Computational Cognitive Science Lab

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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 Nematzadeh, A
SML	Nematzadeh, A., Shekarchi, Z., Griffiths, T. L., & Stevenson, S. (2020). Competition in Cross-situational Word Learning: A Computational Study. (preprint)
S&C	Peterson, J. C., Soulos, P., Nematzadeh, A., & Griffiths, T. L. (2019). Learning to generalize like humans using basic-level object labels. Journal of Vision, 19(10), 60a-60a. (link)
DMRL	Burns, K., Nematzadeh, A., Grant, E., Gopnik, A., & Griffiths, T. L. (2018). Exploiting attention to reveal shortcomings in memory models. Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, 378-380. (pdf)
SC SML	Nematzadeh, A., Burns, K., Grant, E., Gopnik, A., & Griffiths, T. L. (2018). Evaluating theory of mind in question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. (pdf)
S&C SML	Peterson, J. C., Soulos, P., Nematzadeh, A., & Griffiths, T. L. (2018). Learning hierarchical visual representations in deep neural networks using hierarchical linguistic labels. Proceedings of the 40th Annual Conference of the Cognitive Science Society. (pdf)
CD SML	Grant, E., Nematzadeh, A., & Griffiths, T. L. (2017). How can memory-augmented neural networks pass a false-belief task? Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)
SML	Nematzadeh, A., Meylan, S. C., & Griffiths, T. L. (2017). Evaluating vector-space models of word representation, or the unreasonable effectiveness of counting words near other words. Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)