Proceedings of the Annual Meeting of the Cognitive Science Society

In function learning experiments, where participants learnrelationships from sequentially-presented examples, peopleshow a strong tacit expectation that most relationships are lin-ear, and struggle to learn and extrapolate from non-linear rela-tionships. In contrast, experiments with similar tasks wheredata are presented simultaneously – typically using scatterplots – have shown that human learners can discover and ex-trapolate from complex non-linear trends. Do people have dif-ferent expectations in these task types, or can the results beattributed to effects of memory and data availability? In a di-rect comparison of both paradigms, we found that differencesbetween task types can be attributed to data availability. Weshow that a simple memory-limited Bayesian model is consis-tent with human extrapolations for linear data for both highand low data availability. However, our model underestimatesthe participants’ ability to infer non-monotonic functions, es-pecially when data is sparse. This suggest that people trackhigher-order properties of functions when learning and gen-eralizing.