  A hallmark of intelligence is the ability to autonomously learn new flexible,
cognitive behaviors - that is, behaviors where the appropriate action depends
not just on immediate stimuli (as in simple reflexive stimulus-response
associations), but on contextual information that must be adequately acquired,
stored and processed. While many meta-learning algorithms can design agents
that autonomously learn new tasks, cognitive tasks adds another level of
learning and memory to typical ``learning-to-learn'' problems. Here we evolve
neural networks, endowed with plastic connections and neuromodulation, over a
sizable set of simple cognitive tasks adapted from a computational neuroscience
framework. The resulting evolved networks can automatically modify their own
connectivity to acquire a novel simple cognitive task, never seen during
evolution, from stimuli and rewards alone, through the spontaneous operation of
their evolved neural organization and plasticity system. Our results emphasize
the importance of carefully considering the multiple learning loops involved in
the emergence of intelligent behavior.
