On the Distinction Between Perceived Duration and Event Timing - Towards a Unified Model of Time Perception

• Darren Rhodes

• neural and computational bases for the processing of time remains unknown

• he distinction between perceived event timing and perceived duration provides the current for navigating a river of contemporary approaches to time perception

• Recent work has advocated a Bayesian approach to time perception

• This framework has been applied to both duration and perceived timing, where prior expectations about when a stimulus might occur in the future (prior distribution) are combined with current sensory evidence (likelihood function) in order to generate the perception of temporal properties (posterior distribution)

• these models predict that the brain uses temporal expectations to bias perception in a way that stimuli are ‘regularized’ i.e. stimuli look more like what has been seen before

From Perceived Duration to Perceived Timing

• The word ‘perceived’ here, is used in the loosest sense — the above methods cannot demonstrably show changes in low-level sensory processing of time (Rhodes)

A Bayesian Model of Perceived Event Timing

• based on the dynamic updating of temporal expectations
• explain the asymmetries in the detection of irregularity and also in the perceived event timing of stimuli (Di Luca & Rhodes)
• Within a single trial, perceived timing (the posterior distribution) is the result of combining the probability of sensing a stimulus (likelihood) with the time it was expected (prior)
• key tenet of the model is the relaxation of the assumption of normality in the probability distribution
• Probability distributions in the temporal domain are asserted to be necessarily asymmetric due to the way time flows
• The anisotropic nature of time means that evidence accumulated about stimulus timing for the likelihood function can only start after a short delay
• due to neural processing
• a stimulus cannot be sensed before a stimulus is presented
• always the chance it could be perceived a bit later than average due to noise in the sensory system
• As such, the perceived timing of stimuli in an environment where trials are isochronous should exhibit the temporal Regularization effect
• early stimuli should be delayed towards expectation whilst late stimuli should be accelerated
• Stimuli presented on time, in contrast are perceptually accelerated
• stimuli that are presented in a random sequence of irregular timings, should not have any temporal expectations built up
• Therefore, they should not have any modulation of their perceived timing, suggesting that a prior is not built
• An implicit assumption of the model is that noisier measurements should lead to broader likelihood functions that are captured more by the prior probability distributions
• the Bayesian model of perceived timing can explain the delay of early stimuli as well as the acceleration of on time and later than expected stimuli
• Interval models do not make any explicit predictions about changes in the perceived timing of stimuli and as such cannot account for this data.