Learning abstract structure for drawing by efficient motor program induction
Authors: Lucas Tian, Kevin Ellis, Marta Kryven, Josh Tenenbaum
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our behavioral data provides evidence that humans indeed perform few-shot updating of their inductive biases by learning program-like drawing procedures that guide generalization behavior. We describe a program-induction algorithm that discovers new abstract, compositional, drawing routines given the same limited training data given to humans. Moreover, the model s learned drawing behavior captures certain diagnostic features of how humans generalize. These results suggest that abstraction and compositionality are key principles for explaining how humans rapidly learn program-like structure that guides reasoning and planning in drawing. |
| Researcher Affiliation | Academia | 1Brain and Cognitive Sciences, MIT 2Center for Brains, Minds and Machines 3Laboratory of Neural Systems, Rockefeller University |
| Pseudocode | No | The paper describes algorithms conceptually and refers to a figure (Figure 2) for an overall model, but does not present pseudocode in a structured, code-like block. |
| Open Source Code | No | The paper does not provide a direct link to open-source code for the methodology. |
| Open Datasets | No | The paper mentions generating stimuli for training sets but does not provide concrete access information (link, DOI, specific repository, or formal citation) for the dataset used for training. |
| Dataset Splits | No | The paper mentions 250 stimuli generated for each training set and 18 test images, but does not specify validation splits or other detailed dataset split information. |
| Hardware Specification | No | The paper states that the experiment was presented "on a touchscreen device (phone, tablet, or laptop)", but this describes the device used by subjects, not the hardware used to run the models or experiments. No specific GPU/CPU or other experimental hardware details are provided. |
| Software Dependencies | No | The paper mentions "Psi Turk [34] and Raphael Sketch Pad2" and provides a URL for the latter, but does not include specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper describes the experimental procedure for human subjects and conceptual aspects of the model but lacks specific hyperparameters or system-level training settings for the computational model in the main text. |