to these phenomena?ITI S1 FPS2++++++fMTP: A Unifying Computational Framework of Temporal Preparation Across Time ScalesHedderik van Rijn1, Martijn Meeter2Simulations demonstrate that fMTP captures all phenomenaFP ParadigmProbabilityForeperiod (ms)0.500.25400 800 1200 1600Foreperiod (ms)400 800 1200 1600Group Anti-expGroup ExpForeperiod (ms)400 800 1200 1600Group Anti-expGroup ExpPre-acquisition Acquisition Test500 1000 1500400375350325500DataModel1000 1500 500 1000 1500ExpFP Distribution (ms)400 800 1200 1600Anti-expUniformProbability0.500.25Trace ExpressionMemory Strength(a.u.)Previous Trial (n)2 4 6 8TracesFP (ms)Trials15005001000500500 IA500 1000 1500Foreperiod (ms) Foreperiod (ms)Preparation (a.u.)Short-Term Dynamics Long-Term Dynamics500 1000 1500 500 1000 1500ExpUniformAnti-ExpFPn-1 (ms)50010001500fMTP vs HazardfMTP and the Hazard function make different predictions regarding the Gaussian FP distributionDistribution Across BlocksfMTPHazard FunctionProbability0.800.600.400.20Foreperiod (ms)1300 1950 2600CatchForeperiod (ms)1300 1950 2600400375350325ExperimentalAssessmentReaction Time (ms)Empirical dataHazardfMTPReaction Time (ms)The Story FP EffectsfMTP's DynamicsForeperiod (ms)500 1000 1500Anti-expUniformExp400375350325Reaction Time (ms)FPn-1 (ms)400800120016001000500 1500Foreperiod (ms)

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

https://pure.rug.nl/ws/files/109887341/fMTPposter.pdf

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

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Proceedings - University of Groningen