两种基础比率，两种权重：基础比率忽视存在第二个维度
Two base rates, two weights: base-rate neglect has a second axis
- Hunter College and the Graduate Center, CUNY（亨特学院和CUNY研究生院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出基础比率忽视存在第二个维度，将其分为先验概率忽视与线索密度效应，构建含两个权重的贝叶斯框架，提出二者可独立操控的双重分离预测，设计了验证该预测的评分实验。
AI中文摘要：
基础比率忽视通常被视为一种偏差：对先验概率赋予的权重过低。然而，要将观察到的共现转化为有用的判断，需要修正两种基础比率，而非一种。第一种是常见的先验概率，即结果的普遍程度；第二种是线索本身的普遍程度。这是两种独立的偏差，学习者可单独犯其中任意一种。对先验概率修正不足是经典的基础比率忽视；对线索修正不足则是偶然性学习中的线索密度效应，该效应已被长期研究，但此前未被识别为基础比率忽视的一种类型。我们将两种修正表述为单个贝叶斯方程中的两个权重。任务决定了可测量的权重：线索频率权重仅出现在分级评分中，因为二选一测试会抵消该权重。在极端情况下，这两个权重可还原为熟悉的量：基础比率忽视、信号检测标准、邻近度/敏感性/效度三元组，以及因果强度的“提升”（lift）度量。同一线索频率权重也存在于六个标准的学习与记忆模型中；这些模型看似一致，实则仅因常规实验将数据压缩为无法产生分歧的形式。最重要的是，两种忽视应可被独立操控：实验者可改变其中一种而不影响另一种，这是一种双重分离，而单参数模型无法产生该效应。这一预测是该框架的核心，且尚未被验证。本文阐述了该框架及可验证其的评分实验；配套论文则将这两个权重拟合至现有颜色-味觉数据集。
英文摘要：
Base-rate neglect is usually treated as one mistake: giving the prior too little weight. But turning co-occurrences into a judgment means correcting for two base rates, not one -- the prior (how common the outcome is) and the cue's own frequency. Under-correcting the prior is classical base-rate neglect; under-correcting the cue is the cue-density effect, long studied but not usually framed as base-rate neglect's cue-side analogue. We write both corrections as two weights in one Bayesian equation. Response format fixes which weight a study can measure: a graded rating carries the cue-frequency weight in full; a forced choice between two outcomes cancels it and measures a companion weight in odds space instead, the same correction as it acts on the posterior odds rather than on the judged magnitude. The cancellation is exact, so a same-cue choice carries no information about the cue-frequency weight, not even its direction; a choice between two cues rather than two outcomes keeps the term and identifies the weight, and differing cue frequencies is necessary and sufficient for a choice to see it. The same weight appears in six standard learning-and-memory models, some as an exact identity and some only as a limiting or constructed correspondence; when an experiment keeps only the four cells of a cue x outcome contingency table, every model family rich enough to fit them projects onto one scalar coordinate, which alone cannot separate the accounts free to sit anywhere along it. The two neglects should be separately identifiable: an experimenter can move the regressor driving one weight without moving the regressor driving the other -- a two-coefficient separation a one-parameter account cannot produce. This paper lays out the framework and the rating experiment that tests the coefficient separation; a companion paper fits the two weights to an existing colour-flavour dataset.
