We describe a computational model to explain a variety of results in both standard and false recognition. A key attribute of the model is that it uses plausible semantic representations for words, built through exposure to a linguistic corpus. A study list is encoded in the model as a gist trace, similar to the proposal of fuzzy trace theory (Brainerd & Reyna, 2002), but based on realistically structured semantic representations of the component words. The model uses a decision process based on the principles of neural synchronization and information accumulation. The decision process operates by synchronizing a probe with the gist trace of a study context, allowing information to be accumulated about whether the word did or did not occur on the study list, and the efficiency of synchronization determines recognition. We demonstrate that the model is capable of accounting for standard recognition results that are challenging for classic global memory models, and can also explain a wide variety of false recognition effects and make item-specific predictions for critical lures. The model demonstrates that both standard and false recognition results may be explained within a single formal framework by integrating realistic representation assumptions with a simple processing mechanism.
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