Supporting data for "Probabilistic Memory Prioritization Mechanisms in Statistical Learning"

Human learners form conceptual knowledge from statistical learning, an ability of abstracting multiple statistical information across a continuum of probability levels. However, with limited memory and computational resources, how the learning system cope with environmental inputs that embed with multiple forms of information, and multiple types of statistics remains unclear. This thesis investigated this question through three empirical studies. The first study (Chapter 2) developed a novel learning-memory representation paradigm to track the working memory representations of two types of information (i.e., item-specific and abstract) information across high, moderate, and low probabilities during online statistical learning. The second study (Chapter 3) used the electroencephalography approach to further investigate the neural encoding of the abstract and item-specific information across probability levels and non-statistic inputs. The third study (Chapter 4) investigated the online statistical learning of two types of statistics, conditional and distributional. The results show a complex interplay between conditional and distributional learning, regulated by inputs’ probability, structure, and temporal processing. The dataset comprised the dataset for the behavioural experiments in Study 1 (N = 313), the neural data for Study 2 (N = 100) , and the behavioural experiments in Study 3 (N = 245).人类学习者可通过统计学习构建概念性知识，该能力可在连续的概率区间内抽象出多类统计信息。然而，受限于记忆与计算资源，学习系统如何处理嵌入了多种信息形式与多种统计类型的环境输入，这一问题仍有待阐明。本论文通过三项实证研究对该问题展开探究：第一项研究（第2章）开发了全新的学习-记忆表征范式，用于追踪在线统计学习过程中，高、中、低三类概率水平下两类信息（即项目特异性信息与抽象信息）的工作记忆（working memory）表征；第二项研究（第3章）采用脑电图（electroencephalography）技术，进一步探究了不同概率水平下抽象信息与项目特异性信息的神经编码机制，以及非统计输入的相关编码情况；第三项研究（第4章）则针对条件统计与分布统计这两类统计信息的在线统计学习过程展开研究。研究结果显示，条件学习与分布学习之间存在复杂的交互作用，且该交互作用受输入的概率属性、结构特征与时间加工过程调控。本数据集包含三项研究的相关数据：第一项研究（N=313）的行为实验数据、第二项研究（N=100）的神经数据，以及第三项研究（N=245）的行为实验数据。