Large Dataset of Generalization Patterns in the Number Gamedoi:10.7910/DVN/A8ZWLFHarvard Dataverse2018-08-101Bigelow, Eric J.; Piantadosi, Steven T., 2018, "Large Dataset of Generalization Patterns in the Number Game", https://doi.org/10.7910/DVN/A8ZWLF, Harvard Dataverse, V1, UNF:6:zUgVtjc9CKvWc4pB//Qp6A== [fileUNF]Large Dataset of Generalization Patterns in the Number Gamedoi:10.7910/DVN/A8ZWLFBigelow, Eric J.Piantadosi, Steven T.Harvard DataverseBigelow, Eric J.Piantadosi, Steven T.Bigelow, Eric2015-05-19Social SciencesGeneralization; Bayesian inference; Structured cognitive model; Numerical cognition; Concept learning272,700 two-alternative forced choice responses in a simple numerical task modeled after Tenenbaum (1999, 2000), collected from 606 Amazon Mechanical Turk workers. Subjects were shown sets of numbers length 1 to 4 from the range 1 to 100 (e.g. {12, 16}), and asked what other numbers were likely to belong to that set (e.g. 1, 5, 2, 98). Their generalization patterns reflect both rule-like (e.g. “even numbers,” “powers of two”) and distance-based (e.g. numbers near 50) generalization. This data set is available for further analysis of these simple and intuitive inferences, developing of hands-on modeling instruction, and attempts to understand how probability and rules interact in human cognition.2015-03-272015-04-14Technical report describing this dataset to be reviewed by Journal of Open Psychology Data (JOPD). <a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>Tenenbaum, J. B. (2000). Rules and similarity in concept learning. Advances in neural information processing systems, 12, 59-65.Tenenbaum, J. B. (2000). Rules and similarity in concept learning. Advances in neural information processing systems, 12, 59-65.Tenenbaum, J. B. (1999). A Bayesian framework for concept learning (Doctoral dissertation, Massachusetts Institute of Technology).Tenenbaum, J. B. (1999). A Bayesian framework for concept learning (Doctoral dissertation, Massachusetts Institute of Technology).Tenenbaum, J. B. & Griffiths, T. L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24(4), 629-640.Tenenbaum, J. B. & Griffiths, T. L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24(4), 629-640.instructions_rt.tab18483text/tab-separated-valuesUNF:6:iESvM+aEgU47IjH6BHkjKQ==Time spent looking at each instruction pagenumbergame_data.tab27270014text/tab-separated-valuesUNF:6:WXjlwG01u+JA91yxbdD1lQ==Primary dataset, with rows for each response. Includes rating, reaction time, demographics information, & values calculated based on rating. See README.txt for more information.set_descriptions.tab30303text/tab-separated-valuesUNF:6:V6ifJ0pY1z91leZAIj9tpw==SurveySubjective concept descriptions collected during post-experiment questionnaireshow_set_rt.tab90903text/tab-separated-valuesUNF:6:sQfwTes+du4DHwtVfMxRyw==Amount of time spent on each page initially displaying the set to the subject, before they rate corresponding targetsid0.00.01848.0303.5.605.0175.50800268732442304.21699134199145UNF:6:563FkXPiuPpJ16iXMvNZ/g==instruct1848.00.99621212121212131.00.0.2.00.00.8150497923242312UNF:6:wmb5Z7lhwTku1Vyk+RbWdA==rt10442.020164.270021644974.1203980.056602.408619693581848.00.0765.0UNF:6:bdHoOszOBY3JYFyjshlEyw==setUNF:6:USC536CdDHy2F7oU1YPDSQ==id174.93721413408534.302.5302.5605.00.00.0272700.0UNF:6:/sxlEdvcJbaG1ejjKgSUxA==rating1.0272700.0.0.317803447011370370.00.46562346494888470.00.0UNF:6:Wkpy7yRk+7ox2U8ql37JQg==rt1402.75000183625680.029871.01782.78759187141.936.0272294.0406.0UNF:6:+nKd8thj4gecSinq+LIgoQ==target272700.0100.01.028.85608210255984250.050.4583425009167.0.0UNF:6:6eVstPE/hILQMmEU336Hcw==trial129.90372799799133272700.0.224.5449.00.0224.50.0UNF:6:CBf4il/XNiwkXf51ESDJkQ==hits22.010.010.9934653465347570.0272700.09.01.9703060035263393.UNF:6:+mJgExQooUKyVxmhqXlxVw==p272700.00.25636817806481870.00.00.272731.00.3178034028236157.UNF:6:e4D8iciWm0Onh9LkfOuT4g==H0.00.529710.235341743088065630.4513916883755037272700.00.00.69315.UNF:6:H0Qx+3EZ857j06enIf+5xQ==age271800.034.177152317882026900.031.018.010.94116646945509368.0.UNF:6:8pqsbuLvaI9FiPIawy2FoA==firstlangUNF:6:HwHuFcvw6voC79Xf+PXWGw==zipcode.46605.56300.0266400.029748.98383694407799508.01035.050552.793918917916UNF:6:C1SwBmTKAc0HBUrk3Pc3Sg==genderUNF:6:8hSsSl740PyYelv/cfwBxw==educationUNF:6:g7BMn0+wAcBtOxyAQcH/8Q==id605.0302.53030.00.00.0.174.96576800502618302.5UNF:6:cSbo6FYgYg/fVGtYtX0Wxw==setUNF:6:Hkg1VFApr6VcCR2Fy5kKVw==descrUNF:6:M1m5GdaCew2PYjyDr4ed2g==setUNF:6:wD9LySYhDKWvxye1rgc1ew==id302.5174.94651666888330.0605.00.0.302.59090.0UNF:6:xtdR3yB9gQdMXZ69sPidQg==rt678.01510936.027034.4451319661530.09310.9217821782629090.0.3969.0UNF:6:GULEgoD7u4CuQEEJnV3OqQ==plot_all.RGenerate large plot of predictive distributions across all subjects for every set in the dataset (see predictive_all.pdf)text/plain; charset=US-ASCIIplot_compare.RPlot predictive distribution for multiple specified sets to compare. This file also includes a short list of set lists, with interesting patterns to compare.text/plain; charset=US-ASCIIplot_focus.RSame as `plot_compare.R`, but highlighting certain targets to compare how multiple concepts' predictive distributions may reflect common patterns.text/plain; charset=US-ASCIIpredictive_all.pdfLarge plot of predictive distributions across all subjects for every concept in the datasetapplication/pdfREADME.txtDescribes each file in detail, describes each column for .csv filestext/plainrecompute_columns.pyLoad dataset into pandas and recompute 'p', 'H', 'hits', & 'typicality' columnstext/x-python-script