We present the Spreading Activation and Memory PLasticity Model (SAMPL), a computational model of how memory retrieval changes memories. SAMPL restructures memory networks as a function of spreading activation and plasticity. Memory networks are represented as graphs of items in which edge weights capture the strength of association between items. When an item is retrieved, activation spreads across nodes depending on edge weights and the strength of initial activation. A non-monotonic plasticity rule, in turn, updates edge weights following activation. SAMPL simulates human memory phenomena across a number of experiments including retrieval induced forgetting, context-based memory enhancement, and memory synchronization in conversational networks. Our results have implications for theorizing memory disorders such as PTSD and designing computationally assisted conversational therapy.
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