Tests of a Hybrid-Similarity Exemplar Model of Context-Dependent Memorability in a High-Dimensional Real-World Category Domain

We conduct tests of a hybrid-similarity exemplar model on its ability to account for the context-dependent memorability of items embedded in high-dimensional category spaces. According to the model, recognition judgments are based on the summed similarity of test items to studied exemplars. The model allows for the idea that “self-similarity” among objects differs due to matching on highly salient distinctive features. Participants viewed a study list of rock images belonging to geologically defined categories where the number of studied items from each category was manipulated. Following study, the participants’ old-new recognition memory performance was tested. We also manipulated across experiments the nature of the encoding task used during the study phase: Experiment 1 used a category-description matching task, whereas Experiment 2 used more neutral encoding instructions. Hit rates were markedly lower in Experiment 2 than in Experiment 1 and participants relied less on the presence of distinctive features for recognizing old items in the second experiment. With a minimum of parameter estimation, the hybrid-similarity model provided good accounts of a wide variety of fundamental benchmark phenomena across the two experiments. These included changing levels of memorability due to contextual effects of category size, within- and between-category similarity, and the presence of distinctive features. However, the hybrid model and a variety of extensions of the model fell short in accounting for the variability in hit rates within the class of old target items themselves. We discuss future directions for potentially improving upon the current predictions from the model.