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.
翻译:有关生物大脑的基本机制和突发特性的调查有着长期的理论假设和实验结果的历史。今天,科学界倾向于对大脑的认知基础进行单一解释 -- -- 这是一台贝叶斯推断机器。这种当代观点自然是最近围绕计算和认知神经科学的发展动态的强大推动力。特别令人感兴趣的是大脑处理时间流逝的能力 -- -- 这是我们经历的基本层面之一。我们如何利用贝叶西亚大脑假设来解释关于人类时间认知的经验数据?我们能否用贝叶斯模型复制人类对时间认知的偏差?基于代理人的机器学习模型能为研究这个主题提供什么洞察力?在本章中,我们审查一些在时间认知和认知神经科学领域的最新进展,并讨论拜叶斯人处理过程在建立时间模型中的作用。