Cognitive Systems Research最新文献

A novel DE/VS hybrid algorithm for enhanced optimization in numerical and engineering problems 一种新的DE/VS混合算法，用于数值和工程问题的增强优化

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2025-07-17 DOI: 10.1016/j.cogsys.2025.101376

Yiğit Çağatay Kuyu

引用次数: 0

Selective visual memory replay with self-evaluation in cognitive robots based on global workspace framework 基于全局工作空间框架的认知机器人选择性视觉记忆自评价重放

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2025-07-15 DOI: 10.1016/j.cogsys.2025.101377

Wenjie Huang , Antonio Chella , Angelo Cangelosi

{"title":"Selective visual memory replay with self-evaluation in cognitive robots based on global workspace framework","authors":"Wenjie Huang , Antonio Chella , Angelo Cangelosi","doi":"10.1016/j.cogsys.2025.101377","DOIUrl":"10.1016/j.cogsys.2025.101377","url":null,"abstract":"<div><div>Learning capability for artificial systems is a well-studied topic, with various schemes enabling the system to develop knowledge continually. Methods based on memory replay are commonly adopted in the literature. This work presents a consciousness-based model integrated with a continual learning scheme for class-incremental learning in visual recognition. We suggest a reciprocal relation between memory maintenance and the learning activity of the system based on psychological evidence. The memory capability fits the continual learning problem. In return, a self-evaluation of knowledge mechanism is proposed for the robot to discriminate the important learning data during interactions to alleviate the memory constraint without degrading the distribution representation of abnormal data. The implemented robotic agent autonomously puts more effort into learning novel knowledge without human intervention. The cognitive architecture based on the Global Workspace Theory for the robotic agent is presented, with which the agent can automatically associate information from different modalities. Memory consolidation is implemented to run in parallel to the memory formation process. The work is validated in a class-incremental object recognition experiment on a robotic agent. The results show that the agent automatically balances the memory distribution for learning and maintains a relatively small set of samples during learning.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"92 ","pages":"Article 101377"},"PeriodicalIF":2.1,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards naturalized phenomenology: Dynamics of space–time clouds and power law of working memory 走向自然现象学：时空云的动力学与工作记忆的幂律

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2025-07-07 DOI: 10.1016/j.cogsys.2025.101374

Ihor Lubashevsky , Vasily Lubashevskiy

{"title":"Towards naturalized phenomenology: Dynamics of space–time clouds and power law of working memory","authors":"Ihor Lubashevsky , Vasily Lubashevskiy","doi":"10.1016/j.cogsys.2025.101374","DOIUrl":"10.1016/j.cogsys.2025.101374","url":null,"abstract":"<div><div>In this paper, we address the challenge of naturalizing phenomenology by uniting the first-person and third-person perspectives as complementary components in describing human perception. Our approach builds on the concept of space–time clouds (Lubashevsky and Plavinska, <em>Physics of the Human Temporality: Complex Present</em>, Springer, 2021) and introduces a novel formalism of cloud functions to model preconscious information processing in large-scale neural networks. The space–time clouds mathematically represent mental images of physical objects as they are perceived from the first-person perspective, while the cloud functions describe their preconscious representations within the same mathematical framework. The preconscious representations inherit all properties of space–time clouds, except their temporal extent; they are determined completely at each moment in time. The dynamics of cloud functions, governed by brain network activity, is described within a mathematical framework rooted in theories of physical systems, which relies on neural correlates of consciousness and the integrity of mental images. Modality-specific information processing is thought to be responsible for the emergence of high-level preattentive representations. By way of example, we reproduce the properties of the power law of working memory using the developed formalism applied to the recognition of a single scalar physical property. The corresponding governing equation reduces to the Schrödinger equation in imaginary time combined with the Lotka–Volterra model in a Hilbert space.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"92 ","pages":"Article 101374"},"PeriodicalIF":2.1,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144605596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards knowledge autonomy in the Companion cognitive architecture 同伴认知体系结构中的知识自治

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2025-07-05 DOI: 10.1016/j.cogsys.2025.101378

Constantine Nakos, Kenneth D. Forbus

引用次数: 0

The construction and implementation direction of personalized learning model based on multimodal data fusion in the context of intelligent education 智能教育背景下基于多模态数据融合的个性化学习模型构建与实现方向

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2025-07-03 DOI: 10.1016/j.cogsys.2025.101379

Xingle Ji, Lu Sun, Kun Huang

{"title":"The construction and implementation direction of personalized learning model based on multimodal data fusion in the context of intelligent education","authors":"Xingle Ji, Lu Sun, Kun Huang","doi":"10.1016/j.cogsys.2025.101379","DOIUrl":"10.1016/j.cogsys.2025.101379","url":null,"abstract":"<div><div>The rapid development of artificial intelligence (AI) technologies, represented by computer vision, natural language processing, and speech recognition, has brought new opportunities for the advancement of personalized learning within intelligent education. This article utilizes intelligent collection devices such as cameras, electroencephalographs (EEG), eye trackers, smart bracelets, and data gloves to comprehensively collect and analyze data on learners’ voices, videos, texts, breathing, heartbeats, EEG signals, and eye movements. A multimodal dataset for learners is constructed across four dimensions: behavioral representation, physiological information, human–computer interaction, and learning context. By employing natural language processing, speech recognition, computer vision, and physiological information recognition technologies, we extract and analyze the multimodal datasets. This process mines the hidden personalized information of learners, enabling data-driven, real-time, quantified evaluation of their learning states. This study constructs a personalized learning model based on multimodal data fusion within the field of intelligent education by examining the current research landscape, data types, and relevant fusion strategies of this technology. It aims to provide personalized services tailored to the needs of each learner.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"92 ","pages":"Article 101379"},"PeriodicalIF":2.1,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robot manipulation in everyday activities with the CRAM 2.0 cognitive architecture and generalized action plans 机器人在日常活动中的操作与CRAM 2.0认知架构和广义的行动计划

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2025-06-21 DOI: 10.1016/j.cogsys.2025.101375

Michael Beetz , Gayane Kazhoyan , David Vernon

引用次数: 0

Efficient and scalable masked word prediction using concept formation 使用概念形成的高效和可扩展的掩码词预测

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2025-06-04 DOI: 10.1016/j.cogsys.2025.101371

Xin Lian , Zekun Wang , Christopher J. MacLellan

{"title":"Efficient and scalable masked word prediction using concept formation","authors":"Xin Lian , Zekun Wang , Christopher J. MacLellan","doi":"10.1016/j.cogsys.2025.101371","DOIUrl":"10.1016/j.cogsys.2025.101371","url":null,"abstract":"<div><div>This paper introduces Cobweb/4L, a novel approach for efficient language model learning that supports masked word prediction. The approach builds on Cobweb, an incremental system that learns a hierarchy of probabilistic concepts. Each concept stores the frequencies of words that appear in instances tagged with the concept label. The system utilizes an attribute-value representation to encode words and their context into instances. Cobweb/4L uses an information-theoretic variant of category utility as well as a new performance mechanism that leverages multiple concepts to generate predictions. We demonstrate that its new performance mechanism substantially outperforms prior Cobweb performance mechanisms that use only a single node to generate predictions. Further, we demonstrate that Cobweb/4L outperforms transformer-based language models in a low-data setting by learning more rapidly and achieving better final performance. Lastly, we show that Cobweb/4L, which is hyperparameter-free, is robust across varying scales of training data and does not require any manual tuning. This is in contrast to Word2Vec, which performs best with a varying number of hidden nodes that depend on the total amount of training data; this means its hyperparameters must be manually tuned for different amounts of training data. We conclude by discussing future directions for Cobweb/4L.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"92 ","pages":"Article 101371"},"PeriodicalIF":2.1,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Foxtsage vs. Adam: Revolution or evolution in optimization? Foxtsage vs. Adam：优化的革命还是进化？

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2025-05-31 DOI: 10.1016/j.cogsys.2025.101373

Sirwan Abdolwahed Aula , Tarik Ahmed Rashid

{"title":"Foxtsage vs. Adam: Revolution or evolution in optimization?","authors":"Sirwan Abdolwahed Aula , Tarik Ahmed Rashid","doi":"10.1016/j.cogsys.2025.101373","DOIUrl":"10.1016/j.cogsys.2025.101373","url":null,"abstract":"<div><div>Optimisation techniques are crucial in neural network training, influencing predictive performance, convergence efficiency, and computational feasibility. Traditional Optimisers such as Adam offer adaptive learning rates but struggle with convergence stability and hyperparameter sensitivity, whereas SGD provides stability but lacks adaptiveness. We propose Foxtsage, a novel hybrid optimisation algorithm that integrates the FOX-TSA (for global search and exploration) with SGD (for fine-tuned local exploitation) to address these limitations. The proposed Foxtsage Optimiser is benchmarked against the widely used Adam Optimiser across multiple standard datasets, including MNIST, IMDB, and CIFAR-10. Performance is evaluated based on training loss, accuracy, precision, recall, F1-score, and computational time. The study further explores computational complexity and the trade-off between optimisation performance and efficiency. Experimental findings demonstrate that Foxtsage achieves a 42.03% reduction in mean loss (Foxtsage: 9.508, Adam: 16.402) and a 42.19% decrease in loss standard deviation (Foxtsage: 20.86, Adam: 36.085), indicating greater consistency and robustness in optimisation. Additionally, modest improvements are observed in accuracy (0.78%), precision (0.91%), recall (1.02%), and F1-score (0.89%), showcasing better generalization capability. However, these gains come at a significant computational cost, with a 330.87% increase in time mean (Foxtsage: 39.541 sec, Adam: 9.177 sec), raising concerns about practical feasibility in time-sensitive applications. By effectively combining FOX-TSA’s global search power with SGD’s adaptive stability, Foxtsage provides a promising alternative for neural network training. While it enhances performance and robustness, its increased computational overhead presents a critical trade-off. Future work will focus on reducing computational complexity, improving scalability, and exploring its applicability in real-world deep-learning tasks.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"92 ","pages":"Article 101373"},"PeriodicalIF":2.1,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An integrative model of self-efficacy within a computational cognitive architecture 计算认知架构中自我效能的综合模型

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2025-05-27 DOI: 10.1016/j.cogsys.2025.101372

Ron Sun , Sergei Bugrov , David Dai

引用次数: 0

Reactive AI feedback Improves task performance over time 随着时间的推移，反应性AI反馈可以提高任务性能

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2025-05-18 DOI: 10.1016/j.cogsys.2025.101361

Jacquelyn H. Berry

引用次数: 0