INTERVENING CONCEPTS IN MULTIVARIATE ENVIRONMENT

我们建议对人类如何学习干预进行调查和建模 概念。中间的概念，例如 心理学上的饥饿概念，似乎是一种重要而自然的 是人类认知的一部分。在我们的范例中，受试者将被暴露 隐式干预关联的多元投入产出关系 概念。受试者的全局性和逐次尝试的学习过程 将在受试者尝试学习如何预测 给定一组特定输入值的输出。在九个系列中 在实验中，我们计划确定(A)在什么条件下 隐藏的介入性概念可以完全基于 关于投入产出关系的经验，以及(B)机制 用来学习一种隐藏的介入概念。 拟议的实验旨在获得一致的证据。 从广泛和多样的实验操作和 分析方法。实验包括(A)的操作。 在不同类型的因果结构中进行培训和转移， (B)各种教学条件；(B)各种类型的培训 序列，(C)各种类型的刺激条件，以及(D)使用 选择与接受概念学习培训程序的对比。 将使用两种新的和不同类型的分析来分析 结果：(A)无模型的全局(主成分)度量 介入性概念学习将被用来检查 实验操作的全局影响，以及(B) 通过试行学习过程对试行进行微观层面的分析。一 这两种分析方法的重要特点是它们可以 在单个主题级别执行，从而允许 对个体差异的影响的调查 成功迁移的介入性概念的学习 性能。重要的是，结果将被用来比较和 对比被动自适应网络学习模型和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.