What artificial intelligence might teach us about the origin of human language

TL;DR

This paper investigates how AI models trained on sound symbolism data reveal biases related to threat perception, suggesting these biases may reflect evolved cautious behavior in humans.

Contribution

It introduces a hypothesis linking AI classification biases to human error management strategies, supported by experiments with gradient boosting models on East Asian Pokémon names.

Findings

AI models overpredict threatening categories

Biases align with error management theory

Supports hypothesis of evolved cautious behavior

Abstract

This study explores an interesting pattern emerging from research that combines artificial intelligence with sound symbolism. In these studies, supervised machine learning algorithms are trained to classify samples based on the sounds of referent names. Machine learning algorithms are efficient learners of sound symbolism, but they tend to bias one category over the other. The pattern is this: when a category arguably represents greater threat, the algorithms tend to overpredict to that category. A hypothesis, framed by error management theory, is presented that proposes that this may be evidence of an adaptation to preference cautious behaviour. This hypothesis is tested by constructing extreme gradient boosted (XGBoost) models using the sounds that make up the names of Chinese, Japanese and Korean Pokemon and observing classification error distribution.