Log In Sign Up

Learning abstract structure for drawing by efficient motor program induction

by   Lucas Y. Tian, et al.

Humans flexibly solve new problems that differ qualitatively from those they were trained on. This ability to generalize is supported by learned concepts that capture structure common across different problems. Here we develop a naturalistic drawing task to study how humans rapidly acquire structured prior knowledge. The task requires drawing visual objects that share underlying structure, based on a set of composable geometric rules. We show that people spontaneously learn abstract drawing procedures that support generalization, and propose a model of how learners can discover these reusable drawing programs. Trained in the same setting as humans, and constrained to produce efficient motor actions, this model discovers new drawing routines that transfer to test objects and resemble learned features of human sequences. These results suggest that two principles guiding motor program induction in the model - abstraction (general programs that ignore object-specific details) and compositionality (recombining previously learned programs) - are key for explaining how humans learn structured internal representations that guide flexible reasoning and learning.


page 1

page 2

page 3

page 4


Flexible Compositional Learning of Structured Visual Concepts

Humans are highly efficient learners, with the ability to grasp the mean...

Fast and flexible: Human program induction in abstract reasoning tasks

The Abstraction and Reasoning Corpus (ARC) is a challenging program indu...

Explaining Transition Systems through Program Induction

Explaining and reasoning about processes which underlie observed black-b...

Using Program Induction to Interpret Transition System Dynamics

Explaining and reasoning about processes which underlie observed black-b...

Pragmatic inference and visual abstraction enable contextual flexibility during visual communication

Visual modes of communication are ubiquitous in modern life — from maps ...

Learning Abstract Classes using Deep Learning

Humans are generally good at learning abstract concepts about objects an...

Learning a Deep Generative Model like a Program: the Free Category Prior

Humans surpass the cognitive abilities of most other animals in our abil...