Modeling recognition memory using the similarity structure of natural input

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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

12 Citations (Web of Science)


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
Publication statusPublished - 1 Jan 2006

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