PDF(Pubmed)
Abstract：
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 strength 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.
摘要：
解释生产效应的数学模型假设生产导致额外特征的编码，比如语音。这通过编码强度和特征独特性的组合来提高内存，命题理论的实施方面。然而,目前还不清楚为什么生产不同于其他操作，如研究时间和间隔重复，也被认为会影响力量。在这里，我们扩展了注意力子集理论，并提出了基于特征空间维数的解释。具体来说,我们建议从紧凑的特征空间中提取语音特征。更深的特征是从较大的子空间中稀疏选择的。代数和数值解揭示了几个发现，包括生产效果对其他列表项如何编码的依赖性（与其他强度因素不同），以及即使对于同音词也具有生产优势。这将生产置于连续的类似强度的操作中，这些操作在其操作的特征子空间方面有所不同，并基于对特征空间属性的直接操作而产生新颖的预测。
