Brain-AI convergence: Generative world models and hierarchical attention for human intelligence

Recent advances in general-purpose AI provide new insights into how the neocortex and cerebellum, despite their uniform circuit architectures, support diverse functions and human intelligence. Beyond the traditional focus on visual processing, this paper offers a cross-domain comparison of the brain and AI through the lens of world-model-based computation. We argue that both the neocortex and cerebellum predict future world states from past inputs and construct predictive world models through prediction-error learning. These predictive world models are repurposed for sensory comprehension and motor output generation, thereby supporting multi-domain capabilities. Underlying these capabilities and even human-like adaptive intelligence, the neocortex implements hierarchical attention-based processing. Interestingly, autoregressive generative models in transformer-based AI have independently converged on similar computational principles. Together, these shared mechanisms suggest a common computational foundation through which uniform circuits can support cross-domain high-level intelligence in both biological and artificial systems.