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A hallmark of intelligence is the ability to autonomously learn new flexible,cognitive behaviors - that is, behaviors where the appropriate action dependsnot just on immediate stimuli (as in simple reflexive stimulus-responseassociations), but on contextual information that must be adequately acquired,stored and processed. While many meta-learning algorithms can design agentsthat autonomously learn new tasks, cognitive tasks adds another level oflearning and memory to typical ``learning-to-learn'' problems. Here we evolveneural networks, endowed with plastic connections and neuromodulation, over asizable set of simple cognitive tasks adapted from a computational neuroscienceframework. The resulting evolved networks can automatically modify their ownconnectivity to acquire a novel simple cognitive task, never seen duringevolution, from stimuli and rewards alone, through the spontaneous operation oftheir evolved neural organization and plasticity system. Our results emphasizethe importance of carefully considering the multiple learning loops involved inthe emergence of intelligent behavior.

Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning