Department of Psychology and Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada.
School of Psychology, Cardiff University, UK.
Exp Psychol. 2024 Jan;71(1):64-82. doi: 10.1027/1618-3169/a000611.
Mathematical models explaining production effects assume that production leads to the encoding of additional features, such as phonological ones. This improves memory with a combination of encoding strength and feature distinctiveness, implementing aspects of propositional theories. However, it is not clear why production differs from other manipulations such as study time and spaced repetition, which are also thought to influence strength. Here we extend attentional subsetting theory and propose an explanation based on the dimensionality of feature spaces. Specifically, we suggest phonological features are drawn from a compact feature space. Deeper features are sparsely subselected from a larger subspace. Algebraic and numerical solutions shed light on several findings, including the dependency of production effects on how other list items are encoded (differing from other factors) and the production advantage even for homophones. This places production within a continuum of strength-like manipulations that differ in terms of the feature subspaces they operate upon and leads to novel predictions based on direct manipulations of feature-space properties.
数学模型解释生产效应的假设是生产导致额外特征的编码，例如语音特征。这通过编码强度和特征独特性的结合提高了记忆，实现了命题理论的各个方面。然而，目前尚不清楚为什么生产与其他操作（如学习时间和间隔重复）不同，后者也被认为会影响强度。在这里，我们扩展了注意子集理论，并基于特征空间的维度提出了一种解释。具体来说，我们建议语音特征是从一个紧凑的特征空间中抽取出来的。更深层次的特征是从更大的子空间中稀疏地选择出来的。代数和数值解揭示了几个发现，包括生产效应依赖于其他列表项如何被编码（与其他因素不同），以及即使是同音词也具有生产优势。这将生产置于类似于强度的连续体中，这些操作在特征子空间上有所不同，并根据特征空间属性的直接操作产生新的预测。
