Tutorials

Modeling chronological and geospatial language variation

This tutorial provides modeling techniques for predicting language change in apparent time using self-reported questionnaire data in R. Using Sunnmøre Norwegian dialect levelling patterns as a case study, this tutorial shows how Generalized Linear Mixed Models (GLMMs) can be used to identify and forecast language change across different birth cohorts and regions. The tutorial includes methods for calculating derivatives of model predictions, enabling precise pinpointing of key 'tipping points' in the change process (where the change growth curve transitions from gradual to rapid change). Additionally, code is provided for generating dynamic geospatial visualizations from model predictions, visualizing the spread of language change across time and space.
https://osf.io/g2s8n/?view_only=1415cb89e9604c64b5f2f4e21f4b6245

Plotting dynamic scalp topographies

This tutorial illustrates how to produce ERP visualizations in R from raw EEG time series data, including creating grand average waveforms, calculating amplitude differences between condition and sub-conditions, and visualizing these differences as topographic maps over various time windows. The tutorial includes code for generating static scalp topographies over specific time intervals as well as dynamic series that can be used to create GIFs, illustrating how ERPs unfold in real time.
https://osf.io/zsvje/?view_only=d6e30af154764f4687af008cf756f8ea

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