Psychology Department, University of Pittsburgh.
Cogn Sci. 2021 Jul;45(7):e12985. doi: 10.1111/cogs.12985.
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.
从经验中学习因果关系的能力对人类的适应性行为至关重要——选择能带来期望效果的原因。然而，传统的基于经验的学习实验涉及到时间上人为压缩的事件，以至于所有的学习都在几分钟内完成。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到几周的时间里学习因果关系，这就需要长期记忆。413 名参与者完成了一项智能手机研究，该研究比较了每天学习一个因果关系，持续 24 天，与传统的 24 次连续试验范式。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性和预防性因果关系，并且在短期和长期时间框架任务中都表现出了错觉相关。这些结果初步表明，长时间段的基于经验的学习具有与短时间段相似的优势和劣势。然而，随着任务变得更加复杂，长时间的学习可能会受到更大的影响。
