10.7910/DVN/A8ZWLFBigelow, Eric J.Eric J.BigelowUniversity of RochesterPiantadosi, Steven T.Steven T.PiantadosiUniversity of RochesterHarvard Dataverse2018Social SciencesGeneralization; Bayesian inference; Structured cognitive model; Numerical cognition; Concept learningBigelow, Eric J.Eric J.BigelowUniversity of RochesterPiantadosi, Steven T.Steven T.PiantadosiUniversity of Rochester2015-05-192018-08-102015-03-27/2015-04-1421003248213226031434167823740845031075115115178480text/tab-separated-valuestext/tab-separated-valuestext/plain; charset=US-ASCIItext/plain; charset=US-ASCIItext/plain; charset=US-ASCIIapplication/pdftext/plaintext/x-python-scripttext/tab-separated-valuestext/tab-separated-values1.0CC0 1.0272,700 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.Technical report describing this dataset to be reviewed by Journal of Open Psychology Data (JOPD).