A mathematical theory of relational generalization in transitive inference

• 成果类型: Article
• 署名作者: Lippl, Samuel; Kay, Kenneth; Jensen, Greg; Ferrera, Vincent P.; Abbott, L. F.
• 署名单位: Columbia University; Columbia University; Columbia University; Columbia University; Reed College - Portland; Columbia University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-12344
• DOI: 10.1073/pnas.2314511121
• 发表日期: 2024-07-09
• 关键词: complementary learning-systems; cognitive map; conjunctive representations; nonhuman animals; hippocampus; KNOWLEDGE; monkeys; memory; humans; connectionist

摘要

Humans and animals routinely infer relations between different items or events and generalize these relations to novel combinations of items. This allows them to respond appropriately to radically novel circumstances and is fundamental to advanced cognition. However, how learning systems (including the brain) can implement the necessary inductive biases has been unclear. We investigated transitive inference (TI), a classic relational task paradigm in which subjects must learn a relation ( A > B and B > C ) and generalize it to new combinations of items ( A > C ). Through mathematical analysis, we found that a broad range of biologically relevant learning models (e.g. gradient flow or ridge regression) perform TI successfully and recapitulate signature behavioral patterns long observed in living subjects. First, we found that models with item-wise additive representations automatically encode transitive relations. Second, for more general representations, a single scalar conjunctivity factor determines model behavior on TI and, further, the principle of norm minimization (a standard statistical inductive bias) enables models with fixed, partly conjunctive representations to generalize transitively. Finally, neural networks in the rich regime, which enables representation learning and improves generalization on many tasks, unexpectedly show poor generalization and anomalous behavior on TI. We find that such networks implement a form of norm minimization (over hidden weights) that yields a local encoding mechanism lacking transitivity. Our findings show how minimal statistical learning principles give rise to a classical relational inductive bias (transitivity), explain empirically observed behaviors, and establish a formal approach to understanding the neural basis of relational abstraction.