About this Research Topic
The intersection of Artificial Intelligence (AI) and cognitive/behavioral neuroscience offers profound insights into the nature of human knowledge and thought processes. Increasingly sophisticated AI systems are not only modeling but also replicating complex cognitive/behavioral functions such as learning, memory, perception, and problem-solving. This convergence is advancing our understanding of cognitive/behavioral processes and how knowledge is structured and utilized in both humans and machines. As AI continues to evolve, it presents opportunities to refine theoretical frameworks in cognitive/behavioral neuroscience and to develop novel computational models that enhance our grasp of human cognition and behavior.
Such models may have far-reaching applications, such as:
• Improving the safety of AI as they become more human-like and compassionate with an emergent theory of mind, leading to increased public trust in AI.
• Develop better-mixed models that utilize both connectionist and symbolic approaches.
Improved chain of thought reasoning and network dynamics within AI may also lead to many benefits for humanity, as they may solve many urgent problems such as improved disease and mental health diagnostics, ideographic personalized treatment, drug discovery, fraud detection, risk management, efficient transportation such as autonomous vehicles, personalized learning, climate change modeling, efficient disaster response logistics, improved agriculture, and social services for vulnerable populations.
Particularly important within psychology, and computational psychiatry, are novel mental health diagnostics and personalized process-based treatment. AI may help facilitate real-time ideographic mental health diagnostics and form real-time evolving hypergraph models, that map ongoing cognitive/behavioral processes that lead to mental health problems.
The goal of this Research Topic is to address the intricate relationship between AI and cognitive/behavioral accounts of knowledge, reasoning, and theory of mind and how might these be potentially applied to any of the areas highlighted. Specifically, we aim to explore how AI can be used to model cognitive/behavioral processes, as well as how insights from cognitive/behavioral neuroscience can improve AI systems as they transition towards artificial general intelligence (AGI). This reciprocal relationship holds the potential to overcome existing limitations in both fields. By integrating AI with cognitive/behavioral neuroscience, researchers can develop more accurate and comprehensive models of human cognition and AI. This Research Topic seeks to bridge the gap between theoretical constructs and practical implementations, fostering a clearer understanding of how AI can both inform and be informed by cognitive/behavioral neuroscience.
We invite contributions that explore the following themes:
• AI models of cognitive/behavioral processes such as learning, perception, and memory
• Applications of cognitive/behavioral neuroscience findings to improve AI systems
• Comparative studies of human and AI problem-solving strategies
• AI-driven tools for cognitive/behavioral neuroscience research such as mental health diagnostic tools or other areas within society that require AI modeling
• Ethical considerations in the integration of AI with cognitive neuroscience such as trust in AI and safety
• How symbolic and connectionist approaches can be combined to lead to AGI, going beyond existing transformer LLM architecture.
• Exploring the next transformative step in LLM architecture such as knowledge modeling, relational framing, and categorization theory as applied to computational neuroscience
We are interested in a variety of manuscript types, including:
• Original Research articles providing empirical data
• Review articles synthesizing recent advancements
• Technology and code articles
• Hypothesis and Theory articles proposing new models or frameworks
• Perspective articles offering unique viewpoints
• Methods articles detailing novel techniques or tools
Contributors are encouraged to submit interdisciplinary work that unites computational neuroscience and cognitive/behavioral neuroscience, pushing the boundaries of how we understand and utilize AI in the context of human knowledge, reasoning, learning, and theory of mind.
Such models may have far-reaching applications, such as:
• Improving the safety of AI as they become more human-like and compassionate with an emergent theory of mind, leading to increased public trust in AI.
• Develop better-mixed models that utilize both connectionist and symbolic approaches.
Improved chain of thought reasoning and network dynamics within AI may also lead to many benefits for humanity, as they may solve many urgent problems such as improved disease and mental health diagnostics, ideographic personalized treatment, drug discovery, fraud detection, risk management, efficient transportation such as autonomous vehicles, personalized learning, climate change modeling, efficient disaster response logistics, improved agriculture, and social services for vulnerable populations.
Particularly important within psychology, and computational psychiatry, are novel mental health diagnostics and personalized process-based treatment. AI may help facilitate real-time ideographic mental health diagnostics and form real-time evolving hypergraph models, that map ongoing cognitive/behavioral processes that lead to mental health problems.
The goal of this Research Topic is to address the intricate relationship between AI and cognitive/behavioral accounts of knowledge, reasoning, and theory of mind and how might these be potentially applied to any of the areas highlighted. Specifically, we aim to explore how AI can be used to model cognitive/behavioral processes, as well as how insights from cognitive/behavioral neuroscience can improve AI systems as they transition towards artificial general intelligence (AGI). This reciprocal relationship holds the potential to overcome existing limitations in both fields. By integrating AI with cognitive/behavioral neuroscience, researchers can develop more accurate and comprehensive models of human cognition and AI. This Research Topic seeks to bridge the gap between theoretical constructs and practical implementations, fostering a clearer understanding of how AI can both inform and be informed by cognitive/behavioral neuroscience.
We invite contributions that explore the following themes:
• AI models of cognitive/behavioral processes such as learning, perception, and memory
• Applications of cognitive/behavioral neuroscience findings to improve AI systems
• Comparative studies of human and AI problem-solving strategies
• AI-driven tools for cognitive/behavioral neuroscience research such as mental health diagnostic tools or other areas within society that require AI modeling
• Ethical considerations in the integration of AI with cognitive neuroscience such as trust in AI and safety
• How symbolic and connectionist approaches can be combined to lead to AGI, going beyond existing transformer LLM architecture.
• Exploring the next transformative step in LLM architecture such as knowledge modeling, relational framing, and categorization theory as applied to computational neuroscience
We are interested in a variety of manuscript types, including:
• Original Research articles providing empirical data
• Review articles synthesizing recent advancements
• Technology and code articles
• Hypothesis and Theory articles proposing new models or frameworks
• Perspective articles offering unique viewpoints
• Methods articles detailing novel techniques or tools
Contributors are encouraged to submit interdisciplinary work that unites computational neuroscience and cognitive/behavioral neuroscience, pushing the boundaries of how we understand and utilize AI in the context of human knowledge, reasoning, learning, and theory of mind.
Keywords: Cognitive/Behavioral Neuroscience, Artificial Intelligence, Theory of Mind, Human-AI Interaction
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
