Peak Shift and Rules in Human Generalization
Peak Shift and Rules in Human Generalization
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DOI：
10.1037/xlm0000558
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发表时间：
2018-12-01
影响因子：
2.6
通讯作者：
Lovibond, Peter F.
中科院分区：
文献类型：
--
作者：
Lee, Jessica C.;Hayes, Brett K.;Lovibond, Peter F.
两个实验测试了是否可以解释峰移泛化梯度的平均值不同的梯度显示在分组报告不同的泛化规则。在实验中使用的因果判断任务（实验1）和恐惧条件反射范式（实验2），我们发现了一个密切的一致性之间的自我报告的规则和泛化梯度使用连续的刺激维度（色调）。这两个实验也显示了一个整体的峰移梯度后，差分条件反射，但不是在单一的线索条件反射。重要的是，当参与者被分成规则子组时，峰移可以分解为线性梯度和峰值梯度。我们的研究结果强调，需要考虑个体差异的规则，参与者在人类泛化研究，并建议在某些情况下，峰移可能是不同的规则子组的平均结果。
Two experiments tested whether a peak-shifted generalization gradient could be explained by the averaging of distinct gradients displayed in subgroups reporting different generalization rules. Across experiments using a causal judgment task (Experiment 1) and a fear conditioning paradigm (Experiment 2), we found a close concordance between self-reported rules and generalization gradients using a continuous stimulus dimension (hue). Both experiments also showed an overall peak-shifted gradient after differential conditioning, but not after single cue conditioning. Importantly, the peak shift could be decomposed into linear and peaked gradients when participants were divided into rule subgroups. Our results highlight the need to consider individual differences in the rules that participants derive in human generalization studies and suggest that in some situations, peak shift may be a consequence of averaging across diverse rule subgroups.