Bayesian sense of time in biological and artificial brains
This work addresses the fundamental problem of how biological and artificial brains process time, but it is incremental as it reviews existing research rather than presenting new findings.
The paper reviews recent advancements in understanding time perception through the Bayesian brain hypothesis, exploring how Bayesian models can explain human time estimation biases and what insights agent-based machine learning models offer for temporal modeling.
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.