Work

Learning list concepts through program induction

Year: 2018

Type: preprint

Abstract: Humans master complex systems of interrelated concepts like mathematics and natural language. Previous work suggests learning these systems relies on iteratively and directly revising a language-like conceptual representation. We introduce and assess a novel concept learning paradigm called Martha’s Magical Machines that captures complex relationships between concepts. We model human concept learning in this paradigm as a search in the space of term rewriting systems, previously developed as an abstract model of computation. Our model accurately predicts that participants learn some transformations more easily than others and that they learn harder concepts more easily using a bootstrapping curriculum focused on their compositional parts. Our results suggest that term rewriting systems may be a useful model of human conceptual representations.

Source: bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.1101/321505

Authors: Joshua Rule, Eric Schulz, Steven T. Piantadosi, Joshua B. Tenenbaum

Institutions: Massachusetts Institute of Technology, Harvard University, University of Rochester

Language: English

Topic: Categorization, perception, and language

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

SDG: Quality education

Open Access status: green

Funders: National Science Foundation, Companhia Brasileira de Metalurgia e Mineração, Harvard Data Science Initiative, Harvard University, Air Force Office of Scientific Research