Cognitive Systems Research最新文献

Brain and autonomic correlates of procrastination as “active” or “passive” strategy in decision-making 大脑和自主神经将拖延作为决策中的“主动”或“被动”策略

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

Cognitive Systems Research

Pub Date : 2026-01-27 DOI: 10.1016/j.cogsys.2026.101448

Michela Balconi , Katia Rovelli , Roberta A. Allegretta

Procrastination, frequently perceived as a detrimental habit, can be classified into active (“actively to put off tasks”) and passive (“let things go”) forms, each with distinct cognitive, affective, and behavioral characteristics. However, little is known about whether these two forms rely on distinct cognitive, emotional, and physiological mechanisms. This study examined behavioral data, electrophysiological and autonomic measures as arousal indices (skin conductance level and response, heart rate and heart rate variability) in 33 adults during a decision-making scenario. Active and passive procrastination scores were calculated and scales measuring personality traits (Big Five Inventory), decision-making styles (General Decision Making Style), and maximization tendency (Maximization Scale) were collected. Results showed higher response times for active than passive procrastination and highlighted the importance of the alpha band in both forms, though with different functional meanings. Additionally, passive procrastination was linked to increased arousal, suggesting emotion- or avoidance-oriented strategies. Correlation analysis revealed distinct individual factors related to active and passive procrastination, providing deeper insight into the cognitive and physiological aspects of procrastination. In conclusion, these findings highlight procrastination as a heterogeneous phenomenon shaped by individual differences and specific cognitive and affective mechanisms.

拖延症通常被认为是一种有害的习惯，它可以分为主动（“主动推迟任务”）和被动（“放手”）两种形式，每种形式都有不同的认知、情感和行为特征。然而，对于这两种形式是否依赖于不同的认知、情感和生理机制，人们知之甚少。这项研究检查了33名成年人在决策场景中的行为数据、电生理和自主测量作为唤醒指数（皮肤电导水平和反应、心率和心率变异性）。计算主动和被动拖延得分，收集人格特质量表（大五量表）、决策风格量表（一般决策风格量表）和最大化倾向量表（最大化量表）。结果表明，主动拖延比被动拖延反应时间要长，并强调了α带在两种形式中的重要性，尽管其功能意义不同。此外，被动拖延症与觉醒增加有关，表明情绪或回避导向策略。相关分析揭示了与主动和被动拖延症相关的不同个体因素，为拖延症的认知和生理方面提供了更深入的了解。总之，这些发现突出了拖延是一种异质性现象，受个体差异和特定的认知和情感机制的影响。

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引用次数: 0

Robust incremental learning of visual concepts without catastrophic forgetting 没有灾难性遗忘的视觉概念的稳健增量学习

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

Cognitive Systems Research

Pub Date : 2026-01-25 DOI: 10.1016/j.cogsys.2026.101447

Nicki Barari , Xin Lian , Christopher J. MacLellan

Continual learning poses a significant challenge in machine learning, as models often struggle to retain previously learned knowledge when exposed to new data, leading to catastrophic forgetting. In this work, we introduce Cobweb/4V, a novel visual classification method. This approach builds on Cobweb, a human-like learning system that is inspired by the way humans incrementally learn new concepts over time. In this research, we conduct a comprehensive evaluation showcasing Cobweb/4V’s proficiency in learning visual concepts, requiring less data to achieve effective learning outcomes compared to neural network approaches, maintaining stable performance over time, achieving competitive asymptotic behavior, and avoiding catastrophic forgetting. These characteristics align with human learning capabilities, positioning Cobweb/4V as a promising approach for sequential learning and motivating future exploration into its potential to guide the development of neural networks and other machine learning approaches that handle continual learning.

持续学习对机器学习构成了重大挑战，因为模型在接触新数据时往往难以保留以前学过的知识，从而导致灾难性的遗忘。本文介绍了一种新的视觉分类方法——蛛网/4V。这种方法建立在一个类似人类的学习系统Cobweb之上，它的灵感来自于人类随着时间的推移逐渐学习新概念的方式。在这项研究中，我们进行了全面的评估，展示了蛛网/4V在学习视觉概念方面的熟练程度，与神经网络方法相比，需要更少的数据来获得有效的学习结果，随着时间的推移保持稳定的性能，实现竞争性渐近行为，并避免灾难性遗忘。这些特征与人类的学习能力相一致，将蜘蛛网/4V定位为一种有前途的连续学习方法，并激励未来探索其潜力，以指导神经网络和其他处理持续学习的机器学习方法的发展。

引用次数: 0

An exploration of the applicability of information processing theories in road hazard perception context using e-scooter simulator in augmented virtuality scenarios 利用增强虚拟场景下的电动滑板车模拟器，探索信息处理理论在道路危险感知情境中的适用性

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

Cognitive Systems Research

Pub Date : 2026-01-24 DOI: 10.1016/j.cogsys.2026.101446

Martyna Fidler , Robin Palmberg , Yusak Susilo

This paper aims to provide a cognitive perspective on the hazard perception process in the traffic situation based on the conceptual frameworks of two complementary theories of brain information processing, namely, Signal Detection Theory and Predictive Coding Theory. The study uses an augmented virtuality scenario encompassing a simplified traffic situation, where participants are faced with hazard cues characterised by a different degree of predictability. Throughout the experiment, the acceleration and braking behaviour of an e-scooter rider together with the electroencephalography (EEG) data is collected. The developed experimental setup allows for testing the applicability of brain theories in explaining behaviour and cognitive processing of the perception of potential hazards with the ultimate goal to improve road safety. Current findings support the a priori expectations showing that participants create predictions concerning future potential hazards. Produced predictions and the subsequent behaviour are modulated by the degree of ambiguity of hazard cues in line with Signal Detection Theory. Moreover, following Predictive Coding Theory, the predictions improve as more external input is gathered, and the mental model is updated. Complementary to behavioural results the alpha wave is used as a neural marker of hazard predictability. The results provide implications for road safety researchers and practitioners, where the inclusion of a cognitive perspective can guide the more-informed design of road infrastructure as well as in-vehicle human support systems to be more aligned with the processing mechanisms of human cognition and exploit the synergies between them.

本文旨在基于信号检测理论和预测编码理论这两种互补的大脑信息处理理论的概念框架，从认知角度研究交通状况下的危险感知过程。该研究使用了一个包含简化交通状况的增强虚拟场景，参与者面临着不同程度可预测性的危险提示。在整个实验过程中，收集了电动滑板车骑手的加速和制动行为以及脑电图（EEG）数据。开发的实验设置允许测试大脑理论在解释潜在危险感知的行为和认知处理方面的适用性，最终目标是提高道路安全。目前的研究结果支持先验预期，表明参与者会对未来的潜在危险做出预测。根据信号检测理论，产生的预测和随后的行为由危险线索的模糊程度调节。此外，根据预测编码理论，随着更多外部输入的收集和心智模型的更新，预测也会得到改善。作为行为结果的补充，阿尔法波被用作危险可预测性的神经标记。研究结果为道路安全研究人员和从业人员提供了启示，其中包括认知视角可以指导更明智的道路基础设施设计以及车载人类支持系统，使其更符合人类认知的处理机制，并利用它们之间的协同作用。

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引用次数: 0

The ghost of behaviorism: critical reflections on methodological limitations in the research of large language models psychology 行为主义的幽灵：对大语言模型心理学研究方法论局限的批判性反思

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

Cognitive Systems Research

Pub Date : 2026-01-24 DOI: 10.1016/j.cogsys.2026.101445

Zewei Li , Yijin Wang , Qi Wu

This paper provides a critical examination of the methodological paradigms used in the psychological study of Large Language Models. We argue that the nascent field of AI Psychology, much like its human counterpart, is haunted by the ghost of behaviorism, a paradigm that focuses on observable input–output correlations while neglecting internal cognitive mechanisms. In response, a cognitive turn has emerged in the form of Mechanistic Interpretability, a research program that uses neuroscience-inspired techniques to reverse-engineer the internal algorithms of LLMs. While MI represents a significant advancement, we contend that it faces fundamental challenges stemming from the absence of a unifying theoretical framework and a persistent risk of remaining correlational, thus failing to provide true causal understanding. Drawing upon this critique, we propose that the future of LLM psychology lies not in more sophisticated reverse-engineering, but in the development of theory-driven, psychodynamic frameworks. By synthesizing insights from psychoanalytic analogies of internal conflict and desire-driven agent models grounded in the Theory of Needs, we outline a path toward a new science of artificial minds—one that prioritizes the understanding of intrinsic motivation, internal dynamics, and the generative principles of emergent behavior.

本文对大型语言模型的心理学研究中使用的方法论范式进行了批判性的考察。我们认为，新兴的人工智能心理学领域，就像它的人类同行一样，被行为主义的幽灵所困扰，这是一种专注于可观察的输入-输出相关性而忽视内部认知机制的范式。作为回应，一种认知转变以机械可解释性（Mechanistic Interpretability）的形式出现，这是一个研究项目，利用神经科学启发的技术对法学硕士的内部算法进行逆向工程。虽然人工智能代表了一个重大的进步，但我们认为它面临着根本性的挑战，这些挑战源于缺乏统一的理论框架和保持相关性的持续风险，因此无法提供真正的因果理解。根据这一批评，我们提出法学硕士心理学的未来不在于更复杂的逆向工程，而在于理论驱动的心理动力学框架的发展。通过综合精神分析对内部冲突的类比和基于需求理论的欲望驱动的代理人模型的见解，我们概述了一条通往人工思维新科学的道路——这门科学优先考虑对内在动机、内部动力学和紧急行为的生成原则的理解。

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引用次数: 0

Enhancing persuasion with social robots: Leveraging user mood with tailored message style and orientation 通过社交机器人增强说服力：通过量身定制的消息风格和方向来利用用户情绪

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

Cognitive Systems Research

Pub Date : 2026-01-21 DOI: 10.1016/j.cogsys.2026.101443

Eunju Yi , Do-Hyung Park

In the burgeoning landscape of Human-Robot Interaction (HRI), this study explores the intriguing dynamics of robot communication, robot’ speaking style (rhetorical vs. declarative message), and robot’s speaking orientation (prevention vs. promotion-oriented message) and their influence on user’s compliance and intentions in two distinct user moods. While user’s mood, and the robot’s message style and orientation individually did not show a significant effect on compliance, we found interesting interactions: users in a negative mood showed a significant inclination to comply with a robot’s rhetorical or promotion-focused messages, whereas users in a positive mood showed a significant tendency to comply with a robot’s prevention-focused messages. In addition, both declarative and rhetorical messages were acceptable to the positive mood users. The results of this research provide an opportunity to consider the importance of designing communication with technology, given that interactions with technology will occur in our everyday lives through web and mobile applications, AI chatbots, voice assistants, and potentially robots in the near future, which could have an immense impact on individual behavior, society, and life. This research provides new insights into human behavior and its relationship to mood, message style, and orientation, which opens new avenues for the development of emotionally intelligent robots, effective human-robot interaction frameworks, and mood-adaptive messaging systems. Practically, our findings underline the impact of adaptive communication, mood detection techniques, and ethical guidelines in technology design.

在人机交互（HRI）蓬勃发展的背景下，本研究探讨了机器人交流的有趣动态，机器人的说话风格（修辞与陈述信息）和机器人的说话取向（预防与促进导向信息），以及它们在两种不同的用户情绪下对用户依从性和意图的影响。虽然用户的情绪和机器人的信息风格和方向对依从性没有显着影响，但我们发现了有趣的交互：消极情绪的用户表现出明显的倾向于服从机器人的修辞或宣传信息，而积极情绪的用户则表现出明显的倾向于服从机器人的预防信息。此外，陈述性和修辞性的信息对积极情绪使用者来说都是可以接受的。这项研究的结果为考虑设计与技术交流的重要性提供了一个机会，因为与技术的互动将通过网络和移动应用程序、人工智能聊天机器人、语音助手，以及不久的将来可能出现的机器人，在我们的日常生活中发生，这可能对个人行为、社会和生活产生巨大影响。这项研究为人类行为及其与情绪、信息风格和取向的关系提供了新的见解，为开发情商机器人、有效的人机交互框架和情绪适应信息系统开辟了新的途径。实际上，我们的研究结果强调了适应性沟通、情绪检测技术和技术设计中的道德准则的影响。

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引用次数: 0

Cognitive models of reverse engineering processes 逆向工程过程的认知模型

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

Cognitive Systems Research

Pub Date : 2026-01-21 DOI: 10.1016/j.cogsys.2026.101444

Edward A. Cranford , Christian Lebiere , Donald Morrison , Tiffany Hyun-Jin Kim , Jocelyn Rego , Brianna Marsh , Mia Levy , Aidan Barbieux , Froylan Maldonado , Sunny Fugate , Rajan Bhattacharyya

Cognitive models can reproduce many aspects of human performance, including decisions, learning, and workload. Personalized models can be developed by using methodologies such as model tracing that aligns the specific model against the behavior trace of an individual human user. Those models can then be used for various applications involving human–computer interaction and teaming. This paper presents cognitive models of software reverse engineering for cybersecurity tasks such as the analysis of programs containing vulnerabilities. The models, developed using the Adaptive Control of Thought – Rational (ACT-R) cognitive architecture, are validated against human behavioral data. Quantitative measures such as cognitive load can be extracted in real time from the model’s internal representations and correlated with workload measures computed from neurophysiological sensors. Those measures can be used for a variety of purposes including facilitating teaming across different levels of expertise and serving as the basis for providing recommendations that guide users toward more productive activities. The results highlight the utility of the cognitive model in assisting users performing software reverse engineering tasks and for providing insight into the user’s ongoing mental states via cognitive load measures.

认知模型可以再现人类表现的许多方面，包括决策、学习和工作量。个性化模型可以通过使用模型跟踪等方法来开发，这些方法将特定模型与单个人类用户的行为跟踪相匹配。然后，这些模型可以用于涉及人机交互和团队的各种应用程序。本文提出了软件逆向工程的认知模型，用于网络安全任务，如分析包含漏洞的程序。这些模型是使用思维-理性的自适应控制（ACT-R）认知架构开发的，并针对人类行为数据进行了验证。定量测量，如认知负荷，可以从模型的内部表征中实时提取，并与神经生理传感器计算的负荷测量相关联。这些措施可用于各种目的，包括促进跨不同专业水平的团队合作，并作为提供建议的基础，指导用户进行更有成效的活动。结果强调了认知模型在帮助用户执行软件逆向工程任务以及通过认知负荷测量提供对用户持续精神状态的洞察方面的效用。

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引用次数: 0

Developing a cognitively plausible model of lie-truth judgements: An adaptive lie detector account 发展一个认知上似是而非的谎言判断模型：一个适应性测谎仪的描述

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

Cognitive Systems Research

Pub Date : 2026-01-20 DOI: 10.1016/j.cogsys.2026.101434

David Peebles , Chris N.H. Street

To date, no account of lie-truth judgement formation has been capable of explaining how core cognitive mechanisms such as memory encoding and retrieval are employed to reach such judgements. One theory, the Adaptive Lie Detector (ALIED: Street et al., 2016) is sufficiently well defined as to be implemented as a cognitively plausible computational model. Here we describe the first cognitively plausible account of lie-truth judgments and test it in three studies. Developed within the ACT-R cognitive theory (Anderson, 2007), the model provides a close fit to past data (Study 1), predicts novel learning data (Study 2), and generalises to more complex environments with multiple cues (Study 3). In so doing, the cognitive model provides strong support for the assumptions of an adaptive theory of lie-truth judgement, and substantiates a unique prediction of ALIED that lie and truth biases can be considered as functionally equivalent.

迄今为止，没有关于谎言-真实判断形成的解释能够解释核心认知机制（如记忆编码和检索）是如何被用来达成这种判断的。一种理论，自适应测谎仪（ALIED: Street et al., 2016）被充分定义为一种认知上合理的计算模型。在这里，我们描述了第一种认知上可信的谎言-真理判断，并在三个研究中进行了测试。该模型是在ACT-R认知理论（Anderson, 2007）的基础上发展起来的，它与过去的数据（研究1）非常吻合，预测了新的学习数据（研究2），并推广到具有多个线索的更复杂的环境（研究3）。通过这样做，认知模型为谎言-真相判断的适应性理论假设提供了强有力的支持，并证实了ALIED的一个独特预测，即谎言和真相偏见可以被认为在功能上是等同的。

引用次数: 0

Systematic guidelines for extending the appraisal process in computational models of emotion 在情感计算模型中扩展评估过程的系统指南

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

Cognitive Systems Research

Pub Date : 2026-01-16 DOI: 10.1016/j.cogsys.2026.101442

Sergio Castellanos, Enrique Osuna Cuen, Elsa L. Padilla, Luis-Felipe Rodríguez

Influencing factors (IFs) are internal and external elements that modulate the appraisal process and profoundly shape how Appraisal-based Computational Models of Emotion (ACMEs) evaluate emotional stimuli. However, most ACME implementations either omit common IFs (e.g., personality traits, transient moods, cognitive states, and social or cultural norms), incorporate them using ad-hoc strategies, or lack a consistent methodology. To address this gap, we introduce a set of Systematic Guidelines named ACME-IFSG (Appraisal-based Computational Models of Emotion - Influencing Factor Systematic Guidelines). ACME-IFSG is designed to i) formalize IFs using theory-driven constructs and empirical indicators; ii) implement the IF as an autonomous Modulator component; iii) integrate this component through explicit data contracts; and iv) validate system-level coherence. A proof of concept, which follows every phase, stage, and activity outlined in the ACME-IFSG, illustrates how the IF cognitive fatigue can be modeled without modifying the core appraisal mechanisms of ACMEs. This implementation demonstrates how the ACME-IFSG approach enables the systematic incorporation of a complex, cross-disciplinary IF while ensuring theoretical grounding and systematic integration throughout the emotion modeling process.

影响因素是调节评价过程并深刻影响基于评价的情绪计算模型（ACMEs）如何评价情绪刺激的内部和外部因素。然而，大多数ACME实现要么忽略了常见的if（例如，个性特征、短暂情绪、认知状态以及社会或文化规范），要么使用特殊策略将它们合并，要么缺乏一致的方法。为了解决这一差距，我们引入了一套名为ACME-IFSG（基于评估的情感计算模型-影响因素系统指南）的系统指南。ACME-IFSG旨在i)使用理论驱动的结构和经验指标将IFs形式化；ii)实现中频作为自主调制器组件；Iii)通过显式数据契约集成该组件；iv)验证系统级一致性。遵循ACME-IFSG中概述的每个阶段、阶段和活动的概念验证说明了如何在不修改acme核心评估机制的情况下对IF认知疲劳进行建模。这个实现展示了ACME-IFSG方法如何能够系统地整合复杂的跨学科IF，同时确保整个情感建模过程的理论基础和系统集成。

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引用次数: 0

Representativity and univocity of traffic signs and their effect on trajectory movement in a driving-simulation task: regulatory signs 交通标志的代表性和唯一性及其对驾驶模拟任务中轨迹运动的影响：管制标志

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

Cognitive Systems Research

Pub Date : 2026-01-16 DOI: 10.1016/j.cogsys.2026.101435

Jose Luis Vilchez Tornero

Purpose: There is a need to understand how the perception of, attention to and reason with traffic signs influence on driving behavior. The more we know about drivers‘ cognitive processing of them, the better for their response time to those signs and for the decision they take. In previous works, we have shown that the signs that are not-well designed provoke counterproductive effects on movement. Design/methodology/approach: In the present study, regulatory traffic signs in Ecuador are classified by using the criteria of their representativity, their univocity and the numbers of errors participants make when responding to them. Findings: With these criteria, we can detect which traffic signs need to be redesigned. Research limitations/implications: The consequences of traffic accidents are enough important to take this study seriously. In this sense, research must also take a step forward to real-driving contexts in order to reach more ecological conclusions. Practical implications:

This work contributes to the improvement of traffic safety. Originality/value: I develop a new methodology to classify traffic signs from a cognitive Science point of view.

目的：有必要了解交通标志的感知、注意和推理是如何影响驾驶行为的。我们对司机的认知过程了解得越多，他们对这些标志的反应时间和做出的决定就越好。在之前的工作中，我们已经表明，设计不佳的标志会对运动产生适得其反的影响。设计/方法/方法：在本研究中，厄瓜多尔的监管交通标志通过使用其代表性，单一性和参与者在回应时所犯错误的数量的标准进行分类。研究发现：有了这些标准，我们可以发现哪些交通标志需要重新设计。研究局限性/启示：交通事故的后果非常重要，值得我们认真对待这项研究。从这个意义上说，为了得出更多的生态结论，研究还必须向前迈进一步，以真正驱动环境。实际意义：这项工作有助于改善交通安全。原创性/价值：我从认知科学的角度开发了一种新的方法来分类交通标志。

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引用次数: 0

Optimising blockchain security: Computational analysis of adaptive AI coaching 优化区块链安全性：自适应人工智能训练的计算分析

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

Cognitive Systems Research

Pub Date : 2025-12-13 DOI: 10.1016/j.cogsys.2025.101430

Rahma Lakhdim , Jan Treur , Peter H.M.P. Roelofsma

Blockchain networks face evolving security risks that require rapid and consistent responses from employees. This study presents an AI Coach that mirrors human reasoning through stages of context detection, world modeling, belief updating, preparation, execution, and feedback. In doing so, the AI Coach provides cognitive support. The architecture is defined by six types of matrices that include state connectivity, connectivity weights, combination functions, combination function parameters, speed factors, and initial values. In simulations of anomalous transactions, smart contract breaches, consensus delays, and unauthorized access, the AI Coach effectively prioritized critical events and guided response actions, demonstrating its ability to support more structured and efficient security workflows. These results underscore the effectiveness of the AI Coach in improving reliability and responsiveness in blockchain security monitoring.

b区块链网络面临着不断变化的安全风险，需要员工快速一致的响应。这项研究提出了一个人工智能教练，通过情境检测、世界建模、信念更新、准备、执行和反馈等阶段来反映人类的推理。在此过程中，AI教练提供认知支持。该体系结构由六种类型的矩阵定义，包括状态连通性、连通性权重、组合函数、组合函数参数、速度因子和初始值。在异常交易、智能合约违约、共识延迟和未经授权访问的模拟中，AI教练有效地确定了关键事件的优先级，并指导了响应行动，展示了其支持更结构化和更高效的安全工作流程的能力。这些结果强调了人工智能教练在提高区块链安全监测的可靠性和响应能力方面的有效性。

引用次数: 0