Pub Date : 2025-09-10DOI: 10.1016/j.cogsys.2025.101393
Chen Chen , Ruimin Lyu , Guoying Yang , Yuan Liu
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.
{"title":"Human performance in TSP tasks: Based on symbolic cognition","authors":"Chen Chen , Ruimin Lyu , Guoying Yang , Yuan Liu","doi":"10.1016/j.cogsys.2025.101393","DOIUrl":"10.1016/j.cogsys.2025.101393","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"94 ","pages":"Article 101393"},"PeriodicalIF":2.4,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106931","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}
Pub Date : 2025-09-07DOI: 10.1016/j.cogsys.2025.101391
Xiaochun Teng , Jun Yamada
In the square illusion, a square looks taller than it is wide, and in the Helmholtz illusion, a square filled with horizontal lines appears higher than it is wide and a square filled with vertical lines appears wider than it is high. A somewhat analogous pattern of illusion was observed when native Chinese speakers attempted to estimate heights and widths of Chinese characters. We call this illusion the Chinese character illusion, which can be attributable to an imaginary square in which to write characters and also to structural configurations of characters. We briefly discuss the characteristics of the Chinese character illusion and further interesting questions involved in this illusion.
{"title":"The Chinese character illusion","authors":"Xiaochun Teng , Jun Yamada","doi":"10.1016/j.cogsys.2025.101391","DOIUrl":"10.1016/j.cogsys.2025.101391","url":null,"abstract":"<div><div>In the square illusion, a square looks taller than it is wide, and in the Helmholtz illusion, a square filled with horizontal lines appears higher than it is wide and a square filled with vertical lines appears wider than it is high. A somewhat analogous pattern of illusion was observed when native Chinese speakers attempted to estimate heights and widths of Chinese characters. We call this illusion <em>the Chinese character illusion</em>, which can be attributable to an imaginary square in which to write characters and also to structural configurations of characters. We briefly discuss the characteristics of the Chinese character illusion and further interesting questions involved in this illusion.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"94 ","pages":"Article 101391"},"PeriodicalIF":2.4,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050121","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}
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.
{"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}
Pub Date : 2025-09-02DOI: 10.1016/j.cogsys.2025.101392
Sang Hun Kim , Dongkyu Park , Jongmin Lee , So Young Lee , Yosep Chong
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.
{"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}
Pub Date : 2025-09-01DOI: 10.1016/j.cogsys.2025.101397
Eduardo Y. Sakabe , Eduardo Camargo , Alexandre Simões , Esther Colombini , Paula Costa , Ricardo Gudwin
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 scene-based 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.
本文介绍了情节跟踪模块，这是一个认知模块，旨在将跨空间和时间的感官信息编码为称为基于场景的情节的高级语义表示。该模块在认知系统工具包（CST）中实现，为开发需要结构化情景编码的认知模型提供了一个可重用的框架。它的架构基于认知科学的理论见解，并受到人工智能应用的实际需求的影响。为了验证该系统，我们进行了两个主要实验：将该模块应用于Atari River Raid环境中的游戏玩法，以评估感知处理和情节构建；并将其与问答机制相结合，以测试其在下游高级认知过程中的效用。结果表明，该模块生成透明且可解释的表示，支持因果推理、时间推理和基于记忆的查询。通过将基础感知与结构化抽象相结合，情节跟踪模块为推进模块化、可解释和认知启发的人工智能体的设计提供了坚实的基础。
{"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}
Pub Date : 2025-08-30DOI: 10.1016/j.cogsys.2025.101396
Hui Huang , Jiecheng Huangliang , Qinglu Xiao , Ting Zhao , Qingqing Ye , Haokui Xu , Jun Yin
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.
{"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}
Pub Date : 2025-08-30DOI: 10.1016/j.cogsys.2025.101395
Michaela Bocheva
Perceptual uncertainty can be measured as the amount of variability in a Gaussian observation which is logically expected to increase in density as a function of time (number of exposures) and stimulus intensity (Norwich, 1977), thereby making the stimulus representation more accurate. The amount of uncertainty in the observation can simultaneously depend on stimulus novelty and training, task relevance, and whether the stimulus is perceived as a target or a distractor in a psychophysical task. We show that a framework simultaneously incorporating these different sources of noise can explain priming effects where the interference between two signals in a trial is given by the Kullback-Leibler (KL) divergence of their observations, with the target stimulus treated as a reference distribution. Our model predicted response times in a shape discrimination task including items embedded in spatial noise that could unpredictably appear as a target or as a distractor. These results suggest that processing times of the second stimulus can be convincingly modeled as the statistical distance between the noise distributions of two consecutive stimuli.
{"title":"Shannon entropy in visual perception predicts priming effects","authors":"Michaela Bocheva","doi":"10.1016/j.cogsys.2025.101395","DOIUrl":"10.1016/j.cogsys.2025.101395","url":null,"abstract":"<div><div>Perceptual uncertainty can be measured as the amount of variability in a Gaussian observation which is logically expected to increase in density as a function of time (number of exposures) and stimulus intensity (Norwich, 1977), thereby making the stimulus representation more accurate. The amount of uncertainty in the observation can simultaneously depend on stimulus novelty and training, task relevance, and whether the stimulus is perceived as a target or a distractor in a psychophysical task. We show that a framework simultaneously incorporating these different sources of noise can explain priming effects where the interference between two signals in a trial is given by the Kullback-Leibler (KL) divergence of their observations, with the target stimulus treated as a reference distribution. Our model predicted response times in a shape discrimination task including items embedded in spatial noise that could unpredictably appear as a target or as a distractor. These results suggest that processing times of the second stimulus can be convincingly modeled as the statistical distance between the noise distributions of two consecutive stimuli.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"93 ","pages":"Article 101395"},"PeriodicalIF":2.4,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933460","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}
Pub Date : 2025-08-30DOI: 10.1016/j.cogsys.2025.101389
Fateme Akbari , Kamran Sartipi
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.
{"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}
One of the purposes of perception is to bridge between sensors and conceptual understanding. Marr’s Primal Sketch combined initial edge-finding with multiple downstream processes to capture aspects of visual perception such as grouping and stereopsis. Given the progress made in multiple areas of AI since then, we have developed a new framework inspired by Marr’s work, the Hybrid Primal Sketch, which combines computer vision components into an ensemble to produce sketch-like entities which are then further processed by CogSketch, a model of high-level human vision, to produce both more detailed shape representations and scene representations which can be used for data-efficient learning via analogical generalization. This paper describes our theoretical framework, summarizes several previous experiments, and outlines a new experiment in progress on diagram understanding.
{"title":"Hybrid primal sketch: Combining analogy, qualitative representations, and computer vision for scene understanding","authors":"Kenneth D. Forbus , Kezhen Chen , Wangcheng Xu , Madeline Usher","doi":"10.1016/j.cogsys.2025.101390","DOIUrl":"10.1016/j.cogsys.2025.101390","url":null,"abstract":"<div><div>One of the purposes of perception is to bridge between sensors and conceptual understanding. Marr’s <em>Primal Sketch</em> combined initial edge-finding with multiple downstream processes to capture aspects of visual perception such as grouping and stereopsis. Given the progress made in multiple areas of AI since then, we have developed a new framework inspired by Marr’s work, the <em>Hybrid Primal Sketch</em>, which combines computer vision components into an ensemble to produce sketch-like entities which are then further processed by CogSketch, a model of high-level human vision, to produce both more detailed shape representations and scene representations which can be used for data-efficient learning via analogical generalization. This paper describes our theoretical framework, summarizes several previous experiments, and outlines a new experiment in progress on diagram understanding.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"93 ","pages":"Article 101390"},"PeriodicalIF":2.4,"publicationDate":"2025-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913330","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}
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.
{"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}
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