Emergent neural dynamics and geometry for generalization in a transitive inference task.
PLoS Comput Biol
; 20(4): e1011954, 2024 Apr.
Article
in 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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Brain
/
Neural Networks, Computer
/
Cognition
/
Memory, Short-Term
Limits:
Adult
/
Female
/
Humans
/
Male
Language:
En
Journal:
PLoS Comput Biol
Journal subject:
BIOLOGIA
/
INFORMATICA MEDICA
Year:
2024
Document type:
Article
Affiliation country:
United States
Country of publication:
United States