A model of time estimation and error feedback in predictive timing behavior


Two key features of sensorimotor prediction are preprogramming and adjusting of performance based on previous experience. Oculomotor tracking of alternating visual targets provides a simple paradigm to study this behavior in the motor system; subjects make predictive eye movements (saccades) at fast target pacing rates (>0.5 Hz). In addition, the initiation errors (latencies) during predictive tracking are correlated over a small temporal window (correlation window) suggesting that tracking performance within this time range is used in the feedback process of the timing behavior. In this paper, we propose a closed-loop model of this predictive timing. In this model, the timing between movements is based on an internal estimation of stimulus timing (an internal clock), which is represented by a (noisy) signal integrated to a threshold. The threshold of the integrate-to-fire mechanism is determined by the timing between movements made within the correlation window of previous performance and adjusted by feedback of recent and projected initiation error. The correlation window size increases with repeated tracking and was estimated by two independent experiments. We apply the model to several experimental paradigms and show that it produces data specific to predictive tracking: a gradual shift from reaction to prediction on initial tracking, phase transition and hysteresis as pacing frequency changes, scalar property, continuation of predictive tracking despite perturbations, and intertrial correlations of a specific form. These results suggest that the process underlying repetitive predictive motor timing is adjusted by the performance and the corresponding errors accrued over a limited time range and that this range increases with continued confidence in previous performance.

DOI: 10.1007/s10827-008-0102-x

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@article{Joiner2008AMO, title={A model of time estimation and error feedback in predictive timing behavior}, author={Wilsaan M. Joiner and Mark Shelhamer}, journal={Journal of Computational Neuroscience}, year={2008}, volume={26}, pages={119-138} }