人类在TSP任务中的表现：基于符号认知

IF 2.4 3区心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Cognitive Systems Research Pub Date : 2025-09-10 DOI:10.1016/j.cogsys.2025.101393

Chen Chen , Ruimin Lyu , Guoying Yang , Yuan Liu

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引用次数: 0

摘要

随着人类认知研究的深入，了解人类在规划和解决问题时使用的启发式机制对优化算法的设计和改进具有重要意义。本研究旨在探讨人类在解决旅行推销员问题（TSP）时所采用的基于符号特征的启发式策略，并找出提高人类解决TSP问题效率的关键因素。通过分析被试在具有线特征的TSP任务中的表现，实验控制了符号特征的强度和操作方式，并将结果与现有文献中的启发式算法进行比较。结果表明，人类在Line-TSP任务中表现异常出色，其整体表现接近高效启发式算法。符号特征有助于提高人类解决问题的效率，尽管当操作模式类似于手写时，这种效率会略有下降。本研究提出了一种新的求解TSP的启发式机制，为TSP算法的设计和优化提供了新的见解。

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Human performance in TSP tasks: Based on symbolic cognition

As research on human cognition deepens, understanding the heuristic mechanisms humans use in planning and problem-solving is of great significance for the design and improvement of optimization algorithms. This study aims to explore the heuristic strategies based on symbolic features that humans employ when solving the Traveling Salesman Problem (TSP) and to identify key factors that enhance the efficiency of human problem-solving in TSP. By analyzing participants’ performance in TSP tasks with line features (Line-TSP), the experiment controlled the intensity and operational modes of symbolic features and compared the results with heuristic algorithms from existing literature. The results indicate that humans perform exceptionally well in Line-TSP tasks, with their overall performance approaching that of efficient heuristic algorithms. Symbolic features contribute to enhancing human problem-solving efficiency, although this efficiency slightly decreases when the operation mode resembles handwriting. This study proposes a new heuristic mechanism for solving TSP, offering fresh insights for the design and optimization of TSP algorithms.

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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.