About this Research Topic
Prediction is a central mechanism in the human language processing architecture. The psycholinguistic and neurolinguistic literature has seen a lively debate about what form prediction may take and what status it has for language processing in the human mind and brain. While predictions are a ubiquitous finding, the implications of these results for models of language processing differ. For instance, eyetracking data suggest that predictions may rely on sublexical orthographic information in natural reading, while electrophysiological data provide mixed evidence for form-based predictions during reading. Other research has revealed that humans rapidly adapt to text specifics and that their predictive capacity varies, broadly speaking, in accordance with inter- and intra-individual language proficiency, which cuts across the speaker groups (e.g. L1 vs. L2 speakers, skilled vs. untrained readers) traditionally used for experimental contrasts. There is therefore evidence that the kind and strength of linguistic predictions depend on (at least) three sources of variability in language processing: speaker, text genre and experimental method.
The aim of this Research Topic is to develop a better understanding of prediction in light of the three sources of variability in language processing, by providing an overview of state-of-the art research on predictive language processing and by bringing together research from various disciplines.
First, intra-and inter-individual differences and their influence on predictive processes remain underrepresented in experimental research on predictive processing. How do language users differ in their predictive abilities and strategies, and how are these differences shaped by e.g. biological, social and cultural factors?
Second, while language users experience great stylistic diversity in their daily language exposure and use, the majority of language processing research still focuses on a very constrained register of well-controlled sentences composed in the standard language. How are predictions shaped by extra- and meta-linguistic context, such as register/genre or accent/speaker identity, and how may this influence the processing of experimental items in another language or text variety?
Third, the Research Topic invites contributions that make use of a multi-method approach, such as combined behavioral and electrophysiological measures or experimental methods combined with measures extracted from corpus data. What opportunities and challenges do we face when integrating multiple approaches to examine linguistic, experimental and individual differences in human predictive capacity?
We welcome contributions from all areas of empirical psycho- and neurolinguistics, but contributions must explicitly address variability and variation in language and language processing. Relevant topics include individual differences and the impact of genre, modality, register and language variety. Contributions that go beyond single word and single sentence paradigms are especially desirable. Experimental, corpus-based, meta-analytic and review papers, as well as theoretical/opinion pieces are welcome; however, papers of the latter type should support their arguments with substantial empirical evidence from the literature. Particularly desirable are contributions which combine topics and/or methods, such as the impact of an individual's native dialect on processing of constructions that show variability in the standard language (e.g. choice of auxiliary, agreement of mass nouns, etc.) or experimental methods combined with measures extracted from corpus data such as information-theoretic surprisal.
Keywords: prediction, language processing, speaker variability, item variability, multimodal
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
