Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning.

ABSTRACT: The ability to acquire abstract knowledge is a hallmark of human intelligence and is believed by many to be one of the core differences between humans and neural network models. Agents can be endowed with an inductive bias towards abstraction through meta-learning, where they are trained on a distribution of tasks that share some abstract structure that can be learned and applied. However, because neural networks are hard to interpret, it can be difficult to tell whether agents have learned the underlying abstraction, or alternatively statistical patterns that are characteristic of that abstraction. In this work, we compare the performance of humans and agents in a meta-reinforcement learning paradigm in which tasks are generated from abstract rules. We define a novel methodology for build

SUBMITTER: Kumar S 

PROVIDER: S-EPMC10497163 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature