When & Where
When: 15 October 2026, 9:00-10:15
Where: Main Building, room 304
Zoom: https://unibe-ch.zoom.us/j/61375946771
Abstract
During language change, children increment away from parental norms and toward innovative community patterns, eventually emerging as leaders of change (Labov, 2007). While this process is well established through apparent-time and real-time evidence (Labov, 2001; Tagliamonte & D’Arcy, 2009; Holmes-Elliott, 2016, 2020; Smith & Holmes-Elliott, 2022), less is known about how children determine the direction and magnitude of their adjustment.
Recent accounts propose that incrementation is driven by momentum-based learning, whereby children extract age-graded trajectories from the speech community and use them to position themselves at the leading edge of change (Bermúdez-Otero, 2020). This predicts that community-level patterns should affect how easily change is learned: highly variable changes may provide weaker learning signals than those with more consistent trajectories. Evidence from TH-fronting and T-glottaling supports this view (Holmes-Elliott, 2020), but both variables are categorical and socially stigmatized, making it difficult to separate the mechanics of incrementation from social evaluation.
To test predictions about the direction, magnitude, and convergence of incrementation requires fine-grained phonetic analysis of continuous vowel measures rather than categorical variables. As such, the present study examines two non-stigmatized vowel changes, GOOSE-fronting (8,000 tokens) and TRAP-backing (6,500 tokens). Across real time, GOOSE-fronting shows higher rates of predicted incrementation than TRAP-backing (12/13 vs. 10/13 speakers), yet both changes exhibit convergence in Hertz values. These findings suggest that both the strength of apparent-time trajectories and the degree of community variability shape the learning vectors that children extract and internalize during language change. More broadly, these findings demonstrate how fine-grained phonetic analysis can advance our understanding of core sociolinguistic processes, including momentum-based learning as a driver of language change.