Computational Modelling of Differences in Associations for Olfactory Autobiographical Memories across the Lifespan

Abstract

Odour-cued memory retrieval has emerged as a potential intervention for people with dementia due to the unique properties of odour-evoked memories. The design and application of technologies that integrate this strategy require knowing which odours are meaningful to a given person, motivating the systematic modelling of odour-event associations across the lifespan. In this paper, we introduce a computational pipeline that uses a Large Language Model (LLM) to simulate diverse persona profiles and model these associations at scale. We constructed persona-event pairs by operationalising age, gender, nationality, origin environment, and personal values. Then we prompted the LLM to generate an odour description and its most likely source for each pair. A bottom-up open card sorting taxonomy is then applied to cluster the odour sources into 13 odour types, forming an exploratory olfactory memory map, while events are also clustered similarly into 33 event types. Empirical analyses confirm the significance of systematic odour-event associations being encoded in our LLM-generated sample. While interaction effects for lifespan were not significant, the total effects for events, lifespan and cultural indicators were significant. The interaction effects of our cultural indicators were also significant for our sample. This study is a step towards using LLMs as a computational proxy in the field of LLMs for personalised assistive technologies to be adaptive and culturally inclusive in order to aid patients with dementia.