A context noise model of episodic word recognition

Item noise models of recognition assert that interference at retrieval is g enerated by the words from the study list. Context noise models of recognit ion assert that interference at retrieval is generated by the contexts in w hich the test word has appeared. The authors introduce the bind cue decide model of episodic memory, a Bayesian context noise model, and demonstrate h ow it can account for data from the item noise and dual-processing approach es to recognition memory. From the item noise perspective, list strength an d list length effects, the mirror effect for word frequency and concretenes s, and the effects of the similarity of other words in a list are considere d. From the dual-processing perspective, process dissociation data on the e ffects of length, temporal separation of lists, strength, and diagnosticity of context are examined. The authors conclude that the context noise appro ach to recognition is a viable alternative to existing approaches.