A powerful theoretical framework for exploring recognition memory is the global matchingframework, in which a cue’s memory strength reflects the similarity of the retrieval cuesbeing matched against the contents of memory simultaneously. Contributions at retrievalcan be categorized as matches and mismatches to the item and context cues, including theself match (match on item and context), item noise (match on context, mismatch on item),context noise (match on item, mismatch on context), and background noise (mismatch onitem and context). We present a model that directly parameterizes the matches andmismatches to the item and context cues, which enables estimation of the magnitude ofeach interference contribution (item noise, context noise, and background noise). Themodel was fit within a hierarchical Bayesian framework to ten recognition memory datasetsthat employ manipulations of strength, list length, list strength, word frequency, study-testdelay, and stimulus class in item and associative recognition. Estimates of the modelparameters revealed at most a small contribution of item noise that varies by stimulusclass, with virtually no item noise for single words and scenes. Despite the unpopularity ofbackground noise in recognition memory models, background noise estimates dominated atretrieval across nearly all stimulus classes with the exception of high frequency words,which exhibited equivalent levels of context noise and background noise. These parameterestimates suggest that the majority of interference in recognition memory stems fromexperiences acquired prior to the learning episode.
Human Hippocampal and Parahippocampal Activity during Visual Associative Recognition Memory for Spatial and Nonspatial Stimulus Configurations
