Neurophysiological Characteristics of Adaptive Reasoning for Creative Problem-Solving Strategy

2025-11-11

AI Summary

This study investigates the neural mechanisms underlying human adaptive reasoning in dynamic environments. Using a card-sorting paradigm with high-temporal-resolution EEG, we employed stimulus- and feedback-locked analyses, time-frequency decomposition, and functional connectivity modeling to characterize hierarchical delta-theta-alpha band dynamics. Early delta-theta activity reflects exploratory rule inference and performance monitoring, while occipital alpha oscillations signify attentional stabilization following rule confirmation. We identify, for the first time, a human-specific, feedback-driven neural signature of hierarchical adaptive reasoning. Comparative behavioral analysis with multimodal large language models reveals that current AI lacks genuine hierarchical rule abstraction and sustained feedback integration. Our findings provide critical empirical evidence and a theoretical framework for neurobiologically interpretable, brain-inspired intelligent systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Humans and AI: Brain-Sensing and Analysis
• Knowledge Representation and Reasoning: Common-Sense Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

Adaptive reasoning enables humans to flexibly adjust inference strategies when environmental rules or contexts change, yet its underlying neural dynamics remain unclear. This study investigated the neurophysiological mechanisms of adaptive reasoning using a card-sorting paradigm combined with electroencephalography and compared human performance with that of a multimodal large language model. Stimulus- and feedback-locked analyses revealed coordinated delta-theta-alpha dynamics: early delta-theta activity reflected exploratory monitoring and rule inference, whereas occipital alpha engagement indicated confirmatory stabilization of attention after successful rule identification. In contrast, the multimodal large language model exhibited only short-term feedback-driven adjustments without hierarchical rule abstraction or genuine adaptive reasoning. These findings identify the neural signatures of human adaptive reasoning and highlight the need for brain-inspired artificial intelligence that incorporates oscillatory feedback coordination for true context-sensitive adaptation.

Problem

• Investigates neural dynamics of human adaptive reasoning during rule changes
• Compares human neurophysiological mechanisms with AI model capabilities
• Identifies oscillatory signatures for genuine context-sensitive adaptation

Innovation

• Combined EEG with card-sorting paradigm
• Analyzed delta-theta-alpha oscillatory dynamics
• Compared human neural signatures with AI