Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139099
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dc.contributor.authorWang, Dien_US
dc.contributor.authorTan, Ah-Hweeen_US
dc.contributor.authorMiao, Chunyanen_US
dc.contributor.authorMoustafa, Ahmed A.en_US
dc.date.accessioned2020-05-15T07:23:16Z-
dc.date.available2020-05-15T07:23:16Z-
dc.date.issued2019-
dc.identifier.citationWang, D., Tan, A.-H., Miao, C., & Moustafa, A. A. (2019). Modelling autobiographical memory loss across life span. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), 1368-1375. doi:10.1609/aaai.v33i01.33011368en_US
dc.identifier.urihttps://hdl.handle.net/10356/139099-
dc.description.abstractNeurocomputational modelling of long-term memory is a core topic in computational cognitive neuroscience, which is essential towards self-regulating brain-like AI systems. In this paper, we study how people generally lose their memories and emulate various memory loss phenomena using a neurocomputational autobiographical memory model. Specifically, based on prior neurocognitive and neuropsychology studies, we identify three neural processes, namely overload, decay and inhibition, which lead to memory loss in memory formation, storage and retrieval, respectively. For model validation, we collect a memory dataset comprising more than one thousand life events and emulate the three key memory loss processes with model parameters learnt from memory recall behavioural patterns found in human subjects of different age groups. The emulation results show high correlation with human memory recall performance across their life span, even with another population not being used for learning. To the best of our knowledge, this paper is the first research work on quantitative evaluations of autobiographical memory loss using a neurocomputational model.en_US
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en_US
dc.description.sponsorshipMOH (Min. of Health, S’pore)en_US
dc.language.isoenen_US
dc.rights© 2019 Association for the Advancement of Artificial Intelligence. All rights reserved. This paper was published in The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) and is made available with permission of Association for the Advancement of Artificial Intelligence.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleModelling autobiographical memory loss across life spanen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.conferenceThe Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)en_US
dc.contributor.organizationJoint NTU-UBC Research Centre of Excellence in Active Living for the Elderlyen_US
dc.contributor.organizationAlibaba-NTU Singapore Joint Research Instituteen_US
dc.identifier.doi10.1609/aaai.v33i01.33011368-
dc.description.versionAccepted versionen_US
dc.identifier.spage1368en_US
dc.identifier.epage1375en_US
dc.subject.keywordsAutobiographicalen_US
dc.subject.keywordsMemoryen_US
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