时间信息的贝叶斯优化知觉&#x0002A;

陈有国; 彭春花; 刘培朵; 余婕

doi:10.16719/j.cnki.1671-6981.20240102

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心理科学 ›› 2024, Vol. 47 ›› Issue (1) : 11-20. DOI: 10.16719/j.cnki.1671-6981.20240102

基础、实验与工效

时间信息的贝叶斯优化知觉^*

陈有国^**1,2, 彭春花³, 刘培朵^1,2, 余婕^1,2

作者信息 +

Bayes-Optimal Perception of Temporal Information

Chen Youguo^1,2, Peng Chunhua³, Liu Peiduo^1,2, Yu Jie^1,2

Author information +

文章历史 +

摘要

大脑运用贝叶斯推理加工时间信息形成优化知觉。贝叶斯模型在趋中效应、多源时间信息整合、时空干扰效应以及同时性的贝叶斯校准这四个课题上取得了较大的进展。本文在测量-估计-决策三阶段模型的框架下,对以上课题中的贝叶斯模型进行分析。未来需检验不同形式的时间信息先验的合理性,厘清似然性的心理表征,探明时间信息贝叶斯估计的神经基础。应该结合时间信息加工模型与贝叶斯模型的优势,为时间信息加工的认知神经机制研究提供新的思路。

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.

导出引用

陈有国, 彭春花, 刘培朵, 余婕. 时间信息的贝叶斯优化知觉^*[J]. 心理科学. 2024, 47(1): 11-20 https://doi.org/10.16719/j.cnki.1671-6981.20240102

Chen Youguo, Peng Chunhua, Liu Peiduo, Yu Jie. Bayes-Optimal Perception of Temporal Information[J]. Journal of Psychological Science. 2024, 47(1): 11-20 https://doi.org/10.16719/j.cnki.1671-6981.20240102

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