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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Perception
P	Liu, G., Snell, J. C., Griffiths, T. L., & Dubey, R. (2025). Binary climate data visuals amplify perceived impact of climate change. Nature Human Behaviour, 1–10. (pdf)
IB P	Campbell, D., Kumar, S., Giallanza, T., Griffiths, T. L., & Cohen, J. D. (2024). Human-like geometric abstraction in large pre-trained neural networks. 46th Annual Meeting of the Cognitive Science Society. (pdf)
F P	Campbell, D., Rane, S., Giallanza, T., De Sabbata, N., Ghods, K., Joshi, A., Ku, A., Frankland, S. M., Griffiths, T. L., & Cohen, J. D., & Webb, T. W. (2024). Understanding the limits of vision language models through the lens of the binding problem. Advances in Neural Information Processing Systems 38. (pdf)
P	Chen, A., Sucholutsky, I., Russakovsky, O., & Griffiths, T. L. (2024). Analyzing the roles of language and vision in learning from limited data. 46th Annual Meeting of the Cognitive Science Society. (pdf)
DMRL P	Dubey, R., Hardy, M., Griffiths, T., & Bhui, R. (2024). AI-generated visuals of car-free American cities help increase support for sustainable transport policies. Nature Sustainability, 7, 399–403. (pdf)
P S&C	Marjieh, R., Jacoby, N., Peterson, J. C., & Griffiths, T. L. (2024). The Universal Law of Generalization holds for naturalistic stimuli. Journal of Experimental Psychology: General, 153(3), 573–589. (pdf)
IB P	Marjieh, R., Kumar, S., Campbell, D., Zhang, L., Bencomo, G., Snell, J., & Griffiths, T. L. (2024). Using contrastive learning with generative similarity to learn spaces that capture human inductive biases. (preprint)
P	Marjieh, R., van Rijn, P., Sucholutsky, I., Lee, H., Griffiths, T. L., & Jacoby, N. (2024). A rational analysis of the speech-to-song illusion. 46th Annual Meeting of the Cognitive Science Society. (pdf)
P SML	Marjieh, R., van Rijn, P., Sucholutsky, I., Lee, H., Jacoby, N., & Griffiths, T. L. (2024). Characterizing the large-scale structure of grounded semantic networks. (preprint)
P	Niedermann, J. P., Sucholutsky, I., Marjieh, R., Çelen, E., Griffiths, T., Jacoby, N., & van Rijn, P. (2024) Studying the effect of globalization on color perception using multilingual online recruitment and large language models. 46th Annual Meeting of the Cognitive Science Society. (pdf)
P	Rane, S., Ku, A., Baldridge, J., Tenney, I., Griffiths, T. L., & Kim, B. (2024). Can generative multimodal models count to ten? 46th Annual Meeting of the Cognitive Science Society. (pdf)
P SC	Urano, Y., Marjieh, R., Griffiths, T. L., & Jacoby, N. (2024). The influence of social information and presentation interface on aesthetic evaluations. 46th Annual Meeting of the Cognitive Science Society. (pdf)
F P	Zuo, Y., Kayan, K., Wang, M., Jeon, K., Deng, J., & Griffiths, T. L. (2024). Towards foundation models for 3D vision: How close are we? International Conference on 3D Vision 2025. (preprint)
IB P	Campbell, D., Kumar, S., Giallanza, T., Cohen, J. D., & Griffiths, T. L. (2023). Relational constraints on neural networks reproduce human biases towards abstract geometric regularity. (preprint)
P SML	Dedhia, B., Chang, M., Snell, J. C., Griffiths, T. L., & Jha, N. K. (2023). Im-Promptu: In-context composition from image prompts. Advances in Neural Information Processing Systems 37. (preprint)
P S&C	Jha, A., Peterson, J. C., & Griffiths, T. L. (2023). Extracting low‐dimensional psychological representations from convolutional neural networks. Cognitive Science, 47(1), e13226. (pdf)
P S&C	Marjieh, R., Griffiths, T. L., & Jacoby, N. (2023). Musical pitch has multiple psychological geometries. (preprint)
P PR	Marjieh, R., Sucholutsky, I., Langlois, T. A., Jacoby, N., & Griffiths, T. L. (2023) Analyzing diffusion as serial reproduction. Proceedings of the 40th International Conference on Machine Learning (ICML), 202 24005-24019. (preprint)
P SML	Marjieh, R., Sucholutsky, I., van Rijn, P., Jacoby, N., & Griffiths, T. L. (2023). What language reveals about perception: Distilling psychophysical knowledge from large language models. 45th Annual Meeting of the Cognitive Science Society. (pdf)
CD P	Rane, S., Nencheva, M. L., Wang, Z., Lew-Williams, C., Russakovsky, O., & Griffiths, T. L. (2023). Predicting word learning in children from the performance of computer vision systems. 45th Annual Meeting of the Cognitive Science Society. (pdf)
IB P	Sucholutsky, I., & Griffiths, T. L. (2023). Alignment with human representations supports robust few-shot learning. Advances in Neural Information Processing Systems 37. (pdf)
DMRL P	Turner, C. R., Morgan, T., & Griffiths, T. (2023). The joint evolution of sensory systems and decision policy allows cognition. 45th Annual Meeting of the Cognitive Science Society. (pdf)
P SC	Murthy, S. K., Hawkins, R. D., & Griffiths, T. L. (2022). Shades of confusion: Lexical uncertainty modulates ad hoc coordination in an interactive communication task. Cognition, 225, 105152. (pdf)
P S&C	Peterson, J. C., Uddenberg, S., Griffiths, T. L., Todorov, A., & Suchow, J. W. (2022). Deep models of superficial face judgments. Proceedings of the National Academy of Sciences, 119(17), e2115228119. (pdf)
P S&C	Battleday, R. M., Peterson, J. C., & Griffiths, T. L. (2021). From convolutional neural networks to models of higher-level cognition (and back again). Annals of the New York Academy of Sciences. (pdf)
P S&C	Grewal, K., Peterson, J. C., Thompson, B., & Griffiths, T. L. (2021). Exploring the structure of human adjective representations. SVRHM 2021 Workshop @ NeurIPS. (pdf)
CEIL P	Langlois, T. A., Jacoby, N., Suchow, J. W., & Griffiths, T. L. (2021). Serial reproduction reveals the geometry of visuospatial representations. Proceedings of the National Academy of Sciences, 118(13), e2012938118. (pdf)
P	Langlois, T. A., Zhao, H. C., Grant, E., Dasgupta, I., Griffiths, T. L., & Jacoby, N. (2021). Passive attention in artificial neural networks predicts human visual selectivity. Advances in Neural Information Processing Systems 34. (pdf)
P	Tuli, S., Dasgupta, I., Grant, E., & Griffiths, T. L. (2021). Are Convolutional Neural Networks or Transformers more like human vision?. Proceedings of the 43rd Annual Meeting of the Cognitive Science Society. (link)
P S&C	Battleday, R. M., Peterson, J. C., & Griffiths, T. L. (2020). Capturing human categorization of natural images by combining deep networks and cognitive models. Nature Communications, 11(1), 1-14. (pdf)
P S&C	Jha, A., Peterson, J. C., & Griffiths, T. L. (2020). Extracting low-dimensional psychological representations from convolutional neural networks. Proceedings of the 42nd Annual Conference of the Cognitive Science Society. (pdf)
P S&C	Singh, P., Peterson, J. C., Battleday, R. M., & Griffiths, T. L. (2020). End-to-end deep prototype and exemplar models for predicting human behavior. Proceedings of the 42nd Annual Conference of the Cognitive Science Society. (pdf)
DMRL P	Sumers, T. R., Ho, M. K., & Griffiths, T. L. (2020). Show or tell? Demonstration is more robust to changes in shared perception than explanation. Proceedings of the 42nd Annual Conference of the Cognitive Science Society. (pdf)
P S&C	Grant, E., Peterson, J. C., & Griffiths, T. (2019). Learning deep taxonomic priors for concept learning from few positive examples. Proceedings of the 41st Annual Conference of the Cognitive Science Society. (pdf)
P S&C	Peterson, J. C., Battleday, R., Griffiths, T. L., & Russakovsky, O. (2019). Human uncertainty makes classification more robust. Proceedings of the IEEE International Conference on Computer Vision. (pdf)
P S&C	Suchow, J. W., Peterson, J. C., & Griffiths, T. L. (2018). Learning a face space for experiments on human identity. Proceedings of the 40th Annual Conference of the Cognitive Science Society. (pdf)
P PR	Austerweil, J. L., Griffiths, T. L., & Palmer, S. E. (2017). Learning to be (in) variant: Combining prior knowledge and experience to infer orientation invariance in object recognition. Cognitive Science, 41(S5), 1183-1201. (pdf)
CEIL P	Langlois, T. A., Jacoby, N., Suchow, J. W., & Griffiths, T. L. (2017). Uncovering visual priors in spatial memory using serial reproduction. Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)
CI P	Callaway, F., Hamrick, J. B., & Griffiths, T. L. (2017). Discovering simple heuristics from mental simulation. Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)
P S&C	Peterson, J. C., & Griffiths, T. L. (2017). Evidence for the size principle in semantic and perceptual domains. Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)
CI P PR	Hamrick, J. B., Battaglia, P. W., Griffiths, T. L., Tenenbaum, J. B. (2016). Inferring mass in complex scenes by mental simulation. Cognition, 157, 61-76. (pdf)
F P	Griffiths, T. L., Abbott, J. T., & Hsu, A. S. (2016). Exploring human cognition using large image databases. Topics in Cognitive Science, 8(3), 569-588. (pdf)
P S&C	Abbott, J. T., Griffiths, T. L., & Regier, T. (2016). Focal colors across languages are representative members of color categories. Proceedings of the National Academy of Sciences, 113(40), 11178-11183. (pdf)
CEIL P	Suchow, J. W., Pacer, M. D., & Griffiths, T. L. (2016). Design from Zeroth Principles. Proceedings of the 38th Annual Conference of the Cognitive Science Society. (pdf)
P S&C	Peterson, J. C., Abbott, J. T., & Griffiths, T. L. (2016). Adapting deep network features to capture psychological representations. Proceedings of the 38th Annual Conference of the Cognitive Science Society. (pdf) (Winner of the Computational Modeling Prize in Perception/Action)
P RPM	Hamrick, J., Smith, K. A., Griffiths, T. L., & Vul, E. (2015). Think again? The amount of mental simulation tracks uncertainty in the outcome. Proceedings of the 37th Annual Conference of the Cognitive Science Society (pdf)
P S&C	Jia, Y., Abbott, J. T., Austerweil, J. L., Griffiths, T. L., & Darrell, T. (2013). Visual concept learning: Combining machine vision and Bayesian generalization on concept hierarchies. Advances in Neural Information Processing Systems, 26. (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)
P S&C	Austerweil, J. L., & Griffiths, T. L. (2012). Human feature learning. Encyclopedia of the sciences of learning. N. M. Seel, ed. New York: Springer. (book)
P S&C	Abbott, J. T., Regier, T., & Griffiths, T. L. (2012). Predicting focal colors with a rational model of representativeness. Proceedings of the 34th Annual Conference of the Cognitive Science Society. (pdf)
P S&C	Abbott, J. T., Austerweil, J. L., & Griffiths, T. L. (2012). Constructing a hypothesis space from the Web for large-scale Bayesian word learning. Proceedings of the 34th Annual Conference of the Cognitive Science Society. (pdf)
NBM P	Austerweil, J. L., Friesen, A. L., & Griffiths, T. L. (2011). An ideal observer model for identifying the reference frame of objects. Advances in Neural Information Processing Systems, 24. (pdf)
NBM P	Austerweil, J. L., & Griffiths, T. L. (2011). A rational model of the effects of distributional information on feature learning. Cognitive Psychology, 63, 173-209. (pdf)
P SML	Buchsbaum, D., Canini, K. R., & Griffiths, T. L. (2011). Segmenting and recognizing human action using low-level video features. Proceedings of the 33rd Annual Conference of the Cognitive Science Society.(pdf)
NBM P S&C	Austerweil, J. L., & Griffiths, T. L. (2010). Learning invariant features using the Transformed Indian Buffet Process. Advances in Neural Information Processing Systems 23. (pdf)
CEIL P S&C	Xu, J., Griffiths, T. L., & Dowman, M. (2010). Replicating color term universals through human iterated learning. Proceedings of the 32nd Annual Conference of the Cognitive Science Society. (pdf)
P S&C	Austerweil, J. L., & Griffiths, T. L. (2010). Learning hypothesis spaces and dimensions through concept learning. Proceedings of the 32nd Annual Conference of the Cognitive Science Society. (pdf)
P S&C SML	Feldman, N. H., Griffiths, T. L., & Morgan, J. L. (2009). The influence of categories on perception: Explaining the perceptual magnet effect as optimal statistical inference. Psychological Review, 116, 752-782. (pdf)
NBM P S&C	Austerweil, J., & Griffiths, T. L. (2009). Analyzing human feature learning as nonparametric Bayesian inference. Advances in Neural Information Processing Systems 21. (pdf)
NBM P S&C	Austerweil, J. L., & Griffiths, T. L. (2009). The effect of distributional information on feature learning. Proceedings of the 31st Annual Conference of the Cognitive Science Society. (pdf)