Cognitive Systems Research最新文献

{"title":"Theta power increases during intermodal configural learning: A possible mechanism for establishing network communication during stimulus encoding and feature binding","authors":"Boris V. Chernyshev ,&nbsp;Larisa A. Pozniak ,&nbsp;Kristina I. Pultsina ,&nbsp;Andrey O. Prokofyev ,&nbsp;Anna G. Kruychkova ,&nbsp;Vadim L. Ushakov","doi":"10.1016/j.cogsys.2025.101415","DOIUrl":"10.1016/j.cogsys.2025.101415","url":null,"abstract":"<div><div>Configurations are gestalt-like conjunctions of stimuli or stimulus features leading to holistic perception. The current study in humans investigated configural threat learning with bimodal visual-auditory conjunctions. The associative learning task involved classical discriminative conditioning with elemental visual (V), elemental auditory (A) and complex bimodal audiovisual (AV) stimuli, some of which were reinforced and some not. We focused on early theta oscillations (4–7  Hz) evoked by stimuli, and we used data-driven approach to magnetoencephalographic data recorded during participants’ performance on the task. We observed a robust increase in theta-band power in response to reinforced configural audiovisual stimuli (AV+), compared either to non-reinforced audiovisual stimuli (AV−) or to reinforced elemental stimuli (A+ or V+). Notably, the effect in response to the configural stimulus exhibited non-additive properties, indicating emergent integrative processing that extends beyond a simple superposition of its elements. Source localization revealed a distributed network of higher-order associative brain regions specifically engaged during configural learning, including the parahippocampal complex and dorsolateral prefrontal cortex – areas traditionally associated with learning and memory. Significant theta power increases were also observed in the inferior parietal cortex and temporoparietal junction, as well as in the lateral and inferior temporal cortices. These regions, known for their roles in multimodal integration and higher-order cognition, are implicated in relational processing, attentional modulation, and object categorization. Together, these findings underscore the role of theta synchronization in binding complex sensory inputs into unified, higher-level representations during configural learning in humans. We interpret these results in terms of hippocampal-cortical communication and concept formation.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"94 ","pages":"Article 101415"},"PeriodicalIF":2.4,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363503","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}

{"title":"Integrating human–machine systems and digital twin technologies: navigating trust, interoperability, and ethical challenges","authors":"Soheil Sabri ,&nbsp;Mahdi Aghaabbasi ,&nbsp;Simon Reay Atkinson ,&nbsp;Mary Jean Amon ,&nbsp;Peter Hancock ,&nbsp;Roger Azevedo ,&nbsp;Megan Wiedbusch ,&nbsp;Crystal Maraj ,&nbsp;Sean Mondesire ,&nbsp;Bulent Soykan ,&nbsp;Stephen Fiore ,&nbsp;Saeid Nahavandi ,&nbsp;Ghaith Rabadi","doi":"10.1016/j.cogsys.2025.101414","DOIUrl":"10.1016/j.cogsys.2025.101414","url":null,"abstract":"<div><div>This commentary highlights three problems that can emerge by integrating Digital Twin Technology (DTT) and Human–Machine Systems (HMS), drawing insights from Human–Technology Interaction, Systems Engineering and Computer Science, and Learning Sciences experts, who participated in the IEEE SMC Society/SMST Workshop on HMS–DTT, hosted at the University of Central Florida. The paper focuses on ethics, human and data interoperability, and trust issues. Rather than providing a traditional literature review, it consolidates contributions from workshop discussions and highlights the need for transparent, reliable systems, standardized data protocols, and ethical frameworks to guide development and implementation. Synthesizing diverse perspectives underscores the importance of interdisciplinary approaches in realizing the benefits of HMS and DTT integration while mitigating potential risks. Overall, this work aims to inform future research agendas and foster responsible innovation by integrating viewpoints across disciplines in this rapidly evolving field.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"94 ","pages":"Article 101414"},"PeriodicalIF":2.4,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267757","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}

{"title":"Safeguarding autonomy: A focus on machine learning decision systems","authors":"Paula Subías-Beltrán ,&nbsp;Oriol Pujol ,&nbsp;Itziar de Lecuona","doi":"10.1016/j.cogsys.2025.101413","DOIUrl":"10.1016/j.cogsys.2025.101413","url":null,"abstract":"<div><div>As global discourse on AI regulation gains momentum, this paper focuses on delineating the impact of ML on autonomy and fostering awareness. Respect for autonomy is a basic principle in bioethics that establishes people as decision-makers. While the concept of autonomy in the context of ML appears in several European normative publications, it remains a theoretical concept that has yet to be widely accepted in ML practice. Our contribution is to bridge the gap between theory and practice in ML by encouraging the respect of autonomy in ML-aided decision-making. We do this by proposing a clear framework for operationalizing autonomy and identifying the conditioning factors that currently prevent it. Consequently, we focus on the different stages of the ML pipeline to identify the potential effects on ML end-users’ autonomy. To improve its practical utility, we propose a related question for each detected impact, offering guidance for identifying possible focus points to respect ML end-users autonomy in decision-making.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"94 ","pages":"Article 101413"},"PeriodicalIF":2.4,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267759","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}

{"title":"Organizations’ interpersonal activity knowledge graph (IAKG)","authors":"Serge Sonfack Sounchio ,&nbsp;Halguieta Trawina ,&nbsp;Baudelaire Ismael Tankeu Nguekeu ,&nbsp;Laurent Geneste ,&nbsp;Bernard Kamsu-Foguem","doi":"10.1016/j.cogsys.2025.101407","DOIUrl":"10.1016/j.cogsys.2025.101407","url":null,"abstract":"<div><div>Knowledge today supports organizations’ growth, lets them stay competitive, and enables them to design new products and services or make effective decisions. This knowledge is classified into two primary forms: explicit knowledge, which is easy to encode, store, and access, and implicit knowledge, which employees possess regarding products, services, and how they carry out an organization’s activities. Unlike explicit knowledge, implicit knowledge, and particularly organizations’ personal activity knowledge, is challenging to capture, formalize, and reuse. Moreover, the human-centered personal knowledge graph approach is unfit for the personal activity knowledge representation and reasoning. On the one hand, this study describes and depicts the limitations of human-centered personal knowledge graph approaches for representing personal activity knowledge within an organization. Afterward, it elaborates on a personal activity ontology derived from an extension of the activity theory concept established in social sciences. The proposed framework enables the capture, formalization, sharing, and reasoning of personal activity knowledge within an organization.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"94 ","pages":"Article 101407"},"PeriodicalIF":2.4,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267758","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}

{"title":"Eliciting problem specifications for LLM-Modulo cognitive systems","authors":"Robert E. Wray,&nbsp;James R. Kirk,&nbsp;John E. Laird","doi":"10.1016/j.cogsys.2025.101409","DOIUrl":"10.1016/j.cogsys.2025.101409","url":null,"abstract":"<div><div>Large language models (LLMs) offer unprecedented natural-language understanding and generation capabilities. However, evaluations of their ability to demonstrate other cognitive functions, especially various categories of reasoning, have been, at best, mixed. The limited scope of reliable and robust LLM capabilities has resulted in a new class of AI systems, LLM-Modulo AI, in which LLMs are used to contribute to the overall capabilities of an intelligent system. In this paper, we explore the applicability of LLMs for one specific capability acutely missing in most cognitive systems: problem formulation. Cognitive systems generally require a human to translate a problem definition into some specification that the cognitive system can use to attempt to solve the problem or perform the task. We explore how large language models (LLMs) can be utilized to map a problem class, defined in natural language, into a semi-formal specification that can then be utilized by an existing reasoning and learning system to solve instances from the problem class. The result is a Modulo-LLM cognitive system in which the LLM roughly acts as a <em>cognitive task analyst</em>, generating a problem specification that can be used by a typical cognitive system to solve specific problems. The agent uses prompts derived from the definition of problem spaces in the AI literature and general problem-solving strategies (Polya’s <em>How to Solve It</em>). We offer preliminary evidence illustrating the potential for LLM-based problem specification. Such automatic problem specification offers the potential to speed cognitive systems research via disintermediation of problem formulation while also retaining core capabilities of cognitive systems, such as robust inference and online learning.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"94 ","pages":"Article 101409"},"PeriodicalIF":2.4,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267655","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}

{"title":"Estimating the difficulty of abstract classes of problems","authors":"Michael T. Cox ,&nbsp;Kristen Jacobson ,&nbsp;Paul Rademacher ,&nbsp;Laura M. Hiatt ,&nbsp;Mark Roberts","doi":"10.1016/j.cogsys.2025.101412","DOIUrl":"10.1016/j.cogsys.2025.101412","url":null,"abstract":"<div><div>Learning is most effective when an artificial agent (or a human) begins with mastering easier tasks before progressing to more difficult ones. In the reinforcement learning community, this principle has led to the concept of a curriculum, which consists of successively harder training episodes. However, the creation of such episodes requires significant manual effort. Some researchers have semi-automated this process by using specialized graphs to organize learning tasks and order them by increasing difficulty. But the degree to which one task is harder than another remains an open question. In this paper, we present a method for automatically determining the difficulty of an arbitrary task, and hence the difference between associated learning problems. In support of this goal, we will examine the fundamental question of what makes an activity hard rather than seek an incremental improvement in known algorithms or representations. Further, the scope of research will not be limited to machine learning only but will include planning problems as well. We present empirical data to support our claims, and we consider the human–machine problem of choosing good representations related to a curriculum.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"94 ","pages":"Article 101412"},"PeriodicalIF":2.4,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416460","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}

{"title":"General interaction battery: Simple object navigation and affordances (GIBSONA)","authors":"Danaja Rutar ,&nbsp;Alva Markelius ,&nbsp;Wout Schellaert ,&nbsp;José Hernández-Orallo ,&nbsp;Lucy Cheke","doi":"10.1016/j.cogsys.2025.101411","DOIUrl":"10.1016/j.cogsys.2025.101411","url":null,"abstract":"<div><div><em>Perception of affordances</em> is an agent’s capability to identify what action-possibilities exist with a particular object or set of objects, based on its own physical properties and capacities. This capability has been well explored in psychology because perception of affordances provides the basis for understanding and interacting with the world. For the same reason, affordance perception is also crucial for AI research. Most approaches to evaluating AI are <em>task-oriented</em> which means that they are geared towards evaluating aggregate performance on a specific set of tasks, rather than focusing on the nature and degree of underlying <em>capabilities</em> that drive task performance. An alternative approach to measuring performance in AI is <em>capability-oriented</em> evaluation, which aims to measure robust, task-independent capabilities across different conditions and difficulties. This approach allows not only measurement of performance but also prediction of performance on novel challenges that share the same fundamental demands. In the context of affordances, there are currently no clear guidelines as to how such capability-oriented approach should best be implemented; for example, there is much variation in what perception of affordances entails. Perhaps for this reason, no comprehensive battery of affordances tasks for AI currently exists. Building on this gap, the aims of this paper are to first, lay out some candidate guidelines for the construction of capability-oriented task batteries for embodied AI and second, to construct and present a battery GIBSONA that takes a step towards this goal: Assessing perception of a set of affordances in AI, directly following these guidelines.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"94 ","pages":"Article 101411"},"PeriodicalIF":2.4,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221326","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}

{"title":"Biologically inspired computational models of Visuospatial Working Memory: A systematic review","authors":"Viviana Dueñas,&nbsp;Sonia López,&nbsp;José-Antonio Cervantes,&nbsp;Gerardo Ortiz-Torres","doi":"10.1016/j.cogsys.2025.101410","DOIUrl":"10.1016/j.cogsys.2025.101410","url":null,"abstract":"<div><div>Visuospatial working memory is a fundamental cognitive component that enables humans to explore and interact with their visual environment. This paper presents a systematic review of bio-inspired computational models of visuospatial working memory developed over the past 14 years. The review identifies the main bio-inspired and algorithmic approaches used, examines the cognitive functions and brain areas considered in these models, and discusses the strategies employed to evaluate them. Furthermore, it outlines the current challenges in enhancing the design and implementation of such models. The findings from this meta-review are intended to support and guide future research on developing bio-inspired computational models of visuospatial working memory to enhance the cognitive abilities of bio-inspired artificial agents.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"94 ","pages":"Article 101410"},"PeriodicalIF":2.4,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159070","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}

{"title":"Analogical mappings of facts and counterfactuals in the human mind and Peirce’s abduction: limitations in LLMs","authors":"Mariana Olezza","doi":"10.1016/j.cogsys.2025.101408","DOIUrl":"10.1016/j.cogsys.2025.101408","url":null,"abstract":"<div><div>In this work, it is proposed that the human mind engages in an analogical mapping between facts found in “expert knowledge” and the abductive reasoning process described by Charles Sanders Peirce (1839–1914). This mapping connects the human mind with the causal world and enables the generation of hypotheses—whether scientific, artistic, or related to everyday life. Artificial Neural Networks (ANNs), including Large Language Models (LLMs) (<span><span>Vaswani et al., 2017</span></span>) and models incorporating Generative Adversarial Networks (GANs) (<span><span>Goodfellow et al., 2014</span></span>), face two key limitations: (1) They cannot work with counterfactuals, relying only on correlational datasets. (2) They are unable to perform true abductive reasoning. These systems may appear to “create” mappings with varying degrees of amplitude, but this impression arises from hyperparameters—such as Temperature (T) (<span><span>Agarwal et al., 2024</span></span>, <span><span>Peeperkorn et al., 2024</span></span>) and Top–K (<span><span>Noarov et al., 2025</span></span>)—configured by system supervisors or users via prompts. These parameters control the model’s output variability: Temperature influences the distribution of logits, while Top–K limits the prediction to the top K probable tokens, thus managing how deterministic or aleatoric the output becomes.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"94 ","pages":"Article 101408"},"PeriodicalIF":2.4,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106930","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}

{"title":"Emerging synchrony and synchrony transitions and their effects on development of affiliation in social interaction adaptivity: Comparative computational analysis of different synchrony and synchrony transition detection methods","authors":"Sophie C.F. Hendrikse ,&nbsp;Jan Treur ,&nbsp;Sander L. Koole","doi":"10.1016/j.cogsys.2025.101399","DOIUrl":"10.1016/j.cogsys.2025.101399","url":null,"abstract":"<div><div>Interpersonal synchrony often emerges during social interaction and in turn is linked to better interpersonal affiliation. In addition, transitions in synchrony – meaning switching between moving in and out of sync − also occur often. It might be assumed that transitions in synchrony, especially when the extent of synchrony decreases, negatively affect affiliation. Nevertheless, there is empirical evidence indicating that time periods with transitions in synchrony can have an even stronger positive effect on affiliation or liking in comparison to time periods without transitions in synchrony, possibly highlighting that timing of synchrony episodes is of equal importance for being considered as the extent of synchrony episodes is. This paper presents multiple systematic analyses of both phenomena based on an adaptive agent model simulating how persons’ affiliation might benefit both from emerging synchrony and transitions in synchrony. Both for detection of synchrony and detection of synchrony transitions, multiple methods have been proposed in the literature and applied (from an external observer viewpoint) to identify or detect forms of emerging synchrony or synchrony transitions in given pairs of time series. We systematically evaluate through simulations the performance of multiple combinations of synchrony detection methods that have been incorporated in our developed adaptive agent model. These methods model the agent’s subjective detection of synchrony and synchrony transitions. We explored and compared the synchrony scores from the following methods: complemental difference, Pearson correlation coefficient, signal matching and average mutual information. Regarding the transition detection of synchrony scores, we examined the following three methods: standard deviation based, average based, and maximum-minimum based. In a comparative manner we evaluated all 12 combinations of synchrony detection and transition detection methods in our adaptive agent model in simulation experiments for two agents with a setup in which a number of situations were encountered in different (time) episodes. Moreover, also the subjective synchrony and transition detection of each of the two agents were mutually compared and their subjective detections were compared to objective detections from an external observer viewpoint.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"94 ","pages":"Article 101399"},"PeriodicalIF":2.4,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267760","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}