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We report two corpus analyses to examine the impact of animacy, definiteness, givenness and type of referring expression on the ordering of double objects in the spontaneous speech of German-speaking two- to four-year-old children and the child-directed speech of their mothers. The first corpus analysis revealed that definiteness, givenness and type of referring expression influenced word order variation in child language and child-directed speech when the type of referring expression distinguished between pronouns and lexical noun phrases. These results correspond to previous child language studies in English (e.g., de Marneffe et al. 2012). Extending the scope of previous studies, our second corpus analysis examined the role of different pronoun types on word order. It revealed that word order in child language and child-directed speech was predictable from the types of pronouns used. Different types of pronouns were associated with different sentence positions but also showed a strong correlation to givenness and definiteness. Yet, the distinction between pronoun types diminished the effects of givenness so that givenness had an independent impact on word order only in child-directed speech but not in child language. Our results support a multi-factorial approach to word order in German. Moreover, they underline the strong impact of the type of referring expression on word order and suggest that it plays a crucial role in the acquisition of the factors influencing word order variation.
Moving beyond ERP components
(2018)
Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or "components" derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.