Ah yes, I phrased that very poorly, sorry. So from the hyperprior page on wikipedia, “use of a hyperprior allows one to express uncertainty in a hyperparameter: taking a fixed prior is an assumption, varying a hyperparameter of the prior allows one to do sensitivity analysis on this assumption, and taking a distribution on this hyperparameter allows one to express uncertainty in this assumption”
So the thing that is being learned in the latter case that’s not in the former is the distribution of likely parameter values, or the uncertainty around those values in the population. The parameter values can change because they can be drawn from a distribution of values, rather than being fixed to the most likely value