Daikoku Tatsuya, Kamermans Kevin, Minatoya Maiko
Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
Centre for Neuroscience in Education, University of Cambridge, Cambridge, UK.
EXCLI J. 2023 Aug 4;22:828-846. doi: 10.17179/excli2023-6135. eCollection 2023.
Statistical learning starts at an early age and is intimately linked to brain development and the emergence of individuality. Through such a long period of statistical learning, the brain updates and constructs statistical models, with the model's individuality changing based on the type and degree of stimulation received. However, the detailed mechanisms underlying this process are unknown. This paper argues three main points of statistical learning, including 1) cognitive individuality based on "" of prediction, 2) the construction of information "" through chunking, and 3) the acquisition of "1-3Hz that is essential for early language and music learning. We developed a Hierarchical Bayesian Statistical Learning (HBSL) model that takes into account both reliability and hierarchy, mimicking the statistical learning processes of the brain. Using this model, we conducted a simulation experiment to visualize the temporal dynamics of perception and production processes through statistical learning. By modulating the sensitivity to sound stimuli, we simulated three cognitive models with different reliability on bottom-up sensory stimuli relative to top-down prior prediction: hypo-sensitive, normal-sensitive, and hyper-sensitive models. We suggested that statistical learning plays a crucial role in the acquisition of 1-3 Hz rhythm. Moreover, a hyper-sensitive model quickly learned the sensory statistics but became fixated on their internal model, making it difficult to generate new information, whereas a hypo-sensitive model has lower learning efficiency but may be more likely to generate new information. Various individual characteristics may not necessarily confer an overall advantage over others, as there may be a trade-off between learning efficiency and the ease of generating new information. This study has the potential to shed light on the heterogeneous nature of statistical learning, as well as the paradoxical phenomenon in which individuals with certain cognitive traits that impede specific types of perceptual abilities exhibit superior performance in creative contexts.
统计学习始于幼年时期,与大脑发育和个性的形成密切相关。经过如此漫长的统计学习过程,大脑更新并构建统计模型,模型的个性会根据所接收刺激的类型和程度而变化。然而,这一过程背后的详细机制尚不清楚。本文阐述了统计学习的三个主要观点,包括1)基于预测“”的认知个性,2)通过组块构建信息“”,以及3)获取对早期语言和音乐学习至关重要的“1 - 3Hz”。我们开发了一种分层贝叶斯统计学习(HBSL)模型,该模型兼顾了可靠性和层级性,模拟了大脑的统计学习过程。利用这个模型,我们进行了一项模拟实验,以可视化通过统计学习的感知和生成过程的时间动态。通过调节对声音刺激的敏感度,我们模拟了三种对自下而上的感觉刺激相对于自上而下的先验预测具有不同可靠性的认知模型:低敏感模型、正常敏感模型和高敏感模型。我们认为统计学习在1 - 3Hz节奏的获取中起着关键作用。此外,高敏感模型能快速学习感觉统计信息,但会固着于其内部模型,难以生成新信息,而低敏感模型学习效率较低,但可能更易于生成新信息。各种个体特征不一定会比其他特征具有整体优势,因为在学习效率和生成新信息的难易程度之间可能存在权衡。这项研究有可能揭示统计学习的异质性本质,以及具有某些阻碍特定类型感知能力的认知特征的个体在创造性情境中表现出卓越表现的矛盾现象。