Recently, the field of meta-learning, often described as “learning how to learn", has been gaining significant research attention. Its generalization is given by higher-order meta-learning, where multiple levels (orders) of meta-parameters are stacked, leading to systems that learn how to learn how to learn, and so on. Higher-order mutation rates represent an instance of this paradigm used within evolutionary computation. Under such a scheme, the mutation rate of the k-th order mutation rate is determined by the \((k+1)\) -th order mutation rate, and so forth, continuing up to the top order n. In the self-referential variant, the top meta-mutation rate is mutated by itself, thereby removing the need for an additional hyperparameter. While initial experiments employing higher-order mutation rates have yielded promising results, especially in dynamic and adversarial settings, a comprehensive analysis is so far lacking. To address this gap, we provide an empirical study with a focus on interpreting the behavior of higher-order mutation rates, including self-referential ones, under varying selective pressure dynamics (i.e., fitness functions).