[论文] Human Adults and LLMs as Scientists: Who Benefits from Active Explorat

论文概要

研究领域: NLP
作者: Mandana Samiei, Eunice Yiu, Anthony GX-Chen, Dongyan Lin, Jocelyn Shen, Blake A. Richards, Alison Gopnik, Doina Precup
发布时间: 2026-06-04
arXiv: 2606.06464

中文摘要

因果学习文献中一个长期存在的发现是，成年人在识别合取因果规则（即一个效应需要多个原因同时存在）时存在困难，而在析取情境中表现更好。然而，这种"合取障碍"的大多数展示依赖于被动观察范式，证据有限，学习者无法控制证据的生成。本文探讨当成年人通过主动探索获得能动性时，这种偏差是否仍然存在。使用修改后的"blicket detector"任务，成年参与者自由干预以在合取或析取规则结构下识别因果对象。我们表明，主动探索显著改善了成年人的合取因果推理，尽管合取规则仍比析取规则需要更多测试才能推断。我们进一步将人类表现与一系列大语言模型在相同情境下的表现进行比较。虽然一些最先进的模型在假设推断准确性上接近人类水平，但它们往往表现出效率较低的探索策略和类似的合取-析取性能差距。

原文摘要

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 ...

自动采集于 2026-06-08

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