We study the basic computational processes involved in human language and cognition.

overview: colala's research includes computational and experimental approaches to language acquistion, language processing, and understanding the core features of language. Our work draws on corpus methods, behavioral experiments with adults and children, computational and mathematical modeling, as well as state-of-the-art techniques in machine learning, information theory, statistics, computer science, and linguistics.

We collaborate closely with Tedlab at MIT, The CAOS lab at CMU, The Kidd Lab at UC Berkeley, and The Hayden Lab at the University of Minnesota.

We do fieldwork with an indigenous South American population, the Tsimane' of Bolivia, in collaboration with tedlab and CBIDSI to study universal aspects of human language, numerical cognition, and cognitive development.

philosophy: Most research in colala centers on creating fully-formalized (implemented) theories that aim to address basic questions about human language and cognition with convergent evidence across methods. We work to develop open tools, data, and result, and build an atmosphere that is inclusive, scientifically rigorous, and productive.

topics and selected papers:

• Structured (LOT) statistical learning
  
• Concept learning in language
  
• Cognition in indigenous groups
  
• Communicative language theories
  
• Numerical cognition
  
• Methods in psychology and linguistics
  
• Computational cognitive development

facilities: Our experimental facilities include rooms for testing adults and children. Our computational facilities include dedicated HPC cores for running models and analysis, as well as several in-house web, corpus, GPU, and HPC servers.

• prospective students •

graduate students: Prospective graduate students should apply to the Department of Psychology at UC Berkeley and contact Steve. Applications are due in in late November. Graduate students should be hard-working, full of ideas, skilled programmers, and capable of self-directed research. Students interested in the lab should bring strong quantitative skills including knowledge of statistics and programming, as well as serious background in some of the following areas: machine learning, mathematics, theoretical computer science, mathematical logic, cognitive development, language acquisition, language processing, or linguistics.

undergraduates: Undergraduates must be independent, creative, self-motivated, and good at programming. Our undergraduate research opportunities include work on current large-scale projects, as well as opportunities for self-directed research. Undergraduates will typically have a background in psychology, math, computer science, or linguistics. Apply here.

• graduate students •

Rachel Sun works on number and concepts	Umesh Singla is a first year in the lab
Dyana Muller works on concepts and neural computation.	Karthikeya Kaushik works on language and concepts.

• staff •

Holly Palmeri is the primary lab manager.	Gabriella Morales works on probability and inference with children.
Sarah Sommer works on kid number and algorithms.

• postdoctoral researchers •

Emily Sanford works on numerical cognition.	Vijay Marupudi works on number and algorithms.

• principal investigator •

The lab's PI is Steve Piantadosi. He is interested in understanding the computational mechanisms supporting human language learning and use.

•alums•

Isabelle Boni is a postdoc with ManyNumbers	Benjamin Pitt is a faculty member at UMass Amherst.	Michelle Hurst is a faculty member at Rutgers.
David O'Shaughnessy work at the Center for Social Impact	Shengyi Wu is a graduate student at MIT	Margaret Bryer is faculty at the University of Wisconsin-Madison
Sam Cheyette is a postdoc at MIT	Yim Register is a PhD student at UW	Eric Bigelow is a PhD student at Harvard
Frank Mollica is a professor at The University of Melbourne	Tania Cruz is a PhD student at Delaware	Charlene Gallardo is a graduate student at Cal State LA

• collaborators •

Researchers in the lab work in close collaboration with a number of labs around the country:

Josh Tenenbaum	Ted Gibson	Jessica Cantlon
Ben Hayden	Fei Xu	Dick Aslin
Ev Fedorenko	Dan Everett	Noah Goodman
Richard Futrell	Julian Jara-Ettinger	Julien Musolino
Ricardo Godoy	Kyle Mahowald	Isabelle Dautriche
Celeste Kidd	Stephen Ferrigno

submitted
[141]	Kaushik, K., & Piantadosi, S. T. (submitted). Neural state structure from behavior. [bibtex]
[140]	Li, W., Boni, I., DeLong, C., Conti, J., Henderson, M., Bisk, Y., Piantadosi, S. T., & Cantlon, J. (submitted). Uniquely Human Abstractions Emerge with Expanded Information Capacity in Neural Networks. [bibtex]
[139]	Zheng, Y. S., Sarmiento, C., Schwartkopf, A., Morales, G., X, A. J., Piantadosi, S., & Xu, F. (submitted). Evidence of induction by overhypothesis among indigenous Amazonian children and adults. [bibtex]
[138]	Zhu, H., Franch, M., Mickiewicz, E., Belanger, J., Cowan, R. L., Katlowitz, K., Chavez, A. G. L., Chericoni, A., Paulo, D., Yan, X., Bartoli, E., Hennig, J., Provenza, N., Smith, E. H., Piantadosi, S. T., Sheth, S., & Hayden, B. Y. (submitted). A geometric foundation for word meaning in the brain. bioRxiv. [bibtex] [url]
[137]	Zhu, H., Chericoni, A., Ismail, T., Mickiewicz, E., Franch, M., Nigam, T., Yan, X., Belanger, J., Chavez, A. G., Nair, J., Paulo, D., Bartoli, E., Hennig, J. A., Fraczek, T., Provenza, N., Yoo, S. B. M., Sohn, H., Cantlon, J., Piantadosi, S., … Hayden, B. Y. (submitted). A number simplex in the human medial temporal lobe. [bibtex] [url]
in press
[136]	Dedhe, A., Liao, D., Ferrigno, S., Nieder, A., Piantadosi, S. T., & Cantlon, J. (in press). Flexibility of Hierarchical Reasoning across Age and Species. Cognition. [bibtex]
[135]	Franch, M., Mickiewicz, E. A., Belanger, J. L., Chericoni, A., Chavez, A. G., Katlowitz, K. A., Mathura, R., Paulo, D., Bartoli, E., Kemmer, S., et al. (in press). A population code for semantics in human hippocampus. Nature Neuroscience. [bibtex]
[134]	Katlowitz, K. A., Belanger, J. L., Ismail, T., Chavez, A. G., Chericoni, A., Franch, M., Mickiewicz, E. A., Mathura, R. K., Paulo, D. L., Bartoli, E., et al. (in press). Attention is all you need (in the brain): semantic contextualization in human hippocampus. Nature Human Behavior. [bibtex]
2026
[133]	Kean, H., Fung, A., Jaggers, P., Chen, J., Rule, J. S., Benn, Y., Tenenbaum, J. B., Piantadosi, S. T., & Varley, R. A. (2026). Evidence from Formal Logical Reasoning Reveals that the Language of Thought is not Natural Language. Proceedings of the National Academy of Sciences. [bibtex] [url]
[132]	Li, J., Boni, I., Sandwick, L., DeLong, C., Henderston, M. M., Piantadosi, S. T., & Cantlon, J. F. (2026). Continuity in Geometric Intuition Between Humans and Monkeys. Proceedings of the National Academy of Sciences. [bibtex] [url]
[131]	Sanford, E. M., Sommer, S., & Piantadosi, S. T. (2026). Children successfully infer algorithmic properties based on only partial descriptions. In Proceedings of the Annual Meeting of the Cognitive Science Society. [bibtex] [pdf]
[130]	Piantadosi, S. T. (2026). Mathematics and AI Oracles. [bibtex] [pdf]
[129]	Pitt, B., Leib, E., O'Shaughnessy, D., Gallardo, C., Ferrigno, S., & Piantadosi, S. T. (2026). Algorithm induction in indigenous Amazonian children. Nature Communications, 17. [bibtex] [url]
[128]	Wong, A., Morales, G., Piantadosi, S. T., & Xu, F. (2026). Visual preferences and understanding of goal-directed actions across cultures: Evidence from Tsimane’ infants and children. In Proceedings of the Annual Meeting of the Cognitive Science Society. [bibtex] [pdf]
2025
[127]	Alderete, S., Tong, E., Cao, W., Piantadosi, S., & Xu, F. (2025). Young Children's Understanding of Prior and Posterior Probability. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 47). [bibtex]
[126]	Dedhe, A., Kulshrestha, K. N., Kulkarni, S., Piantadosi, S., & Cantlon, J. (2025). Dendrophilia versus continuity in hierarchical reasoning. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 47). [bibtex]
[125]	Li, J., Brownell, L., Sanford, E. M., Wenjie, L., DeLong, C., Piantadosi, S., & Cantlon, J. (2025). The Continuity of Geometric Intuition between Monkeys and Humans. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 47). [bibtex]
[124]	Rule, J., & Piantadosi, S. T. (2025). The End of Radical Concept Nativism. arXiv. [bibtex] [url]
2024
[123]	Borazjanizadeh, N., & Piantadosi, S. T. (2024). Reliable Reasoning Beyond Natural Language. arXiv. [bibtex] [url]
[122]	Cantlon, J. F., & Piantadosi, S. T. (2024). Uniquely human intelligence arose from expanded information capacity. Nature Reviews Psychology. [bibtex] [pdf]
[121]	Cheyette, S. J., Wu, S., & Piantadosi, S. T. (2024). Limited information-processing capacity in vision explains number psychophysics. Psychological Review. [bibtex] [pdf]
[120]	Cheyette, S., & Piantadosi, S. T. (2024). Response to difficulty drives variation in IQ test performance. Open Mind, 8, 265–277. [bibtex] [url]
[119]	Fedorenko, E., Piantadosi, S. T., & Gibson, E. (2024). Language is primarily a tool for communication rather than thought. Nature. [bibtex] [pdf]
[118]	Hurst, M. A., & Piantadosi, S. T. (2024). Continuous and Discrete Proportions Elicit Different Cognitive Strategies. Cognition. [bibtex] [pdf]
[117]	Piantadosi, S. T., & Gallistel, C. R. (2024). Formalising the role of behaviour in neuroscience. European Journal of Neuroscience. [bibtex] [url]
[116]	Piantadosi, S. T., Muller, D. C. Y., Rule, J. S., Kaushik, K., Gorenstein, M., Leib, E. R., & Sanford, E. (2024). Why concepts are (probably) vectors. Trends in Cognitive Sciences. [bibtex] [pdf]
[115]	Piantadosi, S. T. (2024). Modern language models refute Chomsky's approach to language. In E. Gibson & M. Poliak (Eds.), From fieldwork to linguistic theory: A tribute to Dan Everett (Empirically Oriented Theoretical Morphology and Syntax 15) (pp. 353–414). Berlin: Language Science Press. [bibtex] [url]
[114]	Rule, J., Piantadosi, S. T., Cropper, A., Ellis, K., Nye, M., & Tenenbaum, J. B. (2024). Symbolic metaprogram search improves learning efficiency and explains rule learning in humans. Nature Communications. [bibtex] [pdf]
2023
[113]	Chater, N., Perfors, A., & Piantadosi, S. T. (2023). Language processing and language learning. In T. L. Griffiths, N. Chater, & J. Tenenbaum (Eds.), Bayesian Models of Cognition: Reverse Engineering the Mind. [bibtex] [pdf]
[112]	Dedhe, A. M., Clatterbuck, H., Piantadosi, S. T., & Cantlon, J. F. (2023). Origins of Hierarchical Logical Reasoning. Cognitive Science, 47(2). [bibtex] [pdf]
[111]	Dedhe, A. M., Piantadosi, S. T., & Cantlon, J. F. (2023). Cognitive Mechanisms Underlying Recursive Pattern Processing in Human Adults. Cognitive Science, 47(4). [bibtex] [url]
[110]	Hoover, J. L., Sonderegger, M., Piantadosi, S. T., & O'Donnell, T. J. (2023). The Plausibility of Sampling as an Algorithmic Theory of Sentence Processing. Open Mind, 7, 350–391. [bibtex] [url]
[109]	Martí, L., Wu, S., Piantadosi, S. T., & Kidd, C. (2023). Latent diversity in conceptual representation. Open Mind, 7, 79–92. [bibtex] [url]
[108]	O'Shaughnessy, D. M., Cruz, T., Mollica, F., Boni, I., Jara-Ettinger, J., Gibson, E., & Piantadosi, S. T. (2023). Diverse mathematical knowledge among indigenous Amazonians. Proceedings of the National Academy of Sciences, 120(35). [bibtex] [url]
[107]	Perfors, A., Piantadosi, S. T., & Kidd, C. (2023). Trans-inclusive gender categories are cognitively natural. Nature Human Behavior, 7, 1609–1611. [bibtex] [pdf]
[106]	Piantadosi, S. T. (2023). The Algorithmic Origins of Counting. Child Development, 94(6), 1472–1490. [bibtex] [pdf]
[105]	Piantadosi, S. T. (2023). How to enumerate trees from a context-free grammar. arXiv. [bibtex] [url]
[104]	Piantadosi, S. T., Rule, J. S., & Tenenbaum, J. B. (2023). Learning as Bayesian inference over programs. In T. L. Griffiths, N. Chater, & J. Tenenbaum (Eds.), Bayesian Models of Cognition: Reverse Engineering the Mind. [bibtex] [pdf]
[103]	Pitt, B., Casasanto, D., & Piantadosi, S. T. (2023). No clear evidence for a left-to-right mental number line in insects. Proceedings of the National Academy of Sciences (commentary). [bibtex] [url]
[102]	Ryskin, R., Salinas, M. A., Piantadosi, S. T., & Gibson, E. (2023). Real-time pragmatic inference across cultures: evidence from a non-industrialized society. Journal of Experimental Psychology: General, 152(5), 1245–1263. [bibtex] [url]
[101]	Sanford, E. M., & Piantadosi, S. T. (2023). Sampling in Approximate Number Perception. In Proceedings of the Annual Meeting of the Cognitive Science Society. [bibtex] [pdf]
[100]	Yang, H. A., Piantadosi, S. T., & Kidd, C. (2023). Children’s Estimation of Peripheral Information Drives Improvements in Approximate Number Sense. In Proceedings of the Annual Meeting of the Cognitive Science Society. [bibtex] [pdf]
2022
[99]	Boni, I., & Piantadosi, S. T. (2022). Culture and Commutativity. In Proceedings of the Annual Meeting of the Cognitive Science Society. [bibtex] [pdf]
[98]	Boni, I., Jara-Ettinger, J., Sackstein, S., & Piantadosi, S. T. (2022). Verbal counting and the timing of number acquisition in an indigenous Amazonian group. PLOS ONE. [bibtex] [url]
[97]	Holdaway, C., & Piantadosi, S. (2022). Stochastic time-series analyses highlight the day-to-day dynamics of lexical frequencies. Cognitive Science, 46(12). [bibtex] [pdf]
[96]	Hurst, M., & Piantadosi, S. T. (2022). Investigating Adults’ Strategy Use During Proportional Comparison. In Proceedings of the Annual Meeting of the Cognitive Science Society. [bibtex] [pdf]
[95]	Piantadosi, S. T., & Yang, Y. (2022). Reply to Kodner et al: Fundamental misunderstanding of both model and methods. Proceedings of the National Academy of Sciences (response to commentary). [bibtex] [url]
[94]	Piantadosi, S. T., & Hill, F. (2022). Meaning without reference in large language models. arXiv preprint arXiv:2208.02957. [bibtex] [pdf]
[93]	Piantadosi, S. T., & Yang, Y. (2022). Reply to Murphy et al: Program induction can learn language. Proceedings of the National Academy of Sciences (response to commentary). [bibtex] [url]
[92]	Pitt, B., Gibson, E., & Piantadosi, S. T. (2022). Exact number concepts are limited to the verbal count range. Psychological Science, 33(3). [bibtex] [pdf]
[91]	Pitt, B., Carstensen, A., Boni, I., Piantadosi, S. T., & Gibson, E. (2022). Different reference frames on different axes: Space and language in indigenous Amazonians. Science Advances, 8(47). [bibtex] [pdf]
[90]	Rule, J. S., Piantadosi, S. T., & Tenenbaum, J. B. (2022). Learning as programming: Efficient search in models of human concept learning. In Proceedings of the Cognitive Science Society. [bibtex]
[89]	Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022). Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615. [bibtex] [url]
[88]	Yang, Y., & Piantadosi, S. T. (2022). One model for the learning of language. Proceedings of the National Academy of Sciences, 119(5). [bibtex] [pdf]
2021
[87]	Bryer, M. A. H., Koopman, S. E., Cantlon, J. F., Piantadosi, S. T., Maclean, E. L., Baker, J. M., Beran, M. J., Jones, S. M., Jordan, K. E., Mahamane, S., Nieder, A., Perdue, B. M., Range, F., Stevens, J. R., Tomonaga, M., Ujfalussy, D. J., & Vonk, J. (2021). The evolution of quantitative sensitivity. Philosophical Transactions of the Royal Society B, 377(1844). [bibtex] [pdf]
[86]	Cheyette, S. J., Wu, S., & Piantadosi, S. T. (2021). The psychophysics of number arise from resource-limited spatial memory. In Proceedings of the Cognitive Science Society. [bibtex] [pdf]
[85]	Futrell, R., Gibson, E., Tily, H., Blank, I., Vishnevetsky, A., Piantadosi, S. T., & Fedorenko, E. (2021). The Natural Stories Corpus. Language Resources and Evaluation, 55, 63–77. [bibtex] [url]
[84]	Mollica, F., & Piantadosi, S. T. (2021). Logical Word Learning: The case of kinship. Psychonomic Bulletin & Review. [bibtex] [pdf]
[83]	O'Shaughnessy, D. M., Gibson, E., & Piantadosi, S. T. (2021). The Cultural Origins of Symbolic Number. Psychological Review, 129(6). [bibtex] [pdf]
[82]	Piantadosi, S. T. (2021). Probability, Belief, and the Richness of Cognition. In J. Musolino, J. Sommer, & P. Hemmer (Eds.), The Cognitive Science of Belief. Cambridge University Press. [bibtex]
[81]	Piantadosi, S. T. (2021). The computational origin of representation. Minds and Machines, 31(3), 1–58. [bibtex] [pdf]
[80]	Pitt, B., Ferrigno, S., Cantlon, J., Casasanto, D., Gibson, E., & Piantadosi, S. T. (2021). Spatial concepts of number, size, and time in an indigenous culture. Science Advances, 7(33). [bibtex] [url]
[79]	Pitt, B., Carstensen, A., Gibson, E., & Piantadosi, S. T. (2021). Variation in spatial concepts: Different frames of reference on different axes. In Proceedings of the Cognitive Science Society. [bibtex] [pdf]
[78]	Schmidt, B., Piantadosi, S. T., & Mahowald, K. (2021). Uncontrolled corpus composition drives an apparent surge in cognitive distortions (Commentary on Bollen et al.). Proceedings of the National Academy of Sciences (commentary). [bibtex] [url]
2020
[77]	Cheyette, S. J., & Piantadosi, S. T. (2020). A unified account of numerosity perception. Nature Human Behavior, 4, 1265–1272. [bibtex] [pdf]
[76]	Ferrigno, S., Cheyette, S. J., Piantadosi, S. T., & Cantlon, J. F. (2020). Recursive sequence generation in monkeys, children, US adults, and native Amazonians. Science Advances, 6(26). [bibtex] [url]
[75]	Ferrigno, S., Cheyette, S. J., Dedhe, A., Piantadosi, S. T., & Cantlon, J. F. (2020). Simple models of sequential processing cannot explain center-embedded generalizations. Science Advances eLetters. [bibtex] [url]
[74]	Gorenstein, M. A., Cedegao, Z., & Piantadosi, S. T. (2020). A model of temporal connective acquisition. In Proceedings of the Cognitive Science Society. [bibtex] [pdf]
[73]	Lake, B., & Piantadosi, S. T. (2020). People Infer Recursive Visual Concepts from Just a Few Examples. Computational Brain and Behavior, 3, 54–65. [bibtex] [pdf]
[72]	Mollica, F., Siegelman, M., Diachek, E., Piantadosi, S. T., Mineroff, Z., Futrell, R., Kean, H., Qian, P., & Fedorenko, E. (2020). Composition is the core driver of the language-selective network. Neurobiology of Language, 1(1), 104–134. [bibtex] [url]
[71]	Pitt, B., Casasanto, D., Ferrigno, S., Gibson, E., & Piantadosi, S. T. (2020). Multi-directional mappings in the minds of the Tsimane': Size, time, and number on three spatial axes. In Proceedings of the Cognitive Science Society. [bibtex] [pdf]
[70]	Rule, J., Tenenbaum, J., & Piantadosi, S. (2020). The Child as Hacker. Trends in Cognitive Science, 24(11). [bibtex] [pdf]
[69]	Yoo, S. B. M., Tu, J. C., Piantadosi, S. T., & Hayden, B. (2020). The neural basis of predictive pursuit. Nature Neuroscience, 23, 252–259. [bibtex] [url]
2019
[68]	Alonso-Diaz, S., Cantlon, J., & Piantadosi, S. T. (2019). Intrinsic whole number bias in an indigenous population. In Proceedings of the Cognitive Science Society. [bibtex] [pdf]
[67]	Cheyette, S. J., & Piantadosi, S. T. (2019). A primarily serial, foveal accumulator underlies approximate numerical estimation. Proceedings of the National Academy of Sciences, 116(36), 17729–17734. [bibtex] [pdf]
[66]	Musolino, J., d'Agostino, K. L., & Piantadosi, S. T. (2019). Why we should abandon the Semantic Subset Principle. Language Learning and Development, 15(1), 32–46. [bibtex] [pdf]
[65]	Gibson, E., Futrell, R., Piantadosi, S. T., Dautriche, I., Mahowald, K., Bergen, L., & Levy, R. (2019). How Efficiency Shapes Human Language. Trends in Cognitive Science, 23(5), 389–407. [bibtex] [pdf]
[64]	Koopman, S. E., Arre, A., Piantadosi, S. T., & Cantlon, J. F. (2019). One-to-one correspondence without Language. Royal Society Open Science. [bibtex] [url]
[63]	Mollica, F., & Piantadosi, S. T. (2019). Humans store about 1.5 megabytes of information during language acquisition. Royal Society Open Science. [bibtex] [url]
2018
[62]	Alonso-Diaz, S., Cantlon, J. F., & Piantadosi, S. T. (2018). A threshold free model of number comparison. PLOS ONE. https://doi.org/10.1371/journal.pone.0195188 [bibtex] [pdf] [doi]
[61]	Alonso-Diaz, S., Piantadosi, S. T., Hayden, B., & Cantlon, J. F. (2018). Intrinsic whole number bias in humans. Journal of Experimental Psychology: Human Perception and Performance, 44(9), 1472–1481. [bibtex] [pdf]
[60]	Blanchard, T., Piantadosi, S. T., & Hayden, B. (2018). Robust mixture modeling reveals category-free selectivity in reward region neuronal ensembles. Journal of Neurophysiology, 119(4), 1305–1318. [bibtex] [pdf]
[59]	Brabec, M., Behrman, J., Emmett, S., Gibson, E., Kidd, C., Leonard, W., Penny, M., Piantadosi, S. T., Sharma, A., Tanner, S., Undurraga, E. A., & Godoy, R. (2018). Birth season and height among girls and boys below 12 years of age: Lasting effects and catch-up growth among native Amazonians in Bolivia. Annals of Human Biology, 45(4), 299–313. [bibtex] [pdf]
[58]	Mahowald, K., Dautriche, I., Gibson, E., & Piantadosi, S. T. (2018). Word forms are structured for efficient use. Cognitive Science, 42(8), 3116–3134. [bibtex] [pdf]
[57]	Martí, L., Mollica, F., Piantadosi, S. T., & Kidd, C. (2018). Certainty is Primarily Determined by Past Performance during Concept Learning. Open Mind, 2(2), 47–60. [bibtex] [url]
[56]	Oey, L. A., Mollica, F., & Piantadosi, S. T. (2018). Adults use gradient similarity information in compositional rules. In Proceedings of the Cognitive Science Society. [bibtex] [pdf]
[55]	Piantadosi, S. T., Palmeri, H., & Aslin, R. (2018). Limits on Composition of Conceptual Operations in 9-Month-Olds. Infancy, 23(3), 310–324. [bibtex] [pdf]
[54]	Piantadosi, S. T. (2018). One parameter is always enough. AIP Advances, 8. [bibtex] [url]
[53]	Rule, J., Schulz, E., Piantadosi, S. T., & Tenenbaum, J. B. (2018). Learning list concepts through program induction. In Proceedings of the Cognitive Science Society. [bibtex] [pdf]
[52]	Undurraga, E. A., Behrman, J., Emmet, S., Kidd, C., Leonard, W., Piantadosi, S. T., Reyes-Garcia, V., Sharma, A., Zhang, R., & Godoy, R. (2018). Child stunting is associated with weaker human capital among native Amazonians. American Journal of Human Biology, 30(1). [bibtex] [pdf]
2017
[51]	Cheyette, S. J., & Piantadosi, S. T. (2017). Knowledge transfer in a probabilistic Language of Thought. In Proceedings of the Cognitive Science Society. [bibtex] [pdf]
[50]	Dautriche, I., Mahowald, K., Gibson, E., & Piantadosi, S. T. (2017). Wordform similarity increases with semantic similarity: an analysis of 100 languages. Cognitive Science, 41(8), 2149–2169. [bibtex] [pdf]
[49]	Dautriche, I., Mahowald, K., Gibson, E., Christophe, A., & Piantadosi, S. T. (2017). Words cluster phonetically beyond phonotactic regularities. Cognition, 163, 128–145. [bibtex] [pdf]
[48]	Ferrigno, S., Jara-Ettinger, J., Piantadosi, S. T., & Cantlon, J. (2017). Universal and uniquely human factors in spontaneous number perception. Nature Communications, 8. [bibtex] [url]
[47]	Gibson, E., Futrell, R., Jara-Ettinger, J., Mahowald, K., Bergen, L., Ratnasingam, S., Gibson, M., Piantadosi, S. T., & Conway, B. R. (2017). Color naming across languages reflects color use. Proceedings of the National Academy of Sciences, 114(40), 10785–10790. [bibtex] [pdf]
[46]	Mollica, F., & Piantadosi, S. T. (2017). How data drives early word learning: A cross-linguistic waiting time analysis. Open Mind, 1(2), 67–77. [bibtex] [url]
[45]	Mollica, F., & Piantadosi, S. T. (2017). An incremental information-theoretic buffer supports sentence processing. In Proceedings of the Cognitive Science Society. [bibtex] [pdf]
[44]	Mollica, F., Wade, S., & Piantadosi, S. T. (2017). A Rational Constructivist Account of the Characteristic-to-Defining Shift. In Proceedings of the Cognitive Science Society. [bibtex] [pdf]
[43]	Overlan, M. C., Jacobs, R. A., & Piantadosi, S. T. (2017). Learning abstract visual concepts via probabilistic program induction in a Language of Thought. Cognition, 168, 320–334. [bibtex] [pdf]
[42]	Piantadosi, S. T., & Cantlon, J. (2017). True Numerical Cognition in the Wild. Psychological Science, 28(4), 462–469. [bibtex] [pdf]
2016
[41]	Bigelow, E. J., & Piantadosi, S. T. (2016). Inferring priors in compositional cognitive models. In Proceedings of the Cognitive Science Society. [bibtex] [pdf]
[40]	Bigelow, E. J., & Piantadosi, S. T. (2016). A large dataset of generalization patterns in the number game. Journal of Open Psychology Data, 4(1). https://doi.org/10.5334/jopd.19 [bibtex] [url] [doi]
[39]	Futrell, R., Stearns, L., Everett, D. L., Piantadosi, S. T., & Gibson, E. (2016). A Corpus Investigation of Syntactic Embedding in Pirahã. PLOS ONE. [bibtex] [url]
[38]	Jara-Ettinger, J., Piantadosi, S. T., Spelke, E., Levy, R., & Gibson, E. (2016). Mastery of the logic of natural numbers is not the result of mastery of counting: Evidence from late counters. Developmental Science, 20(6), e12459. [bibtex] [pdf]
[37]	Jara-Ettinger, J., Gibson, E., Kidd, C., & Piantadosi, S. T. (2016). Native Amazonian Children Forego Egalitarianism When They Learn to Count. Developmental Science, 19(6), 1104–1110. https://doi.org/10.1111/desc.12351 [bibtex] [pdf] [doi]
[36]	Martí, L., Mollica, F., Piantadosi, S. T., & Kidd, C. (2016). What determines human certainty? In Proceedings of the Cognitive Science Society. [bibtex] [pdf]
[35]	Overlan, M. C., Jacobs, R. A., & Piantadosi, S. T. (2016). A Hierarchical Probabilistic Language-of-Thought Model of Human Visual Concept Learning. In Proceedings of the Cognitive Science Society. [bibtex] [pdf]
[34]	Piantadosi, S. T., & Fedorenko, E. (2016). Infinitely productive language can arise from chance under communicative pressure. Journal of Language Evolution, 2(2), 141–147. [bibtex] [pdf]
[33]	Piantadosi, S. T., & Aslin, R. (2016). Compositional reasoning in early childhood. PLOS ONE. [bibtex] [url]
[32]	Piantadosi, S. T. (2016). Efficient estimation of Weber's W. Behavior Research Methods, 48, 42–52. [bibtex] [pdf]
[31]	Piantadosi, S. T., & Kidd, C. (2016). Endogenous or exogenous? The data don’t say (Commentary on Han, Musolino, & Lidz 2016). Proceedings of the National Academy of Sciences, 113(20). [bibtex] [pdf]
[30]	Piantadosi, S. T., & Kidd, C. (2016). Extraordinary intelligence and the care of infants. Proceedings of the National Academy of Sciences, 113(25), 6874–6879. [bibtex] [pdf]
[29]	Piantadosi, S. T., & Jacobs, R. (2016). Four problems solved by the probabilistic Language of Thought. Current Directions in Psychological Science, 25(1), 54–59. [bibtex] [pdf]
[28]	Piantadosi, S. T., Tenenbaum, J. B., & Goodman, N. D. (2016). The logical primitives of thought: Empirical foundations for compositional cognitive models. Psychological Review, 123(4), 392–424. [bibtex] [pdf]
[27]	Piantadosi, S. T. (2016). A rational analysis of the approximate number system. Psychonomic Bulletin and Review, 1–10. https://doi.org/10.3758/s13423-015-0963-8 [bibtex] [pdf] [doi]
2015
[26]	Alonso-Diaz, S., Cantlon, J., & Piantadosi, S. T. (2015). Cognition in reach: continuous statistical inference in optimal motor planning. In Proceedings of the Cognitive Science Society. [bibtex] [pdf]
[25]	Cantlon, J., Piantadosi, S. T., Ferrigno, S., Hughes, K. D., & Barnard, A. (2015). The origins of counting algorithms. Psychological Science, 26(6), 675–683. [bibtex] [pdf]
[24]	Hemmer, P., Persaud, K., Kidd, C., & Piantadosi, S. T. (2015). Inferring the Tsimane's use of color categories from recognition memory. In Proceedings of the Cognitive Science Society. [bibtex] [pdf]
[23]	Mollica, F., Piantadosi, S. T., & Tanenhaus, M. K. (2015). The perceptual foundation of linguistic context. In Proceedings of the Cognitive Science Society. [bibtex] [pdf]
[22]	Mollica, F., & Piantadosi, S. T. (2015). Towards semantically rich and recursive word learning models. In Proceedings of the Cognitive Science Society. [bibtex] [pdf]
[21]	Pelz, M., Piantadosi, S. T., & Kidd, C. (2015). The dynamics of idealized attention in complex learning environments. In The 5th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics. [bibtex] [pdf]
[20]	Piantadosi, S. T., & Hayden, B. (2015). Utility-free models of binomial choice can replicate predictions of utility models in many conditions. Frontiers in Neuroscience. [bibtex] [url]
[19]	Piantadosi, S. T. (2015). Problems in the philosophy of mathematics: A view from cognitive science. In E. Davis & P. J. Davis (Eds.), Mathematics, Substance and Surmise: Views on the Meaning and Ontology of Mathematics. Springer. [bibtex] [pdf]
[18]	Piantadosi, S. T., & Hayden, B. (2015). Response: "Commentary: Utility-free heuristic models of two-option choice can mimic predictions of utility-stage models under many conditions". Frontiers in Neuroscience, 9(299). https://doi.org/10.3389/fnins.2015.00299 [bibtex] [url] [doi]
2014
[17]	Kidd, C., Piantadosi, S. T., & Aslin, R. N. (2014). The Goldilocks Effect in Infant Auditory Attention. Child Development, 85(5), 1795–1804. https://doi.org/10.1111/cdev.12263 [bibtex] [pdf] [doi]
[16]	Piantadosi, S. T., Jara-Ettinger, J., & Gibson, E. (2014). Children's learning of number words in an indigenous farming-foraging group. Developmental Science, 17(4), 553–563. https://doi.org/10.1111/desc.12078 [bibtex] [pdf] [doi]
[15]	Piantadosi, S. T., & Gibson, E. (2014). Quantitative Standards for Absolute Linguistic Universals. Cognitive Science, 38(4), 736–756. https://doi.org/10.1111/cogs.12088 [bibtex] [pdf] [doi]
[14]	Piantadosi, S. T., Kidd, C., & Aslin, R. N. (2014). Rich analysis and rational models: Inferring individual behavior from infant looking data. Developmental Science, 17(3), 321–337. [bibtex] [pdf]
[13]	Piantadosi, S. T. (2014). Zipf’s word frequency law in natural language: A critical review and future directions. Psychonomic Bulletin & Review, 21(5), 1112–1130. https://doi.org/10.3758/s13423-014-0585-6 [bibtex] [pdf] [doi]
2013
[12]	Piantadosi, S. T., Tily, H., & Gibson, E. (2013). Information content versus word length in natural language: A reply to Ferrer-i-Cancho and Moscoso del Prado Martin [arXiv:1209.1751]. ArXiv e-prints. [bibtex] [url]
2012
[11]	Kidd, C., Piantadosi, S. T., & Aslin, R. N. (2012). The Goldilocks Effect: Human Infants Allocate Attention to Visual Sequences That Are Neither Too Simple Nor Too Complex. PLoS ONE. [bibtex] [url]
[10]	Piantadosi, S. T., Tenenbaum, J. B., & Goodman, N. D. (2012). Bootstrapping in a language of thought: a formal model of numerical concept learning. Cognition, 123(2), 199–217. [bibtex] [pdf]
[9]	Piantadosi, S. T., Stearns, L., Everett, D., & Gibson, E. (2012). A corpus analysis of Pirahã grammar: An investigation of recursion. Talk presented at the LSA (by E. Gibson). [bibtex] [pdf]
2011
[8]	Piantadosi, S. T., Tily, H., & Gibson, E. (2011). The communicative function of ambiguity in language. Cognition, 122(3), 280–291. [bibtex] [pdf]
[7]	Piantadosi, S. T. (2011). Learning and the language of thought [Doctoral dissertation, MIT]. [bibtex] [pdf]
[6]	Piantadosi, S. T., Tily, H., & Gibson, E. (2011). Reply to Reilly and Kean: Clarifications on word length and information content. Proceedings of the National Academy of Sciences (response to commentary), 108(20), E109. [bibtex] [pdf]
[5]	Piantadosi, S. T., Tily, H., & Gibson, E. (2011). Word lengths are optimized for efficient communication. Proceedings of the National Academy of Sciences, 108(9), 3526. [bibtex] [pdf]
2010
[4]	Kidd, C., Piantadosi, S. T., & Aslin, R. N. (2010). The Goldilocks Effect: Infants' preference for visual stimuli that are neither too predictable nor too surprising. In Proceedings of the Cognitive Science Society. [bibtex] [pdf]
[3]	Piantadosi, S. T., Tenenbaum, J. B., & Goodman, N. D. (2010). Beyond Boolean logic: exploring representation languages for learning complex concepts. In Proceedings of the Cognitive Science Society. [bibtex] [pdf]
2009
[2]	Piantadosi, S. T., Tily, H. J., & Gibson, E. (2009). The communicative lexicon hypothesis. In Proceedings of the Cognitive Science Society (pp. 2582–2587). [bibtex] [pdf]
2008
[1]	Piantadosi, S. T., Goodman, N. D., Ellis, B. A., & Tenenbaum, J. B. (2008). A Bayesian model of the acquisition of compositional semantics. In Proceedings of the Cognitive Science Society. [bibtex] [pdf]

• libraries & software •

A number of research software packages are actively developed by colala and available under the GNU Public License:

• LOTlib3 is a Python 3 library for modeling learning complex concepts as compositions of primitives in a language of thought. GPL3


• Fleet is a C++ library with the same goal as LOTlib3, but much faster. This is currently developed most heavily and is used by the lab for a variety of LOT-learning projects. GPL3
  
  
• pychuriso is a python implementation of churiso inference, which learns Church encodings from simple relational datasets. GPL3
  
  
• kelpy (kid experimental library in python) is a library for running simple psychology experiments in python. It is intended primarily for making simple animated displays with simple responses for baby and child experiments. It is built on top of pygame and supportrs Tobii eyetracking. GPL3

• data & code •

Data from all projects completed and in progress is available upon request

• One parameter is always enough Code for Piantadosi (2018)'s paper fitting an arbitrary scatter plot with a single free parameter. GPL3


• English Surprisal estimates -- data from Piantadosi, Tily, Gibson (2011), giving the average in-context surprisal of each word. Note that we now recommend use of ngrampy to compute this, using the free publicly available data from google books. Full data from the paper is available upon request.


• PyMC model and subject data -- from Piantadosi, Kidd, and Aslin (2014), implementing Bayesian, partially-pooled by-subject looking curves relating the surprisal of observed events to infants' probability of terminating attention.


• Open corpus of Pirahã A corpus of shallowly parsed Pirahã -- sentences used in Futrell et al.
  
  
• Set function logical learner data -- from Piantadosi, Tenenbaum, & Goodman 2016, Psych Review. Data on subject learning curves for 108 logical concepts.