Estimating Personal Model Parameters from Utterances in Model-based
Reminiscence
- URL: http://arxiv.org/abs/2208.07087v2
- Date: Thu, 18 Aug 2022 09:53:52 GMT
- Title: Estimating Personal Model Parameters from Utterances in Model-based
Reminiscence
- Authors: Shoki Sakai, Kazuki Itabashi, Junya Morita
- Abstract summary: This study utilized a computational model of personal memory recollection based on a cognitive architecture adaptive control of thought-rational (ACT-R)
We proposed a method for estimating the internal states of users through repeated interactions with the memory model.
Results confirmed the ability of the method to estimate the memory retrieval parameters of the model from the utterances of the user.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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