AI 中文总结
该研究针对跨预测、观察和无监督程序的逆基率效应，提出新理论OSCAR，其可解释人类在三类程序中的个体差异及相关眼动数据，表现与替代模型相当。
AI 中文摘要
逆基率效应是人们在竞争类别间解决歧义时存在的一种稳定偏差，最突出的理论通过预测误差来解释该效应。在两项实验中，我们逐步移除了预测学习设计中提供此类误差信号的元素：首先转向观察学习，随后进入不提供类别标签的无监督程序。该效应仍持续存在——这种非理性偏差独立于监督学习程序。我们提出了一种新理论OSCAR，它整合了经最充分验证的模型的核心计算原则，并基于类似模式补全的自生成反馈运行。OSCAR将反应偏差背后的学习动态扩展至观察和无监督程序。除本文报告的两项新实验外，在一个大型现有监督数据集上评估时，OSCAR的表现与其他模型相当，且是首个能重现人类在所有三种程序中表现出的个体差异模式的模型。该模型还能解释迄今未得到解释的眼动追踪数据，这是其他替代理论都无法做到的。
英文摘要
The inverse base-rate effect is a robust bias in how people resolve ambiguity between competing categories, and the most prominent theories explain it through prediction error. Across two experiments we progressively removed the elements of the predictive-learning design that supply such error signals: first by moving to observational learning, then to an unsupervised procedure in which category labels were not presented. The effect persisted--the irrational bias is independent of supervised learning procedures. We propose a new theory, OSCAR, that integrates core computational principles of the best-validated models and operates on self-generated feedback akin to pattern completion. OSCAR extends the learning dynamics underlying the response bias to observational and unsupervised procedures. Evaluated on a large preexisting supervised dataset in addition to the two new experiments reported here, OSCAR performs competitively against alternatives, and is the first model that reproduces the pattern of individual differences seen in humans across all three procedures. The model provides an explanation of hitherto unexplained eye-tracking data, something none of the alternative accounts provide.
Comments24 pages, 7 figures
