INTERVENING CONCEPTS IN MULTIVARIATE ENVIRONMENT

我们建议调查和模拟人类如何学习干预 理念的 介入概念，例如 饥饿的心理概念，似乎是一个重要的和自然的 人类认知的一部分。 在我们的范例中，实验对象将被暴露在 隐性干预下的多变量投入产出关系 理念的 受试者的整体和逐个试验学习过程 当受试者试图学习如何预测 输出给定一组特定的输入值。 在一系列的九个 实验，我们计划确定（a）在何种条件下， 隐藏的介入概念可以仅仅基于 投入产出关系的经验，以及（B）机制 用来学习一个隐藏的介入概念。 所提出的实验旨在获得收敛的证据 从广泛和各种各样的实验操作， 分析方法 实验包括以下操作：（a） 在不同类型的因果结构之间进行训练和转移， (b)各种教学条件，（B）各种培训 序列，（c）各种类型的刺激条件，和（d）使用 选择与接受概念的学习训练程序。 将使用两种新的不同类型的分析来分析 结果：（a）一个无模式的全球（主成分）措施， 干预概念学习，将用于检查 实验操作的整体效应，以及（B） 微观层面的分析，通过试验学习过程。 一 这两种分析方法的一个重要特点是， 在个体受试者水平上进行，从而允许 研究个体差异的影响， 学习中介概念以促进成功迁移 性能 重要的是，结果将用于比较和 对比被动自适应网络学习模型与主动自适应网络学习模型， 假设检验学习模型

We propose to investigate and model how humans learn intervening concepts. Intervening concepts, such as for example the psychological concept of hunger, seem to be an important and natural part of human cognition. In our paradigm, subjects will be exposed to multivariate input-output relations related by hidden intervening concepts. Subjects' global and trial-by-trial learning processes will be examined as subjects attempt to learn how to predict the outputs given a particular set of input values. In a series of nine experiments, we plan to determine (a) the conditions under which a hidden intervening concept can be learned solely on the basis of experience with the input-output relations, and (b) the mechanisms used to learn a hidden intervening concept. The proposed experiments are designed to obtain converging evidence from a wide range and variety of experimental manipulations and analytical methods. The experiments include manipulations of (a) training and transfer across different types of causal structures, (b) various instructional conditions, (b) various types of training sequences, (c) various types of stimulus conditions, and (d) the use of selection versus reception concept learning training procedures. Two new and different types of analyses will be used to analyze the results: (a) a model-free global (principle component) measure of intervening concept learning which will be used to examine the global effects of the experimental manipulations, and (b) micro-level analyses of the trial by trial learning process. One important feature of both methods of analyses is that they can be performed at the individual subject level, thus permitting an investigation of the implications of individual differences in learning of intervening concepts for successful transfer performance. Importantly, the results will be used to compare and contrast a passive adaptive network learning model versus an active hypothesis testing learning model.