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• W4304606945 abstract "Abstract Relational cognition — the ability to infer relationships that generalize to novel combinations of objects — is fundamental to human and animal intelligence. Despite this importance, it remains unclear how relational cognition is implemented in the brain due in part to a lack of hypotheses and predictions at the levels of collective neural activity and behavior. Here we discovered, analyzed, and experimentally tested neural networks (NNs) that perform transitive inference (TI), a classic relational task (if A > B and B > C, then A > C). We found NNs that (i) generalized perfectly, despite lacking overt transitive structure prior to training, (ii) generalized when the task required working memory (WM), a capacity thought essential to inference in the brain, (iii) emergently expressed behaviors long observed in living subjects, in addition to a novel order-dependent behavior, and (iv) adopted different task solutions yielding alternative behavioral and neural predictions. Further, in a large-scale experiment, we found that human subjects performing WM-based TI showed behavior inconsistent with a class of NNs expressing an intuitive task solution. These findings provide neural insights into a classical relational ability, with wider implications for how the brain realizes relational cognition." @default.
• W4304606945 created "2022-10-12" @default.
• W4304606945 creator A5003487806 @default.
• W4304606945 creator A5028797433 @default.
• W4304606945 creator A5031578230 @default.
• W4304606945 creator A5045967000 @default.
• W4304606945 creator A5048599983 @default.
• W4304606945 creator A5058457117 @default.
• W4304606945 creator A5059572012 @default.
• W4304606945 creator A5079185098 @default.
• W4304606945 date "2022-10-11" @default.
• W4304606945 modified "2023-10-16" @default.
• W4304606945 title "Emergent neural dynamics and geometry for generalization in a transitive inference task" @default.
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