  Enquiries concerning the underlying mechanisms and the emergent properties of
a biological brain have a long history of theoretical postulates and
experimental findings. Today, the scientific community tends to converge to a
single interpretation of the brain's cognitive underpinnings -- that it is a
Bayesian inference machine. This contemporary view has naturally been a strong
driving force in recent developments around computational and cognitive
neurosciences. Of particular interest is the brain's ability to process the
passage of time -- one of the fundamental dimensions of our experience. How can
we explain empirical data on human time perception using the Bayesian brain
hypothesis? Can we replicate human estimation biases using Bayesian models?
What insights can the agent-based machine learning models provide for the study
of this subject? In this chapter, we review some of the recent advancements in
the field of time perception and discuss the role of Bayesian processing in the
construction of temporal models.
