The ability to learn cause–effect relations from experience is critical for humans to behave adaptively — to choose causes that bring about desired effects. However, traditional experiments on experience-based learning involve events that are artificially compressed in time so that all learning occurs over the course of minutes. These paradigms therefore exclusively rely upon working memory. In contrast, in real-world situations we need to be able to learn cause–effect relations over days and weeks, which necessitates long-term memory. 413 participants completed a smartphone study, which compared learning a cause–effect relation one trial per day for 24 days versus the traditional paradigm of 24 trials back- to- back. Surprisingly, we found few differences between the short versus long timeframes. Subjects were able to accurately detect generative and preventive causal relations, and they exhibited illusory correlations in both the short and long timeframe tasks. These results provide initial evidence that experience-based learning over long timeframes exhibits similar strengths and weaknesses as in short timeframes. However, learning over long timeframes may become more impaired with more complex tasks.

中文翻译：

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长期因果学习的准确性：一种生态瞬时实验方法

从经验中学习因果关系的能力对于人类适应性行为至关重要 - 选择带来预期结果的原因。然而，基于经验的学习的传统实验涉及时间被人为压缩的事件，因此所有学习都在几分钟内发生。因此，这些范式完全依赖于工作记忆。相比之下，在现实世界中，我们需要能够在几天和几周内学习因果关系，这需要长期记忆。413 名参与者完成了一项智能手机研究，该研究将每天 1 次试验、连续 24 天学习因果关系与背靠背 24 次试验的传统范式进行了比较。令人惊讶的是，我们发现短期和长期时间框架之间几乎没有差异。受试者能够准确地检测生成和预防因果关系，并且他们在短期和长期任务中都表现出虚幻的相关性。这些结果提供了初步证据，表明基于经验的学习在很长的时间范围内表现出与在短时间范围内相似的优势和劣势。然而，在更复杂的任务中，长时间的学习可能会变得更加困难。