People often prefer simple to complex explanations because they generally have higher prior probability. However, simpler explanations are not always normatively superior because they often do not account for the data as well as complex explanations. How do people negotiate this trade-off between prior probability (favoring simple explanations) and goodness-of-fit (favoring complex explanations)? Here, we argue that people use opponent heuristics to simplify this problem-that people use simplicity as a cue to prior probability but complexity as a cue to goodness-of-fit. Study 1 finds direct evidence for this claim. In subsequent studies, we examine factors that lead one or the other heuristic to predominate in a given context. Studies 2 and 3 find that people have a stronger simplicity preference in deterministic rather than stochastic contexts, while Studies 4 and 5 find that people have a stronger simplicity preference for physical rather than social causal systems, suggesting that people use abstract expectations about causal texture to modulate their explanatory inferences. Together, we argue that these cues and contextual moderators act as powerful constraints that can help to specify the otherwise ill-defined problem of what distributions to use in Bayesian hypothesis comparison.

中文翻译：

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因果解释中的简单性和复杂性偏好：对手启发式解释

人们通常更喜欢简单到复杂的解释，因为它们通常具有更高的先验概率。然而，简单的解释在规范上并不总是优越，因为它们通常不能解释数据以及复杂的解释。人们如何在先验概率（倾向于简单解释）和拟合优度（倾向于复杂解释）之间进行权衡？在这里，我们认为人们使用对手启发式方法来简化这个问题——人们使用简单性作为先验概率的线索，而使用复杂性作为拟合优度的线索。研究 1 找到了这一说法的直接证据。在随后的研究中，我们检查了在给定上下文中导致一种或另一种启发式方法占主导地位的因素。研究 2 和 3 发现人们在确定性而不是随机环境中具有更强的简单偏好，而研究 4 和 5 发现人们对物理而非社会因果系统有更强的简单偏好，这表明人们使用对因果结构的抽象期望来调节他们的解释性推论。总之，我们认为这些线索和上下文调节器充当了强大的约束，可以帮助指定在贝叶斯假设比较中使用什么分布的其他不明确的问题。