Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)
Vinayak Rao, Marc Howard
Semantic memory refers to our knowledge of facts and relationships between con- cepts. A successful semantic memory depends on inferring relationships between items that are not explicitly taught. Recent mathematical modeling of episodic memory argues that episodic recall relies on retrieval of a gradually-changing rep- resentation of temporal context. We show that retrieved context enables the de- velopment of a global memory space that reﬂects relationships between all items that have been previously learned. When newly-learned information is integrated into this structure, it is placed in some relationship to all other items, even if that relationship has not been explicitly learned. We demonstrate this effect for global semantic structures shaped topologically as a ring, and as a two-dimensional sheet. We also examined the utility of this learning algorithm for learning a more realistic semantic space by training it on a large pool of synonym pairs. Retrieved context enabled the model to “infer” relationships between synonym pairs that had not yet been presented.