{'fraction_non_alphanumeric': 0.019963702359346643, 'fraction_numerical': 0.0, 'mean_word_length': 5.5266272189349115, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Model-based Reminiscence: Guiding Mental Time Travel by Cognitive Modeling

This paper proposes an approach to elderly mental care called model-based reminiscence, which utilizes cognitive modeling to guide a user's mental time travel. In this approach, a personalized cognitive model is constructed by implementing a user's lifelog (a photo library) in the ACT-R cognitive architecture. The constructed model retrieves photos based on human memory characteristics such as learning, forgetting, inhibition, and noise. These memory characteristics are regulated with parameter values corresponding to cognitive and emotional health. The authors assumed that a user's mental health could be assessed from their reactions to photo sequences retrieved by models with various parameter settings. The authors also assumed that it would be possible to motivate a user by guiding their memory recall with photo sequences generated from a healthy optimal state model. A simulation study indicates the potential of this approach presenting a variety of model behaviors corresponding cognitive / emotional states.