In this talk, we discuss the role of prediction in shaping language, with a focus on nominal classification systems. Prediction refers to the “pre-activation of linguistic representations, before they have been partially or fully activated by the bottom-up input” (León-Cabrera et al. 2024). Nominal classification systems are grammatical systems that divide the nouns of a language into categories (Aikhenvald 2000; Seifart 2010), and include, e.g., gender and classifier systems. Previous research has shown that both gender (e.g., Dahan et al. 2000; Lew-Williams and Fernald 2007; Wicha et al. 2004) and classifier (Deng et al. 2023: e.g.,; Mitsugi 2020; Cronhamn et al. 2024) marking can help listeners predict upcoming nouns, by narrowing down the range of possible continuations. Although prediction is not the only function of nominal classification, we argue that its communicative benefits help explain why such systems have emerged and persisted typologically. Nominal classification systems are common in the world’s languages – around 50% make use of one. The fact that they are widespread is in a sense unsurprising, given their processing advantages. At the same time, nominal classification systems are difficult to learn and fully master (Aikhenvald 2000), and in some sense class membership of nouns can be seen as a redundant linguistic feature. And indeed, the other half of the world’s languages does perfectly well without any nominal classification system whatsoever. We suggest that this typological variation in nominal classification is rooted in trade-offs between speaker and listener needs. Classification systems, in particular extended ones, serve listener economy by allowing anticipation of upcoming elements. Languages without such systems serve speaker economy by avoiding the cognitive load of maintaining and producing class distinctions. Cross-linguistically, these trade-offs result in an almost equal distribution between languages with and without nominal classification systems.
 

References
Aikhenvald, Alexandra Y. 2000. Classifiers: A typology of noun categorization devices. Oxford: Oxford University Press.
Cronhamn, Sandra, Anna Hjortdal, Franklin da Silva & Mikael Roll. 2024. The predictive function of Baniwa classifiers. Presented at the 57th Annual Meeting of the Societas Linguistica Europaea (SLE2024), Helsinki, Finland.
Dahan, Delphine, Daniel Swingley, Michael K. Tanenhaus & James S. Magnuson. 2000. Linguistic gender and spoken-word recognition in French. Journal of Memory and Language 42(4). 465–480. https://doi.org/10.1006/jmla.1999.2688.
Deng, Xizi, Ashley Farris-Trimble & H. Henny Yeung. 2023. Contextual effects on spoken word processing: An eye-tracking study of the time course of tone and vowel activation in Mandarin. Journal of Experimental Psychology: Learning, Memory, and Cognition 49(7). 1145–1160. https://doi.org/10.1037/xlm0001143.
León-Cabrera, Patricia, Anna Hjortdal, Sabine Gosselke Berthelsen, Antoni Rodríguez-Fornells & Mikael Roll. 2024. Neurophysiological signatures of prediction in language: A critical review of anticipatory negativities. Neuroscience & Biobehavioral Reviews 160. Article 105624. https://doi.org/10.1016/j.neubiorev.2024.105624.
Lew-Williams, Casey & Anne Fernald. 2007. Young children learning Spanish make rapid use of grammatical gender in spoken word recognition. Psychological Science 18(3). 193–198. https://doi.org/10.1111/j.1467-9280.2007.01871.x.
Mitsugi, Sanako. 2020. Generating predictions based on semantic categories in a second language: A case of numeral classifiers in Japanese. International Review of Applied Linguistics in Language Teaching 58(3). 323–349. https://doi.org/10.1515/iral-2017-0118.
Seifart, Frank. 2010. Nominal classification. Language and Linguistics Compass 4(8). 719–736. https://doi.org/10.1111/j.1749-818X.2010.00194.x.
Wicha, Nicole Y. Y., Eva M. Moreno & Marta Kutas. 2004. Anticipating words and their gender: An event-related brain potential study of semantic integration, gender expectancy, and gender agreement in Spanish sentence reading. Journal of Cognitive Neuroscience 16(7). 1272–1288. https://doi.org/10.1162/0898929041920487.

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