Emergent neural dynamics and geometry for generalization in a transitive inference task.
PLoS Comput Biol
; 20(4): e1011954, 2024 Apr.
Article
en En
| MEDLINE
| ID: mdl-38662797
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 to be 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) expressed 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 that characteristically expressed an intuitive task solution. These findings provide neural insights into a classical relational ability, with wider implications for how the brain realizes relational cognition.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Encéfalo
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Redes Neurales de la Computación
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Cognición
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Memoria a Corto Plazo
Límite:
Adult
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Female
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Humans
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Male
Idioma:
En
Revista:
PLoS Comput Biol
Asunto de la revista:
BIOLOGIA
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INFORMATICA MEDICA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos