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基于记忆的自动性理论中从算法到检索的转变。

The transition from algorithm to retrieval in memory-based theories of automaticity.

作者信息

Compton B J, Logan G D

机构信息

Department of Psychology, University of Illinois, Champaign 61820.

出版信息

Mem Cognit. 1991 Mar;19(2):151-8. doi: 10.3758/bf03197111.

Abstract

Two memory-based theories of automaticity were compared. The mixture model and the race model both describe automatization as a transition from algorithmic processing to memory retrieval. The mixture model predicts that, with training, the variability of reaction time will initially increase, and later decrease in a concave downward manner, whereas the race model predicts the variability will decrease only in a concave upward manner. The mixture model predicts that using both algorithm and retrieval on a single trial will be slower than using the algorithm alone, whereas the race model predicts the reverse. The experiments used an alphabet arithmetic task, in which subjects verified equations of the form H + 3 = K and made subjective reports of their strategies on individual trials. Both the variability of reaction times and the pattern of reaction times associated with the strategy reports supported the race model.

摘要

对两种基于记忆的自动化理论进行了比较。混合模型和竞争模型都将自动化描述为从算法处理到记忆检索的转变。混合模型预测,随着训练,反应时间的变异性最初会增加,随后呈向下凹的方式减少,而竞争模型预测变异性只会呈向上凹的方式减少。混合模型预测,在单次试验中同时使用算法和检索会比单独使用算法更慢,而竞争模型则预测相反的情况。实验使用了字母算术任务,其中受试者验证形如H + 3 = K的等式,并在个别试验中对其策略进行主观报告。反应时间的变异性以及与策略报告相关的反应时间模式均支持竞争模型。

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