An adaptive drift-diffusion model of interval timing dynamics

Luzardo, Andre; Ludvig, Elliot Andrew; Rivest, François

doi:10.1016/j.beproc.2013.02.003

Cited by 36 publications

(42 citation statements)

References 43 publications

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“…Despite their limited scope, the results obtained from PRP-based studies are sufficiently consistent to support informative quantitative models of timing dynamics (Luzardo, Ludvig, and Rivest 2013; Staddon, Chelaru, and Higa 2002, 2002b). In contrast, research focused on peak times provides a more comprehensive view of temporally controlled behavior, but the exiguous data it has provided support to seemingly inconsistent findings.…”

Section: Introductionmentioning

confidence: 70%

Adaptation of timing behavior to a regular change in criterion

Sanabria

Oldenburg

2014

Behavioural Processes

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This study examined how operant behavior adapted to an abrupt but regular change in the timing of reinforcement. Pigeons were trained on a fixed interval (FI) 15-s schedule of reinforcement during half of each experimental session, and on an FI 45-s (Experiment 1), FI 60-s (Experiment 2), or extinction schedule (Experiment 3) during the other half. FI performance was well characterized by a mixture of two gamma-shaped distributions of responses. When a longer FI schedule was in effect in the first half of the session (Experiment 1), a constant interference by the shorter FI was observed. When a shorter FI schedule was in effect in the first half of the session (Experiments 1, 2, and 3), the transition between schedules involved a decline in responding and a progressive rightward shift in the mode of the response distribution initially centered around the short FI. These findings are discussed in terms of the constraints they impose to quantitative models of timing, and in relation to the implications for information-based models of associative learning.

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Section: Introductionmentioning

confidence: 70%

Adaptation of timing behavior to a regular change in criterion

Sanabria

Oldenburg

2014

Behavioural Processes

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“…Alternatively, instead of describing time production as a release from a state of inhibition over time, one might also consider that the beta power indexes parameters of an evidence accumulation process that is underlying interval timing (e.g., Balci and Simen, 2014;Luzardo et al, 2011;Simen et al, 2013;Van Rijn et al, 2011). According to accumulation-to-bound models, a decision is reached by accrual of sensory evidence over time.…”

Section: Discussionmentioning

confidence: 98%

Single trial beta oscillations index time estimation

2015

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Recent work shows that putamen-originating beta power oscillations serve as a carrier for temporal information during tapping tasks, with higher beta power associated with longer temporal reproductions. However, given the nature of tapping tasks, it is difficult to determine whether beta power dynamics observed in these tasks are linked to the generation or execution of motor programs or to the internal representation of time. To assess whether recent findings in animals generalize to human studies we reanalyzed existing EEG data of participants who estimated a 2.5s time interval with self-paced onset and offset keypresses. The results showed that the trial-to-trial beta power measured after the onset predicts the produced duration, such that higher beta power indexes longer produced durations. Moreover, although beta power measured before the first key-press also influenced the estimated interval, it did so independently from post-first-keypress beta power. These results suggest that initial motor inhibition plays an important role in interval production, and that this inhibition can be interpreted as a biased starting point of the decision processes involved in time estimation.

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“…An additional set of time-adaptive drift diffusion models (TDDMs) have been recently developed to provide an approach for learning to time intervals (Rivest and Bengio 2011; Simen et al 2011; Luzardo, Ludvig, and Rivest 2013). These models all assume that timing is accomplished by a ramping function, with a learning rule that incorporates prediction error relating to the time of occurrence of reinforcement.…”

Section: Theories Of Timing and Prediction Error Learningmentioning

confidence: 99%

Interactions of timing and prediction error learning

Kirkpatrick

2014

Behavioural Processes

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Timing and prediction error learning have historically been treated as independent processes, but growing evidence has indicated that they are not orthogonal. Timing emerges at the earliest time point when conditioned responses are observed, and temporal variables modulate prediction error learning in both simple conditioning and cue competition paradigms. In addition, prediction errors, through changes in reward magnitude or value alter timing of behavior. Thus, there appears to be a bi-directional interaction between timing and prediction error learning. Modern theories have attempted to integrate the two processes with mixed success. A neurocomputational approach to theory development is espoused, which draws on neurobiological evidence to guide and constrain computational model development. Heuristics for future model development are presented with the goal of sparking new approaches to theory development in the timing and prediction error fields.

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An adaptive drift-diffusion model of interval timing dynamics

Cited by 36 publications

References 43 publications

Adaptation of timing behavior to a regular change in criterion

Adaptation of timing behavior to a regular change in criterion

Single trial beta oscillations index time estimation

Interactions of timing and prediction error learning

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