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  • title: Compositional generalization through abstract representations in human and artificial neural networks
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            Compositional generalization through abstract representations in human and artificial neural networks
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            Compositional generalization through abstract representations in human and artificial neural networks

            Nov 28, 2022

            Speakers

            TI

            Takuya Ito

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            TK

            Tim Klinger

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            DHS

            Douglas H. Schultz

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            About

            Humans have a remarkable ability to rapidly generalize to new tasks that is difficult to reproduce in artificial learning systems.Compositionality has been proposed as a key mechanism supporting generalization in humans, but evidence of its neural implementation and impact on behavior is still scarce. Here we study the computational properties associated with compositional generalization in both humans and artificial neural networks (ANNs) on a highly compositional task. First, we identified beh…

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            NeurIPS 2022

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