A major goal of vision science is to understand how the visual system maintains behaviourally
relevant perceptions given the level of uncertainty in the signals it receives. One proposed solution is
that the visual system applies predictive coding to its inputs based on the integration of prior
knowledge and current stimulus features. However, support for some vital aspects of predictive
coding in the temporal domain is lacking and simpler accounts of temporal integration also exist. The
aim of this thesis was to test two key attributes of predictive coding in time a) does the visual system
apply adaptive weighting to prediction errors and b) can the visual system apply probabilistic
information learnt from stimulus sequences when making predictions. In chapters 3 & 4, we tested
predictive coding’s ideas of how prediction errors are weighted under the theoretical guidance of a
temporal integration model linked to predictive processing, called the Kalman filter. Here, both
experiments supported predictive coding. We showed that, consistent with the Kalman filter, visual
estimates and the way estimation errors were corrected, adapted to stimulus behaviour and viewing
conditions. In chapter 5, we assessed the ability of the visual system to integrate conditional
relationships present in sequences of stimuli when making predictions. To do this, we inserted a
stimulus sequence that changed and omitted trials based on Markov transition probabilities that made some transitions more or less probable and assessed reaction times and omission trial responses.
Reaction time data was consistent with predictive coding, in that more predictable changes elicited
faster responses. Omission trials data, was though, less clear. When faced with no stimulus,
participants did not apply the conditional probabilities in their decisions optimally, instead applying
non optimal decision strategies, inconsistent with predictive coding. In summary, this thesis supports
the predictive coding of temporal integration but questions its application in all situations
