Superregular grammars do not provide additional explanatory power but allow for a compact analysis of animal song
This work addresses the debate on whether human language and animal song differ in computational complexity, suggesting they may be governed by similar grammatical systems, though it is incremental as it builds on prior computational analyses.
The paper tackled the problem of comparing computational complexity between human language and animal song by performing a superregular analysis on gibbon song data, finding that superregular analysis does not increase explanatory power but allows for a more compact analysis with fewer grammatical rules.
A pervasive belief with regard to the differences between human language and animal vocal sequences (song) is that they belong to different classes of computational complexity, with animal song belonging to regular languages, whereas human language is superregular. This argument, however, lacks empirical evidence since superregular analyses of animal song are understudied. The goal of this paper is to perform a superregular analysis of animal song, using data from gibbons as a case study, and demonstrate that a superregular analysis can be effectively used with non-human data. A key finding is that a superregular analysis does not increase explanatory power but rather provides for compact analysis: Fewer grammatical rules are necessary once superregularity is allowed. This pattern is analogous to a previous computational analysis of human language, and accordingly, the null hypothesis, that human language and animal song are governed by the same type of grammatical systems, cannot be rejected.