Learning-to-learn, a progressive acceleration of learning while solving a series of similar problems, represents a core process of knowledge acquisition that draws attention in both neuroscience and artificial intelligence. To investigate its underlying brain mechanism, we trained a recurrent neural network model on arbitrary sensorimotor mappings. The network displayed an exponential speedup in learning. The neural substrate of a schema emerges within a low-dimensional subspace of population activity. Its reuse in new problems facilitates learning by limiting connection weight changes. Since the population trajectory of a recurrent network produces behavior, learning is determined by the vector field changes. We propose a novel analysis of weight-driven vector field changes, which showed that novel stimuli in new problems can distort the schema representation. Weight changes eliminate such distortions and improve the invariance of the reused representations in future learning. The accumulation of such weight changes across problems underlies the learning-to-learn dynamics.

Learning-to-learn, a progressive acceleration of learning while solving a series of similar problems, represents a core process of knowledge acquisition that draws attention in both neuroscience and artificial intelligence. To investigate its underlying brain mechanism, we trained a recurrent neural network model on arbitrary sensorimotor mappings. The network displayed an exponential speedup in learning. The neural substrate of a schema emerges within a low-dimensional subspace of population activity. Its reuse in new problems facilitates learning by limiting connection weight changes. Since the population trajectory of a recurrent network produces behavior, learning is determined by the vector field changes. We propose a novel analysis of weight-driven vector field changes, which showed that novel stimuli in new problems can distort the schema representation. Weight changes eliminate such distortions and improve the invariance of the reused representations in future learning. The accumulation of such weight changes across problems underlies the learning-to-learn dynamics.

Categorization is a fundamental cognitive process by which the brain assigns stimuli to behaviorally meaningful groups. Investigations of visual categorization in primates have identified a hierarchy of cortical areas that are involved in the transformation of sensory information into abstract category representations. However, categorization behaviors are ubiquitous across diverse animal species, even those without a neocortex, motivating the possibility that subcortical regions may contribute to abstract cognition in primates. One candidate structure is the superior colliculus (SC), an evolutionarily conserved midbrain region that, although traditionally thought to mediate only reflexive spatial orienting, is involved in cognitive tasks that require spatial orienting. Here, we reveal a novel role of the primate SC in abstract, higher-order visual cognition. We compared neural activity in the SC and the posterior parietal cortex (PPC), a region previously shown to causally contribute to category decisions, while monkeys performed a visual categorization task in which they report their decisions with a hand movement. The SC exhibits stronger and shorter-latency category encoding than the PPC, and inactivation of the SC markedly impairs monkeys' category decisions. These results extend SC's established role in spatial orienting to abstract, non-spatial cognition.