4 claims in “neuroscience, cognitive science”
Predictive coding—the theory that the brain predicts sensory input and adjusts predictions based on prediction errors—is a plausible computational model for how biological systems learn, and self-supervised learning in AI mirrors this fundamental learning mechanism.
No localized 'IQ gland' or anatomical structure exists in the brain that, if removed or damaged, would eliminate fluid intelligence, because fluid intelligence is not a unified property but rather the recombination of many learned specialized solutions.
The brain operates via a perception-action cycle where it perceives environmental data, formulates a hypothesis or prediction based on that data, and when predictions are wrong, uses the new data to refine future predictions—this is how natural learning works and mirrors the scientific method.
David Marr's three-level theory of understanding (computational theory: what is being computed and why; algorithm: how is it computed; implementation: how is it physically instantiated) is crucial for neuroscience; without knowing what the brain computes and why, examining its architecture and chemistry cannot yield understanding