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  4. Dynamic Bayesian Models of Time Within and Across the Senses




Dynamic Bayesian Models of Time Within and Across the Senses





Darren Rhodes, Nottingham Trent University


Perception can be understood as an active process in which sensory samples are combined with prior expectations to shape perceptual content and experience. A prominent example of the influence of priors on perception is that manually reproduced temporal durations are biased towards the mean of previously experienced durations. However, little is known about how prior expectations are acquired and maintained in environments in which multiple competing cues may indicate whether a given prior should be applied in that specific context. Over a series of experiments, we have tested such notions - combining psychophysics, Bayesian modelling, and causal inference to get at the underlying foundations of time perception. We generally account for our findings within a Bayesian framework, in which duration priors are iteratively updated depending on determination of a common or distinct origin between successive events. Overall, our data paper to show that the human brain can acquire and maintain multiple perceptual priors based on differences in stimulus properties both within and across the senses.

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When and where

2.00pm - 3.00pmWednesday 27th March 2019

D427 Rhind Building City, University of London St John Street London EC1R 0JD United Kingdom

Contact Details

CLS Research Events

City, University of London
Northampton Square
United Kingdom
0207 040 3410

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