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Probabilistic Inference: Task Dependency and Individual Differences of Probability Weighting Revealed by Hierarchical Bayesian Modeling

机译：概率推论：分层贝叶斯模型揭示了任务相关性和概率加权的个体差异

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摘要

Cognitive determinants of probabilistic inference were examined using hierarchical Bayesian modeling techniques. A classic urn-ball paradigm served as experimental strategy, involving a factorial two (prior probabilities) by two (likelihoods) design. Five computational models of cognitive processes were compared with the observed behavior. Parameter-free Bayesian posterior probabilities and parameter-free base rate neglect provided inadequate models of probabilistic inference. The introduction of distorted subjective probabilities yielded more robust and generalizable results. A general class of (inverted) S-shaped probability weighting functions had been proposed; however, the possibility of large differences in probability distortions not only across experimental conditions, but also across individuals, seems critical for the model's success. It also seems advantageous to consider individual differences in parameters of probability weighting as being sampled from weakly informative prior distributions of individual parameter values. Thus, the results from hierarchical Bayesian modeling converge with previous results in revealing that probability weighting parameters show considerable task dependency and individual differences. Methodologically, this work exemplifies the usefulness of hierarchical Bayesian modeling techniques for cognitive psychology. Theoretically, human probabilistic inference might be best described as the application of individualized strategic policies for Bayesian belief revision.
机译：使用分层贝叶斯建模技术检查了概率推断的认知决定因素。一个经典的n球范式作为实验策略，涉及阶乘两个（先验概率）乘两个（可能性）设计。五个认知过程的计算模型与观察到的行为进行了比较。无参数的贝叶斯后验概率和无参数的基本速率忽略不充分提供了概率推论模型。扭曲的主观概率的引入产生了更可靠和可推广的结果。提出了一般类的（倒）S形概率加权函数。但是，不仅在实验条件下，而且在个体之间，概率失真都可能存在较大差异，这对于模型的成功至关重要。从各个参数值​​的信息量较弱的先验分布中采样时，考虑概率加权参数中的各个差异似乎也很有利。因此，分层贝叶斯建模的结果与先前的结果收敛，表明概率加权参数显示出相当大的任务依赖性和个体差异。从方法上讲，这项工作例证了贝叶斯分层建模技术对认知心理学的有用性。从理论上讲，人类概率推断最好描述为个性化战略策略在贝叶斯信念修正中的应用。

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