Cognitive Systems Research最新文献_第2页

Semantic neighborhood in cognitive modeling 认知建模中的语义邻域

IF 2.4 3区心理学

Cognitive Systems Research Pub Date : 2025-09-04 DOI: 10.1016/j.cogsys.2025.101398

Viacheslav Wolfengagen , Larisa Ismailova , Sergey Kosikov

{"title":"Semantic neighborhood in cognitive modeling","authors":"Viacheslav Wolfengagen , Larisa Ismailova , Sergey Kosikov","doi":"10.1016/j.cogsys.2025.101398","DOIUrl":"10.1016/j.cogsys.2025.101398","url":null,"abstract":"<div><div>The notion of semantic neighborhood is introduced and its properties are studied. The semantic neighborhood of a concept is understood as a commutative diagram characterizing the associated natural transformations over the representing functors. They characterize the behavior of the domains over which individual variables can range. The domains consist of generalized elements implementing the representation of an individual-as-a-process. Such a parametrization is based on the idea of the stage of knowledge that is achieved along evolvents. Another parameter is the properties of individual domains. As an example, the problem of habitability of variable domains and the corresponding problem of transmigration of individuals are considered, for which a solution is given. The life cycle of individuals and the possible elevation and spread of the effect of their entanglement are analyzed. Following the way of computational thinking, decomposition of the overall information task is performed in a standard manner. An evolvent system is chosen as a pattern, reflecting the idea of existence and becoming. When abstracting, how one class emerges from another is revealed, which is the content of some resulting mathematical assumptions. As a result, algorithmization becomes possible, involving parametrization, indexing, and functors.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"93 ","pages":"Article 101398"},"PeriodicalIF":2.4,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004618","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

Humanoid artificial consciousness designed with Large Language Model based on psychoanalysis and personality theory 基于精神分析和人格理论的大语言模型设计的类人人工意识

IF 2.4 3区心理学

Cognitive Systems Research Pub Date : 2025-09-02 DOI: 10.1016/j.cogsys.2025.101392

Sang Hun Kim , Dongkyu Park , Jongmin Lee , So Young Lee , Yosep Chong

{"title":"Humanoid artificial consciousness designed with Large Language Model based on psychoanalysis and personality theory","authors":"Sang Hun Kim , Dongkyu Park , Jongmin Lee , So Young Lee , Yosep Chong","doi":"10.1016/j.cogsys.2025.101392","DOIUrl":"10.1016/j.cogsys.2025.101392","url":null,"abstract":"<div><div>Human consciousness is still a concept hard to define with current scientific understanding. Although Large Language Models (LLMs) have recently demonstrated significant advancements across various domains including translation and summarization, human consciousness is not something to imitate with current upfront technology owing to so-called hallucination. This study, therefore, proposes a novel approach to address these challenges by integrating psychoanalysis and the Myers–Briggs Type Indicator (MBTI) into constructing consciousness and personality modules. We developed three artificial consciousnesses (self-awareness, unconsciousness, and preconsciousness) based on the principles of psychoanalysis. Additionally, we designed 16 characters with different personalities representing the sixteen MBTI types, with several attributes such as needs, status, and memories. To determine if our model’s artificial consciousness exhibits human-like cognition, we created ten distinct situations considering seven attributes such as emotional understanding and logical thinking. The decision-making process of artificial consciousness and the final action were evaluated in three ways: survey evaluation, three-tier classification via ChatGPT, and qualitative review. Both quantitative and qualitative analyses indicated a high likelihood of well-simulated consciousness, although the difference in response between different characters and consciousnesses was not very significant. This implies that the developed models incorporating elements of psychoanalysis and personality theory can lead to building a more intuitive and adaptable AI system with humanoid consciousness. Therefore, this study contributes to opening up new avenues for improving AI interactions in complex cognitive contexts.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"94 ","pages":"Article 101392"},"PeriodicalIF":2.4,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050122","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 episode encoding mechanism for cognitive architectures 认知架构的情节编码机制

IF 2.4 3区心理学

Cognitive Systems Research Pub Date : 2025-09-01 DOI: 10.1016/j.cogsys.2025.101397

Eduardo Y. Sakabe , Eduardo Camargo , Alexandre Simões , Esther Colombini , Paula Costa , Ricardo Gudwin

{"title":"An episode encoding mechanism for cognitive architectures","authors":"Eduardo Y. Sakabe , Eduardo Camargo , Alexandre Simões , Esther Colombini , Paula Costa , Ricardo Gudwin","doi":"10.1016/j.cogsys.2025.101397","DOIUrl":"10.1016/j.cogsys.2025.101397","url":null,"abstract":"<div><div>This paper introduces the Episode Tracker Module, a cognitive module designed to encode sensory information across space and time into high-level semantic representations known as <em>scene-based</em> episodes. Implemented within the Cognitive Systems Toolkit (CST), the module provides a reusable framework for developing cognitive models that require structured episodic encoding. Its architecture is grounded in theoretical insights from cognitive science and shaped by practical requirements for artificial intelligence applications. To validate the system, we conducted two main experiments: applying the module to gameplay in the Atari River Raid environment to evaluate perceptual processing and episode construction; and integrating it with a question-and-answering mechanism to test its utility in downstream high-level cognitive processes. Results show that the module produces transparent and interpretable representations that support causal inference, temporal reasoning, and memory-based querying. By combining grounded perception with structured abstraction, the Episode Tracker Module offers a robust foundation for advancing the design of modular, interpretable, and cognitively inspired artificial agents.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"94 ","pages":"Article 101397"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145011236","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

Positivity bias in generalizing moral impressions from individuals to groups 将道德印象从个人推广到群体的积极性偏差

IF 2.4 3区心理学

Cognitive Systems Research Pub Date : 2025-08-30 DOI: 10.1016/j.cogsys.2025.101396

Hui Huang , Jiecheng Huangliang , Qinglu Xiao , Ting Zhao , Qingqing Ye , Haokui Xu , Jun Yin

{"title":"Positivity bias in generalizing moral impressions from individuals to groups","authors":"Hui Huang , Jiecheng Huangliang , Qinglu Xiao , Ting Zhao , Qingqing Ye , Haokui Xu , Jun Yin","doi":"10.1016/j.cogsys.2025.101396","DOIUrl":"10.1016/j.cogsys.2025.101396","url":null,"abstract":"<div><div>Individuals should seek to become aware of the valence (positive/negative) of their moral impressions of others, as it determines whether the observed individuals may be helpful or harmful. Studies have documented the capacity to form moral impressions of groups by integrating individual moral characters, but whether and how moral valence influences the generalization of moral impressions from individuals to groups remain unknown, especially in dynamic learning contexts. In the present study, participants sequentially predicted and observed positive (helping) or negative (hindering) behaviors of two group members to form moral impressions about these individuals. They reported their moral impressions of an unknown group member with no trait-implying behavior and of the entire group. Experiments 1 and 2 demonstrated greater generalization of positive moral impressions than of negative moral impressions from individuals to groups, indicating a group-level positivity bias. Experiment 3 manipulated participants’ prior beliefs through preexposure to group moral impressions. The results revealed that the group-level positivity bias persisted after the initial formation of positive group impressions, while the initial formation of negative impressions led to a corresponding negativity bias. The Bayesian findings suggested that the observed positivity bias in morality generalization within groups can be attributed to a prior belief in the positivity of groups. Thus, social groups not only influence the selection of shared moral characters among their members but also contribute to prior knowledge shaping group moral impressions. This prior belief functions as a default assumption in social evaluations.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"93 ","pages":"Article 101396"},"PeriodicalIF":2.4,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933458","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

Shannon entropy in visual perception predicts priming effects 视觉知觉中的香农熵预测启动效应

IF 2.4 3区心理学

Cognitive Systems Research Pub Date : 2025-08-30 DOI: 10.1016/j.cogsys.2025.101395

Michaela Bocheva

引用次数: 0

Modeling behavioral deviations in ADLs using Inverse Reinforcement Learning 用逆强化学习建模adl的行为偏差

IF 2.4 3区心理学

Cognitive Systems Research Pub Date : 2025-08-30 DOI: 10.1016/j.cogsys.2025.101389

Fateme Akbari , Kamran Sartipi

{"title":"Modeling behavioral deviations in ADLs using Inverse Reinforcement Learning","authors":"Fateme Akbari , Kamran Sartipi","doi":"10.1016/j.cogsys.2025.101389","DOIUrl":"10.1016/j.cogsys.2025.101389","url":null,"abstract":"<div><div>The detection of abnormalities in Activities of Daily Living (ADLs) has garnered significant attention in recent studies, with many employing deep learning techniques. This paper introduces a novel approach to analyzing ADL sequences, aimed at identifying meaningful deviations from an individual’s routine behavior. Our method offers several benefits for older adults, including timely care, early detection of health conditions to prevent deterioration, reduced monitoring burden on family members, and enhanced self-sufficiency without disrupting daily activities. We propose an Inverse Reinforcement Learning (IRL)-based method to detect behavioral abnormalities in older adults by analyzing ADL sequences. Our approach models the problem of abnormality detection in behavior sequences as a Markov Chain model. By applying the IRL method, we infer the reward function that motivates individuals to perform ADL from observed behavior trajectories. This inferred reward function is then used to identify potential behavior abnormalities through a threshold-based mechanism, where sequences with rewards below a specified threshold are flagged as potential abnormalities.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"93 ","pages":"Article 101389"},"PeriodicalIF":2.4,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933470","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

Hybrid primal sketch: Combining analogy, qualitative representations, and computer vision for scene understanding 混合原始草图：结合类比、定性表示和计算机视觉来理解场景

IF 2.4 3区心理学

Cognitive Systems Research Pub Date : 2025-08-24 DOI: 10.1016/j.cogsys.2025.101390

Kenneth D. Forbus , Kezhen Chen , Wangcheng Xu , Madeline Usher

引用次数: 0

Distinct neural correlates of active listening and passive listening to emotional narratives 情绪叙事的主动倾听和被动倾听的不同神经关联

IF 2.4 3区心理学

Cognitive Systems Research Pub Date : 2025-08-21 DOI: 10.1016/j.cogsys.2025.101394

Ruei-Jyun Hung , Po-Yu Wang , Intan Low , Yong-Sheng Chen , Li-Fen Chen

{"title":"Distinct neural correlates of active listening and passive listening to emotional narratives","authors":"Ruei-Jyun Hung , Po-Yu Wang , Intan Low , Yong-Sheng Chen , Li-Fen Chen","doi":"10.1016/j.cogsys.2025.101394","DOIUrl":"10.1016/j.cogsys.2025.101394","url":null,"abstract":"<div><div>In social communication, emotional responses and feedback often emerge from dynamic interactions, such as active listening. However, most previous research on electroencephalography (EEG)-based emotion recognition has relied on datasets collected from passive participants in controlled and isolated environments. This study addressed this gap by introducing a more naturalistic experimental design. We aimed at revealing the neural correlates of active and passive listening to emotional narratives, thereby simulating real-world social interactions. Using deep learning-based EEG emotion recognition, we employed a rhythm-specific convolutional neural network (CNN) combined with occlusion sensitivity analysis to investigate critical rhythmic and spatial information. Without prior feature engineering, the proposed approach achieved approximately 88% accuracy in distinguishing Happy/Sad from Neutral emotions. Our results highlighted the importance of gamma-band signals in emotion recognition, particularly in the left frontocentral and right temporal regions. We also identified the significant roles played by the left central-parietal, right parietal, and occipital regions during active listening to emotional narratives. This study demonstrates the feasibility of capturing essential rhythmic and spatial information through a rhythm-specific convolutional neural network combined with occlusion sensitivity analysis. This approach provides a robust foundation for uncovering the neural correlates of naturalistic emotional communication, paving the way for future research in social neuroscience.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"93 ","pages":"Article 101394"},"PeriodicalIF":2.4,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926355","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

A machine-readable metadata model for intelligent data analysis 用于智能数据分析的机器可读元数据模型

IF 2.4 3区心理学

Cognitive Systems Research Pub Date : 2025-08-10 DOI: 10.1016/j.cogsys.2025.101388

Xavier Angerri, Joan Vázquez, Karina Gibert

{"title":"A machine-readable metadata model for intelligent data analysis","authors":"Xavier Angerri, Joan Vázquez, Karina Gibert","doi":"10.1016/j.cogsys.2025.101388","DOIUrl":"10.1016/j.cogsys.2025.101388","url":null,"abstract":"<div><div>Metadata, or data about data, is essential in Knowledge Discovery from Data (KDD) and Artificial Intelligence (AI) processes, providing details about the meanings and technical aspects of dataset variables. Historically, research has focused on software to store and manage metadata, mainly describing data structure and formats for analyst understanding. However, little has been done to analyze the metadata required for automating advanced KDD processes. Traditionally, metadata creation has been a manual process, relying on analysts to gather information from stakeholders. This paper introduces the GeMeDaFi methodology, enabling stakeholders to automatically generate machine-readable metadata files, facilitating the automatic management of metadata. GeMeDaFi is a key component of the AM4IDA methodology, which guides intelligent data analysis using automatically generated metadata. This process spans from data preprocessing to result interpretation, including modeling. The metadata file, based on the MdM formal model, incorporates semantic information from stakeholders and the dataset’s structure, supporting automated intelligent preprocessing and analysis. The proposal also enhances the INSESS methodology for intelligent data analysis and has been applied in four real-world scenarios. The primary contributions are the significant reduction in time and errors in creating metadata files, accelerating the preprocessing phase, and enabling automation in the analytical step of KDD processes.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"93 ","pages":"Article 101388"},"PeriodicalIF":2.4,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933459","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

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