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反馈时滞和反馈类型对知觉类别学习的影响:多系统的局限性。

The effect of feedback delay and feedback type on perceptual category learning: the limits of multiple systems.

机构信息

School of Psychology, University of Adelaide, Adelaide, South Australia, Australia.

出版信息

J Exp Psychol Learn Mem Cogn. 2012 Jul;38(4):840-59. doi: 10.1037/a0027867.

Abstract

Evidence that learning rule-based (RB) and information-integration (II) category structures can be dissociated across different experimental variables has been used to support the view that such learning is supported by multiple learning systems. Across 4 experiments, we examined the effects of 2 variables, the delay between response and feedback and the informativeness of feedback, which had previously been shown to dissociate learning of the 2 types of category structure. Our aim was twofold: first, to determine whether these dissociations meet the more stringent inferential criteria of state-trace analysis and, second, to determine the conditions under which they can be observed. Experiment 1 confirmed that a mask-filled feedback delay dissociated the learning of RB and II category structures with minimally informative (yes/no) feedback and also met the state-trace criteria for the involvement of multiple latent variables. Experiment 2 showed that this effect is eliminated when a less similar, fixed pattern mask is presented in the interval between response and feedback. Experiment 3 showed that the selective effect of feedback delay on II learning is reduced with fully informative feedback (in which the correct category is specified after an incorrect response) and that feedback type did not dissociate RB and II learning. Experiment 4 extended the results of Experiment 2, showing that the differential effect of feedback delay is eliminated when a fixed pattern mask is used. These results pose important challenges to models of category learning, and we discuss their implications for multiple learning system models and their alternatives.

摘要

证据表明,基于规则(RB)和信息整合(II)类别结构的学习可以在不同的实验变量中分离,这支持了这样一种观点,即这种学习是由多个学习系统支持的。在 4 项实验中,我们研究了 2 个变量的影响,即反应和反馈之间的延迟以及反馈的信息量,这两个变量以前被证明可以分离这两种类型的类别结构的学习。我们的目的有两个:首先,确定这些分离是否符合状态轨迹分析更严格的推理标准;其次,确定可以观察到这些分离的条件。实验 1 证实,掩蔽填充反馈延迟可以分离 RB 和 II 类别结构的学习,具有最小信息量(是/否)的反馈,并且也符合涉及多个潜在变量的状态轨迹标准。实验 2 表明,当在反应和反馈之间呈现不那么相似的固定模式掩蔽时,这种效果就会消除。实验 3 表明,反馈延迟对 II 学习的选择性影响随着完全信息反馈(在错误响应后指定正确类别)而减少,并且反馈类型不会分离 RB 和 II 学习。实验 4 扩展了实验 2 的结果,表明当使用固定模式掩蔽时,反馈延迟的差异效应被消除。这些结果对类别学习模型提出了重要挑战,我们讨论了它们对多个学习系统模型及其替代模型的影响。

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