Department of Psychology, University of California, Berkeley, Berkeley, CA 94720-1650, USA.
J Exp Psychol Gen. 2011 Nov;140(4):725-43. doi: 10.1037/a0024899.
Predicting the future is a basic problem that people have to solve every day and a component of planning, decision making, memory, and causal reasoning. In this article, we present 5 experiments testing a Bayesian model of predicting the duration or extent of phenomena from their current state. This Bayesian model indicates how people should combine prior knowledge with observed data. Comparing this model with human judgments provides constraints on possible algorithms that people might use to predict the future. In the experiments, we examine the effects of multiple observations, the effects of prior knowledge, and the difference between independent and dependent observations, using both descriptions and direct experience of prediction problems. The results indicate that people integrate prior knowledge and observed data in a way that is consistent with our Bayesian model, ruling out some simple heuristics for predicting the future. We suggest some mechanisms that might lead to more complete algorithmic-level accounts.
预测未来是人们每天都必须解决的基本问题，也是规划、决策、记忆和因果推理的组成部分。在本文中，我们提出了 5 个实验，测试了一种从当前状态预测现象持续时间或程度的贝叶斯模型。该贝叶斯模型表明了人们应该如何将先验知识与观测数据相结合。将该模型与人类判断进行比较，为人们可能用于预测未来的可能算法提供了限制。在实验中，我们使用描述和直接经验预测问题，研究了多次观测、先验知识的影响，以及独立和依赖观测之间的差异。结果表明，人们以符合我们贝叶斯模型的方式整合先验知识和观测数据，排除了一些用于预测未来的简单启发式方法。我们提出了一些可能导致更完整的算法级解释的机制。
