Linguistic Assessment Criteria for Explaining Language Change: A Case Study on Syncretism in German Definite Articles

Is regularisation uniform across linguistic levels? Comparing learning and production of unconditioned probabilistic variation in morphology and word order.

“…Indeed, regularisation (i.e., the reduction, elimination or conditioning of variation) has been documented extensively in natural languages across linguistic levels like phonology, morphology, syntax and the lexicon, including in language acquisition (Fraser, Bellugi, & Brown 1963;Newport 1999;Ross & Newport 1996;Singleton & Newport 2004), in language change (Hare & Elman 1995;Schilling-Estes & Wolfram 1994;van Trijp 2013), and in language formation (Bickerton 2015;DeGraff 1999;McWhorter 2005;Senghas & Coppola 2001;Senghas, Newport, & Supalla 1997;Siegel 2004;Spears 2008;Winford 2003). In addition, experimental studies involving artificial language learning experiments report regularisation behaviour during learning and production of probabilistic variation of diverse linguistic elements, across different linguistic levels: morphology (e.g., Hudson Kam & Newport 2005Perfors 2016;Schumacher, Pierrehumbert, & LaShell 2014;Smith & Wonnacott 2010), word order (e.g., Culbertson, Smolensky, & Legendre 2012;Fehér, Ritt, Wonnacott, & Smith 2016), and the lexicon (e.g., Ferdinand et al 2017;Reali & Griffiths 2009) .…”

Markov Models for Multi-state Language Change

“….) all the way to simulation studies (Baxter et al, 2009;Beuls & Steels, 2013;Blythe & Croft, 2012;Landsbergen et al, 2010;Lestrade, 2015;Nowak et al, 2001;Pijpops et al, 2015;Steels, 2016;Van Trijp, 2013), the main workhorse today is generalized linear (mixed)models, with time as an explanatory variable, often under multivariate control or in interaction with other covariates (see, a.o., De Cuypere, 2015;Fonteyn & Van de Pol, 2016;Gries & Hilpert, 2010;Petré & Van de Velde, 2018;Rosemeyer, 2016Rosemeyer, , 2014Wolk et al, 2013;Zimmermann, 2017). This may seem reasonable, as regressing competing variants or frequency metrics on time can accommodate the typical S-curve that is observed in many cases of diachronic change (Blythe & Croft, 2012;Denison, 2003;Feltgen et al, 2017;Kroch, 1989;Pintzuk, 2003;Weinreich et al, 1968, p. 113).…”

The Emergence of Modern Languages: Has Human Self-Domestication Optimized Language Transmission?

“…Specifically, in exoteric communities, the need for decontextualized language use may have driven morphological structure toward greater informativeness based on degree of transparency and regularity to support greater communicative efficiency, e.g., via morphological devices that mark long-distance agreement patterns or that allow for immediate thematic role assignment, to handle the increased lexical and syntactic complexity needed for more sophisticated information transmission. At the same time, the drive toward reduced cognitive effort may act to put a cap on morphological richness, i.e., on the number of obligatorily marked grammatical features, compressing morphological paradigms so as to result in some degree of inflectional neutralization and syncretism (van Trijp, 2013 ). The extant morpho-syntactic variation found in modern exoteric languages presumably reflects different solutions to the trade-off between communicative efficiency of morphological systems given lexical and syntactic complexity and the cognitive effort required for processing these systems (Piantadosi et al, 2012 ; Kemp et al, 2018 ).…”