Bayesian optimization of time perception.

Area of Science:

• Cognitive Neuroscience
• Computational Neuroscience
• Psychology

Background:

• Precise timing is vital for decision-making and behavioral control.
• Subjective time perception is susceptible to distortions from temporal contexts.

Purpose of the Study:

• To review progress in understanding temporal calibration.
• To integrate a Bayesian framework with information-processing models of timing.
• To offer a new perspective on timing distortions.

Main Methods:

• Application of a Bayesian framework to contextual calibration in timing.
• Integration of Bayesian principles with information-processing models (clock, memory, decision stages).

Main Results:

• Contextual biases in timing, contrary to popular belief, optimize performance in noisy environments.
• Bayesian framework components align with established timing model stages.
• This integrated framework explains previously difficult-to-explain timing distortions.

Conclusions:

• Temporal calibration and Bayesian inference are key to understanding optimal timing under uncertainty.
• An integrated Bayesian and information-processing approach provides novel insights into time perception and its distortions.