启发式
计算机科学
加权
一致性(知识库)
稳健性(进化)
人工智能
边界判定
机器学习
认知心理学
计量经济学
数据挖掘
心理学
分类器(UML)
数学
基因
操作系统
放射科
医学
生物化学
化学
作者
Moshe Glickman,Rani Moran,Marius Usher
标识
DOI:10.1038/s41562-022-01318-6
摘要
Evidence integration is a normative algorithm for choosing between alternatives with noisy evidence, which has been successful in accounting for vast amounts of behavioural and neural data. However, this mechanism has been challenged by non-integration heuristics, and tracking decision boundaries has proven elusive. Here we first show that the decision boundaries can be extracted using a model-free behavioural method termed decision classification boundary, which optimizes choice classification based on the accumulated evidence. Using this method, we provide direct support for evidence integration over non-integration heuristics, show that the decision boundaries collapse across time and identify an integration bias whereby incoming evidence is modulated based on its consistency with preceding information. This consistency bias, which is a form of pre-decision confirmation bias, was supported in four cross-domain experiments, showing that choice accuracy and decision confidence are modulated by stimulus consistency. Strikingly, despite its seeming sub-optimality, the consistency bias fosters performance by enhancing robustness to integration noise. Glickman et al. identify an evidence integration bias whereby the relative weighting of incoming information towards a decision is increased based on its consistency with preceding evidence, resulting in a pre-decision confirmation bias.
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