Temporal foreknowledge: Anticipation and prospective correction of timing errors by diffusion.

Area of Science:

• Cognitive Neuroscience
• Computational Neuroscience
• Behavioral Science

Background:

• Recent research indicates humans and rodents possess temporal error monitoring (TEM) capabilities.
• Electrophysiological data suggests TEM-related neural signals precede observable timing behavior.

Purpose of the Study:

• To present a drift-diffusion model of real-time TEM incorporating prospective behavioral modulation.
• To explain how timing errors are read out rather than detected retrospectively.
• To account for task representation dependency in reward-rate maximizing timing behavior.

Main Methods:

• Development of a drift-diffusion model for real-time temporal error monitoring.
• Integration of prospective within-trial behavioral modulation elements sensitive to task representations.
• Analysis of psychophysical and statistical features of timing behavior.

Main Results:

• The model predicts the read-out of timing signals before behavior manifestation.
• The model demonstrates translation of error signals into improved timing accuracy within trials.
• The model explains reward-rate maximizing behavior through task representation dependency.

Conclusions:

• The proposed model provides a new theoretical foundation for temporal error monitoring.
• The model offers testable predictions for future behavioral and electrophysiological studies.
• Real-time error monitoring enables prospective, within-trial error corrections and improved timing precision.