The Emergence of Organizing Structure in Conceptual Representation

TL;DR

This paper presents a new computational model for discovering organizing structures in concepts, emphasizing a broad hypothesis space and sparsity, which better explains human-like conceptual organization and property induction.

Contribution

It introduces a flexible model that allows structural forms to emerge from sparsity, reducing the need for predefined forms and capturing complex, intuitive, and non-intuitive structures.

Findings

Model learns complex structures without explicit forms

Predicts human property induction judgments

Supports a wide range of structural discoveries

Abstract

Both scientists and children make important structural discoveries, yet their computational underpinnings are not well understood. Structure discovery has previously been formalized as probabilistic inference about the right structural form --- where form could be a tree, ring, chain, grid, etc. [Kemp & Tenenbaum (2008). The discovery of structural form. PNAS, 105(3), 10687-10692]. While this approach can learn intuitive organizations, including a tree for animals and a ring for the color circle, it assumes a strong inductive bias that considers only these particular forms, and each form is explicitly provided as initial knowledge. Here we introduce a new computational model of how organizing structure can be discovered, utilizing a broad hypothesis space with a preference for sparse connectivity. Given that the inductive bias is more general, the model's initial knowledge shows little qualitative resemblance to some of the discoveries it supports. As a consequence, the model can also learn complex structures for domains that lack intuitive description, as well as predict human property induction judgments without explicit structural forms. By allowing form to emerge from sparsity, our approach clarifies how both the richness and flexibility of human conceptual organization can coexist.