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CI Causal Induction
CD Cognitive Development
CEIL Cultural Evolution and Iterated Learning
DMRL Decision Making and Reinforcement Learning
E Education
F Foundations
IB Inductive Biases
NBM Nonparametric Bayesian Models
P Perception
PR Probabilistic Reasoning
RPM Rational Process Models
S&C Similarity and Categorization
SC Social Cognition
SML Statistical Models of Language
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By Mansinghka, V. | F
SC
| Collins, K. M., Sucholutsky, I., Bhatt, U., Chandra, K., Wong, L., Lee, M., Zhang, C. E., Zhi-Xuan, T., Ho, M., Mansinghka, V., Weller, A., Tenenbaum, J. B., & Griffiths, T. L. (2024). Building machines that learn and think with people. Nature Human Behaviour, 8(10), 1851-1863. (pdf)
| CI
P
| Sanborn, A. N., Mansinghka, V. K., & Griffiths, T. L. (2013). Reconciling intuitive physics and Newtonian mechanics for colliding objects. Psychological Review, 120, 411-437. (pdf)
| CI
| Sanborn, A. N., Mansinghka, V. K., & Griffiths, T. L. (2009). A Bayesian framework for modeling intuitive dynamics. Proceedings of the 31st Annual Conference of the Cognitive Science Society. (pdf)
| SML
| Frank, M. C., Goldwater, S., Mansinghka, V., Griffiths, T., & Tenenbaum, J. B. (2007). Modeling human performance in statistical word segmentation. Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. (pdf)
| CI
NBM
| Mansinghka, V. K., Kemp, C., Tenenbaum, J. B., & Griffiths, T. L. (2006). Structured priors for structure learning. Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI 2006). (pdf)
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