become even stretched to cope with an entire range of different tasks. This could lead to a “cascade” of fluid adaptivities piled upon each other in a hierarchical fashion. Interactions between such fluid adaptations might give rise to higher-level function, e.g. when predictive components influence each other mutually.

So far, AI research has primarily focused on crystallized adaptivity and has only recently begun to make some inroads into the layers of this fluid adaptivity hierarchy. Biological systems, in contrast, face very dynamic and constantly changing scenarios that force them from the outset to realize significant fluid adaptivity hierarchies, enabling them to cope with strongly varying contexts and tasks, interact with other animate agents which themselves are adaptive, and to even come up with rapid solutions when the abilities of their body are changed, e.g. due to injuries. While the developed fluid adaptivity works on fast time scales ranging from seconds to fractions of their lifetime, it has been created by a form of crystallized adaptivity that works on much slower time scales, including the very slow time scale of biological evolution. Biological evolution has created brains that complement evolutionary crystallized adaptivity with much faster neural adaptivity that blends learning on 