Probability distributions are central to Bayesian accounts of cognition, but behavioral assessments do not directly measure them. Posterior distributions are typically computed from collections of individual participant actions, yet are used to draw conclusions about the internal structure of participant beliefs. Also not explicitly measured are the prior distributions that distinguish Bayesian models from others by representing initial states of belief. Instead, priors are usually derived from experimenters' intuitions or model assumptions and applied equally to all participants. Here we present three experiments using "Plinko", a behavioral task in which participants estimate distributions of ball drops over all available outcomes and where distributions are explicitly measured before any observations. In Experiment 1, we show that participant priors cluster around prototypical probability distributions (Gaussian, bimodal, etc.), and that prior cluster membership may indicate learning ability. In Experiment 2, we highlight participants' ability to update to unannounced changes of presented distributions and how this ability is affected by environmental manipulation. Finally, in Experiment 3, we verify that individual participant priors are reliable representations and that learning is not impeded when faced with a physically implausible ball drop distribution that is dynamically defined according to individual participant input. This task will prove useful in more closely examining mechanisms of statistical learning and mental model updating without requiring many of the assumptions made by more traditional computational modeling methodologies.
翻译:概率分布是Bayesian认知账户的核心,但行为评估并不直接测量它们。 前景分布通常是从单个参与者行动收集的集合中计算出来的,但用于就参与者信仰的内部结构作出结论。 也没有明确测量以前通过代表初始信仰状态区分Bayesian模型和其他模型的分布。 相反, 先前分配通常来自实验者的直觉或模型假设,并平等地适用于所有参与者。 我们在这里介绍了三个实验, 使用“ Plinko” 进行的行为性任务, 参与者在其中估计球在所有现有结果上的分布, 在任何观察之前对分布进行明确测量。 在实验1中, 我们显示参与者先前围绕原概率分布( Gausian, 双式等) 进行分类, 而先前的分组成员资格可能表明学习能力。 在实验2中, 我们强调参与者能够更新未经宣布的分布变化以及这种能力如何受到环境操纵的影响。 最后, 在实验3中, 我们核实, 个人参与者以前的表现是可靠的, 当面对物理上无法预测的球的分布在任何观察之前都得到明确测量的分布时, 。 在实际无法想象的球流传的分布上, 需要更动态的模型的模型中, 将仔细地研究更多的统计分析。