How Occam's razor guides human decision-making.
How Occam's razor guides human decision-making.
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奥卡姆剃刀如何指导人类决策。
DOI：
10.1101/2023.01.10.523479
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Gold,JoshuaI
中科院分区：
文献类型：
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作者：
Piasini,Eugenio;Liu,Shuze;Chaudhari,Pratik;Balasubramanian,Vijay;Gold,JoshuaI
奥卡姆剃刀的原则是，在其他条件相同的情况下，更简单的解释应该比更复杂的解释更受欢迎。这一原则被认为指导着人类的决策，但这种指导的性质尚不清楚。在这里，我们使用预先注册的行为实验来表明，对于不确定数据，人们倾向于在两种可选解释中选择较简单的那一种。这些偏好与惩罚过度灵活性的正式模型选择理论的预测相匹配。当我们不仅考虑最佳解释，而且考虑所有可能的相关解释时，这些惩罚就会出现。我们进一步表明，这些简单的偏好在人类中持续存在，但在某些人工神经网络中却没有，即使它们是不适应的。我们的研究结果表明，统计模型选择的原则概念，包括整合可能的潜在原因，以避免过度拟合噪声观测，可能在人类决策中发挥核心作用。
Occam’s razor is the principle that, all else being equal, simpler explanations should be preferred over more complex ones. This principle is thought to guide human decision-making, but the nature of this guidance is not known. Here we used preregistered behavioral experiments to show that people tend to prefer the simpler of two alternative explanations for uncertain data. These preferences match predictions of formal theories of model selection that penalize excessive flexibility. These penalties emerge when considering not just the best explanation but the integral over all possible, relevant explanations. We further show that these simplicity preferences persist in humans, but not in certain artificial neural networks, even when they are maladaptive. Our results imply that principled notions of statistical model selection, including integrating over possible, latent causes to avoid overfitting to noisy observations, may play a central role in human decision-making.