主动探索能显著提升成人对复合因果关系的判断能力。
Human Adults and LLMs as Scientists: Who Benefits from Active Exploration?
- 让成人主动干预实验，而非被动观察，以测试因果规则。
- 主动探索使复合规则推理准确率大幅提高，但仍需更多测试。
- 大模型虽推理准确率接近人类，但探索策略效率更低。
因果学习研究中长期发现，成人难以识别需要多个原因同时存在的复合因果规则，而在只需一个原因的析取规则中表现更好。然而，这些结论多基于无法控制证据生成的被动观察实验。本文通过改进的“blicket检测器”任务，让成人主动干预以发现因果对象，在复合或析取规则结构下进行测试。结果表明，主动探索显著提升了成人对复合因果规则的推理能力，尽管仍需更多测试才能确认。我们进一步将人类表现与多种大型语言模型对比：部分先进模型在假设推断准确率上接近人类水平，但探索策略效率较低，且在复合与析取规则间的性能差距相似。
原文摘要 · Abstract (English)
A long-standing finding in the causal learning literature is that adults struggle to identify conjunctive causal rules, where an effect requires the simultaneous presence of multiple causes, while performing better in disjunctive settings. However, most demonstrations of this ``conjunctive handicap'' rely on passive observation paradigms with limited evidence, where learners have no control over evidence generation. This paper asks whether this bias persists when adults are granted agency through active exploration. Using a modified ``blicket detector'' task, adult participants freely intervened to identify causal objects under conjunctive or disjunctive rule structures. We show that active exploration substantially improves adults' conjunctive causal reasoning, although conjunctive rules still require more tests to infer than disjunctive rules. We further compare human performance to a range of large language models in the same setting. While some state-of-the-art models approach human-level performance on hypothesis inference accuracy, they often exhibit less efficient exploration strategies and similar conjunctive-disjunctive performance gaps.
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