Dataset of an Inferred Bayesian Model of Word Learning

Theories of word learning differentially weigh the role of repeated experience with a novel item, leading to internalization of statistical regularities over time, and the learners use of prior knowledge to infer in-the-moment. Bayesian theories suggest both are critical, but which is weighed more heavily depends on how ambiguous the situation is. To examine this interplay and how it relates to memory, we adapted a Bayesian model of learning (Tenanbaum, Kemp, Griffiths, & Goodman, 2011; Xu & Tenanbaum, 2007) to an inferential word learning task of novel animals, as outline in the following article: “Bayesians learn best: an inferred Bayesian model accounts for individual differences in prior knowledge use during word learning.” Briefly, the model used (i) contextual information provided in the task, quantified by collecting norms for how informative each trial was (likelihood) and (ii) participant’s trial selection accuracy (posterior distribution) to (iii) infer their prior distribution, a proxy for their belief before exposure to the contextual information. Trial accuracy data for the word learning task was collected on one day, and free recall and recognition memory of learned animal names was completed the next day. Norms for how informative each trial was to guide correct selection were collected in a single session with a separate group of participants. Primary data include trial informativeness norms and trial accuracy in the task, both of which were used as input for the Bayesian model. The model infers prior distribution shape parameters from task accuracy and trial norms, completed using the Excel add-in Solver. This is also included in the primary dataset. Output of the model were used to mathematically derive measures of central tendency and spread for participants’ inferred prior distributions, included in the Secondary dataset. These values, along with average block accuracy, were regressed for each participant to examine change across the task. Output from these regressions (slope, intercept and error terms) were used in the statistical analyses with memory measures, which can be found in the Secondary data.

词汇学习理论对新异项目的重复体验的作用权重存在差异，该差异会随时间推移推动统计规律的认知内化，同时学习者会借助先验知识开展即时推断。贝叶斯理论（Bayesian theories）认为二者均为关键要素，但二者的权重占比取决于当前情境的模糊程度。为探究这一交互作用及其与记忆的关联，我们将贝叶斯学习模型（Bayesian learning model，Tenanbaum、Kemp、Griffiths与Goodman，2011；Xu与Tenanbaum，2007）适配至新异动物的推断式词汇学习任务，详见后文论文"贝叶斯学习者表现最优：推断式贝叶斯模型解释词汇学习过程中先验知识运用的个体差异"。简言之，该模型通过（i）任务中提供的情境信息——通过收集各试次的信息性常模对其似然（likelihood）项进行量化——（ii）参与者的试次选择正确率（即后验分布（posterior distribution）），来（iii）推断其先验分布（prior distribution），该分布可作为参与者接触情境信息前的信念的代理指标。词汇学习任务的试次正确率数据于单日采集，而对已习得动物名称的自由回忆（free recall）与再认记忆（recognition memory）测试于次日完成。用于指导正确选择的各试次信息性常模，由另一组参与者在单次实验中采集。原始数据集（Primary dataset）包含任务中的试次信息性常模与试次正确率，二者均作为贝叶斯模型的输入数据。模型通过Excel插件规划求解（Solver）完成计算，从任务正确率与试次常模中推断先验分布的形状参数，该部分数据同样收录于原始数据集（Primary dataset）。模型的输出被用于数学推导参与者所推断先验分布的集中趋势与离散程度指标，此类指标收录于辅助数据集（Secondary dataset）。我们将此类数值与平均区块正确率（block accuracy）相结合，对每位参与者开展回归分析，以探究任务全程的表现变化。上述回归分析的结果（斜率（slope）、截距（intercept）与误差项（error terms））被用于结合记忆指标开展统计分析，相关分析结果可见于辅助数据集（Secondary dataset）。

搜集汇总

背景与挑战

背景概述

该数据集基于贝叶斯模型研究单词学习过程，重点关注重复经验与先验知识推断的交互作用及其对记忆的影响。数据集包含试验信息性规范、任务准确率等主要数据，以及推断先验分布的统计指标等次要数据，用于分析学习动态和记忆表现。数据集适用于语言学习、记忆模型等领域的研究，采用CC BY 4.0许可证开放访问。