人类成年人与大语言模型作为科学家:主动探索谁更受益? / Human Adults and LLMs as Scientists: Who Benefits from Active Exploration?
1️⃣ 一句话总结
本文通过一项交互式实验发现,当允许主动探索时,成年人识别复杂因果规则(如多个原因同时出现才导致结果)的能力显著提升,但这类规则仍比简单规则更难;而先进的大语言模型虽然在推断准确率上接近人类,但探索效率较低,同样存在类似的难度差距。
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.
人类成年人与大语言模型作为科学家:主动探索谁更受益? / Human Adults and LLMs as Scientists: Who Benefits from Active Exploration?
本文通过一项交互式实验发现,当允许主动探索时,成年人识别复杂因果规则(如多个原因同时出现才导致结果)的能力显著提升,但这类规则仍比简单规则更难;而先进的大语言模型虽然在推断准确率上接近人类,但探索效率较低,同样存在类似的难度差距。
源自 arXiv: 2606.06464