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Discovering prominence and its role in language processing: An individual (differences) approach

Alday, P. M., Schlesewsky, M., & Bornkessel-Schlesewsky, I. (2015). Discovering prominence and its role in language processing: An individual (differences) approach. Linguistics Vanguard, 1(1), 201-213. doi:10.1515/lingvan-2014-1013.
It has been suggested that, during real time language comprehension, the human language processing system attempts to identify the argument primarily responsible for the state of affairs (the “actor”) as quickly and unambiguously as possible. However, previous work on a prominence (e.g. animacy, definiteness, case marking) based heuristic for actor identification has suffered from underspecification of the relationship between different cue hierarchies. Qualitative work has yielded a partial ordering of many features (e.g. MacWhinney et al. 1984), but a precise quantification has remained elusive due to difficulties in exploring the full feature space in a particular language. Feature pairs tend to correlate strongly in individual languages for semantic-pragmatic reasons (e.g., animate arguments tend to be actors and actors tend to be morphosyntactically privileged), and it is thus difficult to create acceptable stimuli for a fully factorial design even for binary features. Moreover, the exponential function grows extremely rapidly and a fully crossed factorial design covering the entire feature space would be prohibitively long for a purely within-subjects design. Here, we demonstrate the feasibility of parameter estimation in a short experiment. We are able to estimate parameters at a single subject level for the parameters animacy, case and number. This opens the door for research into individual differences and population variation. Moreover, the framework we introduce here can be used in the field to measure more “exotic” languages and populations, even with small sample sizes. Finally, pooled single-subject results are used to reduce the number of free parameters in previous work based on the extended Argument Dependency Model (Bornkessel-Schlesewsky and Schlesewsky 2006, 2009, 2013, in press; Alday et al. 2014).
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