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Usage-based linguistics abounds with studies that use statistical classification models to analyze either textual corpus data or behavioral experimental data. Yet, before we can draw conclusions from statistical models of empirical data that we can feed back into cognitive linguistic theory, we need to assess whether the text-based models are cognitively plausible and whether the behavior-based models are linguistically accurate. In this paper, we review four case studies that evaluate statistical classification models of richly annotated linguistic data by explicitly comparing the performance of a corpus-based model to the behavior of native speakers. The data come from four different languages (Arabic, English, Estonian, and Russian) and pertain to both lexical as well as syntactic near-synonymy. We show that behavioral evidence is needed in order to fine tune and improve statistical models built on data from a corpus. We argue that methodological pluralism is the key for a cognitively realistic linguistic theory.

The cognitive plausibility of statistical classification models: Comparing textual and behavioral evidence