Chaining models of serial recall can produce positional errors

IF 2.2 4区 心理学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Journal of Mathematical Psychology Pub Date : 2022-08-01 DOI:10.1016/j.jmp.2022.102677
Jeremy B. Caplan , Amirhossein Shafaghat Ardebili , Yang S. Liu
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引用次数: 4

Abstract

A major argument for positional-coding over associative chaining models of immediate serial recall has been the high probability that an error from a prior list will appear in its correct serial-position, so-called “protrusions.” Here we show that a chaining model can produce protrusions if it includes three characteristics that have been incorporated into published chaining models: (a) a “start-signal” item is associated with all first list-items, (b) memory is not cleared following each list, and (c) the retrieval cue for each item is always the full non-redintegrated retrieved information, regardless of the response. The model covertly recalls all studied lists in parallel (weighted by recency), such that when prior-list items intrude, they predominantly occur at the correct output position. In addition to fitting prior protrusion data, we report two new data sets that question the ubiquity of the simple protrusion-dominance characteristic. These findings show that protrusions cannot falsify an associative basis for serial-order memory and speak to the plausibility of mixture models.

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串联回忆模型会产生位置误差
位置编码优于即时序列回忆联想链模型的一个主要论点是,先前列表中的错误很有可能出现在正确的序列位置上,即所谓的“突起”。在这里,我们表明,如果一个链模型包含了三个特征,那么它就可以产生突出点:(a)一个“开始信号”项目与所有的第一个列表项目相关联,(b)记忆在每个列表之后都没有被清除,(c)每个项目的检索线索总是完整的未重新整合的检索信息,而不管响应如何。该模型隐式地并行召回所有研究过的列表(按近时性加权),这样,当先验列表条目入侵时,它们主要出现在正确的输出位置。除了拟合先前的突出数据外,我们报告了两个新的数据集,这些数据集质疑了简单的突出优势特征的普遍性。这些发现表明,突起不能伪造序列顺序记忆的联想基础,并说明混合模型的合理性。
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来源期刊
Journal of Mathematical Psychology
Journal of Mathematical Psychology 医学-数学跨学科应用
CiteScore
3.70
自引率
11.10%
发文量
37
审稿时长
20.2 weeks
期刊介绍: The Journal of Mathematical Psychology includes articles, monographs and reviews, notes and commentaries, and book reviews in all areas of mathematical psychology. Empirical and theoretical contributions are equally welcome. Areas of special interest include, but are not limited to, fundamental measurement and psychological process models, such as those based upon neural network or information processing concepts. A partial listing of substantive areas covered include sensation and perception, psychophysics, learning and memory, problem solving, judgment and decision-making, and motivation. The Journal of Mathematical Psychology is affiliated with the Society for Mathematical Psychology. Research Areas include: • Models for sensation and perception, learning, memory and thinking • Fundamental measurement and scaling • Decision making • Neural modeling and networks • Psychophysics and signal detection • Neuropsychological theories • Psycholinguistics • Motivational dynamics • Animal behavior • Psychometric theory
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