Our ability to learn a wide range of behavioral tasks is essential for responding appropriately to sensory stimuli according to behavioral demands, but the underlying neural mechanism has been rarely examined by neurophysiological recordings in the same subjects across learning. To understand how learning new behavioral tasks impacts underlying neuronal representations, we recorded from posterior parietal cortex (PPC) before and after training on a visual motion categorization task. Here we show that categorization training influenced cognitive encoding in PPC, with a marked enhancement of memory-related delay-period encoding during the categorization task which was absent during a motion discrimination task prior to categorization training. In contrast, the prefrontal cortex (PFC) exhibited strong delay-period encoding during both discrimination and categorization tasks. This reveals a dissociation between PFC’s and PPC’s roles in working memory, with general engagement of PFC across multiple tasks, in contrast with more task-specific mnemonic encoding in PPC.

In contrast to feedforward architecture commonly used in deep networks at the core of today's AI revolution, the biological cortex is endowed with an abundance of feedback projections. Feedback signaling is often difficult to differentially identify, and its computational roles remain poorly understood. Here, we investigated a cognitive phenomenon, called categorical perception (CP), that reveals the influences of high-level category learning on low-level feature-based perception, as a putative signature of top-down signaling. By examining behavioral data from a visual motion delayed matching experiment in non-human primates, we found that, after categorization training, motion directions closer to (respectively, away from) a category center became more (less) difficult to discriminate. This distance-dependent discrimination performance change along the dimension relevant to the learned categories provides direct evidence for the CP phenomenon.To explain this experimental finding, we developed a neural circuit model that incorporated key neurophysiological findings in visual categorization, working memory and decision making. Our model accounts for the behavioral data indicative of CP, pinpoints its circuit basis, suggests novel experimentally testable predictions and provides a functional explanation for its existence. Our work shows that delayed matching paradigms in non-human primates combined with biologicallybased modeling can serve as a promising model system for elucidating the neural mechanisms of CP, as a manifestation of top-down signaling in the cortex. 2 Significant StatementCategorical perception is a cognitive phenomenon revealing the influences of high-level category learning on low-level feature-based perception. However, its underlying neural mechanisms are largely unknown. Here, we found behavioral evidence for this phenomenon from a visual motion delayed matching experiment in non-human primates. We developed a neural circuit model that can account for this behavioral data, pinpoints its circuit basis, suggests novel experimentally testable predictions and provides a functional explanation for its existence. Our work shows that delayed matching paradigms in non-human primates combined with biologically-based modeling can serve as a promising model system for elucidating the neural mechanisms of categorical perception, as a manifestation of top-down signaling in the cortex.3