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

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. & COPY; 2023 Elsevier Inc. All rights reserved.