关键词: 时间信息, 贝叶斯推理, 趋中效应, 时间信息整合, 时空干扰效应, 同时性的贝叶斯校准

Abstract: The Bayesian theory is widely used in cognitive science. Previous studies have proven that Bayesian inferences cause many illusions in temporal cognition. From the perspective of Bayesian inference, we analyzed the processing mechanism of the central tendency effect, integration of multisource temporal information, spatiotemporal interference effect, and Bayesian calibration of simultaneity. This study has crucial implications for research on temporal information processing; additionally, it helps other researchers in cognitive science to understand the basic methods of modeling mental processes using the Bayesian theory.
Temporal information processing is reinterpreted using a Bayesian model. The clock, reference memory, working memory, and decision-making in the temporal information processing model were analogized with the likelihood, prior, posterior, and loss functions of Bayesian inference. The central tendency effect is known as the Vierordt’s law, in which participants underestimate long time intervals and overestimate short time intervals in various time intervals. A three-stage Bayesian model is constructed to explain the mechanism of the central tendency effect. In daily life, the integration of multiple sources of temporal information is necessary. The maximum likelihood estimation model proposes that the brain uses Bayes’ rule to integrate temporal information from different sources, reducing uncertainty and increasing estimation reliability. The causal inference model uses a hierarchical Bayesian model to determine whether different temporal information originate from the same source or different sources. The spatiotemporal interference effect involves the mutual interference between spatial and temporal information, among which the Kappa effect has significantly progressed. The Kappa effect is a spatiotemporal illusion in which the irrelevant distance between the stimuli systematically distorts the perception of elapsed time between sensory stimuli. An algebraic model assumes that the perceived interstimulus time is a weighted average of actual and expected times, calculated as a ratio of known distance and velocity. This algebraic model was rewritten as a Bayesian model with a constant-speed hypothesis. A logarithmic constant-velocity model was proposed by integrating the Weber-Fechner law with an algebraic model. The logarithmic constant-velocity model considers that the deceleration tendency of the Kappa effect is driven by the Weber-Fechner law. The fitness of the logarithmic model for the Kappa effect behavior data is better than that of the original constant-velocity model. Additionally, priors influence the simultaneity of temporal order perception. During the temporal-order judgment task, participants learned the statistical distribution of temporal-order information as a prior. The prior and likelihood of the temporal-order information are then integrated using the Bayes’ rule, which is similar to the central tendency effect.
Future research on Bayes-optimal perception of temporal information should answer the following questions. (1) To test the rationality of the prior proposed in previous studies, neuroscience should incorporate the Bayesian model to test priors using electrophysiological evidence. (2) To clarify the mental representation of the likelihood of temporal information, neural activity in the dorsolateral striatum is associated with objective temporal coding. Neural activity can be measured to decode the likelihood of temporal information and to clarify whether the likelihood of temporal information has a normal or lognormal distribution. (3) To identify the neural basis of the Bayesian estimation of temporal information, further evidence is needed to identify whether temporal Bayesian estimation is specific to the parietal cortex or involves a larger neural network. The advantages of the temporal information processing and Bayesian models should be combined to provide new ideas for research on the cognitive and neural mechanisms of temporal information processing.

Key words: temporal information, Bayesian inference, central tendency effect, temporal information integration, spatiotemporal interference effect, Bayesian calibration of simultaneity