Adaptive network modeling for joint action and memory recall for elderly by detecting interpersonal synchrony

IF 2.1 3区心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Cognitive Systems Research Pub Date : 2024-08-30 DOI:10.1016/j.cogsys.2024.101280

Yijie Xu , Sophie C.F. Hendrikse , Jan Treur , Peter H.M.P. Roelofsma

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引用次数: 0

Abstract

This paper explores the potential of adaptive network modeling for joint action and memory recall among elderly through detecting interpersonal synchrony. With the aging population increasing, there is a crucial need to focus on the health and social interaction of older adults. Based on research of the significance of social interaction and memory use for the elderly, as well as the role of interpersonal synchrony in joint action, this paper aims to analyse computationally how to enhance positive effects of social interactions among older individuals by applying an adaptive network model. The research examines the concept of interpersonal synchrony and its impact on joint action, memory, and emotional well-being in elderly populations. Through simulation experiments and analysis, the study demonstrates the potential benefits for music in memory recall for older adults with cognitive decline, highlighting the importance of social interaction and emotional resonance. This study offers a valuable contribution to understanding and improving social interactions and memory recall among the elderly.

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通过检测人际同步，为老年人的联合行动和记忆回忆建立自适应网络模型

本文通过检测人际同步性，探索自适应网络建模在老年人联合行动和记忆回忆方面的潜力。随着老龄化人口的增加，关注老年人的健康和社会交往成为当务之急。基于社会交往和记忆使用对老年人的意义以及人际同步在联合行动中的作用的研究，本文旨在通过计算分析如何应用自适应网络模型来增强老年人社会交往的积极作用。研究探讨了人际同步的概念及其对老年人联合行动、记忆和情绪健康的影响。通过模拟实验和分析，该研究证明了音乐对认知能力下降的老年人记忆回忆的潜在益处，强调了社会互动和情感共鸣的重要性。这项研究为了解和改善老年人的社会互动和记忆回忆做出了宝贵贡献。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cognitive Systems Research 工程技术-计算机：人工智能

CiteScore

9.40

自引率

5.10%

发文量

审稿时长

>12 weeks

期刊介绍： Cognitive Systems Research is dedicated to the study of human-level cognition. As such, it welcomes papers which advance the understanding, design and applications of cognitive and intelligent systems, both natural and artificial. The journal brings together a broad community studying cognition in its many facets in vivo and in silico, across the developmental spectrum, focusing on individual capacities or on entire architectures. It aims to foster debate and integrate ideas, concepts, constructs, theories, models and techniques from across different disciplines and different perspectives on human-level cognition. The scope of interest includes the study of cognitive capacities and architectures - both brain-inspired and non-brain-inspired - and the application of cognitive systems to real-world problems as far as it offers insights relevant for the understanding of cognition. Cognitive Systems Research therefore welcomes mature and cutting-edge research approaching cognition from a systems-oriented perspective, both theoretical and empirically-informed, in the form of original manuscripts, short communications, opinion articles, systematic reviews, and topical survey articles from the fields of Cognitive Science (including Philosophy of Cognitive Science), Artificial Intelligence/Computer Science, Cognitive Robotics, Developmental Science, Psychology, and Neuroscience and Neuromorphic Engineering. Empirical studies will be considered if they are supplemented by theoretical analyses and contributions to theory development and/or computational modelling studies.