Understanding Creativity Across Brains and Machines Through the GEEC Framework

Abstract

Creativityis often viewed as one of the defining hallmarks of intelligence, yet the mechanisms underlying creative behavior remain poorly understood. This question has become increasingly important as artificial intelligence systems demonstrate remarkable abilities to generate novel text, images, music, and code, raising the possibility of more general forms of machine creativity and, ultimately, Artificial General Intelligence (AGI)—the capacity for flexible intelligence across diverse tasks and domains. Although creativity is frequently treated as a singular capability, evidence from cognitive neuroscience suggests that it instead emerges from interactions among multiple brain systems responsible for generative, evaluative, and exploratory processes regulated by coordination. In this paper, we propose the Generative–Evaluative–Exploratory–Coordination (GEEC) framework, a neuroscience-inspired functional framework that conceptualizes creativity as an emergent property of three primary creative processes—generation, evaluation, and exploration—whose interactions are dynamically regulated by a higher-order coordination process. Generative processes produce novel ideas through imagination and associative recombination; evaluative processes assess their utility and coherence; exploratory processes drive curiosity and learning under uncertainty; and coordination is conceptualized as a meta-process that regulates transitions among the other components. We review the neural mechanisms underlying these functions and examine how they have informed modern computational approaches, including generative models, reinforcement learning, meta-learning, neuro-symbolic systems, and cognitive architectures. Rather than viewing neuroscience as a roadmap to AGI, we argue that it provides a source of hypotheses about the computational principles that support creative behavior. The GEEC framework emphasizes functional correspondences rather than mechanistic equivalence and proposes that creativity is inherently multicomponent, emerging from dynamic interactions among generative, evaluative, and exploratory processes regulated through coordination. By synthesizing perspectives from neuroscience, cognitive science, and artificial intelligence, GEEC offers a unifying framework for understanding creativity across brains and machines.