探索抽象规则推理和问题解决中的人类行为：认知抽象与推理语料库
Exploring Human Behavior During Abstract Rule Inference and Problem Solving with the Cognitive Abstraction and Reasoning Corpus
AI总结：
CogARC通过研究人类在抽象规则推理中的行为，揭示了人们在不确定性下的策略适应与泛化能力。
摘要：
人类在抽象推理中表现出显著的灵活性，能够快速从稀疏的例子中学习和应用规则。为了研究这种能力背后的认知策略，我们引入了认知抽象与推理语料库（CogARC），这是抽象与推理语料库（ARC）的一个多样化的人类适应子集，后者最初是为在人工智能中基准测试抽象推理而开发的。在两项实验中，CogARC被分发给总共260名人类参与者，他们自由地解决了75个抽象视觉推理问题。成功需要从少量例子中推断出输入-输出规则，将测试输入转换为一个正确的测试输出。参与者的行为在高时间分辨率下被记录，包括例子的观看、编辑序列和多尝试提交。参与者总体上表现良好（实验1（n=40）的平均准确率约为90%，实验2（n=220）约为80%），但表现在问题和参与者之间差异很大。更困难的问题导致更长的思考时间和更广泛的解决方案策略差异。在整个任务过程中，参与者响应速度加快，但准确性略有下降，这表明对任务结构的熟悉度增加，而不是规则学习能力的提高。重要的是，即使在问题解决轨迹不同长度和流畅度的情况下，错误的解决方案往往高度收敛。一些轨迹直接高效地向稳定结果发展，而另一些则涉及扩展探索或部分重新启动后才收敛。这些发现突显了CogARC作为研究人类抽象推理的丰富行为环境，提供了关于人们如何泛化、误泛化以及在不确定性下适应策略的见解。
英文摘要：
Humans exhibit remarkable flexibility in abstract reasoning, and can rapidly learn and apply rules from sparse examples. To investigate the cognitive strategies underlying this ability, we introduce the Cognitive Abstraction and Reasoning Corpus (CogARC), a diverse human-adapted subset of the Abstraction and Reasoning Corpus (ARC) which was originally developed to benchmark abstract reasoning in artificial intelligence. Across two experiments, CogARC was administered to a total of 260 human participants who freely generated solutions to 75 abstract visual reasoning problems. Success required inferring input-output rules from a small number of examples to transform the test input into one correct test output. Participants' behavior was recorded at high temporal resolution, including example viewing, edit sequences, and multi-attempt submissions. Participants were generally successful (mean accuracy ~90% for experiment 1 (n=40), ~80% for experiment 2 (n=220) across problems), but performance varied widely across problems and participants. Harder problems elicited longer deliberation times and greater divergence in solution strategies. Over the course of the task, participants initiated responses more quickly but showed a slight decline in accuracy, suggesting increased familiarity with the task structure rather than improved rule-learning ability. Importantly, even incorrect solutions were often highly convergent, even when the problem-solving trajectories differed in length and smoothness. Some trajectories progressed directly and efficiently toward a stable outcome, whereas others involved extended exploration or partial restarts before converging. Together, these findings highlight CogARC as a rich behavioral environment for studying human abstract reasoning, providing insight into how people generalize, misgeneralize, and adapt their strategies under uncertainty.
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