基于自适应同质性的人类学习、同步和社会联系多自适应网络模型

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

Cognitive Systems Research Pub Date : 2023-11-29 DOI:10.1016/j.cogsys.2023.101187

Yelyzaveta Mukeriia , Jan Treur , Sophie Hendrikse

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引用次数: 0

摘要

本文提出了一个融合多种适应机制的多适应网络模型，重点分析了五种适应机制。其中两个是通过学习对他人做出反应来解决一阶适应问题，另一个是通过同质性与他人建立联系来解决一阶适应问题。其他三种适应机制是通过同质性对Hebbian学习和结合速度的二阶适应，以及同质性临界点的二阶适应。本文对这些概念及其在受控学习中的作用进行了全面的解释。

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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A multi-adaptive network model for human Hebbian learning, synchronization and social bonding based on adaptive homophily

This paper present a multi-adaptive network model integrating multiple adaptation mechanisms, specifically focusing on five types of such adaptation mechanisms. Two of them address first-order adaptation by learning of responding on others and first-order adaptation by bonding with others based on homophily. Three other adaptation mechanisms addressed are second-order adaptation of the speed of both Hebbian learning and bonding by homophily, and second-order adaptation of the homophily tipping point. The paper provides a comprehensive explanation of these concepts and their role in controlled adaptation within the diverse contextual scenarios of the paper.

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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.