I would say that what humans do to learn has some elements of some machine learning approaches (Naive Bayes classifier comes to mind) on an unconscious level, but humans have a wild mix of different approaches to learning and even a single human employs many ways of capturing knowledge, and also, the imperfect and messy ways that humans capture and store knowledge is a critical feature of humanness.

I think we have to at least add the capacity to create links that were not learned through reasoning.

The difference in people is that our brains are continuously learning and LLMs are a static state model after being trained. To take your example about brute forcing more data, we've been doing that the second we were born. Every moment of every second we've had sound, light, taste, noises, feelings, etc, bombarding us nonstop. And our brains have astonishing storage capacity. AND our neurons function as both memory and processor (a holy grail in computing).

Sure, we have a ton of advantages on the hardware/wetware side of things. Okay, and technically the data-side also, but the idea of us learning from fewer examples isn't exactly right. Even a 5 year old child has "trained" far longer than probably all other major LLMs being used right now combined.