Neurophysiological Characteristics of Adaptive Reasoning for Creative Problem-Solving Strategy

TL;DR

This study uncovers neural oscillation patterns associated with human adaptive reasoning during creative problem-solving, contrasting it with AI models that lack hierarchical rule abstraction, emphasizing the importance of brain-inspired feedback mechanisms.

Contribution

It identifies specific neurophysiological signatures of adaptive reasoning and demonstrates the limitations of current large language models in replicating hierarchical rule inference.

Findings

Delta-theta-alpha dynamics linked to reasoning processes

Occipital alpha stabilizes attention after rule discovery

AI models lack hierarchical rule abstraction capabilities

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.