A dynamic model of context-based retrieval

We propose a comprehensive model of how experiences are encoded and retrieved from memory. At the core of the model is a dynamic retrieval process incorporating two essential mechanisms: iterative retrieval, whereby information is sequentially sampled from memory to access the full history of experiences; and competitive retrieval, whereby the most prominent features in memory inhibit the recollection of other features. Together with context-based encoding, the model quantitatively explains well-known facts about response order and inter-response times in recall experiments. We show that our retrieval process maps closely to existing decision frameworks, such as drift–diffusion models, suggesting that the memory system plays a fundamental role in a wide-ranging set of decision-making settings. • We propose a model of encoding, retrieval, and response incorporating rich memory dynamics. • Information is sequentially sampled from memory and competes for retrieval. • The model can explain recall and response-time data in free-recall experiments. • The model sheds light on the role of memory in established decision frameworks, including drift-diffusion models.