steven t. piantadosi spiantado@gmail.com @spiantado google scholar github lab cv

about

I am a professor at UC Berkeley in psychology and neuroscience, where I head the computation and language lab (colala). I also sometimes teach math at Mt. Tamalpais College. My research uses formal computational methods and behavioral experiments to study how people learn language and create conceptual systems. You can read about some of my work on information and language, language acquisition, ambiguity, and the evolution of human-like cognition.

I am an NCSE Steve who really likes free software, wikipedia, and accordions.

My partner, Meghan Dorrian, is a bay area architect at Young America Creative.

I was one of nine faculty and students in a lawsuit against the University of Rochester over harassment and retaliation by the administration.... In 2018, I resigned from the University of Rochester's department of Brain and Cognitive Sciences over its mishandling of a series of sexual harassment complaints from sixteen students and subsequent retaliation against those who complained (lawsuit). We released a public response to the University's paid investigation, which confirmed egregious behavior but which led to no consequences. Many students from the department who are not plaintiffs have spoken out in venues including Nature and The Chronicle of Higher Education, especially following the University of Rochester's de-anonymization of 23 witnesses in the case. Jessica Cantlon and Celeste Kidd were honored as two of Time's People of the Year in 2018 for their whistleblowing in the case. Women in Cognitive Science awarded Dick Aslin an Honorary Award for Enduring Leadership in 2018, and his Atkinson Prize from the National Academy of Sciences highlighted his "leadership in the field by standing up to support his students, especially women, when they face injustice." In 2020, the lawsuit was settled and the university's new administration thanked the plaintiffs for their efforts. [more]

representative publications

Diverse mathematical knowledge among indigenous Amazonians.

One model for the learning of language.

The communicative function of ambiguity in language.

Zipf’s word frequency law in natural language: A critical review and future directions.

Bootstrapping in a language of thought: A formal model of numerical concept learning.

A unified account of numerosity perception.

courses

• Fall 2023 (undergraduate) - Insect Cognition
• Fall 2023 (undergraduate, Mt. Tamalpais College, co-instructor) - Math 50B
• Spring 2023 (graduate) - Data Analysis
• Fall 2022 (graduate) - Bayesian models in cognition
• Spring 2022 (undergraduate) - Computational Cognitive Science
• Spring 2021 (graduate) - Data Analysis
• Spring 2020 (undergraduate) - Computational Cognitive Science
• Spring 2019 (undergraduate) - Computational Cognitive Science
• Spring 2018 (undergraduate) - Cognition
• Fall 2017 (graduate) - Behavioral Methods
• Spring 2017 (undergraduate) - Cognition
• Fall 2016 (graduate) - Information theory in cognitive science and neuroscience
• Spring 2016 (undergraduate) - Cognition
• Fall 2015 (graduate) - Behavioral Methods
• Spring 2014 (undergraduate) - Cognition

data + code

All data and code from papers published and in progress is available upon request.

I develop several free libraries for research in cognitive science:

• Fleet - An in-progress C++ library for "language of thought" models
• LOTlib3 - inference in "language of thought models", program induction as a cognitive theory
• kelpy - Library for constructing kid experiments on touchscreens and eyetrackers
• pychuriso - A python implementation of churiso

publications

under review
[141]	Rule, J., Piantadosi, S.T. (under review). The End of Radical Concept Nativism. [email]
[140]	Rule, J., Piantadosi, S.T., Tenenbaum, J.B. (under review). Efficient learning of symbolic concepts via metaprogram search. [email]
[139]	Register, Y., Mollica, F., Piantadosi, S.T. (under review). Semantic verification is flexible and sensitive to context. [email]
[138]	Pitt, B., Leib, E., O'Shaughnessy, D., Gallardo, C., Ferrigno, S., Piantadosi, S.T. (under review). Algorithm induction in indigenous Amazonian children.
[137]	Leib, E., Bunge, S., Piantadosi, S.T. (under review). Indigenous Amazonians spontaneously use space to offload cognitive demands.
[136]	Hurst, M.A., Piantadosi, S.T. (under review). Continuous and Discrete Proportions Elicit Different Cognitive Strategies. [email]
[135]	Fedorenko, E., Piantadosi, S.T., Gibson, E. (under review). Language is a tool for communication, not complex thought. [email]
in press
[134]	Piantadosi, S.T., Muller, D.C., Rule, J.S., Kaushik, K., Gorenstein, M., Leib, E.R., Sanford, E. (in press). How cognitive science (probably) figured out concepts. Trends in Cognitive Sciences. [email]
[133]	Piantadosi, S.T., Gallistel, C.R. (in press). Formalizing the Role of Behavior in Neuroscience. European Journal of Neuroscience. [email]
[132]	Cheyette, S.J., Wu, S., Piantadosi, S.T. (in press). Limited information-processing capacity in vision explains number psychophysics. Psychological Review. [email]
2024
[131]	Piantadosi, S.T. (2024). Modern language models refute Chomsky's approach to language. in From fieldwork to linguistic theory: A tribute to Dan Everett. E. Gibson, M. Poliak, (Eds). Language Science Press. [pdf]
[130]	Cheyette, S., Piantadosi, S.T. (2024). Response to difficulty drives variation in IQ test performance. Open Mind, 8, 265-277. [pdf]
[129]	Cantlon, J.F., Piantadosi, S.T. (2024). Information capacity sparked human intelligence. Nature Reviews Psychology. [pdf]
2023
[128]	Yang, H.A., Piantadosi, S.T., Kidd, C. (2023). Children’s Estimation of Peripheral Information Drives Improvements in Approximate Number Sense. Proceedings of the Annual Meeting of the Cognitive Science Society. [pdf]
[127]	Sanford, E.M., Piantadosi, S.T. (2023). Sampling in Approximate Number Perception. Proceedings of the Annual Meeting of the Cognitive Science Society. [pdf]
[126]	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. [pdf]
[125]	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). [pdf]
[124]	Piantadosi, S.T., Rule, J.S., Tenenbaum, J.B. (2023). Learning as Bayesian inference over programs. in Bayesian Models of Cognition: Reverse Engineering the Mind. T. L. Griffiths, N. Chater, J. Tenenbaum, (Eds). [pdf]
[123]	Piantadosi, S.T. (2023). How to enumerate trees from a context-free grammar. arXiv. [pdf]
[122]	Piantadosi, S.T. (2023). The Algorithmic Origins of Counting. Child Development, 94(6), 1472-1490. [pdf]
[121]	Perfors, A., Piantadosi, S.T., Kidd, C. (2023). Trans-inclusive gender categories are cognitively natural. Nature Human Behavior, 7, 1609-1611. [pdf]
[120]	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). [pdf]
[119]	Martí, L., Wu, S., Piantadosi, S.T., Kidd, C. (2023). Latent diversity in conceptual representation. Open Mind, 7, 79-92. [pdf]
[118]	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. [pdf]
[117]	Dedhe, A.M., Piantadosi, S.T., Cantlon, J.F. (2023). Cognitive Mechanisms Underlying Recursive Pattern Processing in Human Adults. Cognitive Science, 47(4). [pdf]
[116]	Dedhe, A.M., Clatterbuck, H., Piantadosi, S.T., Cantlon, J.F. (2023). Origins of Hierarchical Logical Reasoning. Cognitive Science, 47(2). [pdf]
[115]	Chater, N., Perfors, A., Piantadosi, S.T. (2023). Language processing and language learning. in Bayesian Models of Cognition: Reverse Engineering the Mind. T. L. Griffiths, N. Chater, J. Tenenbaum, (Eds). [pdf]
2022
[114]	Yang, Y., Piantadosi, S.T. (2022). One model for the learning of language. Proceedings of the National Academy of Sciences, 119(5). [pdf] [tldr] A program-learning model can build generative systems for formal languages.
[113]	Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., others, (2022). Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615. [pdf]
[112]	Rule, J.S., Piantadosi, S.T., Tenenbaum, J.B. (2022). Learning as programming: Efficient search in models of human concept learning. Proceedings of the Cognitive Science Society.
[111]	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). [pdf]
[110]	Pitt, B., Gibson, E., Piantadosi, S.T. (2022). Exact number concepts are limited to the verbal count range. Psychological Science, 33(3). [pdf] [tldr] Indigenous Amazonians only exactly manipulate cardinalities that have verbal labels.
[109]	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). [pdf]
[108]	Piantadosi, S.T., Hill, F. (2022). Meaning without reference in large language models. arXiv preprint arXiv:2208.02957. [pdf] [tldr] Large language models probably implement some simple version of conceptual role semantics, a leading account of how human concepts work.
[107]	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). [pdf] [correction] This paper incorrectly stated that an n-gram model can capture all finite-state languages. It should state that an n-gram model can capture the finite-state languages that Koder et al. examined.
[106]	Hurst, M., Piantadosi, S.T. (2022). Investigating Adults’ Strategy Use During Proportional Comparison. Proceedings of the Annual Meeting of the Cognitive Science Society. [pdf]
[105]	Holdaway, C., Piantadosi, S. (2022). Stochastic time-series analyses highlight the day-to-day dynamics of lexical frequencies. Cognitive Science, 46(12). [pdf]
[104]	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. [pdf]
[103]	Boni, I., Piantadosi, S.T. (2022). Culture and Commutativity. Proceedings of the Annual Meeting of the Cognitive Science Society. [pdf] [tldr] Cultural support in the form of education or commerce is likely necessary for children to learn commutativity in arithmetic.
2021
[102]	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). [pdf]
[101]	Pitt, B., Carstensen, A., Gibson, E., Piantadosi, S.T. (2021). Variation in spatial concepts: Different frames of reference on different axes. Proceedings of the Cognitive Science Society. [pdf] [tldr] Tsimane' participants use different frames of reference on different spatial axes.
[100]	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). [pdf] [tldr] Tsimane' participants do not map number, size, and time to spatial axes in the same directions
[99]	Piantadosi, S.T. (2021). The computational origin of representation. Minds and Machines, 31(3), 1-58. [pdf]
[98]	Piantadosi, S.T. (2021). Probability, Belief, and the Richness of Cognition. in The Cognitive Science of Belief. J. Musolino, J. Sommer, P. Hemmer, (Eds). Cambridge University Press.
[97]	O'Shaughnessy, D.M., Gibson, E., Piantadosi, S.T. (2021). The Cultural Origins of Symbolic Number. Psychological Review, 129(6). [pdf]
[96]	Mollica, F., Piantadosi, S.T. (2021). Logical Word Learning: The case of kinship. Psychonomic Bulletin & Review. [pdf] [tldr] A program-learning model can acquire key aspects of kinship terms
[95]	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. [pdf]
[94]	Cheyette, S.J., Wu, S., Piantadosi, S.T. (2021). The psychophysics of number arise from resource-limited spatial memory. Proceedings of the Cognitive Science Society. [pdf] [tldr] Imperfect visual memory can derive Weber's law, subitizing, and number psychophysics.
[93]	Bryer, M.A., 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). [pdf]
2020
[92]	Yoo, S.B.M., Tu, J.C., Piantadosi, S.T., Hayden, B. (2020). The neural basis of predictive pursuit. Nature Neuroscience, 23, 252-259. [pdf] [tldr] Monkeys predict where prey will go by extrapolating with physics
[91]	Rule, J., Tenenbaum, J., Piantadosi, S. (2020). The Child as Hacker. Trends in Cognitive Science, 24(11). [pdf] [tldr] Children must be able to learn over computationally-sophisticated representations. Maybe they are like programmers.
[90]	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. Proceedings of the Cognitive Science Society. [pdf] [tldr] The Tsimane' do not put size, time, and number into space in the same way, arguing against theories of a generalized magnitude system.
[89]	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. [pdf]
[88]	Lake, B., Piantadosi, S.T. (2020). People Infer Recursive Visual Concepts from Just a Few Examples. Computational Brain and Behavior, 3, 54-65. [pdf] [tldr] Um read the title.
[87]	Gorenstein, M.A., Cedegao, Z., Piantadosi, S.T. (2020). A model of temporal connective acquisition. Proceedings of the Cognitive Science Society. [pdf] [tldr] A LOT model can learn temporal connectives.
[86]	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. [pdf]
[85]	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). [pdf] [tldr] Humans really like to make recursive generalizations; monkeys will, but less so.
[84]	Cheyette, S.J., Piantadosi, S.T. (2020). A unified account of numerosity perception. Nature Human Behavior, 4, 1265-1272. [pdf]
2019
[83]	Mollica, F., Piantadosi, S.T. (2019). Humans store about 1.5 megabytes of information during language acquisition. Royal Society Open Science. [pdf] [tldr] Some rough estimates show that people know about 1.5 megabytes of information about language.
[82]	Koopman, S.E., Arre, A., Piantadosi, S.T., Cantlon, J.F. (2019). One-to-one correspondence without Language. Royal Society Open Science. [pdf]
[81]	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. [pdf] [tldr] Pressures for efficient usage can be found in the properties of human language.
[80]	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. [pdf] [tldr] Children don't learn language by starting with the most specific possible meaning.
[79]	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. [pdf] [tldr] What people estimate for a number depends on how many items they looked at.
[78]	Alonso-Diaz, S., Cantlon, J., Piantadosi, S.T. (2019). Intrinsic whole number bias in an indigenous population. Proceedings of the Cognitive Science Society. [pdf]
2018
[77]	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). [pdf]
[76]	Rule, J., Schulz, E., Piantadosi, S.T., Tenenbaum, J.B. (2018). Learning list concepts through program induction. Proceedings of the Cognitive Science Society. [pdf]
[75]	Piantadosi, S.T. (2018). One parameter is always enough. AIP Advances, 8. [pdf] [tldr] A single real number free parameter can fit any scatter plot.
[74]	Piantadosi, S.T., Palmeri, H., Aslin, R. (2018). Limits on Composition of Conceptual Operations in 9-Month-Olds. Infancy, 23(3), 310-324. [pdf] [tldr] Infants don't seem to correctly predict what happens when you put together two novel functions.
[73]	Oey, L.A., Mollica, F., Piantadosi, S.T. (2018). Adults use gradient similarity information in compositional rules. Proceedings of the Cognitive Science Society. [pdf] [tldr] In learnings tasks, people combine rules with continuous variables to form concepts (but our theories don't do this).
[72]	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. [pdf] [tldr] People's confidence that they have learned something correctly is primarily based on how well they seem themselves doing, not on an idealized model of how certain they should be.
[71]	Mahowald, K., Dautriche, I., Gibson, E., Piantadosi, S.T. (2018). Word forms are structured for efficient use. Cognitive Science, 42(8), 3116-3134. [pdf][data] [tldr] Words that people use the most tend to have the easiest forms.
[70]	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. [pdf]
[69]	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. [pdf]
[68]	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. [pdf]
[67]	Alonso-Diaz, S., Cantlon, J.F., Piantadosi, S.T. (2018). A threshold free model of number comparison. PLOS ONE. [pdf] [tldr] [doi] Hand movements to the larger of two numbers reflect show that people continuously update their certainty throughout movement.
2017
[66]	Piantadosi, S.T., Cantlon, J. (2017). True Numerical Cognition in the Wild. Psychological Science, 28(4), 462-469. [pdf] [tldr] Wild primates make decisions about where to go according to number systems we can study in the lab and in people.
[65]	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. [pdf]
[64]	Mollica, F., Wade, S., Piantadosi, S.T. (2017). A Rational Constructivist Account of the Characteristic-to-Defining Shift. Proceedings of the Cognitive Science Society. [pdf]
[63]	Mollica, F., Piantadosi, S.T. (2017). An incremental information-theoretic buffer supports sentence processing. Proceedings of the Cognitive Science Society. [pdf] [tldr] Reading time patterns suggest that people store linguistic information in a FIFO buffer.
[62]	Mollica, F., Piantadosi, S.T. (2017). How data drives early word learning: A cross-linguistic waiting time analysis. Open Mind, 1(2), 67-77. [pdf] [tldr] The distribution of ages at which children learn early words suggests that they need about 10-20 informative examples of each.
[61]	Gibson, E., Piantadosi, S.T., Jara-Ettinger, J., Levy, R. (2017). The use of a computer display exaggerates the connection between exact and approximate number ability in remote populations. Open Mind, 1(3), 159-168. [pdf]
[60]	Gibson, E., Piantadosi, S.T., Levy, R. (2017). Post Hoc Analysis Decisions Drive the Reported Reading Time Effects in Hackl, Koster-Hale & Varvoutis (2012). Journal of Semantics, 34(3), 539-546. [pdf]
[59]	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. [pdf]
[58]	Ferrigno, S., Jara-Ettinger, J., Piantadosi, S.T., Cantlon, J. (2017). Universal and uniquely human factors in spontaneous number perception. Nature Communications, 8. [pdf] [tldr] Indigneous adults, US adults, US kids, and primates all prefer to generalize according to number, instead of correlated dimensions like area.
[57]	Dautriche, I., Mahowald, K., Gibson, E., Christophe, A., Piantadosi, S.T. (2017). Words cluster phonetically beyond phonotactic regularities. Cognition, 163, 128-145. [pdf] [tldr] Words in language are more similar to each other than you would expect by chance.
[56]	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. [pdf] [tldr] Words that sound similar tend to have similar meanings.
[55]	Cheyette, S.J., Piantadosi, S.T. (2017). Knowledge transfer in a probabilistic Language of Thought. Proceedings of the Cognitive Science Society. [pdf]
[54]	Ballard, I., Miller, E., Piantadosi, S., Goodman, N., McClure, S. (2017). Beyond Reward Prediction Errors: Human Striatum Updates Rule Values During Learning. Cerebral Cortex, 28(11), 3965-3975. [pdf]
2016
[53]	Piantadosi, S.T. (2016). A rational analysis of the approximate number system. Psychonomic Bulletin and Review, 1-10. [pdf] [tldr] [doi] The properties of the Approximate Number System can be explained by thinking about how to represent numbers with a low probability of confusion, relative to how often you have to represent them.
[52]	Piantadosi, S.T., Tenenbaum, J., Goodman, N. (2016). The logical primitives of thought: Empirical foundations for compositional cognitive models. Psychological Review, 123(4), 392-424. [pdf] [tldr] By modeling learning, we can infer what operations people are likely to put together to form complex concepts.
[51]	Piantadosi, S.T., Jacobs, R. (2016). Four problems solved by the probabilistic Language of Thought. Current Directions in Psychological Science, 25(1), 54-59. [pdf]
[50]	Piantadosi, S.T., Kidd, C. (2016). Extraordinary intelligence and the care of infants. Proceedings of the National Academy of Sciences, 113(25), 6874-6879. [pdf] [tldr] Human-like intelligence may have evolved because helpless kids require smart parents, smart parents require big brains, and big brains lead to even more helpless kids.
[49]	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). [pdf]
[48]	Piantadosi, S.T. (2016). Efficient estimation of Weber's W. Behavior Research Methods, 48, 42-52. [pdf]
[47]	Piantadosi, S.T., Aslin, R. (2016). Compositional reasoning in early childhood. PLOS ONE. [pdf] [tldr] Children can predict what happens when you put together two novel functions.
[46]	Piantadosi, S.T., Fedorenko, E. (2016). Infinitely productive language can arise from chance under communicative pressure. Journal of Language Evolution, 2, 141-147. [pdf] [tldr] Having brains that are complex won't tend to give you languages that generate a lot of sentences, but needing to communicate many meanings will.
[45]	Overlan, M.C., Jacobs, R.A., Piantadosi, S.T. (2016). A Hierarchical Probabilistic Language-of-Thought Model of Human Visual Concept Learning. Proceedings of the Cognitive Science Society. [pdf]
[44]	Martí, L., Mollica, F., Piantadosi, S.T., Kidd, C. (2016). What determines human certainty?. Proceedings of the Cognitive Science Society. [pdf]
[43]	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. [pdf] [doi]
[42]	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. [pdf] [tldr] Children's learning of counting is not closely tied to their understanding of quantity more abstractly.
[41]	Futrell, R., Stearns, L., Everett, D.L., Piantadosi, S.T., Gibson, E. (2016). A Corpus Investigation of Syntactic Embedding in Pirahã. PLOS ONE. [pdf][data] [tldr] There is no clear evidence for recursive structures in Piraha
[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). [pdf][data] [doi]
[39]	Bigelow, E.J., Piantadosi, S.T. (2016). Inferring priors in compositional cognitive models. Proceedings of the Cognitive Science Society. [pdf]
2015
[38]	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). [pdf] [doi]
[37]	Piantadosi, S.T. (2015). Problems in the philosophy of mathematics: A view from cognitive science. in Mathematics, Substance and Surmise: Views on the Meaning and Ontology of Mathematics. E. Davis, P. J. Davis, (Eds). Springer. [pdf] [tldr] Philosophy is mainly just people failing to see that their own concepts are really imprecise.
[36]	Piantadosi, S.T., Hayden, B. (2015). Utility-free models of binomial choice can replicate predictions of utility models in many conditions. Frontiers in Neuroscience. [pdf] [tldr] You can make decisions which are equivalent to expected value decisions without ever computing expected value.
[35]	Pelz, M., Piantadosi, S.T., Kidd, C. (2015). The dynamics of idealized attention in complex learning environments. The 5th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics. [pdf]
[34]	Mollica, F., Piantadosi, S.T. (2015). Towards semantically rich and recursive word learning models. Proceedings of the Cognitive Science Society. [pdf]
[33]	Mollica, F., Piantadosi, S.T., Tanenhaus, M.K. (2015). The perceptual foundation of linguistic context. Proceedings of the Cognitive Science Society. [pdf]
[32]	Hemmer, P., Persaud, K., Kidd, C., Piantadosi, S.T. (2015). Inferring the Tsimane's use of color categories from recognition memory. Proceedings of the Cognitive Science Society. [pdf]
[31]	Gibson, E., Jacobson, P., Graff, P., Mahowald, K., Fedorenko, E., Piantadosi, S.T. (2015). A pragmatic account of complexity in definite Antecedent-Contained-Deletion relative clauses. Journal of Semantics, 32(4). [pdf]
[30]	Cantlon, J., Piantadosi, S.T., Ferrigno, S., Hughes, K., Barnard, A. (2015). The origins of counting algorithms. Psychological Science, 26(6), 675-683. [pdf] [tldr] Monkeys can follow a procedure very similar to counting, but approximate.
[29]	Alonso-Diaz, S., Cantlon, J., Piantadosi, S.T. (2015). Cognition in reach: continuous statistical inference in optimal motor planning. Proceedings of the Cognitive Science Society. [pdf]
2014
[28]	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. [pdf] [tldr] [doi] All theories of Zipf's law stink.
[27]	Piantadosi, S.T., Kidd, C., Aslin, R. (2014). Rich analysis and rational models: Inferring individual behavior from infant looking data. Developmental Science, 17(3), 321-337. [pdf] [tldr] You can find U-shaped attentional patterns within individual kids.
[26]	Piantadosi, S.T., Gibson, E. (2014). Quantitative Standards for Absolute Linguistic Universals. Cognitive Science, 38(4), 736-756. [pdf] [tldr] [doi] You can get statistical evidence that certain features of language are impossible, but it probably takes more languages than actually exist.
[25]	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. [pdf] [tldr] [doi] Children in the Tsimane', an indigneous group in Bolivia, go through the exact same series of stages in learning number words as US kids, but take much longer to do so.
[24]	Kidd, C., Piantadosi, S.T., Aslin, R.N. (2014). The Goldilocks Effect in Infant Auditory Attention. Child Development, 85(5), 1795-1804. [pdf] [tldr] [doi] Infants show U-shaped attentional preferences where they tend to look away from things that are too complex, or too simple.
2013
[23]	Rieth, C., Piantadosi, S.T., Smith, K., Vul, E. (2013). Put your money where your mouth is: Incentivizing the Truth by Making Nonreplicability Costly. European Journal of Personality. [pdf]
[22]	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. [pdf] [tldr] Random typing models of language make absolutely no sense.
[21]	Gibson, E., Bergen, L., Piantadosi, S.T. (2013). The rational integration of noise and prior semantic expectation: Evidence for a noisy-channel model of sentence interpretation. Proceedings of the National Academy of Sciences, 11(20), 8051-8056. [pdf]
[20]	Gibson, E., Piantadosi, S.T., Fedorenko, E. (2013). Quantitative methods in syntax / semantics research: A response to Sprouse & Almeida. Language and Cognitive Processes, 28(3), 229-240. [pdf]
2012
[19]	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).. [pdf]
[18]	Piantadosi, S.T., Tenenbaum, J., Goodman, N. (2012). Bootstrapping in a language of thought: a formal model of numerical concept learning. Cognition, 123(2), 199-217. [pdf] [tldr] Children may learn counting by inferring the right (latent) algorithm to explain how their parents use number words.
[17]	Mahowald, K., Fedorenko, E., Piantadosi, S.T., Gibson, E. (2012). Info/information theory: speakers actively choose shorter words in predictable contexts. Cognition, 126(2), 313-318. [pdf]
[16]	Kidd, C., Piantadosi, S.T., Aslin, R. (2012). The Goldilocks Effect: Human Infants Allocate Attention to Visual Sequences That Are Neither Too Simple Nor Too Complex. PLoS ONE. [pdf] [tldr] Infants show U-shaped attentional preferences where they tend to look away from things that are too complex, or too simple.
[15]	Gibson, E., Piantadosi, S.T., Brink, K., Bergen, L., Lim, E., Saxe, R. (2012). A noisy-channel account of crosslinguistic word order variation. Psychological Science, 24(7), 1079-1088. [pdf]
[14]	Fedorenko, E., Piantadosi, S., Gibson, E. (2012). Processing Relative Clauses in Supportive Contexts. Cognitive Science, 36(3), 1-27. [pdf]
[13]	Fedorenko, E., Piantadosi, S.T., Gibson, E. (2012). The interaction of syntactic and lexical information sources in language processing: The case of the noun--verb ambiguity. Journal of Cognitive Science. [pdf]
2011
[12]	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. [pdf] [tldr] Word length is better explained by a word's predictability than its frequency.
[11]	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. [pdf]
[10]	Piantadosi, S.T. (2011). "Learning and the language of thought". Ph.D. dissertation, MIT. [pdf] [tldr] Learners have to infer algorithms in order to show human-like competence in number, language, and logical concepts.
[9]	Piantadosi, S.T., Tily, H., Gibson, E. (2011). The communicative function of ambiguity in language. Cognition, 122(3), 280--291. [pdf] [tldr] Contrary to Chomsky's claims, ambiguity is good for communication in language because it prevents you from saying things that are redundant with the context.
[8]	Gibson, E., Piantadosi, S.T., Fedorenko, K. (2011). Using Mechanical Turk to Obtain and Analyze English Acceptability Judgments. Language and Linguistics Compass, 5(8), 509-524. [pdf]
2010
[7]	Piantadosi, S.T., Crutchfield, J. (2010). How the Dimension of Space Affects the Products of Pre-Biotic Evolution: The Spatial Population Dynamics of Structural Complexity and The Emergence of Membranes. Santa Fe Institute Working Paper arXiv:1010.5019. [pdf]
[6]	Piantadosi, S.T., Tenenbaum, J., Goodman, N. (2010). Beyond Boolean logic: exploring representation languages for learning complex concepts. Proceedings of the Cognitive Science Society. [pdf] [tldr] You can model learning curves to see which basis of logical operations people likely use.
[5]	Kidd, C., Piantadosi, S.T., Aslin, R. (2010). The Goldilocks Effect: Infants' preference for visual stimuli that are neither too predictable nor too surprising. Proceedings of the Cognitive Science Society. [pdf] [tldr] Infants show U-shaped attentional preferences where they tend to look away from things that are too complex, or too simple.
2009
[4]	Tily, H., Piantadosi, S.T. (2009). Refer efficiently: Use less informative expressions for more predictable meanings. Proceedings of the workshop on the production of referring expressions: Bridging the gap between computational and empirical approaches to reference. [pdf]
[3]	Piantadosi, S.T., Tily, H., Gibson, E. (2009). The communicative lexicon hypothesis. Proceedings of the Cognitive Science Society, 2582-2587. [pdf] [tldr] Lexicons in natural language are strongly shaped by a pressure for communicative efficiency.
2008
[2]	Piantadosi, S.T. (2008). Symbolic dynamics on free groups. Discrete and Continuous Dynamical Systems, 20(3), 725-738. [pdf]
[1]	Piantadosi, S.T., Goodman, N., Ellis, B., Tenenbaum, J.B. (2008). A Bayesian model of the acquisition of compositional semantics. Proceedings of the Cognitive Science Society. [pdf]

phd thesis

My thesis studied learning in language of thought models, in which learners compose simple functions in order to express complex concepts like those needed for natural language. A precis is available here.