Rapid runtime learning by curating small datasets of high-quality items obtained from memory.

ABSTRACT: We propose the "runtime learning" hypothesis which states that people quickly learn to perform unfamiliar tasks as the tasks arise by using task-relevant instances of concepts stored in memory during mental training. To make learning rapid, the hypothesis claims that only a few class instances are used, but these instances are especially valuable for training. The paper motivates the hypothesis by describing related ideas from the cognitive science and machine learning literatures. Using computer simulation, we show that deep neural networks (DNNs) can learn effectively from small, curated training sets, and that valuable training items tend to lie toward the centers of data item clusters in an abstract feature space. In a series of three behavioral experiments, we show that people can als

SUBMITTER: German JS 

PROVIDER: S-EPMC10578607 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature