  Animals are equipped with a rich innate repertoire of sensory, behavioral and
motor skills, which allows them to interact with the world immediately after
birth. At the same time, many behaviors are highly adaptive and can be tailored
to specific environments by means of learning. In this work, we use
mathematical analysis and the framework of meta-learning (or 'learning to
learn') to answer when it is beneficial to learn such an adaptive strategy and
when to hard-code a heuristic behavior. We find that the interplay of
ecological uncertainty, task complexity and the agents' lifetime has crucial
effects on the meta-learned amortized Bayesian inference performed by an agent.
There exist two regimes: One in which meta-learning yields a learning algorithm
that implements task-dependent information-integration and a second regime in
which meta-learning imprints a heuristic or 'hard-coded' behavior. Further
analysis reveals that non-adaptive behaviors are not only optimal for aspects
of the environment that are stable across individuals, but also in situations
where an adaptation to the environment would in fact be highly beneficial, but
could not be done quickly enough to be exploited within the remaining lifetime.
Hard-coded behaviors should hence not only be those that always work, but also
those that are too complex to be learned within a reasonable time frame.
