Estimating Personal Model Parameters from Utterances in Model-based Reminiscence

TL;DR

This paper presents a method to estimate users' internal memory and mood states through interactions with a personalized ACT-R based reminiscence model, enhancing tailored mental health support.

Contribution

It introduces a novel approach to infer internal user states from utterances using a cognitive architecture-based memory model, advancing personalized reminiscence therapy.

Findings

The method can estimate memory retrieval parameters from user utterances.

It can detect changes in user mood caused by system interaction.

The approach demonstrates feasibility for personalized mental health support.

Abstract

Reminiscence therapy is mental health care based on the recollection of memories. However, the effectiveness of this method varies amongst individuals. To solve this problem, it is necessary to provide more personalized support; therefore, this study utilized a computational model of personal memory recollection based on a cognitive architecture adaptive control of thought-rational (ACT-R). An ACT-R memory model reflecting the state of users is expected to facilitate personal recollection. In this study, we proposed a method for estimating the internal states of users through repeated interactions with the memory model. The model, which contains the lifelog of the user, presents a memory item (stimulus) to the user, and receives the response of the user to the stimulus, based on which it adjusts the internal parameters of the model. Through the repetition of these processes, the parameters of the model will reflect the internal states of the user. To confirm the feasibility of the proposed method, we analyzed utterances of users when using a system that incorporates this model. The results confirmed the ability of the method to estimate the memory retrieval parameters of the model from the utterances of the user. In addition, the ability of the method to estimate changes in the mood of the user caused by using the system was confirmed. These results support the feasibility of the interactive method for estimating human internal states, which will eventually contribute to the ability to induce memory recall and emotions for our well-being.