Investigating variability in morphological processing with Bayesian distributional models.

We investigated the processing of morphologically complex words adopting an approach that goes beyond estimating average effects and allows testing predictions about variability in performance. We tested masked morphological priming effects with English derived ('printer') and inflected ('printed') forms priming their stems ('print') in non-native speakers, a population that is characterized by large variability. We modeled reaction times with a shifted-lognormal distribution using Bayesian distributional models, which allow assessing effects of experimental manipulations on both the mean of the response distribution ('mu') and its standard deviation ('sigma'). Our results show similar effects on mean response times for inflected and derived primes, but a difference between the two on the sigma of the distribution, with inflectional priming increasing response time variability to a significantly larger extent than derivational priming. This is in line with previous research on non-native processing, which shows more variable results across studies for the processing of inflected forms than for derived forms. More generally, our study shows that treating variability in performance as a direct object of investigation can crucially inform models of language processing, by disentangling effects which would otherwise be indistinguishable. We therefore emphasize the importance of looking beyond average performance and testing predictions on other parameters of the distribution rather than just its central tendency.