Abstract
Despite its name, associative recognition is a paradigm thought to rely on memory recall. However, it remains unclear how associative information may be represented and retrieved from memory and what its relationship to other information, such as item memory, is. Here, we propose a computational model of associative recognition, where relational information is accessed in a generic, multistage retrieval process. The model explains the relative difficulty of associative recognition compared with item recognition, the difference in experimental outcomes when different types of lures are used, as well as the conditions leading to the emergence of associative ROC curves with different shapes.
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