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Modeling Recognition Memory Using the Similarity Structure of Natural Input
Author(s) -
Lacroix Joyca P. W.,
Murre Jaap M. J.,
Postma Eric O.,
Herik H. Jaap
Publication year - 2006
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1207/s15516709cog0000_48
Subject(s) - computer science , artificial intelligence , similarity (geometry) , pattern recognition (psychology) , natural (archaeology) , feature (linguistics) , recognition memory , memory model , representation (politics) , preprocessor , process (computing) , facial recognition system , perception , computer vision , image (mathematics) , cognition , psychology , shared memory , linguistics , philosophy , archaeology , neuroscience , politics , law , political science , history , operating system
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).