Learning a Bayesian prior in interval timing

Abstract

Behavioral studies on perceptual learning (PL) often attributed an improvement in task performance to an enhancement in sensory processing of stimuli. However, the framework of Bayesian inference suggests that perceptual improvements can arise from learning-induced changes either in a likelihood function or in a prior expectation for sensory input. We developed and adapted Bayesian observer models to long-term changes in interval timing (IT) performance by human subjects to assess relative contributions of the prior and likelihood to PL of IT.

While subjects were viewing a small bar that drifted for a while and disappeared, we estimated subjective time intervals ([DELTA]Ts) from subjects’ natural reactions to the reappearance of the invisible bar at a designated location, with the speed and distance of invisible motion varied trial to trial. Ten subjects performed this task over 10 daily sessions for [DELTA]Ts ranging from 0.5 to 6.5 sec. 

In terms of timing accuracy, the trend that short and long [DELTA]Ts were overestimated and underestimated, respectively, was evident in all subjects and became stronger over sessions. In contrast, timing precision gradually improved over session for the entire set of sampled [DELTA]Ts. These seemingly contradictory dynamics of PL in accuracy and precision were captured simultaneously by Bayesian models, in which subjective timing is determined jointly by the prior and likelihood function for IT. The best among several nested models was a simple model in which only a single Gaussian function and a single coefficient of variance were set free to describe the prior and the likelihood functions, respectively. Interestingly, it was the spread of the prior, not the likelihood, that changed steadily in the model fit to the across-session data, suggesting that the improvements in timing precision observed in our and previous studies arose as the prior became sharpened through massive training.
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