Cognitive Science and Mapping

Últimos artículos

• Converting Sequenced Fuzzy Cognitive Maps to Causal Virtual Worlds with Large Video Generators

  Akash Kumar Panda, Olaoluwa Adigun, Bart Kosko · 15 de septiembre de 2026

  We show how users can create and manipulate causal virtual worlds with large-language-model (LLM) and large-video-model agents. The approach uses feedback fuzzy cognitive maps (FCMs) both to model the granular causal structure of the virtual world and to guide its causal evolution. The local causal …

• When Can Large Reasoning Models Save Thinking? Mechanistic Analysis of Behavioral Divergence in Reasoning

  Rongzhi Zhu, Yi Liu, Jiancheng Wang, Xiangyu Liu, Zequn Sun, Yiwei Wang, Yu Deng, Zijian Zhou, Wei Hu · 3 de septiembre de 2026

  Large reasoning models (LRMs) have achieved remarkable success on complex tasks, yet their tendency to "overthink" leads to inefficiencies. Although "save-thinking" prompts are intended to mitigate this issue, we find that LRMs still frequently enter the "Still-thinking" mode instead of the expected…

• Toward a New Science of AI as Cognitive Infrastructure

  Giuseppe Riva · 28 de agosto de 2026

  Contemporary human-AI interaction research overlooks how AI systems fundamentally reshape human cognition pre-consciously, a critical blind spot for understanding distributed cognition. This paper introduces "Cognitive Infrastructure Studies" (CIS) as a new interdisciplinary domain to reconceptualiz…

• A Taxonomy of Cognitive Capability Gaps in Generative and Agentic AI

  Taye Akinrele, Sindhuja Penchala, Noorbakhsh Amiri Golilarz, Sudip Mittal, Shahram Rahimi · 4 de agosto de 2026

  Cognitive AI seeks to move beyond language generation and autonomous task execution toward systems capable of sustained reasoning, adaptive behavior, persistent memory, and self-regulation. While generative and agentic AI have demonstrated impressive capabilities across a wide range of tasks, many f…

• Beyond Directed Acyclic Graphs: Causal Zeros and Causal Differential Equations

  Sergei V. Kalinin · 28 de julio de 2026

  Pearl's structural causal model (SCM) framework, built on directed acyclic graphs (DAGs) and the do-calculus, is the dominant formal language for causal reasoning. Yet it carries two structural restrictions: every relationship must be pre-specified as a directed causal edge, and feedback cycles are …

• LLM for the development of FCM

  Alexis Kafantaris · 7 de julio de 2026

  This article is about the development of a fuzzy cognitive map using a local large language model. In the light of recent advances it is evident that large language models, and even local large language models are capable of extracting quantities from textual data. In other words, a local LLM like Q…

• MAGIK: Mapping to Analogous Goals via Imagination-enabled Knowledge Transfer

  Ajsal Shereef Palattuparambil, Thommen George Karimpanal, Santu Rana · 3 de julio de 2026

  Humans excel at analogical reasoning - applying knowledge from one task to a related one with minimal relearning. In contrast, reinforcement learning (RL) agents typically require extensive retraining even when new tasks share structural similarities with previously learned ones. In this work, we pr…

• Learning by Surprise: Adaptive Mitigation of Model Collapse in Large Language Models

  Daniele Gambetta, Gizem Gezici, Fosca Giannotti, Dino Pedreschi, Alistair Knott, Luca Pappalardo · 1 de julio de 2026

  As AI-generated content increasingly populates the web, generative AI models are at growing risk of being trained on their own outputs, a process known as AI autophagy. This feedback loop has been shown to induce model collapse, typically characterized by a loss of diversity in generated content. Ho…

• Unsupervised Cognition

  Alfredo Ibias, Hector Antona, Guillem Ramirez-Miranda, Enric Guinovart, Eduard Alarcon · 2 de junio de 2026

  Unsupervised learning methods have a soft inspiration in cognition models. To this day, the most successful unsupervised learning methods revolve around clustering samples in a mathematical space. In this paper we propose a primitive-based, unsupervised learning approach for decision-making inspired…

• Agentic Chunking and Bayesian De-chunking of AI Generated Fuzzy Cognitive Maps: A Model of the Thucydides Trap

  Akash Kumar Panda, Olaoluwa Adigun, Bart Kosko · 19 de mayo de 2026

  We automatically generate feedback causal fuzzy cognitive maps (FCMs) from text by teaching large-language-model agents to break the text into overlapping chunks of text. Convex mixing of these chunk FCMs gives a representative cyclic FCM knowledge graph. The text chunks can have different levels of…

• The Design and Composition of Structural Causal Decision Processes

  Sebastian Benthall, Alan Lujan · 6 de mayo de 2026

  We present two new classes of causal models of decision-making agents. Our approach is motivated by the needs of modeling the economics of computing systems. These systems are composed of subsystems and can exhibit endogenous limits on cognitive resources and value discounting. Structural Causal Dec…

• Generative midtended cognition and Artificial Intelligence. Thinging with thinging things

  Xabier E. Barandiaran, Marta P\'erez-Verdugo · 21 de abril de 2026

  This paper introduces the concept of ``generative midtended cognition'', exploring the integration of generative AI with human cognition. The term "generative" reflects AI's ability to iteratively produce structured outputs, while "midtended" captures the potential hybrid (human-AI) nature of the pr…

• Cognitive Loop of Thought: Reversible Hierarchical Markov Chain for Efficient Mathematical Reasoning

  Jia-Chen Zhang, Yu-Jie Xiong, Zheng Zhou · 9 de abril de 2026

  Multi-step Chain-of-Thought (CoT) has significantly advanced the mathematical reasoning capabilities of LLMs by leveraging explicit reasoning steps. However, the widespread adoption of Long CoT often results in sequence lengths that exceed manageable computational limits. While existing approaches a…

• Non-monotonic causal discovery with Kolmogorov-Arnold Fuzzy Cognitive Maps

  Jose L. Salmeron · 8 de abril de 2026

  Fuzzy Cognitive Maps constitute a neuro-symbolic paradigm for modeling complex dynamic systems, widely adopted for their inherent interpretability and recurrent inference capabilities. However, the standard FCM formulation, characterized by scalar synaptic weights and monotonic activation functions,…

• Socially Minded Intelligence: How Individuals, Groups, and Artificial Intelligence Can Make Each Other Smarter (or Not)

  William J. Bingley, S. Alexander Haslam, Janet Wiles · 30 de marzo de 2026

  A core part of human intelligence is the ability to work flexibly with others to achieve goals. The incorporation of artificial agents into human spaces is making increasing demands on artificial intelligence (AI) to demonstrate and facilitate this ability. However, this kind of flexibility is not w…

• Cognitive Spillover in Human-AI Teams

  Christoph Riedl, Saiph Savage, Josie Zvelebilova · 24 de marzo de 2026

  AI is not only a neutral tool in team settings; it influence the social and cognitive fabric of collaboration. Across two randomized experiments, we demonstrate that AI exposure produces causal spillover into human-human interaction -- affecting shared language, collective attention, shared mental m…

• Looking Through Glass Box

  Alexis Kafantaris · 9 de marzo de 2026

  This essay is about a neural implementation of the fuzzy cognitive map, the FHM, and corresponding evaluations. Firstly, a neural net has been designed to behave the same way that an FCM does; as inputs it accepts many fuzzy cognitive maps and propagates them in order to learn causality patterns. Mo…

• Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond

  Takaaki Fujita, Florentin Smarandache · 24 de febrero de 2026

  Combinatorics studies how discrete objects can be counted, arranged, and combined under specified rules. Motivated by uncertainty in real-world data and decisions, modern set-theoretic formalisms such as fuzzy sets, neutrosophic sets, rough sets, soft sets, and plithogenic sets have been developed. …

• Emergent Cognitive Convergence via Implementation: Structured Cognitive Loop Reflecting Four Theories of Mind

  Myung Ho Kim · 9 de febrero de 2026

  We report a structural convergence among four influential theories of mind: Kahneman dual-system theory, Friston predictive processing, Minsky society of mind, and Clark extended mind, emerging unintentionally within a practical AI architecture known as Agentic Flow. Designed to address limitations …

• Cognitive AI framework 2.0: advances in the simulation of human thought

  Rommel Salas-Guerra · 23 de enero de 2026

  The Human Cognitive Simulation Framework proposes a governed cognitive AI architecture designed to improve personalization, adaptability, and long-term coherence in human AI interaction. The framework integrates short-term memory (conversation context), long-term memory (interaction context), cognit…

• The Agentic Leash: Extracting Causal Feedback Fuzzy Cognitive Maps with LLMs

  Akash Kumar Panda, Olaoluwa Adigun, Bart Kosko · 5 de enero de 2026

  We design a large-language-model (LLM) agent that extracts causal feedback fuzzy cognitive maps (FCMs) from raw text. The causal learning or extraction process is agentic both because of the LLM's semi-autonomy and because ultimately the FCM dynamical system's equilibria drive the LLM agents to fetc…

• External Hippocampus: Topological Cognitive Maps for Guiding Large Language Model Reasoning

  Jian Yan · 23 de diciembre de 2025

  This paper proposes the External Hippocampus framework, which models language model reasoning from a cognitive dynamics perspective as the flow of information energy in semantic space. Unlike traditional weight-space optimization methods, this framework constructs topological cognitive maps through …

• Relational Dissonance in Human-AI Interactions: The Case of Knowledge Work

  Emrecan Gulay, Eleonora Picco, Enrico Glerean, Corinna Coupette · 8 de diciembre de 2025

  When AI systems allow human-like communication, they elicit increasingly complex relational responses. Knowledge workers face a particular challenge: They approach these systems as tools while interacting with them in ways that resemble human social interaction. To understand the relational contexts…

• Integrating Semantic Communication and Human Decision-Making into an End-to-End Sensing-Decision Framework

  Edgar Beck, Hsuan-Yu Lin, Patrick R\"uckert, Yongping Bao, Bettina von Helversen, Sebastian Fehrler, Kirsten Tracht, Armin Dekorsy · 1 de diciembre de 2025

  As early as 1949, Weaver defined communication in a very broad sense to include all procedures by which one mind or technical system can influence another, thus establishing the idea of semantic communication. With the recent success of machine learning in expert assistance systems where sensed info…

• Probing the Critical Point (CritPt) of AI Reasoning: a Frontier Physics Research Benchmark

  Minhui Zhu, Minyang Tian, Xiaocheng Yang, Tianci Zhou, Lifan Yuan, Penghao Zhu, Eli Chertkov, Shengyan Liu, Yufeng Du, Ziming Ji, Indranil Das, Junyi Cao, Yufeng Du, Jiabin Yu, Peixue Wu, Jinchen He, Yifan Su, Yikun Jiang, Yujie Zhang, Chang Liu, Ze-Min Huang, Weizhen Jia, Yunkai Wang, Farshid Jafarpour, Yong Zhao, Xinan Chen, Jessie Shelton, Aaron W. Young, John Bartolotta, Wenchao Xu, Yue Sun, Anjun Chu, Victor Colussi, Chris Akers, Nathan Brooks, Wenbo Fu, Jinchao Zhao, Marvin Qi, Anqi Mu, Yubo Yang, Allen Zang, Yang Lyu, Peizhi Mai, Christopher Wilson, Xuefei Guo, Juntai Zhou, Daniel Inafuku, Chi Xue, Luyu Gao, Ze Yang, Ya\"ir Hein, Yonatan Kahn, Kevin Zhou, Di Luo, John Drew Wilson, Jarrod T. Reilly, Dmytro Bandak, Ofir Press, Liang Yang, Xueying Wang, Hao Tong, Nicolas Chia, Eliu Huerta, Hao Peng · 21 de noviembre de 2025

  While large language models (LLMs) with reasoning capabilities are progressing rapidly on high-school math competitions and coding, can they reason effectively through complex, open-ended challenges found in frontier physics research? And crucially, what kinds of reasoning tasks do physicists want L…