A key component of humans' striking creativity in solving problems is our ability to construct novel descriptions to help us characterize novel concepts. Bongard problems (BPs), which challenge the problem solver to come up with a rule for distinguishing visual scenes that fall into two categories, provide an elegant test of this ability. BPs are challenging for both human and machine category learners because only a handful of example scenes are presented for each category, and they often require the open-ended creation of new descriptions. A new type of BP called physical Bongard problems (PBPs) is introduced, which requires solvers to perceive and predict the physical spatial dynamics implicit in the depicted scenes. The perceiving and testing hypotheses on structures (PATHS) computational model, which can solve many PBPs, is presented and compared to human performance on the same problems. PATHS and humans are similarly affected by the ordering of scenes within a PBP. Spatially or temporally juxtaposing similar (relative to dissimilar) scenes promotes category learning when the scenes belong to different categories but hinders learning when the similar scenes belong to the same category. The core theoretical commitments of PATHS, which we believe to also exemplify open-ended human category learning, are (a) the continual perception of new scene descriptions over the course of category learning; (b) the context-dependent nature of that perceptual process, in which the perceived scenes establish the context for the perception of subsequent scenes; (c) hypothesis construction by combining descriptions into explicit rules; and (d) bidirectional interactions between perceiving new aspects of scenes and constructing hypotheses for the rule that distinguishes categories. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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概念学习期间的感知和模拟。

人类在解决问题时具有惊人创造力的一个关键组成部分是我们构建新描述以帮助我们表征新概念的能力。邦加德问题 (BP) 要求问题解决者提出区分属于两类的视觉场景的规则，为这种能力提供了一种优雅的测试。BP 对于人类和机器类别学习者来说都具有挑战性，因为每个类别只提供了少数示例场景，而且它们通常需要开放式地创建新的描述。引入了一种称为物理邦加德问题 (PBP) 的新型 BP，它要求求解器感知和预测所描绘场景中隐含的物理空间动态。提出了结构感知和测试假设 (PATHS) 计算模型，该模型可以解决许多 PBP，并与人类在相同问题上的表现进行比较。PATHS 和人类同样受到 PBP 中场景顺序的影响。当场景属于不同类别时，在空间或时间上并置相似（相对于不相似）场景会促进类别学习，但当相似场景属于同一类别时会阻碍学习。我们相信 PATHS 的核心理论承诺也体现了开放式人类类别学习，即（a）在类别学习过程中不断感知新场景描述；(b) 该感知过程的上下文相关性质，其中感知的场景为后续场景的感知建立了上下文；(c) 通过将描述组合成明确的规则来构建假设；(d)感知场景的新方面和为区分类别的规则构建假设之间的双向相互作用。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。