When people seek to understand concepts from an incomplete set of examples and counterexamples, there is usually an exponentially large number of classification rules that can correctly classify the observed data, depending on which features of the examples are used to construct these rules. A mechanistic approximation of human concept-learning should help to explain how humans prefer some rules over others when there are many that can be used to correctly classify the observed data. Here, we exploit the tools of propositional logic to develop an experimental framework that controls the minimal rules that are simultaneously consistent with the presented examples. For example, our framework allows us to present participants with concepts consistent with a disjunction and also with a conjunction, depending on which features are used to build the rule. Similarly, it allows us to present concepts that are simultaneously consistent with two or more rules of different complexity and using different features. Importantly, our framework fully controls which minimal rules compete to explain the examples and is able to recover the features used by the participant to build the classification rule, without relying on supplementary attention-tracking mechanisms (e.g. eye-tracking). We exploit our framework in an experiment with a sequence of such competitive trials, illustrating the emergence of various transfer effects that bias participants’ prior attention to specific sets of features during learning.

当人们试图从一组不完整的例子和反例中理解概念时，通常会有成倍数量的分类规则可以正确地对观察到的数据进行分类，具体取决于样本的哪些特征用于构建这些规则。人类概念学习的机械近似应该有助于解释当有许多规则可用于正确分类观察到的数据时，人类如何更喜欢某些规则。在这里，我们利用命题逻辑的工具来开发一个实验框架，该框架控制与给出的示例同时一致的最小规则。例如，我们的框架允许我们向参与者呈现与析取一致的概念，并且使用连词，具体取决于用于构建规则的功能。类似地，它允许我们呈现同时符合两个或多个不同复杂性并使用不同特征的规则的概念。重要的是，我们的框架完全控制了哪些最小规则竞争解释示例，并且能够恢复参与者用来构建分类规则的特征，而不依赖于补充的注意力跟踪机制（例如眼球跟踪）。我们在一系列此类竞争性试验的实验中利用我们的框架，说明了各种转移效应的出现，这些转移效应使参与者在学习过程中对特定特征集的先前注意力有偏向。