Using a Neurocomputational Autobiographical Memory Model to Study Memory Loss
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Autobiographical memory (AM) is a core component of human life and plays an important role in self-identification. Various conceptual models have been proposed to explain its functionalities and describe its dynamics. However, most existing computational AM models do not distinguish AM from other long-term memory. Specifically, during model design, the unique features and the memory encoding, storage, and retrieval procedures of AM were not taken into consideration in prior models. In this chapter, we introduce our neurocomputational AM model, which is consistent with Conway and Pleydell-Pearce’s model in terms of both the network structure and dynamics. We further propose how to apply our parameterized computational model to quantitatively study memory loss in people of different age groups. As such, we provide a suitable tool to evaluate the effect of different memory loss phases in a rapid and quantitative manner, which may be difficult or impossible in experimental studies on human subjects.
KeywordsAutobiographical memory Neurocomputational model Memory loss Cognitive modeling
This research is supported, in part, by the National Research Foundation, Prime Minister’s Office, Singapore, under its IDM Futures Funding Initiative and the Singapore Ministry of Health under its National Innovation Challenge on Active and Confident Ageing (NIC Project No. MOH/NIC/COG04/2017).
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