IEEE CogMI 2026 (IEEE International Conference on Cognitive Machine Intelligence) is an academic conference held in San Jose, California, USA on 2026-11-04. The paper submission deadline is 2026-08-15. Acceptance notifications are sent on 2026-09-20.
Cognitive machine intelligence (CogMI) takes up one of the central questions in modern AI research: what does it mean for an artificial system to perceive, reason, remember, decide, and communicate, and what can the cognitive, neural, and behavioral sciences tell us about how to build, study, and deploy such systems well? As AI becomes infrastructure for scientific discovery, healthcare, and the coordination of complex social and economic systems, the answers to these open questions have practical consequences. Making progress on them calls for work that is both technically rigorous and interdisciplinary.
The goal of the IEEE Conference on Cognitive Machine Intelligence (IEEE CogMI) is to create a forum for research that places cognition at the center of how we build, study, and deploy AI and machine learning. We welcome contributions that draw on theories of human cognition to inform the design of AI, that analyze cognitive-like phenomena in artificial systems, and that build AI capable of richer cognitive behavior. The conference is deliberately interdisciplinary, bringing together researchers and practitioners from computer science and engineering, psychology and neuroscience, the social and behavioral sciences, philosophy, and ethics, legal studies, and public policy. We are especially interested in work that treats AI as a partner to human judgment rather than a substitute for it, and that examines the conditions under which human–machine collaboration produces reliable and accountable systems.
List of Topics
Topics of interest include, but are not limited to:
Cognitive Foundations and Architectures
Cognitive architectures, computational models of mind, and dual-process (System 1/System 2) theories in AI, including agent architectures, models of executive function, adaptive behavior, bounded rationality, and developmental learning trajectories
Neuroscience-inspired AI (NeuroAI) and neuromorphic computing, including brain-inspired architectures, biologically plausible learning rules, neural coding principles, spiking neural networks, predictive coding, and free-energy frameworks
Memory, attention, and information processing in artificial systems, including memory-augmented networks, episodic replay, models of forgetting and consolidation, selective attention, cognitive load, information bottlenecks, chunking, and content-addressable memory
Reasoning and Knowledge
Commonsense, causal, and abstract reasoning, including analogical and relational reasoning, compositional generalization, spatial and temporal reasoning, counterfactual thinking, and reasoning under uncertainty and incomplete information
Neuro-symbolic AI and hybrid architectures, including integrating neural learning with symbolic knowledge representation, differentiable logic programming, neural theorem proving, and bridging perception, language, and formal reasoning
Knowledge representation, ontology, and conceptual structure, including concept formation and categorization, belief revision, epistemic reasoning, knowledge graphs, commonsense knowledge bases, and schema-based understanding of events
Probabilistic and Bayesian models of cognition, including probabilistic computing, Bayesian learning, rational analysis, reasoning under uncertainty, and probabilistic programming as cognitive modeling frameworks
Language, Communication, and Meaning
Large language and vision-language models as cognitive and linguistic objects of study, including what LLMs and VLLMs reveal and fail to reveal about human cognition, language, vision, and meaning; multimodal cognition and visual-linguistic binding; scaling laws and intelligence; in-context learning as cognitive flexibility; chain-of-thought as verbal reasoning
Natural language processing, generation, and speech through a cognitive lens, including computational psycholinguistics, models of language acquisition, semantic grounding and compositionality, pragmatics, discourse, text analytics, and speech recognition/generation
Dialogue systems, virtual agents, chatbots, and conversational AI, including communication and common ground theory, cooperative dialogue, audience design, perspective-taking, and interactive storytelling
Perception, Embodiment, and Action
Perception, grounding, and embodied cognition, including multimodal integration, language grounding to vision and robotics, affordance learning, intuitive physics, object permanence, active perception, and simulation-based understanding
Computer vision and image processing as cognitive perception, including visual reasoning, scene understanding, visual question answering, saliency, gaze modeling, and cognitive models of visual search and recognition
Cognitive robotics, autonomous systems, and human-robot interaction, including sensorimotor learning, cognitive motor planning, spatial navigation, embodied interaction, situated cognition in physical agents, and collaborative robot behavior
Learning and Adaptation
Machine learning, neural networks, and deep learning as models of cognition, including few-shot and meta-learning as rapid generalization, curriculum and continual learning, transfer and domain adaptation, inductive biases as cognitive constraints, and self-supervised learning as perceptual development
Reinforcement learning, planning, and goal-directed behavior, including model-based vs. model-free learning, hierarchical goal decomposition, world models, imagination and look-ahead, curiosity-driven exploration, and test-time adaptive reasoning
Learning with relational and graph-structured data, including graph neural networks for relational and structural cognition, knowledge organization, relational reasoning, and learning over interconnected conceptual structures
Social Cognition and Behavior
Theory of mind, social reasoning, and multi-agent cognition, including modeling beliefs, desires, and intentions of others; joint attention; cooperation and coordination; cultural transmission; negotiation; mechanism design; and algorithmic game theory
Social computing, computational social science, and social psychology of AI, including modeling collective behavior and social dynamics, group decision-making, social norms, social influence, opinion formation, and AI-mediated social interaction
Affective computing, emotion, and behavioral science, including emotion recognition and modeling, sentiment analysis, empathy in AI, behavioral models of decision-making, heuristics and biases, and the cognitive-behavioral foundations of human-AI systems
Creativity and Imagination
Computational creativity, imagination, and generative cognition, including AI for art, music, design, and scientific ideation; divergent thinking; conceptual blending; generative models as accounts of imagination; evaluating novelty and meaning; and ethical issues of creative AI (authorship, appropriation)
Human-AI Interaction and Trust
Human-AI collaboration and mixed-initiative systems, including augmented cognition, human-in-the-loop learning, cognitive aspects of shared decision-making, adaptive interfaces, designing for appropriate mental models of AI, and cognitive workload in human-AI teams
Trust, reliance, and mental models of intelligent machines, including cognitive foundations of trust calibration, over-reliance and under-reliance, transparency as a trust mechanism, and building/maintaining trust in AI across contexts
Explainability, Safety, and Alignment
Explainability, interpretability, and transparency, including cognitive models of explanation, XAI methods (feature attribution, concept-based, natural language), probing neural representations, attention visualization, argumentative explanations, and human evaluation of explanations
AI safety, value alignment, and responsible AI, including bias, fairness, and equity through cognitive and social science lenses; reward modeling and preference specification; adversarial robustness; moral reasoning; accountability; and the cognitive science of AI risk perception
Privacy, security, and adversarial machine learning, including privacy-preserving AI, adversarial resilience as cognitive reliability, deceptive alignment, and the societal-cognitive impacts of surveillance and data practices
Evaluation, Philosophy, and Societal Impact
Cognitive benchmarking and psychometric evaluation of AI, including testing AI on human cognitive tasks, comparing human and machine reasoning/perception/language, behavioral experiments with AI, compositionality and systematicity metrics, and evaluation methodology for cognitive AI
Consciousness, metacognition, and self-awareness in AI, including introspection, confidence calibration, self-monitoring and self-correction, computational theories of consciousness, self-models, and philosophical questions about machine understanding
Philosophy of AI, ethics, and the nature of intelligence, including the grounding problem, narrow vs. general intelligence, moral status of cognitive AI, governance and regulation, and the distinction between understanding and pattern matching
Societal and cognitive impacts of AI, including effects on human attention, memory, skill, and autonomy; AI in education, health, science, business, and public welfare; cognitive ergonomics of AI-generated content; and responsible deployment across application domains
Cross-Cutting and Emerging
Emerging frontiers in cognitive AI, including agentic AI and autonomous tool use, foundation models as cognitive models, retrieval-augmented cognition, multi-agent cognitive ecosystems, AI for scientific discovery, developmental AI, world models as internal simulators, synthetic data as cognitive rehearsal, and AI-assisted cognitive science
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