Skip to main content
Open Access Publications from the University of California

Retention of Exemplar-Specific Information in Learning of Real-World High-Dimensional Categories: Evidence from Modeling of Old-New Item Recognition


Participants learned to classify a set of rock images into geologically-defined science categories. We then investigated the nature of their category-based memory representations by collecting old-new recognition data in a subsequent transfer phase. An exemplar model provided better qualitative accounts of the old-new recognition data than did a prototype or clustering model. However, to account for the variability in recognition probabilities among the old training items themselves, a hybrid-similarity exemplar model was needed that took account of distinctive features present in the items. The study is among the first to use computational models for making detailed quantitative predictions of old-new recognition probabilities for individual items embedded in complex, high-dimensional similarity spaces.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View