Learning of bimodally distributed quantities

Abstract

Previous research has shown that people are able to use distributional information about the environment to make inferences. However, how people learn these probability distributions is less well understood, especially for those that are not normal or unimodal. In this paper we focus on how people learn probability distributions that are bimodal. We examined on how the distance between the two peaks of a bimodal distribution and the numbers of observations influence how participants learn each distribution, using two types of stimuli with different degrees of perceptual noise. Overall, participants were able to learn the various distributions quickly and accurately. However, their performance is moderated by stimuli type---whether participants were learning a distribution over numbers (low noise) or over sizes of circles (high noise). This work suggests that although people are able to quickly learn a variety of distributions, many factors may influence their performance.


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