Bayesian sense of time in biological and artificial brains

TL;DR

This paper reviews recent advancements in understanding how Bayesian inference models explain biological and artificial brains' perception of time, highlighting the role of Bayesian processing in temporal cognition.

Contribution

It synthesizes recent developments in Bayesian models of time perception and discusses their implications for biological and artificial intelligence.

Findings

Bayesian models can replicate human time estimation biases.

Recent models improve understanding of temporal processing in brains.

Bayesian approach offers a unified framework for time perception studies.

Abstract

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.