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…
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