  This study constructs machine learning algorithms that are trained to
classify samples using sound symbolism, and then it reports on an experiment
designed to measure their understanding against human participants. Random
forests are trained using the names of Pokemon, which are fictional video game
characters, and their evolutionary status. Pokemon undergo evolution when
certain in-game conditions are met. Evolution changes the appearance,
abilities, and names of Pokemon. In the first experiment, we train three random
forests using the sounds that make up the names of Japanese, Chinese, and
Korean Pokemon to classify Pokemon into pre-evolution and post-evolution
categories. We then train a fourth random forest using the results of an
elicitation experiment whereby Japanese participants named previously unseen
Pokemon. In Experiment 2, we reproduce those random forests with name length as
a feature and compare the performance of the random forests against humans in a
classification experiment whereby Japanese participants classified the names
elicited in Experiment 1 into pre-and post-evolution categories. Experiment 2
reveals an issue pertaining to overfitting in Experiment 1 which we resolve
using a novel cross-validation method. The results show that the random forests
are efficient learners of systematic sound-meaning correspondence patterns and
can classify samples with greater accuracy than the human participants.
