Computational AM Model for Memory Retrieval and Wander in Reminiscence

doi:10.13438/j.cnki.jdzk.2020.01.003

Abstract

Abstract:

For capturing autobiographical memory, a computational AM model for autobiographical memory is proposed. It is a three-layer network structure. Its bottom layer encodes event-specific knowledge comprising 5W1H and provides retrieval clues. The middle layer encodes events by associating the event-specific knowledge, and the top layer encodes episodes by associating related events. By following the bottom-up memory search procedure, the corresponding event and episode can be identified in the middle and top layers respectively. At the same time, the AM model can simulate the phenomenon of wandering in reminiscence through the retrieval process of regular memory. Experimental results based on a group of data sets show that the proposed computational AM model not only has robust and flexible memory retrieval, but, compared with the commonly used memory retrieval model based on keyword-based query method, has better response performance as well to noisy memory retrieval cues; it can also simulate the phenomenon of wandering in reminiscence.

Key words: artificial intelligence, cognitive model, autobiographical memory, wander in reminiscence

TAN Chengbing, ZHAN Lin, YAN Li, CHEN Bo. Computational AM Model for Memory Retrieval and Wander in Reminiscence[J]. Journal of Jishou University（Natural Sciences Edition), DOI: 10.13438/j.cnki.jdzk.2020.01.003.

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