Dichotomy in perceptual learning of interval timing: calibration of mean accuracy and precision differ in specificity and time course

Abstract

Sohn H, Lee SH. Dichotomy in perceptual learning of interval timing: calibration of mean accuracy and precision differ in specificity and time course. J Neurophysiol 109: 344 -362, 2013. First published October 17, 2012 doi:10.1152/jn.01201.2011.-Our brain is inexorably confronted with a dynamic environment in which it has to fine-tune spatiotemporal representations of incoming sensory stimuli and commit to a decision accordingly. Among those representations needing constant calibration is interval timing, which plays a pivotal role in various cognitive and motor tasks. To investigate how perceived time interval is adjusted by experience, we conducted a human psychophysical experiment using an implicit interval-timing task in which observers responded to an invisible bar drifting at a constant speed. We tracked daily changes in distributions of response times for a range of physical time intervals over multiple days of training with two major types of timing performance, mean accuracy and precision. We found a decoupled dynamics of mean accuracy and precision in terms of their time course and specificity of perceptual learning. Mean accuracy showed feedback-driven instantaneous calibration evidenced by a partial transfer around the time interval trained with feedback, while timing precision exhibited a long-term slow improvement with no evident specificity. We found that a Bayesian observer model, in which a subjective time interval is determined jointly by a prior and likelihood function for timing, captures the dissociative temporal dynamics of the two types of timing measures simultaneously. Finally, the model suggested that the width of the prior, not the likelihoods, gradually shrinks over sessions, substantiating the important role of prior knowledge in perceptual learning of interval timing. time perception; perceptual learning; mean accuracy and precision; Bayesian observer model; prior and likelihood TO ACT ON OBJECTS in an ever-changing and noisy environment, our brain has to dynamically calibrate its spatiotemporal representations of the objects by interacting with the environment.

Generalization of prior information for rapid Bayesian time estimation

“…1D shows simulated datasets for each duration distribution condition along with the best-fitting priors. Differences in the magnitude of compression between conditions are captured by a change in the width of the prior, consistent with previous evidence showing that observers can implicitly learn the variance of a stimulus distribution (22,23,29). Changes in the indifference point are consistent with a lateral shift in the prior distribution toward recently presented stimulus values.…”

Training-induced dynamics of accuracy and precision in human motor control

“…Skilled motor performance is suggested to be associated with improved accuracy and lower trial-to-trial variability compared with novice performance 26, 27 . The central nervous system dynamically calibrates the spatiotemporal representations of the object during a motor skill task despite the noisy, ever-changing environment 28 . The results of the present study shows a significant dynamic change in accuracy of controlled forces during the initial stage (first slope) of targeting high hold forces during OMT and a significant dynamic change in both accuracy and precision during the final stage (second slope) of targeting high hold forces during DMT.…”

Bayesian Models of Interval Timing and Distortions in Temporal Memory as a Function of Parkinson’s Disease and Dopamine-Related Error Processing

“…This statistical information influences the determination of temporal judgments even in cases where the current trial may simply require the reproduction of a signal duration that was presented to the participant just moments before the "go" signal to respond on the current trial (Acerbi et al, 2012;Cicchini et al, 2012;Jazayeri and Shadlen, 2010). Moreover, a Bayesian observer model, in which the subjective duration of an event is determined jointly by a prior and a likelihood function for timing, captures the temporal dynamics of accuracy and precision in a manner suggesting that the width of the prior, not the likelihoods, gradually decreases over training sessions, again indicating the relevance of prior knowledge for interval timing (Sohn and Lee, 2013). Based on these prior arguments, a Bayesian-inspired model was used to simulate duration reproductions in the pi procedure using 7-and 14-s target durations as well as 8-and 21-s target durations (Malapani et al, 1998b) as illustrated in Figure 11.7 and Table 11.2.…”