Department Linguistik
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In eye-movement control during reading, advanced process-oriented models have been developed to reproduce behavioral data. So far, model complexity and large numbers of model parameters prevented rigorous statistical inference and modeling of interindividual differences. Here we propose a Bayesian approach to both problems for one representative computational model of sentence reading (SWIFT; Engbert et al., Psychological Review, 112, 2005, pp. 777-813). We used experimental data from 36 subjects who read the text in a normal and one of four manipulated text layouts (e.g., mirrored and scrambled letters). The SWIFT model was fitted to subjects and experimental conditions individually to investigate between- subject variability. Based on posterior distributions of model parameters, fixation probabilities and durations are reliably recovered from simulated data and reproduced for withheld empirical data, at both the experimental condition and subject levels. A subsequent statistical analysis of model parameters across reading conditions generates model-driven explanations for observable effects between conditions.
Advocating the inclusion of older adults in digital language learning technology and research
(2021)
Apples and oranges
(2021)
Despite scarce empirical evidence, introducing new vocabulary in semantic categories has long been standard in second language teaching. We examined the effect of learning context on encoding, immediate recall and integration of new vocabulary into semantic memory by contrasting categorically related (novel names for familiar concepts blocked by semantic category) and unrelated (mixed semantic categories) learning contexts. Two learning sessions were conducted 24 hours apart, with each participant exposed to both contexts. Subsequently, a test phase examined picture naming, translation and picture-word interference tasks. Compared to the unrelated context, the categorically related context resulted in poorer naming accuracy in the learning phase, slower response latencies at the immediate recall tasks and greater semantic interference in the picture-word interference task (picture naming in L1 with semantically related novel word distractors). We develop a theoretical account of word learning that attributes observed differences to episodic rather than semantic memory.
In successful communication, the literal meaning of linguistic utterances is often enriched by pragmatic inferences. Part of the pragmatic reasoning underlying such inferences has been successfully modeled as Bayesian goal recognition in the Rational Speech Act (RSA) framework. In this paper, we try to model the interpretation of question-answer sequences with narrow focus in the answer in the RSA framework, thereby exploring the effects of domain size and prior probabilities on interpretation. Should narrow focus exhaustivity inferences be actually based on Bayesian inference involving prior probabilities of states, RSA models should predict a dependency of exhaustivity on these factors. We present experimental data that suggest that interlocutors do not act according to the predictions of the RSA model and that exhaustivity is in fact approximately constant across different domain sizes and priors. The results constitute a conceptual challenge for Bayesian accounts of the underlying pragmatic inferences.
Argument mining on twitter
(2021)
In the last decade, the field of argument mining has grown notably. However, only relatively few studies have investigated argumentation in social media and specifically on Twitter. Here, we provide the, to our knowledge, first critical in-depth survey of the state of the art in tweet-based argument mining. We discuss approaches to modelling the structure of arguments in the context of tweet corpus annotation, and we review current progress in the task of detecting argument components and their relations in tweets. We also survey the intersection of argument mining and stance detection, before we conclude with an outlook.
Usage-based theories assume that all aspects of language processing are shaped by the distributional properties of the language. The frequency not only of words but also of larger chunks plays a major role in language processing. These theories predict that the frequency of phrases influences the time needed to prepare these phrases for production and their acoustic duration. By contrast, dominant psycholinguistic models of utterance production predict no such effects. In these models, the system keeps track of the frequency of individual words but not of co-occurrences. This study investigates the extent to which the frequency of phrases impacts naming latencies and acoustic duration with a balanced design, where the same words are recombined to build high- and low-frequency phrases. The brain signal of participants is recorded so as to obtain information on the electrophysiological bases and functional locus of frequency effects. Forty-seven participants named pictures using high- and low-frequency adjective-noun phrases. Naming latencies were shorter for high-frequency than low-frequency phrases. There was no evidence that phrase frequency impacted acoustic duration. The electrophysiological signal differed between high- and low-frequency phrases in time windows that do not overlap with conceptualization or articulation processes. These findings suggest that phrase frequency influences the preparation of phrases for production, irrespective of the lexical properties of the constituents, and that this effect originates at least partly when speakers access and encode linguistic representations. Moreover, this study provides information on how the brain signal recorded during the preparation of utterances changes with the frequency of word combinations.
Perceptual narrowing in the domain of face perception typically begins to reduce infants' sensitivity to differences distinguishing other-race faces from approximately 6 months of age. The present study investigated whether it is possible to re-sensitize Caucasian 12-month-old infants to other-race Asian faces through statistical learning by familiarizing them with different statistical distributions of these faces. The familiarization faces were created by generating a morphed continuum from one Asian face identity to another. In the unimodal condition, infants were familiarized with a frequency distribution wherein they saw the midpoint face of the morphed continuum the most frequently. In the bimodal condition, infants were familiarized with a frequency distribution wherein they saw faces closer to the endpoints of the morphed continuum the most frequently. After familiarization, infants were tested on their discrimination of the two original Asian faces. The infants' looking times during the test indicated that infants in the bimodal condition could discriminate between the two faces, while infants in the unimodal condition could not. These findings therefore suggest that 12-month-old Caucasian infants could be re-sensitized to Asian faces by familiarizing them with a bimodal frequency distribution of such faces.
Comparisons of equality with German so ... wie, and the relationship between degrees and properties
(2021)
We present a compositionally transparent, unified semantic analysis of two kinds of so ... wie-equative constructions in German, namely degree equatives and property equatives in the domain of individuals or events. Unlike in English and many other European languages (Haspelmath & Buchholz 1998, Rett 2013), both equative types in German feature the parameter marker so, suggesting a unified analysis. We show that the parallel formal expression of German degree and property equatives is accompanied by a parallel syntactic distribution (in predicative, attributive, and adverbial position), and by identical semantic properties: Both equative types allow for scope ambiguities, show negative island effects out of context, and license the negative polarity item uberhaupt 'at all' in the complement clause. As the same properties are also shared by German comparatives, we adopt the influential quantificational analysis of comparatives in von Stechow (1984ab), Heim (1985, 2001, 2007), and Beck (2011), and treat both German equative types in a uniform manner as expressing universal quantification over sets of degrees or over sets of properties (of individuals or events). Conceptually, the uniform marking of degree-related and property-related meanings is expected given that the abstract semantic category degree (type ) can be reconstructed in terms of equivalence classes, i.e., ontologically simpler sets of individuals (type ) or events (type ). These are found in any language, showing that whether or not a language makes explicit reference to degrees (by means of gradable adjectives, degree question words, degree-only equatives) does not follow on general conceptual or semantic grounds, but is determined by the grammar of that language.
Coordinated subjects often show variable number agreement with the finite verb, but linguistic approaches to this phenomenon have rarely been informed by systematically collected data. We report the results from three experiments investigating German speakers' agreement preferences with complex subjects joined by the correlative conjunctions sowohl horizontal ellipsis als auch ('both horizontal ellipsis and'), weder horizontal ellipsis noch ('neither horizontal ellipsis nor') or entweder horizontal ellipsis oder ('either horizontal ellipsis or'). We examine to what extent conjunction type and a conjunct's relative proximity to the verb affect the acceptability and processibility of singular vs. plural agreement. Experiment 1 was an untimed acceptability rating task, Experiment 2 a timed sentence completion task, and Experiment 3 was a self-paced reading task. Taken together, our results show that number agreement with correlative coordination in German is primarily determined by a default constraint triggering plural agreement, which interacts with linear order and semantic factors. Semantic differences between conjunctions only affected speakers' agreement preferences in the absence of processing pressure but not their initial agreement computation. The combined results from our offline and online experimental measures of German speakers' agreement preferences suggest that the constraints under investigation do not only differ in their relative weighting but also in their relative timing during agreement computation.
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.