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Prediction is often regarded as a central and domain-general aspect of cognition. This proposal extends to language, where predictive processing might enable the comprehension of rapidly unfolding input by anticipating upcoming words or their semantic features. To make these predictions, the brain needs to form a representation of the predictive patterns in the environment. Predictive processing theories suggest a continuous learning process that is driven by prediction errors, but much is still to be learned about this mechanism in language comprehension. This thesis therefore combined three electroencephalography (EEG) experiments to explore the relationship between prediction and implicit learning at the level of meaning.
Results from Study 1 support the assumption that the brain constantly infers und updates probabilistic representations of the semantic context, potentially across multiple levels of complexity. N400 and P600 brain potentials could be predicted by semantic surprise based on a probabilistic estimate of previous exposure and a more complex probability representation, respectively.
Subsequent work investigated the influence of prediction errors on the update of semantic predictions during sentence comprehension. In line with error-based learning, unexpected sentence continuations in Study 2 ¬– characterized by large N400 amplitudes ¬– were associated with increased implicit memory compared to expected continuations. Further, Study 3 indicates that prediction errors not only strengthen the representation of the unexpected word, but also update specific predictions made from the respective sentence context. The study additionally provides initial evidence that the amount of unpredicted information as reflected in N400 amplitudes drives this update of predictions, irrespective of the strength of the original incorrect prediction.
Together, these results support a central assumption of predictive processing theories: A probabilistic predictive representation at the level of meaning that is updated by prediction errors. They further propose the N400 ERP component as a possible learning signal. The results also emphasize the need for further research regarding the role of the late positive ERP components in error-based learning. The continuous error-based adaptation described in this thesis allows the brain to improve its predictive representation with the aim to make better predictions in the future.
Previous studies on native language (L1) anaphor resolution have found that monolingual native speakers are sensitive to syntactic, pragmatic, and semantic constraints on pronouns and reflexive resolution. However, most studies have focused on English and other Germanic languages, and little is currently known about the online (i.e., real-time) processing of anaphors in languages with syntactically less restricted anaphors, such as Turkish. We also know relatively little about how 'non-standard' populations such as non-native (L2) speakers and heritage speakers (HSs) resolve anaphors.
This thesis investigates the interpretation and real-time processing of anaphors in German and in a typologically different and as yet understudied language, Turkish. It compares hypotheses about differences between native speakers' (L1ers) and L2 speakers' (L2ers) sentence processing, looking into differences in processing mechanisms as well as the possibility of cross-linguistic influence. To help fill the current research gap regarding HS sentence comprehension, it compares findings for this group with those for L2ers.
To investigate the representation and processing of anaphors in these three populations, I carried out a series of offline questionnaires and Visual-World eye-tracking experiments on the resolution of reflexives and pronouns in both German and Turkish. In the German experiments, native German speakers as well as L2ers of German were tested, while in the Turkish experiments, non-bilingual native Turkish speakers as well as HSs of Turkish with L2 German were tested. This allowed me to observe both cross-linguistic differences as well as population differences between monolinguals' and different types of bilinguals' resolution of anaphors.
Regarding the comprehension of Turkish anaphors by L1ers, contrary to what has been previously assumed, I found that Turkish has no reflexive that follows Condition A of Binding theory (Chomsky, 1981). Furthermore, I propose more general cross-linguistic differences between Turkish and German, in the form of a stronger reliance on pragmatic information in anaphor resolution overall in Turkish compared to German.
As for the processing differences between L1ers and L2ers of a language, I found evidence in support of hypotheses which propose that L2ers of German rely more strongly on non-syntactic information compared to L1ers (Clahsen & Felser, 2006, 2017; Cunnings, 2016, 2017) independent of a potential influence of their L1. HSs, on the other hand, showed a tendency to overemphasize interpretational contrasts between different Turkish anaphors compared to monolingual native speakers. However, lower-proficiency HSs were likely to merge different forms for simplified representation and processing. Overall, L2ers and HSs showed differences from monolingual native speakers both in their final interpretation of anaphors and during online processing. However, these differences were not parallel between the two types of bilingual and thus do not support a unified model of L2 and HS processing (cf. Montrul, 2012).
The findings of this thesis contribute to the field of anaphor resolution by providing data from a previously unexplored language, Turkish, as well as contributing to research on native and non-native processing differences. My results also illustrate the importance of considering individual differences in the acquisition process when studying bilingual language comprehension. Factors such as age of acquisition, language proficiency and the type of input a language learner receives may influence the processing mechanisms they develop and employ, both between and within different bilingual populations.
The topic of synchronization forms a link between nonlinear dynamics and neuroscience. On the one hand, neurobiological research has shown that the synchronization of neuronal activity is an essential aspect of the working principle of the brain. On the other hand, recent advances in the physical theory have led to the discovery of the phenomenon of phase synchronization. A method of data analysis that is motivated by this finding - phase synchronization analysis - has already been successfully applied to empirical data. The present doctoral thesis ties up to these converging lines of research. Its subject are methodical contributions to the further development of phase synchronization analysis, as well as its application to event-related potentials, a form of EEG data that is especially important in the cognitive sciences. The methodical contributions of this work consist firstly in a number of specialized statistical tests for a difference in the synchronization strength in two different states of a system of two oscillators. Secondly, in regard of the many-channel character of EEG data an approach to multivariate phase synchronization analysis is presented. For the empirical investigation of neuronal synchronization a classic experiment on language processing was replicated, comparing the effect of a semantic violation in a sentence context with that of the manipulation of physical stimulus properties (font color). Here phase synchronization analysis detects a decrease of global synchronization for the semantic violation as well as an increase for the physical manipulation. In the latter case, by means of the multivariate analysis the global synchronization effect can be traced back to an interaction of symmetrically located brain areas.<BR> The findings presented show that the method of phase synchronization analysis motivated by physics is able to provide a relevant contribution to the investigation of event-related potentials in the cognitive sciences.