The CognextAI Imperative: Charting the Cognitive Transition from Biological to Artificial, Hybrid, and Emerging Intelligence

1. Introduction: The Cognitive Crossroads — When Intelligence Meets Its Own Creation

Humanity stands at a cognitive crossroads unprecedented in its intellectual history. For millennia, the study of cognition was the exclusive province of philosophy and, more recently, psychology and neuroscience—disciplines focused on understanding the biological brain and its remarkable capacities for perception, memory, reasoning, and consciousness. The emergence of artificial intelligence (AI) in the mid-twentieth century introduced a new kind of cognitive agent: one that is silicon-based, algorithmically driven, and capable of feats of computation that vastly exceed human capabilities in specific domains.

The past decade has witnessed an acceleration of this trajectory that few could have predicted. Deep learning, large language models, and increasingly autonomous AI systems are no longer mere tools but active participants in cognitive processes. They generate text, create art, make decisions, and even engage in what appears to be reasoning. The boundaries between biological and artificial cognition are blurring, and with them, the very definition of intelligence. This is not merely a technological development; it is a cognitive transformation that challenges the foundations of how we understand knowing, thinking, and being intelligent (CognextAI, 2026).

The inaugural volume of CognextAI — Journal of Cognition, Artificial and Emerging Intelligence arrives at this critical juncture, dedicated to investigating the complex, multifaceted relationship between biological cognition and artificial intelligence. The journal's name is deliberately constructed to capture its intellectual mission: "Cog" evokes cognition, knowing, and cognitive processes; "Next" signals a transition—from biological cognition toward technologically mediated, hybrid, distributed, artificial, or otherwise emergent cognitive configurations; and "AI" completes this conceptual transformation, understanding artificial intelligence not merely as a technological domain but as a potential modifier of cognition itself (CognextAI, 2026).

This editorial sets forth the journal's vision: to serve as the premier scholarly platform for research exploring how biological and artificial intelligence interact, converge, diverge, and generate new forms of cognition and intelligence. The journal does not merely ask "What is intelligence?" but rather "How is intelligence being transformed, and what are the implications of this transformation for science, society, and humanity?" As the journal's foundational statement emphasizes, CognextAI is not simply "cognition plus artificial intelligence"—it represents the possibility of a transition from biological cognition toward technologically mediated, hybrid, distributed, artificial, or otherwise emergent cognitive configurations (CognextAI, 2026).

2. The Conceptual Architecture of CognextAI: Deconstructing the Name

The journal's name is not a mere label but an intellectual statement that embodies its core mission. Understanding its conceptual architecture is essential for appreciating the journal's unique contribution.

2.1 "Cog": The Domain of Cognition

The first component, "Cog," evokes cognition, cognitive, cognize, and the broader concept of knowing. It encompasses the processes through which information is perceived, represented, learned, remembered, interpreted, integrated, reasoned about, and transformed into knowledge or action. This is the traditional domain of cognitive science, psychology, neuroscience, and philosophy of mind. CognextAI is deliberately positioned within this unresolved conceptual territory, recognizing that cognition remains a contested and evolving concept.

Cognition is not a monolithic phenomenon. It encompasses perception, memory, reasoning, decision-making, language, and emotion. It operates at multiple levels, from neural processes to conscious experience. It is shaped by embodiment, culture, and environment. The study of cognition has revealed its remarkable complexity and diversity, challenging any simple definition.

2.2 "Next": The Temporal and Conceptual Transition

The "Next" component marks both temporal and conceptual movement. It signals that we are not merely documenting cognition as it has been understood, but rather investigating what cognition is becoming. The "Next" implies transition—from biological cognition toward technologically mediated, hybrid, distributed, artificial, or otherwise emergent cognitive configurations. This transition is not guaranteed to be progressive; it is a transformation that must be investigated empirically and critically.

The journal deliberately avoids treating this transition as synonymous with progress. The emergence of a new cognitive form does not necessarily mean the emergence of a better cognitive form. As the journal's foundational text emphasizes, "CogNextAI therefore does not assume progress; it assumes change, and change must be investigated empirically" (CognextAI, 2026). This critical stance distinguishes the journal from purely techno-optimistic publications and positions it as a platform for rigorous, evidence-based inquiry.

2.3 "AI": The Transformative Force

The addition of "AI" completes this conceptual transformation. Artificial intelligence is understood not merely as a technological domain but as a potential modifier of cognition itself. AI systems are not neutral tools; they actively shape how we think, learn, and know. They filter information, suggest answers, and influence decisions. They extend our cognitive capacities while also constraining them.

2.4 Evolutionary Perspective

This perspective also places CognextAI at the intersection of cognitive science and evolutionary theory. Human cognition is the outcome of biological evolution and has been shaped by constraints related to embodiment, metabolism, survival, reproduction, development, social interaction, and environmental adaptation. CognextAI therefore considers evolutionary deviation and maladaptation legitimate subjects of scientific investigation.

The evolution of cognition is not a linear progression toward an ideal form. It is a branching, contingent process shaped by multiple factors. The emergence of AI represents a new branch in this evolutionary trajectory, one that may lead to forms of intelligence that are radically different from biological cognition. Understanding this evolution requires a broad interdisciplinary perspective that integrates cognitive science, evolutionary biology, and artificial intelligence.

3. The Intellectual Landscape: From Classical Cognitivism to Emergent Intelligence

The study of cognition has undergone several paradigm shifts over the past century. Understanding these shifts is essential for appreciating the current intellectual landscape and the unique contribution of CognextAI.

3.1 The Classical Cognitivist Paradigm

Classical cognitivism, dominant from the 1950s through the 1980s, conceptualized the mind as an information-processing system, analogous to a computer. Cognition was understood as symbol manipulation, with mental representations, rules, and algorithms as the central explanatory constructs. This paradigm was deeply influential in both psychology and the early development of artificial intelligence, as researchers sought to model human reasoning with computer programs.

The classical cognitivist paradigm made significant contributions, including the development of computational models of cognition, the identification of cognitive heuristics and biases, and the establishment of cognitive science as an interdisciplinary field. However, it faced significant challenges. It struggled to account for the embodied, situated, and dynamic nature of human cognition. It also proved difficult to scale symbolic AI to handle the messy, context-dependent complexity of real-world perception and action.

3.2 The Embodied and Enactive Turn

The embodied and enactive approaches to cognition, emerging in the 1980s and 1990s, offered a radical alternative. These approaches argued that cognition is not merely computational but is shaped by the body, the environment, and the organism's active engagement with the world. Cognition was re-conceptualized as sense-making, as the organism generates meaning through its interactions with its environment.

This shift had profound implications for our understanding of intelligence. It suggested that intelligence is not a property of the brain alone but emerges from the dynamic coupling of brain, body, and environment. It also highlighted the importance of values, emotions, and embodiment in cognition, challenging the reductionist view of intelligence as pure rationality. The embodied approach has influenced robotics, human-computer interaction, and the development of AI systems that interact with the physical world.

3.3 The Rise of Artificial Intelligence

The development of artificial intelligence has followed its own trajectory, moving from symbolic approaches to connectionist and, more recently, deep learning architectures. The current generation of AI systems, particularly large language models, have achieved remarkable capabilities, including fluent language generation, text summarization, and even creative writing. These systems are not explicitly programmed with rules but learn from vast datasets, developing complex statistical representations of language and knowledge.

However, AI systems remain fundamentally different from biological intelligence. They lack embodiment, emotions, consciousness, and the deep integration with the environment that characterizes human cognition. They are also prone to significant errors, biases, and hallucinations, raising questions about their reliability and trustworthiness. The limitations of current AI systems highlight the unique characteristics of biological cognition and the challenges of replicating it artificially.

3.4 The Emergence of Hybrid and Distributed Cognition

The most recent development is the emergence of hybrid systems that integrate biological and artificial intelligence. These range from brain-computer interfaces to AI-assisted decision-making systems to the simple fact that humans increasingly use AI tools to augment their cognitive processes. Hybrid cognition is not a simple addition of AI capabilities to human capabilities but a transformation of cognition itself, as human and machine intelligence co-evolve and co-construct meaning.

Distributed cognition, a related concept, emphasizes that cognitive processes are distributed across people, artifacts, and environments. In the context of AI, distributed cognition implies that intelligence is not located in any single agent but emerges from the interactions among multiple agents—human, artificial, and environmental.

4. The Interdisciplinary Imperative: Integrating Disparate Fields

The study of cognition and artificial intelligence is inherently interdisciplinary. It requires integration of:

• Cognitive science: understanding the nature of mental processes, including perception, memory, reasoning, and decision-making.
• Neuroscience: investigating the neural substrates of cognition and the mechanisms of brain function.
• Artificial intelligence: developing algorithms, models, and systems that exhibit intelligent behavior.
• Machine learning: creating systems that learn from data and experience.
• Computational neuroscience: building computational models of neural systems.
• Psychology: studying cognitive processes in humans and other animals.
• Philosophy of mind: examining the nature of consciousness, intentionality, and mental representation.
• Neuropsychology: exploring the relationship between brain structure and cognitive function.
• Human-AI interaction: designing and evaluating interfaces that support effective collaboration between humans and AI.
• Neurotechnology: developing technologies that interface with the nervous system.
• Consciousness studies: investigating the nature of subjective experience.
• Evolutionary cognition: understanding the evolutionary origins of cognitive capacities.
• Ethics and policy: examining the societal implications of AI and emerging cognitive technologies.
• Epistemology: investigating the nature of knowledge, justification, and belief in the context of AI.
• Anthropology and sociology: examining how AI shapes and is shaped by human culture and social structures.

5. Key Research Themes and Emerging Questions

The journal invites research on a wide range of themes at the intersection of cognition, artificial intelligence, and emerging intelligence. Key areas of investigation include:

5.1 The Nature of Intelligence

What is intelligence? Is it a single, unitary capacity, or is it a diverse set of capacities? How does biological intelligence differ from artificial intelligence, and are these differences fundamental or merely contingent? Is there a unified framework that can account for both biological and artificial intelligence?

These questions are not merely semantic but have profound implications for how we develop AI systems and assess their capabilities. If intelligence is fundamentally embodied and embedded in a living organism, then artificial intelligence may never achieve true intelligence. Conversely, if intelligence is essentially information processing, then AI systems may eventually surpass human capabilities. The journal encourages research that examines these foundational questions from multiple perspectives.

5.2 The Convergence and Divergence of Biological and Artificial Intelligence

What are the points of convergence between biological and artificial intelligence? Both can learn, adapt, and make decisions. Both can process information and generate outputs. Both can exhibit behaviors that appear intelligent.

However, there are also significant divergences. Biological intelligence is embodied, emotional, conscious, and shaped by evolutionary history. Artificial intelligence is typically disembodied, lacking emotions and consciousness, and designed for specific tasks. Will these divergences persist, or will AI systems eventually acquire the characteristics of biological intelligence? Could biological intelligence be enhanced by AI, leading to new forms of hybrid cognition? The journal encourages research that explores both the convergences and divergences between biological and artificial intelligence.

5.3 Hybrid Intelligence and Human-AI Collaboration

One of the most promising and consequential developments is the emergence of hybrid intelligence—systems that integrate human and AI capabilities to achieve outcomes that neither could achieve alone. Hybrid intelligence takes many forms, from AI assistants that support human decision-making to brain-computer interfaces that enable direct neural communication with AI.

Research on hybrid intelligence addresses questions such as: How can human and AI capabilities be optimally integrated? What are the cognitive, social, and ethical implications of hybrid intelligence? How do humans adapt to working with AI, and how do AI systems adapt to working with humans? What is the role of trust, transparency, and explainability in human-AI collaboration? The journal encourages research that explores both the technical and human dimensions of hybrid intelligence.

5.4 Emerging Forms of Intelligence

The term "emerging intelligence" captures the possibility that new forms of intelligence may arise from the interaction of biological and artificial systems, from the development of AI systems that surpass human intelligence, or from the emergence of AI systems with novel cognitive capacities.

Potential emerging forms of intelligence include: collective intelligence (the intelligence of groups, enhanced by AI), swarm intelligence (the decentralized intelligence of multiple agents), AI consciousness (whether AI systems could become conscious), and post-human intelligence (intelligence that transcends human cognitive limitations). These possibilities raise profound questions about the future of intelligence and humanity's place in the cognitive landscape. The journal encourages research that is speculative, visionary, and forward-looking, as well as research that is grounded in current empirical evidence.

5.5 The Epistemological Implications of AI

The increasing use of AI raises fundamental epistemological questions. How can we know whether AI-generated knowledge is reliable? What is the epistemic status of AI-generated content? How do AI systems shape our understanding of the world, and what are the implications of this for science, education, and society?

These questions are particularly pressing in the context of large language models, which can generate highly coherent and plausible-sounding text that may be factually inaccurate. The epistemology of AI is not merely a philosophical issue but has practical implications for how we evaluate, trust, and use AI-generated information. The journal encourages research that examines these epistemological questions from multiple disciplinary perspectives.

5.6 Ethical and Societal Implications

The ethical implications of AI and emerging cognitive technologies are extensive. They include: privacy, surveillance, autonomy, accountability, bias, fairness, transparency, and the potential for misuse. The development of increasingly capable AI systems raises concerns about job displacement, economic inequality, and the concentration of power.

The ethical issues are not limited to the risks of AI but also include the potential benefits: improved healthcare, education, scientific discovery, and environmental management. How can we maximize the benefits while minimizing the risks? What governance structures are needed to ensure that AI is developed and used responsibly? The journal encourages research that examines both the risks and benefits of AI from ethical, legal, and policy perspectives.

5.7 Consciousness and Subjectivity

The question of AI consciousness is one of the most debated and contentious issues in the field. Could AI systems be conscious, and if so, how would we know? What is the relationship between consciousness and intelligence? Does consciousness require biological embodiment, or could it emerge in non-biological systems?

These questions are not merely academic; they have profound implications for how we treat AI systems. If AI systems could be conscious, they would have moral status, and we would owe them ethical consideration. The question of AI consciousness also bears on the nature of intelligence itself, suggesting that intelligence is not simply a matter of information processing but involves subjective experience. The journal encourages research that examines consciousness from multiple disciplinary perspectives, including neuroscience, philosophy, and AI.

5.8 The Evolution of Cognition

Cognition has evolved over billions of years, from simple bacteria to complex human intelligence. The emergence of AI represents a new phase in this evolutionary trajectory, potentially leading to forms of intelligence that are radically different from biological intelligence.

Understanding the evolution of cognition can inform the development of AI. Evolutionary principles such as adaptation, selection, and exaptation may provide insights into how intelligence emerges and how it can be designed. Conversely, AI may provide new models for understanding biological cognition, enabling new insights into human intelligence. The journal encourages research that explores the evolutionary dimensions of cognition and AI.

5.9 Cognitive Deviation and Maladaptation

As the journal's foundational text emphasizes, "CogNextAI therefore considers evolutionary deviation and maladaptation legitimate subjects of scientific investigation" (CognextAI, 2026). The emergence of AI may lead to cognitive changes that are not necessarily adaptive or beneficial. These changes may include cognitive biases, dependencies on AI systems, and the erosion of certain cognitive capacities.

6. The Journal's Vision: Advancing Knowledge at the Intersection

CognextAI is dedicated to advancing scientific understanding at the intersection of cognition, artificial intelligence, and emerging intelligence. Its vision is guided by several core principles:

6.1 Interdisciplinary Integration

The journal recognizes that the study of intelligence cannot be confined to any single discipline. It actively promotes interdisciplinary dialogue and welcomes contributions that integrate insights from multiple fields. This integration is essential for addressing the complex, multifaceted questions that arise at the intersection of cognition and AI. The journal is positioned at the convergence of cognitive science, neuroscience, artificial intelligence, philosophy of mind, and emerging technologies (CognextAI, 2026).

6.2 Rigorous Peer Review and Open Access

The journal operates on a rigorous peer-review model, ensuring the quality and integrity of published research. It is open access, making its content freely available to researchers, policymakers, and the public worldwide. This accessibility is crucial for accelerating the dissemination of knowledge and fostering global collaboration.

6.3 Continuous Publication

The journal operates on a continuous publication model, meaning that accepted articles are published online without delay (CognextAI, 2026). This approach accelerates the dissemination of research and ensures that new findings are available as soon as they are ready.

6.4 Forward-Looking Perspective

The journal is committed to exploring the future of intelligence. It welcomes research that is speculative, visionary, and forward-looking, as well as research that is grounded in current empirical evidence. The journal recognizes that the most important questions about intelligence may not yet have been formulated and that a commitment to exploration and discovery is essential.

6.5 Critical Stance

The journal maintains a critical stance toward technological determinism and simplistic narratives of progress. It encourages research that examines the complex, contingent, and often unpredictable ways in which AI and emerging technologies shape cognition and society.

7. Ethical Imperatives and Societal Responsibility

The development of AI and emerging cognitive technologies is not a purely technical endeavor; it is a deeply ethical and societal one. CognextAI is committed to addressing these ethical dimensions and to promoting responsible research and innovation.

7.1 Responsible AI Development

The journal encourages research that considers the ethical implications of AI systems and promotes responsible development. This includes research on fairness, accountability, transparency, and explainability. It also includes research on the societal impacts of AI, such as job displacement, economic inequality, and the concentration of power.

The journal recognizes that AI is not value-neutral but reflects the values, biases, and assumptions of its creators. Ensuring that AI systems are fair, just, and beneficial to all is a critical challenge that requires interdisciplinary collaboration. The journal encourages research that examines these ethical dimensions from multiple perspectives.

7.2 Human-Centered AI

The journal promotes a human-centered approach to AI, which emphasizes the importance of designing AI systems that enhance human well-being and flourishing. This includes a focus on human-AI collaboration, trust, and usability. It also includes a commitment to ensuring that AI systems are accessible, inclusive, and respectful of human autonomy.

Human-centered AI recognizes that technology should serve human needs and values, not the reverse. It emphasizes the importance of designing AI systems that are understandable, controllable, and aligned with human goals. The journal encourages research that examines how AI can be designed and used to promote human flourishing.

7.3 The Role of Governance and Policy

The journal recognizes the importance of governance and policy in shaping the development and use of AI. It encourages research on policy frameworks, regulatory approaches, and governance structures that can ensure the responsible and beneficial development of AI.

7.4 Public Engagement and Education

The journal recognizes that the future of intelligence is a matter of public concern and that public engagement is essential for ensuring that AI is developed in a way that reflects societal values. The journal encourages research on public perceptions of AI, public engagement strategies, and educational approaches to AI literacy.

Public engagement and education are essential for building trust in AI and ensuring that its benefits are widely shared. The journal encourages research that examines how to communicate AI research to the public, how to engage diverse communities in AI governance, and how to educate the next generation about AI and its implications.

8. Conclusion: The Next Phase of Cognition

We are entering the next phase of cognition—a phase in which artificial intelligence is not merely a tool but a transformative force reshaping the nature of intelligence itself. This transformation is both exciting and daunting, filled with opportunities and challenges. It calls for a new kind of scholarship, one that is interdisciplinary, forward-looking, and deeply engaged with the ethical and societal implications of new technologies.

CognextAI — Journal of Cognition, Artificial and Emerging Intelligence is dedicated to this scholarship. We welcome contributions from researchers, scientists, clinicians, and scholars across all relevant fields. We invite you to join us in exploring the convergence, divergence, and co-evolution of biological and artificial intelligence, and in advancing a scientifically grounded and ethically responsible understanding of the future of intelligence.

The questions we face are profound: What is intelligence, and how is it being transformed? What are the implications of this transformation for science, society, and humanity? How can we ensure that the future of intelligence is beneficial, just, and meaningful?

These questions are not merely academic; they are the defining questions of our era. The answers will shape the future of humanity and the nature of intelligence itself. We invite you to contribute to this vital enterprise.

As the journal's foundational text reminds us, CognextAI "is a name that invites inquiry into the next phase of cognition—a phase in which artificial intelligence is not merely a tool but a transformative force. It is a name that refuses easy answers, resists technological determinism, and opens space for critical, empirical, and interdisciplinary investigation into what cognition is, what it is becoming, and what it might yet be" (CognextAI, 2026).