Bridging Minds and Machines: Toward an Integration of AI and Cognitive Science

• URL: http://arxiv.org/abs/2508.20674v1
• Date: Thu, 28 Aug 2025 11:26:17 GMT
• Title: Bridging Minds and Machines: Toward an Integration of AI and Cognitive Science
• Authors: Rui Mao, Qian Liu, Xiao Li, Erik Cambria, Amir Hussain,
• Abstract summary: Cognitive Science has profoundly shaped disciplines such as Artificial Intelligence (AI), Philosophy, Psychology, Neuroscience, Linguistics, and Culture.<n>Many breakthroughs in AI trace their roots to cognitive theories, while AI itself has become an indispensable tool for advancing cognitive research.<n>We argue that the future of AI within Cognitive Science lies not only in improving performance but also in constructing systems that deepen our understanding of the human mind.
• Score: 48.38628297686686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cognitive Science has profoundly shaped disciplines such as Artificial Intelligence (AI), Philosophy, Psychology, Neuroscience, Linguistics, and Culture. Many breakthroughs in AI trace their roots to cognitive theories, while AI itself has become an indispensable tool for advancing cognitive research. This reciprocal relationship motivates a comprehensive review of the intersections between AI and Cognitive Science. By synthesizing key contributions from both perspectives, we observe that AI progress has largely emphasized practical task performance, whereas its cognitive foundations remain conceptually fragmented. We argue that the future of AI within Cognitive Science lies not only in improving performance but also in constructing systems that deepen our understanding of the human mind. Promising directions include aligning AI behaviors with cognitive frameworks, situating AI in embodiment and culture, developing personalized cognitive models, and rethinking AI ethics through cognitive co-evaluation.

Related papers

• Who Is Doing the Thinking? AI as a Dynamic Cognitive Partner: A Learner-Informed Framework [0.0]
  This study proposes a framework positioning AI as a dynamic cognitive partner whose function shifts across learning situations.<n>We identified nine interrelated dimensions through which learners described AI as partnering with their cognition.<n>Students distinguished between productive support that extends understanding and unproductive reliance that replaces cognitive effort, indicating situational awareness of when AI should and should not be used.
  arXiv  Detail & Related papers  (2026-02-17T15:07:06Z)
• AI Awareness [8.537898577659401]
  We look at the emerging landscape of AI awareness, which includes metacognition, self-awareness, social awareness, and situational awareness.<n>We discuss the risks associated with AI awareness, including key topics in AI safety, alignment, and broader ethical concerns.
  arXiv  Detail & Related papers  (2025-04-25T16:03:50Z)
• Aligning Generalisation Between Humans and Machines [74.120848518198]
  AI technology can support humans in scientific discovery and forming decisions, but may also disrupt democracies and target individuals.<n>The responsible use of AI and its participation in human-AI teams increasingly shows the need for AI alignment.<n>A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise.
  arXiv  Detail & Related papers  (2024-11-23T18:36:07Z)
• Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
  We examine what is known about human wisdom and sketch a vision of its AI counterpart.<n>We argue that AI systems particularly struggle with metacognition.<n>We discuss how wise AI might be benchmarked, trained, and implemented.
  arXiv  Detail & Related papers  (2024-11-04T18:10:10Z)
• Is artificial consciousness achievable? Lessons from the human brain [0.0]
  We analyse the question of developing artificial consciousness from an evolutionary perspective. We take the evolution of the human brain and its relation with consciousness as a reference model. We propose to clearly specify what is common and what differs in AI conscious processing from full human conscious experience.
  arXiv  Detail & Related papers  (2024-04-18T12:59:44Z)
• The Interplay of Learning, Analytics, and Artificial Intelligence in Education: A Vision for Hybrid Intelligence [0.45207442500313766]
  I challenge the prevalent narrow conceptualisation of AI as tools, and argue for the importance of alternative conceptualisations of AI. I highlight the differences between human intelligence and artificial information processing, and posit that AI can also serve as an instrument for understanding human learning. The paper presents three unique conceptualisations of AI: the externalization of human cognition, the internalization of AI models to influence human mental models, and the extension of human cognition via tightly coupled human-AI hybrid intelligence systems.
  arXiv  Detail & Related papers  (2024-03-24T10:07:46Z)
• Advancing Explainable AI Toward Human-Like Intelligence: Forging the Path to Artificial Brain [0.7770029179741429]
  The intersection of Artificial Intelligence (AI) and neuroscience in Explainable AI (XAI) is pivotal for enhancing transparency and interpretability in complex decision-making processes. This paper explores the evolution of XAI methodologies, ranging from feature-based to human-centric approaches. The challenges in achieving explainability in generative models, ensuring responsible AI practices, and addressing ethical implications are discussed.
  arXiv  Detail & Related papers  (2024-02-07T14:09:11Z)
• AI for Mathematics: A Cognitive Science Perspective [86.02346372284292]
  Mathematics is one of the most powerful conceptual systems developed and used by the human species. Rapid progress in AI, particularly propelled by advances in large language models (LLMs), has sparked renewed, widespread interest in building such systems.
  arXiv  Detail & Related papers  (2023-10-19T02:00:31Z)
• Advancing Perception in Artificial Intelligence through Principles of Cognitive Science [6.637438611344584]
  We focus on the cognitive functions of perception, which is the process of taking signals from one's surroundings as input, and processing them to understand the environment. We present a collection of methods in AI for researchers to build AI systems inspired by cognitive science.
  arXiv  Detail & Related papers  (2023-10-13T01:21:55Z)
• World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges [51.92834011423463]
  We focus on the two concepts of world models and predictive coding. In neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment.
  arXiv  Detail & Related papers  (2023-01-14T06:38:14Z)
• Trustworthy AI: A Computational Perspective [54.80482955088197]
  We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
  arXiv  Detail & Related papers  (2021-07-12T14:21:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.