Understanding the development of non-linear processes such as economic or populationgrowth is an important prerequisite for informed decisions in those areas. In the function-learningparadigm, people’s understanding of the function rule that underlies the to-be predicted process istypically measured by means of extrapolation accuracy. Here we argue, however, that even thoughaccurate extrapolation necessitates rule-learning, the reverse does not necessarily hold: Inaccurateextrapolation does not exclude rule-learning. Experiment 1 shows that more than one third of participants who would be classified as “exemplar-based learners” based on their extrapolation accuracy were able to identify the correct function shape and slope in a rule-selection paradigm, demonstrating accurate understanding of the function rule. Experiment 2 shows that higher proportions of rule learning than rule-application in the function learning paradigm is not due to (i) higher a priori probabilities to guess the correct rule in the rule-selection paradigm; nor is it due to (ii) a lack of simultaneous access to all function values in the function-learning paradigm. We conclude that rule application is not tantamount to rule-learning, and that assessing rule-learning via extrapolation accuracy underestimates the proportion of rule learners in function-learning experiments.
Extrapolation accuracy underestimates rule learning: Evidence from the function-learning paradigm
