An issue that is actively explored in the contemporary linguistics literature is how to account for probabilistic generalizations, for which there are currently various competing theories. To bear on this debate, Hayes (2021) proposes that we examine a grammatical framework in terms of its quantitative signature, typical probabilistic patterns that the framework is predicted to generate. In this research report, I zoom in on the quantitative signature of maximum entropy harmonic grammar (MaxEnt HG), because this framework has proven useful in modeling probabilistic generalizations across different linguistic domains. Given a linear scale of violations of one constraint, MaxEnt yields a sigmoid curve. When another constraint is relevant and can be violated multiple times, this sigmoid curve can be shifted, yielding multiple sigmoid curves, which results in a stripy wug-shaped curve. Expanding upon a previous study (Kawahara 2020b), the current experiment demonstrates that we observe a stripy wug-shaped curve in a particularly clear manner in patterns of sound symbolism, systematic associations between sounds and meanings. Concretely, the experiment with Japanese speakers shows that the judgment of Pokémons’ evolution status is affected by the mora counts of nonce names, resulting in a sigmoid curve, and that this sigmoid curve is shifted according to the number of voiced obstruents contained in the names. The overall results suggest that MaxEnt is a useful tool for modeling systematic sound-meaning correspondences.*.
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