Data Availability and Function Extrapolation

Pablo León Villagrá, Irina Preda, Christopher Lucas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

In function learning experiments, where participants learn relationships from sequentially-presented examples, people show a strong tacit expectation that most relationships are linear, and struggle to learn and extrapolate from non-linear relationships. In contrast, experiments with similar tasks where data are presented simultaneously – typically using scatter plots – have shown that human learners can discover and extrapolate from complex non-linear trends. Do people have different expectations in these task types, or can the results be attributed to effects of memory and data availability? In a direct comparison of both paradigms, we found that differences between task types can be attributed to data availability. We show that a simple memory-limited Bayesian model is consistent with human extrapolations for linear data for both high and low data availability. However, our model underestimates the participants’ ability to infer non-monotonic functions, especially when data is sparse. This suggest that people track higher-order properties of functions when learning and generalizing.
Keywords: function learning, function estimation, resource rationality
Original languageEnglish
Title of host publicationProceedings of 40th Annual Meeting of the Cognitive Science Society
Place of PublicationMadison, United States
Number of pages6
Publication statusPublished - 2018
Event40th Annual Meeting of the Cognitive Science Society - Madison, United States
Duration: 25 Jul 201828 Jul 2018


Conference40th Annual Meeting of the Cognitive Science Society
Abbreviated titleCogSci 2018
Country/TerritoryUnited States
Internet address


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