How do native speakers process texts with anomalous learner syntax? Second-language learners of Norwegian, and other verb-second (V2) languages, frequently place the verb in third position (e.g., *Adverbial-Subject-Verb), although it is mandatory for the verb in these languages to appear in second position (Adverbial-Verb-Subject). In an eye-tracking study, native Norwegian speakers read sentences with either grammatical V2 or ungrammatical verb-third (V3) word order. Unlike previous eye-tracking studies of ungrammaticality, which have primarily addressed morphosyntactic anomalies, we exclusively manipulate word order with no morphological or semantic changes. We found that native speakers reacted immediately to ungrammatical V3 word order, indicated by increased fixation durations and more regressions out on the subject, and subsequently on the verb. Participants also recovered quickly, already on the following word. The effects of grammaticality were unaffected by the length of the initial adverbial. The study contributes to future models of sentence processing which should be able to accommodate various types of “noisy” input, that is, non-standard variation. Together with new studies of processing of other L2 anomalies in Norwegian, the current findings can help language instructors and students prioritize which aspects of grammar to focus on.

Grammar anomalies are frequent in texts produced by second language learners. When describing these anomalies, two main issues arise: What is anomalous use of grammar? And how are grammar anomalies distinct from orthographic and lexical anomalies? We review earlier error-definitions and suggest defining grammar anomalies according to an explicit norm instead of L1 usage. We propose a broader definition of grammar than in previous studies, based on Boye & Harder (2012). The distinction between grammar, lexicon and orthography is illustrated with data from 28 adult L1 English learners of L2 Danish. In the corpus, 55.9 % of the anomalies were related to grammar. Finally, we discuss how definitions and procedures can be used in future studies of naturally occurring grammar anomalies.

Grammar errors are a natural part of everyday written communication. They are not a uniform group, but vary from morphological errors to ungrammatical word order and involve different types of word classes. In this study, we examine whether some types of naturally occurring errors attract more attention than others during reading, measured by detection rates. Data from 211 Danish high school students were included in the analysis. They each read texts containing different types of errors: syntactic errors (verb-third word order), morphological agreement errors (verb conjugations; gender mismatches in NPs) and orthographic errors. Participants were asked to underline all errors they detected while reading for comprehension. We examined whether there was a link between the type of errors that participants did not detect, the type of errors which they produce themselves (as measured in a subsequent grammar quiz), and the type of errors that are typical of high school students in general (based on error rates in a corpus). If an error is infrequent in production, it may cause a larger surprisal effect and be more attended to. For the three subtypes of grammar errors (V3 word order, verb errors, NP errors), corpus error rates predicted detection rates for most conditions. Yet, frequency was not the only possible explanation, as phonological similarity to the correct form is entangled with error frequency. Explicit grammatical awareness also played a role. The more correct answers participants had in the grammar tasks in the quiz, the more errors they detected. Finally, we found that the more annoyed with language errors participants reported to be, the more errors they detected. Our study did not measure eye movements, but the differences in error detection patterns point to shortcomings of existing eye-tracking models. Understanding the factors that govern attention and reaction to everyday grammar errors is crucial to developing robust eye-tracking processing models which can accommodate non-standard variation. Based on our results, we give our recommendations for current and future processing models.