Toward an Information-Theoretic Model of Morphological Fusion Based on an Efficient Tradeoff of Memory and Surprisal

Inflectional morphology refers to the mapping from grammatical information to surface forms, which are typically realized as morphemes. This mapping often exhibits fusion, where several abstract features are expressed in a single morpheme that cannot be decomposed into meaningful parts. Here, we discuss crosslinguistic generalizations of morphological fusion. We argue that fusion reflects principles of efficient processing, as formalized by the memory-surprisal tradeoff (Hahn, Degen, & Futrell 2021), which is based on information-theoretic models of language processing from psycholinguistics. We first show that the existence of fusion itself can, in some situations, lead communicative codes to be more efficient under our processing model. Particularly, we reveal via simulation that the fusion of highly correlated features is more efficient for processing, whereas agglutination is more efficient when features are less correlated. We next discuss crosslinguistic patterns of fusion in real languages. First, we analyze well-known generalizations about features that are commonly fused across languages (e.g. tense, aspect, and mood), as well as a typological pattern regarding suppletion. In both cases, we find that the universals we study tend to reflect a tendency toward more efficient structure under our model of language processing. Finally, we use paradigm and frequency data from four languages to study informational fusion, a gradable measure of fusion defined in Rathi et al. 2021. We find that informational fusion is higher when features are highly correlated, which suggests that gradable fusion is also influenced by optimization for the memory-surprisal tradeoff.