From List Learning to Semantic Knowledge: Search and Learning of Associative Memory

Meanings—semantics—matter in the study of cognition. Recent advances in computational modeling have demonstrated how semantic information can be incorporated into episodic or experiential free recall tasks to better account for participant performance. However, one limitation of current uses of semantic representations with the SAM model is a disconnect between how the semantic representations are created and the method of learning used by SAM. By extending the contextual representation used in SAM to incorporate both temporal drift and item-based drift, inspired by the Temporal Context Model (TCM), we demonstrated how the SAM learning mechanism can be used to create semantic representations.