Sparse attentional subsetting of item features and list-composition effects on recognition memory

IF 2.2 4区 心理学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Jeremy B. Caplan
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Abstract

Although knowledge is extremely high-dimensional, human episodic memory performance appears extremely low-dimensional, focused largely on stimulus-features that distinguish list items from one another. A cognitively plausible way this tension could be addressed is if selective attention selects a small number of features from each item. I consider an ongoing debate about whether stronger items (better encoded) interfere more than weaker items (less well encoded) with probe items during old/new episodic recognition judgements. This is called the list-strength effect, concerning whether or not effects of encoding strength are larger in lists of mixed strengths than in pure lists of a single strength. Analytic derivations with Anderson’s (1970) matched filter model show how storing only a small subset of features within high-dimensional representations, and assuming those same subsets tend to reiterate themselves item-wise at test, can support high recognition performance. In the sparse regime, the model produces a list-strength effect that is small in magnitude, resembling previous findings of so-called null list-strength effects. When the attended feature space is compact, such as for phonological features, attentional subsetting cannot be sparse. This introduces non-negligible cross-talk from other list items, producing a large-magnitude list-strength effect, similar to what is observed for the production effect (better recognition when reading aloud). This continuum-based account implies the existence of a continuous range of magnitudes of list-composition effects, including occasional inverted list-strength effects. This lays the foundation for propagating effects of task-relevant attention to sparse subsets of features through a broad range of models of memory behaviour.

项目特征的稀疏注意子集和列表组成对识别记忆的影响
虽然知识是高维的,但人类情景记忆的表现似乎是极低维的,主要集中在区分列表项目的刺激特征上。解决这种紧张关系的一种认知上可行的方法是,选择性注意从每个项目中选择少量特征。我考虑了一个正在进行的争论,即在新旧情景识别判断中,较强的项目(编码较好)是否比较弱的项目(编码较差)对探测项目的干扰更大。这被称为列表强度效应,涉及编码强度的影响是否在混合强度列表中比在单一强度的纯列表中更大。Anderson(1970)匹配过滤器模型的解析推导表明,如何在高维表示中仅存储一小部分特征,并假设这些相同的子集倾向于在测试中重复自己的项目,可以支持高识别性能。在稀疏状态下,模型产生的列表强度效应很小,类似于先前所谓的空列表强度效应的发现。当被关注的特征空间是紧凑的,例如语音特征,注意子集不能是稀疏的。这就引入了来自其他列表项的不可忽略的串扰,产生了一个大范围的列表强度效应,类似于我们观察到的生产效应(大声朗读时更好的识别)。这种基于连续的解释意味着存在一个连续范围的列表组成效应,包括偶尔的反向列表强度效应。这为通过广泛的记忆行为模型将任务相关注意的效应传播到特征的稀疏子集奠定了基础。
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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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