Human concept learning is known to be impaired by conceptual complexity: Simpler concepts are easier to learn, and more complex ones are more difficult. However, the simplicity bias has been studied almost exclusively in the context of deterministic concepts defined over Boolean features and is comparatively unexplored in the more general case of probabilistic concepts defined over continuous features. This article reports a series of experiments in which subjects were asked to learn probabilistic concepts defined over a novel 2D continuous feature space. Each concept was a mixture of several distinct Gaussian components, and the complexity of the concepts was varied by manipulating the positions of the mixture components relative to each other while holding the number of components constant. The results confirm that the positioning of mixture components strongly impacts learning, independent of the intrinsic statistical separability of the concepts, which was manipulated independently. Moreover, the results point to an information-theoretic basis framework for quantifying the complexity of probabilistic concepts, centered on the notion of compressive complexity: Simple concepts are those that can be approximately recovered from a projection of the concept onto a lower dimensional feature space, while more complex concepts are those that can only be represented by combining features. The framework provides a consistent, coherent, and broadly applicable measure of the complexity of probabilistic concepts. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

中文翻译：

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概率定义概念的简单性和复杂性。

众所周知，人类概念学习会受到概念复杂性的损害：更简单的概念更容易学习，而更复杂的概念则更困难。然而，简单偏差几乎完全是在对布尔特征定义的确定性概念的背景下进行研究的，而在对连续特征定义的概率概念的更一般情况下相对未被探索。本文报告了一系列实验，在这些实验中，受试者被要求学习在新的二维连续特征空间上定义的概率概念。每个概念都是几个不同的高斯分量的混合物，并且通过纵混合成分相对于彼此的位置来改变概念的复杂性，同时保持分量数量不变。结果证实，混合成分的定位对学习有很大影响，与概念的内在统计可分离性无关，而概念是独立纵的。此外，研究结果指出了一个以压缩复杂性概念为中心的量化概率概念复杂性的信息论基础框架：简单概念是那些可以从概念投影到较低维度特征空间中近似恢复的概念，而更复杂的概念是那些只能通过组合特征来表示的概念。该框架为概率概念的复杂性提供了一致、连贯且广泛适用的衡量标准。（PsycInfo 数据库记录 （c） 2025 APA，保留所有权利）。