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In present-day German we find new word order options, particularly well-known from Turkish-German bilingual speakers in the contexts of new urban dialects, which allow violations of the canonical verb-second position in independent declarative clauses. In these cases, two positions are occupied in the forefield in front of the finite verb, usually by an adverbial and a subject, which identify, at the level of information structure, frame-setter and topic, respectively. Our study investigates the influence of verbal versus language -independent information-structural preferences for this linearisation, comparing Turkish-German multilingual speakers who have grown up in Germany with monolingual German and Turkish speakers. For tasks, in which grammatical restrictions were largely minimised, the results indicate a general tendency to place verbs in a position after the frame-setter and the topic; in addition, we found language-specific influences that distinguish Turkish-German and monolingual German speakers from monolingual Turkish ones. We interpret this as evidence for an information-structural motivation for verb-third, and for a clear dominance of German for Turkish-German speakers in Germany.
Speech scientists have long noted that the qualities of naturally-produced vowels do not remain constant over their durations regardless of being nominally "monophthongs" or "diphthongs". Recent acoustic corpora show that there are consistent patterns of first (F1) and second (F2) formant frequency change across different vowel categories. The three Australian English (AusE) close front vowels /i:, 1, i/ provide a striking example: while their midpoint or mean F1 and F2 frequencies are virtually identical, their spectral change patterns distinctly differ. The results indicate that, despite the distinct patterns of spectral change of AusE /i:, i, la/ in production, its perceptual relevance is not uniform, but rather vowel-category dependent.
Spectral change and duration as cues in Australian English listeners' front vowel categorization
(2018)
Australian English /iː/, /ɪ/, and /ɪə/ exhibit almost identical average first (F1) and second (F2) formant frequencies and differ in duration and vowel inherent spectral change (VISC). The cues of duration, F1 × F2 trajectory direction (TD) and trajectory length (TL) were assessed in listeners' categorization of /iː/ and /ɪə/ compared to /ɪ/. Duration was important for distinguishing both /iː/ and /ɪə/ from /ɪ/. TD and TL were important for categorizing /iː/ versus /ɪ/, whereas only TL was important for /ɪə/ versus /ɪ/. Finally, listeners' use of duration and VISC was not mutually affected for either vowel compared to /ɪ/.
We used event-related potentials (ERPs) to investigate the neurocognitive mechanisms associated with processing light verb constructions such as "give a kiss". These constructions consist of a semantically underspecified light verb ("give") and an event nominal that contributes most of the meaning and also activates an argument structure of its own ("kiss"). This creates a mismatch between the syntactic constituents and the semantic roles of a sentence. Native speakers read German verb-final sentences that contained light verb constructions (e.g., "Julius gave Anne a kiss"), non-light constructions (e.g., "Julius gave Anne a rose"), and semantically anomalous constructions (e.g., 'Julius gave Anne a conversation"). ERPs were measured at the critical verb, which appeared after all its arguments. Compared to non-light constructions, the light verb constructions evoked a widely distributed, frontally focused, sustained negative-going effect between 500 and 900 ms after verb onset. We interpret this effect as reflecting working memory costs associated with complex semantic processes that establish a shared argument structure in the light verb constructions.
Aspect splits can affect agreement, Case, and even preposition insertion. This paper discusses the functional ‘why’ and the theoretical ‘how’ of aspect splits. Aspect splits are an economical way to mark aspect by preserving or suppressing some independent element in one aspect. In formal terms, they are produced in the same way as coda conditions in phonology, with positional/contextual faithfulness.This approach captures the additive effects of cross-cutting splits. Aspect splits are analyzed here from Hindi, Nepali, Yucatec Maya, Chontal, and Palauan.
We used Chinese prenominal relative clauses (RCs) to test the predictions of two competing accounts of sentence comprehension difficulty: the experience-based account of Levy () and the Dependency Locality Theory (DLT; Gibson, ). Given that in Chinese RCs, a classifier and/or a passive marker BEI can be added to the sentence-initial position, we manipulated the presence/absence of classifiers and the presence/absence of BEI, such that BEI sentences were passivized subject-extracted RCs, and no-BEI sentences were standard object-extracted RCs. We conducted two self-paced reading experiments, using the same critical stimuli but somewhat different filler items. Reading time patterns from both experiments showed facilitative effects of BEI within and beyond RC regions, and delayed facilitative effects of classifiers, suggesting that cues that occur before a clear signal of an upcoming RC can help Chinese comprehenders to anticipate RC structures. The data patterns are not predicted by the DLT, but they are consistent with the predictions of experience-based theories.
The field of cognitive aging has seen considerable advances in describing the linguistic and semantic changes that happen during the adult life span to uncover the structure of the mental lexicon (i.e., the mental repository of lexical and conceptual representations). Nevertheless, there is still debate concerning the sources of these changes, including the role of environmental exposure and several cognitive mechanisms associated with learning, representation, and retrieval of information. We review the current status of research in this field and outline a framework that promises to assess the contribution of both ecological and psychological aspects to the aging lexicon.
Reflecting in written form on one's teaching enactments has been considered a facilitator for teachers' professional growth in university-based preservice teacher education. Writing a structured reflection can be facilitated through external feedback. However, researchers noted that feedback in preservice teacher education often relies on holistic, rather than more content-based, analytic feedback because educators oftentimes lack resources (e.g., time) to provide more analytic feedback. To overcome this impediment to feedback for written reflection, advances in computer technology can be of use. Hence, this study sought to utilize techniques of natural language processing and machine learning to train a computer-based classifier that classifies preservice physics teachers' written reflections on their teaching enactments in a German university teacher education program. To do so, a reflection model was adapted to physics education. It was then tested to what extent the computer-based classifier could accurately classify the elements of the reflection model in segments of preservice physics teachers' written reflections. Multinomial logistic regression using word count as a predictor was found to yield acceptable average human-computer agreement (F1-score on held-out test dataset of 0.56) so that it might fuel further development towards an automated feedback tool that supplements existing holistic feedback for written reflections with data-based, analytic feedback.
Successful sentence comprehension requires the comprehender to correctly figure out who did what to whom. For example, in the sentence John kicked the ball, the comprehender has to figure out who did the action of kicking and what was being kicked. This process of identifying and connecting the syntactically-related words in a sentence is called dependency completion. What are the cognitive constraints that determine dependency completion? A widely-accepted theory is cue-based retrieval. The theory maintains that dependency completion is driven by a content-addressable search for the co-dependents in memory. The cue-based retrieval explains a wide range of empirical data from several constructions including subject-verb agreement, subject-verb non-agreement, plausibility mismatch configurations, and negative polarity items.
However, there are two major empirical challenges to the theory: (i) Grammatical sentences’ data from subject-verb number agreement dependencies, where the theory predicts a slowdown at the verb in sentences like the key to the cabinet was rusty compared to the key to the cabinets was rusty, but the data are inconsistent with this prediction; and, (ii) Data from antecedent-reflexive dependencies, where a facilitation in reading times is predicted at the reflexive in the bodybuilder who worked with the trainers injured themselves vs. the bodybuilder who worked with the trainer injured themselves, but the data do not show a facilitatory effect.
The work presented in this dissertation is dedicated to building a more general theory of dependency completion that can account for the above two datasets without losing the original empirical coverage of the cue-based retrieval assumption. In two journal articles, I present computational modeling work that addresses the above two empirical challenges.
To explain the grammatical sentences’ data from subject-verb number agreement dependencies, I propose a new model that assumes that the cue-based retrieval operates on a probabilistically distorted representation of nouns in memory (Article I). This hybrid distortion-plus-retrieval model was compared against the existing candidate models using data from 17 studies on subject-verb number agreement in 4 languages. I find that the hybrid model outperforms the existing models of number agreement processing suggesting that the cue-based retrieval theory must incorporate a feature distortion assumption.
To account for the absence of facilitatory effect in antecedent-reflexive dependencies, I propose an individual difference model, which was built within the cue-based retrieval framework (Article II). The model assumes that individuals may differ in how strongly they weigh a syntactic cue over a number cue. The model was fitted to data from two studies on antecedent-reflexive dependencies, and the participant-level cue-weighting was estimated. We find that one-fourth of the participants, in both studies, weigh the syntactic cue higher than the number cue in processing reflexive dependencies and the remaining participants weigh the two cues equally. The result indicates that the absence of predicted facilitatory effect at the level of grouped data is driven by some, not all, participants who weigh syntactic cues higher than the number cue. More generally, the result demonstrates that the assumption of differential cue weighting is important for a theory of dependency completion processes. This differential cue weighting idea was independently supported by a modeling study on subject-verb non-agreement dependencies (Article III).
Overall, the cue-based retrieval, which is a general theory of dependency completion, needs to incorporate two new assumptions: (i) the nouns stored in memory can undergo probabilistic feature distortion, and (ii) the linguistic cues used for retrieval can be weighted differentially. This is the cumulative result of the modeling work presented in this dissertation.
The dissertation makes an important theoretical contribution: Sentence comprehension in humans is driven by a mechanism that assumes cue-based retrieval, probabilistic feature distortion, and differential cue weighting. This insight is theoretically important because there is some independent support for these three assumptions in sentence processing and the broader memory literature. The modeling work presented here is also methodologically important because for the first time, it demonstrates (i) how the complex models of sentence processing can be evaluated using data from multiple studies simultaneously, without oversimplifying the models, and (ii) how the inferences drawn from the individual-level behavior can be used in theory development.
In syntactic dependency trees, when arcs are drawn from syntactic heads to dependents, they rarely cross. Constraints on these crossing dependencies are critical for determining the syntactic properties of human language, because they define the position of natural language in formal language hierarchies. We study whether the apparent constraints on crossing syntactic dependencies in natural language might be explained by constraints on dependency lengths (the linear distance between heads and dependents). We compare real dependency trees from treebanks of 52 languages against baselines of random trees which are matched with the real trees in terms of their dependency lengths. We find that these baseline trees have many more crossing dependencies than real trees, indicating that a constraint on dependency lengths alone cannot explain the empirical rarity of crossing dependencies. However, we find evidence that a combined constraint on dependency length and the rate of crossing dependencies might be able to explain two of the most-studied formal restrictions on dependency trees: gap degree and well-nestedness.