@article {5062,
	title = {Plans or Outcomes: How Do We Attribute Intelligence to Others?},
	journal = {Cognitive Science},
	volume = {45},
	year = {2021},
	month = {09/2021},
	issn = {0364-0213},
	doi = {10.1111/cogs.v45.910.1111/cogs.13041},
	url = {https://onlinelibrary.wiley.com/toc/15516709/45/9},
	author = {Marta Kryven and Ullman, Tomer D. and Cowan, William and Joshua B. Tenenbaum}
}
@article {4955,
	title = {Vector-based pedestrian navigation in cities},
	journal = {Nature Computational Science},
	volume = {1},
	year = {2021},
	month = {10/2021},
	pages = {678 - 685},
	abstract = {<p>How do pedestrians choose their paths within city street networks? Researchers have tried to shed light on this matter through strictly controlled experiments, but an ultimate answer based on real-world mobility data is still lacking. Here, we analyze salient features of human path planning through a statistical analysis of a massive dataset of GPS traces, which reveals that (1) people increasingly deviate from the shortest path when the distance between origin and destination increases and (2) chosen paths are statistically different when origin and destination are swapped. We posit that direction to goal is a main driver of path planning and develop a vector-based navigation model; the resulting trajectories, which we have termed pointiest paths, are a statistically better predictor of human paths than a model based on minimizing distance with stochastic effects. Our findings generalize across two major US cities with different street networks, hinting to the fact that vector-based navigation might be a universal property of human path planning.</p>
},
	doi = {10.1038/s43588-021-00130-y},
	url = {https://www.nature.com/articles/s43588-021-00130-y},
	author = {Bongiorno, Christian and Zhou, Yulun and Marta Kryven and Theurel, David and Rizzo, Alessandro and Santi, Paolo and Joshua B. Tenenbaum and Ratti, Carlo}
}
@conference {4702,
	title = {Learning abstract structure for drawing by efficient motor program induction},
	booktitle = {Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020)},
	year = {2020},
	month = {12/2020},
	abstract = {<p>Humans flexibly solve new problems that differ from those previously practiced. This ability to flexibly generalize is supported by learned concepts that represent useful 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 figures that share underlying structure, based on a set of composable geometric rules and simple objects. We show that people spontaneously learn abstract drawing procedures that support generalization, and propose a model of how learners can discover these reusable drawing procedures. Trained in the same setting as humans, and constrained to produce efficient motor actions, this model discovers new drawing program subroutines that generalize to test figures and resemble learned features of human behavior. These results suggest that two principles guiding motor program induction in the model - abstraction (programs can reflect high-level structure that ignores figure-specific details) and compositionality (new programs are discovered by recombining previously learned programs) - are key for explaining how humans learn structured internal representations that guide flexible reasoning and learning.</p>
},
	url = {https://papers.nips.cc/paper/2020/hash/1c104b9c0accfca52ef21728eaf01453-Abstract.html},
	author = {Lucas Tian and Kevin Ellis and Marta Kryven and Joshua B. Tenenbaum}
}
@article {4514,
	title = {Choosing a Transformative Experience },
	year = {2019},
	month = {07/2019},
	author = {Marta Kryven and Niemi, L. and Paul, L. and Joshua B. Tenenbaum}
}
@article {4512,
	title = { Does intuitive inference of physical stability interruptattention?},
	year = {2019},
	month = {07/2019},
	author = {Marta Kryven and Scholl, B. and Joshua B. Tenenbaum}
}
