UC Davis

Prediction plays a critical role in comprehending human language. Many theoretical and computational models have attempted to characterize how we use context to facilitate language processing in noisy environments either with or without relying on predictive processing. Despite these attempts, we do not yet have a complete understanding of the role of prediction in language processing. Predictive coding models have recently gained popularity as potential architectures for the role of prediction during language comprehension. These models suggest that predictions about bottom-up inputs are continuously generated from higher cortical levels to lower levels in a hierarchical manner – i.e., a particular level generates predictions about the next lower level. As bottom-up input is encounter by each level of processing, prediction error is computed by comparing the input with the top-down prediction. The goal of this dissertation was to assess whether predictive coding models can account for the time course of information retrieval during predictive language processing. Specifically, the studies described examine the time course of pre-activations of lexical and sub-lexical features in both monolinguals and bilinguals using a combination of decoding electroencephalogram (EEG) with machine-learning classifiers and mass univariate event-related potential analysis. Chapter 1 describes an experiment that compared three frequently used models for signal classification – support vector machines (SVM), linear discriminant analysis (LDA), and random forest (RF) to determine which is best-suited for analyzing word pair prediction paradigms. Results showed that SVM was the best performing classifier of the three within two data sets from separate visual word priming paradigms. Chapter 2 describes an experiment which used EEG decoding with SVM classifiers and mass univariate ERP analyses to identify the time course of information retrieval prior to the onset of accurately predicted, related but inaccurately predicted, and unrelated target words during a visual word priming prediction paradigm. In addition to this pre-stimulus information retrieval, these analyses were used to investigate the effects of prediction error. The results of this study showed that semantic information, such as concreteness, is retrieved earlier than visual feature information, like word length, and that unrelated words had greater prediction error than predicted or related but inaccurately predicted words. Finally, Chapter 3 describes an experiment that extends the results of Chapter 2 by using the same paradigm and analyses with Spanish-English bilingual participants. The results of this study showed that bilinguals reading words in their second language (L2) retrieve anticipated information in a similar fashion as monolinguals. Semantic information preceded visual information and unrelated words showed evidence of greater prediction error than did predicted or related words that were not accurately predicted. Together, these experiments support predictive coding models of language processing in both monolinguals and bilinguals during word recognition. Both groups predict higher-level features (concreteness) before lower-level features (word length) of anticipated words and calculate prediction error when they make inaccurate predictions.