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 •

Mark Gorenstein works on concepts, language, and the brain	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	Celeste Kidd	Dick Aslin
Ev Fedorenko	Dan Everett	Noah Goodman
Richard Futrell	Julian Jara-Ettinger	Julien Musolino
Ricardo Godoy	Kyle Mahowald	Isabelle Dautriche
Stephen Ferrigno

2025
[126]	The End of Radical Concept Nativism (Joshua Rule and Steven T. Piantadosi), In , 2025. [bibtex] [url]
2024
[125]	Reliable Reasoning Beyond Natural Language (Nasim Borazjanizadeh and Steven T. Piantadosi), In arXiv, 2024. [bibtex] [url]
[124]	Uniquely human intelligence arose from expanded information capacity (Jessica F. Cantlon and Steven T. Piantadosi), In Nature Reviews Psychology, 2024. [bibtex] [pdf]
[123]	Limited information-processing capacity in vision explains number psychophysics (Samuel J. Cheyette, Shengyi Wu and Steven T. Piantadosi), In Psychological Review, 2024. [bibtex] [pdf]
[122]	Response to difficulty drives variation in IQ test performance (Sam Cheyette and Steven T. Piantadosi), In Open Mind, volume 8, 2024. [bibtex] [url]
[121]	Language is primarily a tool for communication rather than thought (Evelina Fedorenko, Steven T. Piantadosi and Edward Gibson), In Nature, 2024. [bibtex] [pdf]
[120]	Continuous and Discrete Proportions Elicit Different Cognitive Strategies (Michelle A. Hurst and Steven T. Piantadosi), In Cognition, 2024. [bibtex] [pdf]
[119]	Formalising the role of behaviour in neuroscience (Steven T. Piantadosi and Charles R. Gallistel), In European Journal of Neuroscience, 2024. [bibtex] [url]
[118]	Why concepts are (probably) vectors (Steven T. Piantadosi, Dyana C.Y. Muller, Joshua S. Rule, Karthikeya Kaushik, Mark Gorenstein, Elena R. Leib and Emily Sanford), In Trends in Cognitive Sciences, 2024. [bibtex] [pdf]
[117]	Modern language models refute Chomsky's approach to language (Steven T. Piantadosi), Chapter in From fieldwork to linguistic theory: A tribute to Dan Everett (Empirically Oriented Theoretical Morphology and Syntax 15) (Edward Gibson, Moshe Poliak, eds.), Berlin: Language Science Press, 2024. [bibtex] [url]
[116]	Symbolic metaprogram search improves learning efficiency and explains rule learning in humans (Josh Rule, Steven T. Piantadosi, Andrew Cropper, Kevin Ellis, Maxwell Nye and Joshua B. Tenenbaum), In Nature Communications, 2024. [bibtex] [pdf]
2023
[115]	Language processing and language learning (Nick Chater, Andy Perfors and Steven T. Piantadosi), Chapter in Bayesian Models of Cognition: Reverse Engineering the Mind (Thomas L. Griffiths, Nick Chater, Joshua Tenenbaum, eds.), 2023. [bibtex] [pdf]
[114]	Origins of Hierarchical Logical Reasoning (Abhishek M. Dedhe, Hayley Clatterbuck, Steven T. Piantadosi and Jessica F. Cantlon), In Cognitive Science, volume 47, 2023. [bibtex] [pdf]
[113]	Cognitive Mechanisms Underlying Recursive Pattern Processing in Human Adults (Abhishek M. Dedhe, Steven T. Piantadosi and Jessica F. Cantlon), In Cognitive Science, volume 47, 2023. [bibtex] [url]
[112]	The Plausibility of Sampling as an Algorithmic Theory of Sentence Processing (Jacob Louis Hoover, Morgan Sonderegger and Steven T. Piantadosi), In Open Mind, volume 7, 2023. [bibtex] [url]
[111]	Latent diversity in conceptual representation (Louis Martí, Shengyi Wu, Steven T. Piantadosi and Celeste Kidd), In Open Mind, volume 7, 2023. [bibtex] [url]
[110]	Diverse mathematical knowledge among indigenous Amazonians (David M. O'Shaughnessy, Tania Cruz, Francis Mollica, Isabelle Boni, Julian Jara-Ettinger, Edward Gibson and Steven T. Piantadosi), In Proceedings of the National Academy of Sciences, volume 120, 2023. [bibtex] [url]
[109]	Trans-inclusive gender categories are cognitively natural (Andrew Perfors, Steven T. Piantadosi and Celeste Kidd), In Nature Human Behavior, volume 7, 2023. [bibtex] [pdf]
[108]	The Algorithmic Origins of Counting (Steven T. Piantadosi), In Child Development, volume 94, 2023. [bibtex] [pdf]
[107]	How to enumerate trees from a context-free grammar (Steven T. Piantadosi), In arXiv, 2023. [bibtex] [url]
[106]	Learning as Bayesian inference over programs (Steven T. Piantadosi, Joshua S. Rule and Joshua B. Tenenbaum), Chapter in Bayesian Models of Cognition: Reverse Engineering the Mind (Thomas L. Griffiths, Nick Chater, Joshua Tenenbaum, eds.), 2023. [bibtex] [pdf]
[105]	No clear evidence for a left-to-right mental number line in insects (Benjamin Pitt, Daniel Casasanto and Steven T. Piantadosi), In Proceedings of the National Academy of Sciences (commentary), 2023. [bibtex] [url]
[104]	Real-time pragmatic inference across cultures: evidence from a non-industrialized society (Rachel Ryskin, Miguel Angel Salinas, Steven T. Piantadosi and Edward Gibson), In Journal of Experimental Psychology: General, volume 152, 2023. [bibtex] [url]
[103]	Sampling in Approximate Number Perception (Emily M. Sanford and Steven T. Piantadosi), In Proceedings of the Annual Meeting of the Cognitive Science Society, 2023. [bibtex] [pdf]
[102]	Children’s Estimation of Peripheral Information Drives Improvements in Approximate Number Sense (Huiwen Alex Yang, Steven T. Piantadosi and Celeste Kidd), In Proceedings of the Annual Meeting of the Cognitive Science Society, 2023. [bibtex] [pdf]
2022
[101]	Culture and Commutativity (Boni, Isabelle and Piantadosi, Steven T), In Proceedings of the Annual Meeting of the Cognitive Science Society, 2022. [bibtex] [pdf]
[100]	Verbal counting and the timing of number acquisition in an indigenous Amazonian group (Isabelle Boni, Julian Jara-Ettinger, Sophie Sackstein and Steven T. Piantadosi), In PLOS ONE, 2022. [bibtex] [url]
[99]	Stochastic time-series analyses highlight the day-to-day dynamics of lexical frequencies (Cameron Holdaway and Steven Piantadosi), In Cognitive Science, volume 46, 2022. [bibtex] [pdf]
[98]	Investigating Adults’ Strategy Use During Proportional Comparison (Hurst, Michelle and Piantadosi, Steven T), In Proceedings of the Annual Meeting of the Cognitive Science Society, 2022. [bibtex] [pdf]
[97]	Reply to Kodner et al: Fundamental misunderstanding of both model and methods (Steven T. Piantadosi and Yuan Yang), In Proceedings of the National Academy of Sciences (response to commentary), 2022. [bibtex] [url]
[96]	Meaning without reference in large language models (Steven T. Piantadosi and Felix Hill), In arXiv preprint arXiv:2208.02957, 2022. [bibtex] [pdf]
[95]	Reply to Murphy et al: Program induction can learn language (Steven T. Piantadosi and Yuan Yang), In Proceedings of the National Academy of Sciences (response to commentary), 2022. [bibtex] [url]
[94]	Exact number concepts are limited to the verbal count range (Benjamin Pitt, Edward Gibson and Steven T. Piantadosi), In Psychological Science, volume 33, 2022. [bibtex] [pdf]
[93]	Different reference frames on different axes: Space and language in indigenous Amazonians (Benjamin Pitt, Alexandra Carstensen, Isabelle Boni, Steven T. Piantadosi and Edward Gibson), In Science Advances, volume 8, 2022. [bibtex] [pdf]
[92]	Learning as programming: Efficient search in models of human concept learning (Rule, Joshua S, Piantadosi, Steven T and Tenenbaum, Joshua B), In Proceedings of the Cognitive Science Society, 2022. [bibtex]
[91]	Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (Srivastava, Aarohi, Rastogi, Abhinav, Rao, Abhishek, Shoeb, Abu Awal Md, Abid, Abubakar, Fisch, Adam, Brown, Adam R, Santoro, Adam, Gupta, Aditya, Garriga-Alonso, Adrià and others), In arXiv preprint arXiv:2206.04615, 2022. [bibtex] [url]
[90]	One model for the learning of language (Yuan Yang and Steven T. Piantadosi), In Proceedings of the National Academy of Sciences, volume 119, 2022. [bibtex] [pdf]
2021
[89]	The evolution of quantitative sensitivity (Margaret A.H. Bryer, Sarah E. Koopman, Jessica F. Cantlon, Steven T. Piantadosi, E. L. Maclean, Joseph M. Baker, Michael J. Beran, Sarah M. Jones, Kerry E. Jordan, Salif Mahamane, Andreas Nieder, Bonnie M. Perdue, Frederike Range, Jeffrey R. Stevens, Masaki Tomonaga, Dorottya J. Ujfalussy and Jennifer Vonk), In Philosophical Transactions of the Royal Society B, volume 377, 2021. [bibtex] [pdf]
[88]	The psychophysics of number arise from resource-limited spatial memory (Samuel J. Cheyette, Shengyi Wu and Steven T. Piantadosi), In Proceedings of the Cognitive Science Society, 2021. [bibtex] [pdf]
[87]	The Natural Stories Corpus (Richard Futrell, Edward Gibson, Hal Tily, Idan Blank, Anastasia Vishnevetsky, Steven T. Piantadosi and Evelina Fedorenko), In Language Resources and Evaluation, volume 55, 2021. [bibtex] [url]
[86]	Logical Word Learning: The case of kinship (Francis Mollica and Steven T. Piantadosi), In Psychonomic Bulletin & Review, 2021. [bibtex] [pdf]
[85]	The Cultural Origins of Symbolic Number (David M. O'Shaughnessy, Edward Gibson and Steven T. Piantadosi), In Psychological Review, volume 129, 2021. [bibtex] [pdf]
[84]	Probability, Belief, and the Richness of Cognition (Steven T. Piantadosi), Chapter in The Cognitive Science of Belief (Julien Musolino, Joseph Sommer, Pernille Hemmer, eds.), Cambridge University Press, 2021. [bibtex]
[83]	The computational origin of representation (Steven T. Piantadosi), In Minds and Machines, volume 31, 2021. [bibtex] [pdf]
[82]	Spatial concepts of number, size, and time in an indigenous culture (Benjamin Pitt, Stephen Ferrigno, Jessica Cantlon, Daniel Casasanto, Edward Gibson and Steven T. Piantadosi), In Science Advances, volume 7, 2021. [bibtex] [url]
[81]	Variation in spatial concepts: Different frames of reference on different axes (Benjamin Pitt, A. Carstensen, E. Gibson and Steven T. Piantadosi), In Proceedings of the Cognitive Science Society, 2021. [bibtex] [pdf]
[80]	Uncontrolled corpus composition drives an apparent surge in cognitive distortions (Commentary on Bollen et al.) (Benjamin Schmidt, Steven T. Piantadosi and Kyle Mahowald), In Proceedings of the National Academy of Sciences (commentary), 2021. [bibtex] [url]
2020
[79]	A unified account of numerosity perception (Samuel J. Cheyette and Steven T. Piantadosi), In Nature Human Behavior, volume 4, 2020. [bibtex] [pdf]
[78]	Recursive sequence generation in monkeys, children, US adults, and native Amazonians (Stephen Ferrigno, Samuel J. Cheyette, Steven T. Piantadosi and Jessica F. Cantlon), In Science Advances, volume 6, 2020. [bibtex] [url]
[77]	Simple models of sequential processing cannot explain center-embedded generalizations (Stephen Ferrigno, Samuel J. Cheyette, Abhishek Dedhe, Steven T. Piantadosi and Jessica F. Cantlon), In Science Advances eLetters, 2020. [bibtex] [url]
[76]	A model of temporal connective acquisition (Mark A. Gorenstein, Zhang Cedegao and Steven T. Piantadosi), In Proceedings of the Cognitive Science Society, 2020. [bibtex] [pdf]
[75]	People Infer Recursive Visual Concepts from Just a Few Examples (B. Lake and Steven T. Piantadosi), In Computational Brain and Behavior, volume 3, 2020. [bibtex] [pdf]
[74]	Composition is the core driver of the language-selective network (Francis Mollica, Matthew Siegelman, Evgeniia Diachek, Steven T. Piantadosi, Zachary Mineroff, Richard Futrell, H. Kean, P. Qian and Evelina Fedorenko), In Neurobiology of Language, volume 1, 2020. [bibtex] [url]
[73]	Multi-directional mappings in the minds of the Tsimane': Size, time, and number on three spatial axes (Benjamin Pitt, Daniel Casasanto, Stephen Ferrigno, Edward Gibson and Steven T. Piantadosi), In Proceedings of the Cognitive Science Society, 2020. [bibtex] [pdf]
[72]	The Child as Hacker (Josh Rule, Josh Tenenbaum and Steven Piantadosi), In Trends in Cognitive Science, volume 24, 2020. [bibtex] [pdf]
[71]	The neural basis of predictive pursuit (Seng Bum Michael Yoo, Jiaxin Cindy Tu, Steven T. Piantadosi and Benjamin Hayden), In Nature Neuroscience, volume 23, 2020. [bibtex] [url]
2019
[70]	Intrinsic whole number bias in an indigenous population (Santiago Alonso-Diaz, Jessica Cantlon and Steven T. Piantadosi), In Proceedings of the Cognitive Science Society, 2019. [bibtex] [pdf]
[69]	A primarily serial, foveal accumulator underlies approximate numerical estimation (Samuel J. Cheyette and Steven T. Piantadosi), In Proceedings of the National Academy of Sciences, volume 116, 2019. [bibtex] [pdf]
[68]	Why we should abandon the Semantic Subset Principle (Julien Musolino, Kelsey Laity d'Agostino and Steven T. Piantadosi), In Language Learning and Development, volume 15, 2019. [bibtex] [pdf]
[67]	How Efficiency Shapes Human Language (Edward Gibson, Richard Futrell, Steven T. Piantadosi, Isabelle Dautriche, Kyle Mahowald, Leon Bergen and Roger Levy), In Trends in Cognitive Science, volume 23, 2019. [bibtex] [pdf]
[66]	One-to-one correspondence without Language (Sarah E. Koopman, Alyssa Arre, Steven T. Piantadosi and Jessica F. Cantlon), In Royal Society Open Science, 2019. [bibtex] [url]
[65]	Humans store about 1.5 megabytes of information during language acquisition (Francis Mollica and Steven T. Piantadosi), In Royal Society Open Science, 2019. [bibtex] [url]
2018
[64]	A threshold free model of number comparison (Santiago Alonso-Diaz, Jessica F. Cantlon and Steven T. Piantadosi), In PLOS ONE, 2018. [bibtex] [pdf] [doi]
[63]	Intrinsic whole number bias in humans (Santiago Alonso-Diaz, Steven T. Piantadosi, Benjamin Hayden and Jessica F. Cantlon), In Journal of Experimental Psychology: Human Perception and Performance, volume 44, 2018. [bibtex] [pdf]
[62]	Robust mixture modeling reveals category-free selectivity in reward region neuronal ensembles (Tommy Blanchard, Steven T. Piantadosi and Benjamin Hayden), In Journal of Neurophysiology, volume 119, 2018. [bibtex] [pdf]
[61]	Birth season and height among girls and boys below 12 years of age: Lasting effects and catch-up growth among native Amazonians in Bolivia (Marek Brabec, Jere Behrman, Susan Emmett, Edward Gibson, Celeste Kidd, William Leonard, Mary Penny, Steven T. Piantadosi, Abhishek Sharma, Susan Tanner, Eduardo A. Undurraga and Ricardo Godoy), In Annals of Human Biology, volume 45, 2018. [bibtex] [pdf]
[60]	Word forms are structured for efficient use (K. Mahowald, I. Dautriche, E. Gibson and Steven T. Piantadosi), In Cognitive Science, volume 42, 2018. [bibtex] [pdf]
[59]	Certainty is Primarily Determined by Past Performance during Concept Learning (Louis Martí, Francis Mollica, Steven T. Piantadosi and Celeste Kidd), In Open Mind, volume 2, 2018. [bibtex] [url]
[58]	Adults use gradient similarity information in compositional rules (Lauren A. Oey, Francis Mollica and Steven T. Piantadosi), In Proceedings of the Cognitive Science Society, 2018. [bibtex] [pdf]
[57]	Limits on Composition of Conceptual Operations in 9-Month-Olds (Steven T. Piantadosi, Palmeri, Holly and Aslin, Richard), In Infancy, volume 23, 2018. [bibtex] [pdf]
[56]	One parameter is always enough (Steven T. Piantadosi), In AIP Advances, volume 8, 2018. [bibtex] [url]
[55]	Learning list concepts through program induction (Joshua Rule, Eric Schulz, Steven T. Piantadosi and Joshua B. Tenenbaum), In Proceedings of the Cognitive Science Society, 2018. [bibtex] [pdf]
[54]	Child stunting is associated with weaker human capital among native Amazonians (Eduardo A. Undurraga, Jere Behrman, Susan Emmet, Celeste Kidd, William Leonard, Steven T. Piantadosi, V. Reyes-Garcia, Abhishek Sharma, Rebecca Zhang and Ricardo Godoy), In American Journal of Human Biology, volume 30, 2018. [bibtex] [pdf]
2017
[53]	Knowledge transfer in a probabilistic Language of Thought (Samuel J. Cheyette and Steven T. Piantadosi), In Proceedings of the Cognitive Science Society, 2017. [bibtex] [pdf]
[52]	Wordform similarity increases with semantic similarity: an analysis of 100 languages (Isabelle Dautriche, Kyle Mahowald, Edward Gibson and Steven T. Piantadosi), In Cognitive Science, volume 41, 2017. [bibtex] [pdf]
[51]	Words cluster phonetically beyond phonotactic regularities (I Dautriche, K. Mahowald, E. Gibson, A. Christophe and Steven T. Piantadosi), In Cognition, volume 163, 2017. [bibtex] [pdf]
[50]	Universal and uniquely human factors in spontaneous number perception (Stephen Ferrigno, Julian Jara-Ettinger, Steven T. Piantadosi and Jessica Cantlon), In Nature Communications, volume 8, 2017. [bibtex] [url]
[49]	Color naming across languages reflects color use (Gibson, Edward, Futrell, Richard, Jara-Ettinger, Julian, Mahowald, Kyle, Bergen, Leon, Ratnasingam, Sivalogeswaran, Gibson, Mitchell, Piantadosi, Steven T and Conway, Bevil R), In Proceedings of the National Academy of Sciences, National Acad Sciences, volume 114, 2017. [bibtex] [pdf]
[48]	How data drives early word learning: A cross-linguistic waiting time analysis (Frank Mollica and Steven T. Piantadosi), In Open Mind, volume 1, 2017. [bibtex] [url]
[47]	An incremental information-theoretic buffer supports sentence processing (Francis Mollica and Steven T. Piantadosi), In Proceedings of the Cognitive Science Society, 2017. [bibtex] [pdf]
[46]	A Rational Constructivist Account of the Characteristic-to-Defining Shift (Francis Mollica, Shirlene Wade and Steven T. Piantadosi), In Proceedings of the Cognitive Science Society, 2017. [bibtex] [pdf]
[45]	Learning abstract visual concepts via probabilistic program induction in a Language of Thought (Overlan, Matthew C, Jacobs, Robert A and Piantadosi, Steven T), In Cognition, volume 168, 2017. [bibtex] [pdf]
[44]	True Numerical Cognition in the Wild (Steven T. Piantadosi and Jessica Cantlon), In Psychological Science, volume 28, 2017. [bibtex] [pdf]
2016
[43]	Inferring priors in compositional cognitive models (Eric J. Bigelow and Steven T. Piantadosi), In Proceedings of the Cognitive Science Society, 2016. [bibtex] [pdf]
[42]	A large dataset of generalization patterns in the number game (Eric J. Bigelow and Steven T. Piantadosi), In Journal of Open Psychology Data, volume 4, 2016. [bibtex] [url] [doi]
[41]	A Corpus Investigation of Syntactic Embedding in Pirahã (Richard Futrell, Laura Stearns, Daniel L. Everett, Steven T. Piantadosi and Edward Gibson), In PLOS ONE, 2016. [bibtex] [url]
[40]	Mastery of the logic of natural numbers is not the result of mastery of counting: Evidence from late counters (Julian Jara-Ettinger, Steven T. Piantadosi, Elizabeth Spelke, Roger Levy and Edward Gibson), In Developmental Science, volume 20, 2016. [bibtex] [pdf]
[39]	Native Amazonian Children Forego Egalitarianism When They Learn to Count (Julian Jara-Ettinger, E. Gibson, C. Kidd and Steven T. Piantadosi), In Developmental Science, volume 19, 2016. [bibtex] [pdf] [doi]
[38]	What determines human certainty? (Louis Martí, Francis Mollica, Steven T. Piantadosi and Celeste Kidd), In Proceedings of the Cognitive Science Society, 2016. [bibtex] [pdf]
[37]	A Hierarchical Probabilistic Language-of-Thought Model of Human Visual Concept Learning (Matthew C. Overlan, Robert A. Jacobs and Steven T. Piantadosi), In Proceedings of the Cognitive Science Society, 2016. [bibtex] [pdf]
[36]	Infinitely productive language can arise from chance under communicative pressure (Steven T. Piantadosi and Evelina Fedorenko), In Journal of Language Evolution, volume 2, 2016. [bibtex] [pdf]
[35]	Compositional reasoning in early childhood (Steven T. Piantadosi and R. Aslin), In PLOS ONE, 2016. [bibtex] [url]
[34]	Efficient estimation of Weber's W (Steven T. Piantadosi), In Behavior Research Methods, volume 48, 2016. [bibtex] [pdf]
[33]	Endogenous or exogenous? The data don’t say (Commentary on Han, Musolino, & Lidz 2016) (Steven T. Piantadosi and Celeste Kidd), In Proceedings of the National Academy of Sciences, volume 113, 2016. [bibtex] [pdf]
[32]	Extraordinary intelligence and the care of infants (Steven T. Piantadosi and Celeste Kidd), In Proceedings of the National Academy of Sciences, volume 113, 2016. [bibtex] [pdf]
[31]	Four problems solved by the probabilistic Language of Thought (Steven T. Piantadosi and Robbie Jacobs), In Current Directions in Psychological Science, volume 25, 2016. [bibtex] [pdf]
[30]	The logical primitives of thought: Empirical foundations for compositional cognitive models (Steven T. Piantadosi, J.B. Tenenbaum and N.D. Goodman), In Psychological Review, volume 123, 2016. [bibtex] [pdf]
[29]	A rational analysis of the approximate number system (Steven T. Piantadosi), In Psychonomic Bulletin and Review, 2016. [bibtex] [pdf] [doi]
2015
[28]	Cognition in reach: continuous statistical inference in optimal motor planning (Santiago Alonso-Diaz, Jessica Cantlon and Steven T. Piantadosi), In Proceedings of the Cognitive Science Society, 2015. [bibtex] [pdf]
[27]	The origins of counting algorithms (J. Cantlon, Steven T. Piantadosi, S. Ferrigno, K.D. Hughes and A. Barnard), In Psychological Science, volume 26, 2015. [bibtex] [pdf]
[26]	Inferring the Tsimane's use of color categories from recognition memory (Pernille Hemmer, Kimele Persaud, Celeste Kidd and Steven T. Piantadosi), In Proceedings of the Cognitive Science Society, 2015. [bibtex] [pdf]
[25]	The perceptual foundation of linguistic context (Francis Mollica, Steven T. Piantadosi and Michael K. Tanenhaus), In Proceedings of the Cognitive Science Society, 2015. [bibtex] [pdf]
[24]	Towards semantically rich and recursive word learning models (Francis Mollica and Steven T. Piantadosi), In Proceedings of the Cognitive Science Society, 2015. [bibtex] [pdf]
[23]	The dynamics of idealized attention in complex learning environments (Madeline Pelz, Steven T. Piantadosi and Celeste Kidd), In The 5th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, 2015. [bibtex] [pdf]
[22]	Utility-free models of binomial choice can replicate predictions of utility models in many conditions (Steven T. Piantadosi and B. Hayden), In Frontiers in Neuroscience, 2015. [bibtex] [url]
[21]	Problems in the philosophy of mathematics: A view from cognitive science (Steven T. Piantadosi), Chapter in Mathematics, Substance and Surmise: Views on the Meaning and Ontology of Mathematics (Ernest Davis, Philip J. Davis, eds.), Springer, 2015. [bibtex] [pdf]
[20]	Response: "Commentary: Utility-free heuristic models of two-option choice can mimic predictions of utility-stage models under many conditions" (Piantadosi, Steven T and Hayden, Benjamin), In Frontiers in Neuroscience, volume 9, 2015. [bibtex] [url] [doi]
2014
[19]	The Goldilocks Effect in Infant Auditory Attention (Kidd, Celeste, Piantadosi, Steven T. and Aslin, Richard N.), In Child Development, volume 85, 2014. [bibtex] [pdf] [doi]
[18]	Children's learning of number words in an indigenous farming-foraging group (Piantadosi, Steven T., Jara-Ettinger, Julian and Gibson, Edward), In Developmental Science, volume 17, 2014. [bibtex] [pdf] [doi]
[17]	Quantitative Standards for Absolute Linguistic Universals (Piantadosi, Steven T. and Gibson, Edward), In Cognitive Science, volume 38, 2014. [bibtex] [pdf] [doi]
[16]	Rich analysis and rational models: Inferring individual behavior from infant looking data (Steven T. Piantadosi, C. Kidd and R.N. Aslin), In Developmental Science, volume 17, 2014. [bibtex] [pdf]
[15]	Zipf’s word frequency law in natural language: A critical review and future directions (Steven T. Piantadosi), In Psychonomic Bulletin & Review, Springer US, volume 21, 2014. [bibtex] [pdf] [doi]
2013
[14]	Information content versus word length in natural language: A reply to Ferrer-i-Cancho and Moscoso del Prado Martin [arXiv:1209.1751] (Steven T. Piantadosi, H. Tily and E. Gibson), In ArXiv e-prints, 2013. [bibtex] [url]
2012
[13]	The Goldilocks Effect: Human Infants Allocate Attention to Visual Sequences That Are Neither Too Simple Nor Too Complex (C. Kidd, Steven T. Piantadosi and R.N. Aslin), In PLoS ONE, 2012. [bibtex] [url]
[12]	Bootstrapping in a language of thought: a formal model of numerical concept learning (Piantadosi, Steven T., Tenenbaum, J.B. and Goodman, N.D), In Cognition, volume 123, 2012. [bibtex] [pdf]
[11]	A corpus analysis of Pirahã grammar: An investigation of recursion (Steven T. Piantadosi, L. Stearns, D. Everett and E. Gibson), In Talk presented at the LSA (by E. Gibson)., 2012. [bibtex] [pdf]
2011
[10]	The communicative function of ambiguity in language (Piantadosi, Steven T., Tily, H. and Gibson, E.), In Cognition, volume 122, 2011. [bibtex] [pdf]
[9]	Learning and the language of thought (Steven T. Piantadosi), PhD thesis, MIT, 2011. [bibtex] [pdf]
[8]	Reply to Reilly and Kean: Clarifications on word length and information content (Piantadosi, Steven T., Tily, H. and Gibson, E.), In Proceedings of the National Academy of Sciences (response to commentary), National Acad Sciences, volume 108, 2011. [bibtex] [pdf]
[7]	Word lengths are optimized for efficient communication (Piantadosi, Steven T., Tily, H. and Gibson, E.), In Proceedings of the National Academy of Sciences, National Acad Sciences, volume 108, 2011. [bibtex] [pdf]
2010
[6]	The Goldilocks Effect: Infants' preference for visual stimuli that are neither too predictable nor too surprising (Kidd, C., Piantadosi, Steven T. and Aslin, R.N.), In Proceedings of the Cognitive Science Society, 2010. [bibtex] [pdf]
[5]	Beyond Boolean logic: exploring representation languages for learning complex concepts (Piantadosi, Steven T., Tenenbaum, J.B. and Goodman, N.D.), In Proceedings of the Cognitive Science Society, 2010. [bibtex] [pdf]
2009
[4]	The communicative lexicon hypothesis (Piantadosi, Steven T., Tily, H.J. and Gibson, E.), In Proceedings of the Cognitive Science Society, 2009. [bibtex] [pdf]
2008
[3]	A Bayesian model of the acquisition of compositional semantics (Piantadosi, Steven T., Goodman, N.D., Ellis, B.A. and Tenenbaum, Joshua B.), In Proceedings of the Cognitive Science Society, 2008. [bibtex] [pdf]
under review
[2]	Indigenous Amazonians spontaneously use space to offload cognitive demands (Elena Leib, Silvia Bunge and Steven T. Piantadosi), In , under review. [bibtex]
[1]	Algorithm induction in indigenous Amazonian children (Benjamin Pitt, Elena Leib, David O'Shaughnessy, Charlene Gallardo, Stephen Ferrigno and Steven T. Piantadosi), In , under review. [bibtex]

• 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.