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This eye-tracking study establishes basic benchmarks of eye movements during reading in heritage language (HL) by Russian-speaking adults and adolescents of high (n = 21) and low proficiency (n = 27). Heritage speakers (HSs) read sentences in Cyrillic, and their eye movements were compared to those of Russian monolingual skilled adult readers, 8-year-old children and L2 learners. Reading patterns of HSs revealed longer mean fixation durations, lower skipping probabilities, and higher regressive saccade rates than in monolingual adults. High-proficient HSs were more similar to monolingual children, while low-proficient HSs performed on par with L2 learners. Low-proficient HSs differed from high-proficient HSs in exhibiting lower skipping probabilities, higher fixation counts, and larger frequency effects. Taken together, our findings are consistent with the weaker links account of bilingual language processing as well as the divergent attainment theory of HL.
During reading or listening, people can generate predictions about the lexical and morphosyntactic properties of upcoming input based on available context. Psycholinguistic experiments that study predictability or control for it conventionally rely on a human-based approach and estimate predictability via the cloze task. Our study investigated an alternative corpus-based approach for estimating predictability via language predictability models. We obtained cloze and corpus-based probabilities for all words in 144 Russian sentences, correlated the two measures, and found a strong correlation between them. Importantly, we estimated how much variance in eye movements registered while reading the same sentences was explained by each of the two probabilities and whether the two probabilities explain the same variance. Along with lexical predictability (the activation of a particular word form), we analyzed morphosyntactic predictability (the activation of morphological features of words) and its effect on reading times over and above lexical predictability. We found that for predicting reading times, cloze and corpus-based measures of both lexical and morphosyntactic predictability explained the same amount of variance. However, cloze and corpus-based lexical probabilities both independently contributed to a better model fit, whereas for morphosyntactic probabilities, the contributions of cloze and corpus-based measures were interchangeable. Therefore, morphosyntactic but not lexical corpus-based probabilities can substitute for cloze probabilities in reading experiments. Our results also indicate that in languages with rich inflectional morphology, such as Russian, when people engage in prediction, they are much more successful in predicting isolated morphosyntactic features than predicting the particular lexeme and its full morphosyntactic markup.
The goal of this dissertation is to empirically evaluate the predictions of two classes of models applied to language processing: the similarity-based interference models (Lewis & Vasishth, 2005; McElree, 2000) and the group of smaller-scale accounts that we will refer to as faulty encoding accounts (Eberhard, Cutting, & Bock, 2005; Bock & Eberhard, 1993). Both types of accounts make predictions with regard to processing the same class of structures: sentences containing a non-subject (interfering) noun in addition to a subject noun and a verb. Both accounts make the same predictions for processing ungrammatical sentences with a number-mismatching interfering noun, and this prediction finds consistent support in the data. However, the similarity-based interference accounts predict similar effects not only for morphosyntactic, but also for the semantic level of language organization. We verified this prediction in three single-trial online experiments, where we found consistent support for the predictions of the similarity-based interference account. In addition, we report computational simulations further supporting the similarity-based interference accounts. The combined evidence suggests that the faulty encoding accounts are not required to explain comprehension of ill-formed sentences.
For the processing of grammatical sentences, the accounts make conflicting predictions, and neither the slowdown predicted by the similarity-based interference account, nor the complementary slowdown predicted by the faulty encoding accounts were systematically observed. The majority of studies found no difference between the compared configurations. We tested one possible explanation for the lack of predicted difference, namely, that both slowdowns are present simultaneously and thus conceal each other. We decreased the amount of similarity-based interference: if the effects were concealing each other, decreasing one of them should allow the other to surface. Surprisingly, throughout three larger-sample single-trial online experiments, we consistently found the slowdown predicted by the faulty encoding accounts, but no effects consistent with the presence of inhibitory interference.
The overall pattern of the results observed across all the experiments reported in this dissertation is consistent with previous findings: predictions of the interference accounts for the processing of ungrammatical sentences receive consistent support, but the predictions for the processing of grammatical sentences are not always met. Recent proposals by Nicenboim et al. (2016) and Mertzen et al. (2020) suggest that interference might arise only in people with high working memory capacity or under deep processing mode. Following these proposals, we tested whether interference effects might depend on the depth of processing: we manipulated the complexity of the training materials preceding the grammatical experimental sentences while making no changes to the experimental materials themselves. We found that the slowdown predicted by the faulty encoding accounts disappears in the deep processing mode, but the effects consistent with the predictions of the similarity-based interference account do not arise.
Independently of whether similarity-based interference arises under deep processing mode or not, our results suggest that the faulty encoding accounts cannot be dismissed since they make unique predictions with regard to processing grammatical sentences, which are supported by data. At the same time, the support is not unequivocal: the slowdowns are present only in the superficial processing mode, which is not predicted by the faulty encoding accounts. Our results might therefore favor a much simpler system that superficially tracks number features and is distracted by every plural feature.