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主观记忆强度的分布:列表强度和反应偏差。

The distribution of subjective memory strength: list strength and response bias.

机构信息

Department of Psychology, Syracuse University, Syracuse, NY 13244, USA.

出版信息

Cogn Psychol. 2009 Dec;59(4):297-319. doi: 10.1016/j.cogpsych.2009.07.003. Epub 2009 Sep 17.

Abstract

Models of recognition memory assume that memory decisions are based partially on the subjective strength of the test item. Models agree that the subjective strength of targets increases with additional time for encoding however the origin of the subjective strength of foils remains disputed. Under the fixed strength assumption the distribution of memory strength for foils is invariant across experimental manipulations of encoding. For example, the subjective strength of foils may depend solely on the pre-experimental history of the item, thus encoding manipulations have no impact. In contrast, under the differentiation assumption the subjective strength of foils depends on the nature of the traces stored in episodic memory. If those traces are well encoded, the subjective strength of foils will be lower than the case where noisy traces are stored (e.g., when targets received minimal encoding). The fixed strength and differentiation accounts are tested by measuring direct ratings of memory strength. In Experiments 1 and 2, item strength is varied via repetition and in Experiment 3 response bias is varied via the relative proportion of targets on the test list. For all experiments empirical distributions of memory strength were obtained and compared to the distributions predicted by the two accounts. The differentiation assumption provides the most parsimonious account of the data.

摘要

记忆识别模型假设记忆决策部分基于测试项目的主观强度。这些模型一致认为,目标的主观强度会随着编码时间的增加而增加,然而,干扰项的主观强度的来源仍然存在争议。在固定强度假设下,干扰项的记忆强度分布在编码的实验操作中是不变的。例如,干扰项的主观强度可能仅取决于项目的预实验历史,因此编码操作没有影响。相比之下,在区分假设下,干扰项的主观强度取决于在情景记忆中存储的痕迹的性质。如果这些痕迹被很好地编码,那么干扰项的主观强度将低于存储噪声痕迹的情况(例如,当目标只进行了最少的编码时)。固定强度和区分假设通过测量记忆强度的直接评分来检验。在实验 1 和 2 中,通过重复来改变项目的强度,在实验 3 中,通过测试列表上目标的相对比例来改变反应偏差。对于所有实验,都获得了记忆强度的经验分布,并将其与两个假设的预测分布进行了比较。区分假设为数据提供了最简约的解释。

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