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共变学习中判断与结果预期测量之间的分离:一种信号检测理论方法。

Dissociation between judgments and outcome-expectancy measures in covariation learning: a signal detection theory approach.

作者信息

Perales José C, Catena Andrés, Shanks David R, González José A

机构信息

Facultad de Psicología Experimental, Departamento de Psicologia Experimental, Universidad de Granada, Granada, Spain.

出版信息

J Exp Psychol Learn Mem Cogn. 2005 Sep;31(5):1105-20. doi: 10.1037/0278-7393.31.5.1105.

Abstract

A number of studies using trial-by-trial learning tasks have shown that judgments of covariation between a cue c and an outcome o deviate from normative metrics. Parameters based on trial-by-trial predictions were estimated from signal detection theory (SDT) in a standard causal learning task. Results showed that manipulations of P(c) when contingency (deltaP) was held constant did not affect participants' ability to predict the appearance of the outcome (d') but had a significant effect on response criterion (c) and numerical causal judgments. The association between criterion c and judgment was further demonstrated in 2 experiments in which the criterion was directly manipulated by linking payoffs to the predictive responses made by learners. In all cases, the more liberal the criterion c was, the higher judgments were. The results imply that the mechanisms underlying the elaboration of judgments and those involved in the elaboration of predictive responses are partially dissociable.

摘要

一些使用逐次试验学习任务的研究表明,线索c和结果o之间的协变判断偏离了规范指标。在一个标准的因果学习任务中,基于逐次试验预测的参数是根据信号检测理论(SDT)估计出来的。结果显示,在 contingency(deltaP)保持不变时,对P(c)的操纵并不影响参与者预测结果出现的能力(d'),但对反应标准(c)和数字因果判断有显著影响。在两个实验中进一步证明了标准c与判断之间的关联,在这两个实验中,通过将收益与学习者做出的预测反应相联系,直接操纵了标准。在所有情况下,标准c越宽松,判断就越高。结果表明,判断细化背后的机制与预测反应细化所涉及的机制部分是可分离的。

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