Complexity in complementation: understanding long-term change in verb complementation in terms of inter- and intra-individual variation

The PhD project to be carried out by Eleanor Smith is part of an FWO junior research project. The main objective is to establish the impact of interindividual differences in cognitive representations on long-term population-level language change. In examining how the individual and community levels interact, the project seeks to contribute to a theory of language as a complex adaptive system. This theory views language as a self-organizing network which at the macro-level shows properties that are not recurrent at the individual level and yet emerge out of complex behaviour at that level. A more specific goal is to chart, and explain, the range of variation in the English complementation system by studying variation between patterns such as ‘I remember that a detective came in’ and ‘I remember a detective coming in’. While a certain amount of variation can probably be accounted for by a desire of varying itself, it has been shown  that the choice between complement variants is influenced by factors such as animacy (human or abstract) or clause length. At the same time existing studies have experienced difficulties with robustly accounting for the variation by means of population-level (social) variables only. When social clues are insufficient to determine usage, cognitive mechanisms may come into play that are different between individuals and therefore cannot easily be averaged over. This project seeks to advance our insight in the functionality of abstract grammatical variation of this kind by putting individual-level analysis more central. The data for the project will be collected as part of the PhD and will consist of a sample of the collected works of 40 prolific 18th-19th century authors. The theoretical framework will be that of construction grammar (e.g. Goldberg 2006, Traugott & Trousdale 2013) and cognitive science more generally. In addition to preparing a PhD thesis, the PhD student will collaborate with the supervisors of the project as  regards maintenance of the database and different more general aspects of the project (connecting the results with lifespan and complementation research carried out previously by the supervisors; opportunities and limitations of deep learning to assist the analysis). The PhD student will also be  encouraged to publish at least two papers in international journals or monographs during the PhD.