Modeling recognition memory using the similarity structure of natural input

Joyca Lacroix, J.M.J. Murre, E.O. Postma, H.J. van den Herik

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

The natural input memory (NIM) model is a new model for recognition memory that operates on natural visual input. A biologically informed perceptual preprocessing method takes local samples (eye fixations) from a natural image and translates these into a feature-vector representation. During recognition, the model compares incoming preprocessed natural input to stored representations. By complementing the recognition memory process with a perceptual front end, the NIM model is able to make predictions about memorability based directly on individual natural stimuli. We demonstrate that the NIM model is able to simulate experimentally obtained similarity ratings and recognition memory for individual stimuli (i.e., face images).
Original languageEnglish
Pages (from-to)121-145
JournalCognitive Science
Volume30
DOIs
Publication statusPublished - 1 Jan 2006

Cite this

Lacroix, J., Murre, J. M. J., Postma, E. O., & van den Herik, H. J. (2006). Modeling recognition memory using the similarity structure of natural input. Cognitive Science, 30, 121-145. https://doi.org/10.1207/s15516709cog0000_48