Publications

Abstract

In order to decode the message of a speaker, listeners have to recognize individual words in the speaker's utterance.

Abstract

Many people speak a second language next to their mother tongue. How do they learn this language and how does the brain process it compared to the native language? A second language can be learned without explicit instruction. Our brains automatically pick up grammatical structures, such as word order, when these structures are repeated frequently during learning. The learning takes place within hours or days and the same brain areas, such as frontal and temporal brain regions, that process our native language are very quickly activated. When people master a second language very well, even the same neuronal populations in these language brain areas are involved. This is especially the case when the grammatical structures are similar. In conclusion, it appears that a second language builds on the existing cognitive and neural mechanisms of the native language as much as possible.

Abstract

ISO Technical Committee 37, Terminology and other language and content resources, established an ISO 12620:2009 based Data Category Registry (DCR), called ISOcat (see http://www.isocat.org), to foster semantic interoperability of linguistic resources. However, this goal can only be met if the data categories are reused by a wide variety of linguistic resource types. A resource indicates its usage of data categories by linking to them. The small DC Reference XML vocabulary is used to embed links to data categories in XML documents. The link is established by an URI, which servers as the Persistent IDentifier (PID) of a data category. This paper discusses the efforts to mimic the same approach for RDF-based resources. It also introduces the RDF quad store based Relation Registry RELcat, which enables ontological relationships between data categories not supported by ISOcat and thus adds an extra level of linguistic knowledge.

Abstract

5.1 Einführung in den Forschungsbereich Die Psycholinguistik ist der Bereich der Linguistik, der sich mit dem Zusammenhang zwischen menschlicher Sprache und dem Denken und anderen mentalen Prozessen beschäftigt, d.h. sie stellt sich einer Reihe von essentiellen Fragen wie etwa (1) Wie schafft es unser Gehirn, im Wesentlichen akustische und visuelle kommunikative Informationen zu verstehen und in mentale Repräsentationen umzusetzen? (2) Wie kann unser Gehirn einen komplexen Sachverhalt, den wir anderen übermitteln wollen, in eine von anderen verarbeitbare Sequenz von verbalen und nonverbalen Aktionen umsetzen? (3) Wie gelingt es uns, in den verschiedenen Phasen des Lebens Sprachen zu erlernen? (4) Sind die kognitiven Prozesse der Sprachverarbeitung universell, obwohl die Sprachsysteme derart unterschiedlich sind, dass sich in den Strukturen kaum Universalien finden lassen?

Abstract

In recent decades, neuroimaging studies on the neural infrastructure of language are usually (or mostly) conducted with certain on-line language processing tasks. These functional neuroimaging studies helped to localize the language areas in the brain and to investigate the brain activity during explicit language processing. However, little is known about what is going on with the language areas when the brain is ‘at rest’, i.e., when there is no explicit language processing running. Taking advantage of the fcMRI and DTI techniques, this thesis is able to investigate the language function ‘off-line’ at the neuronal network level and the connectivity among language areas in the brain. Based on patient studies, the traditional, classical model on the perisylvian language network specifies a “Broca’ area – Arcuate Fasciculus – Werinicke’s area” loop (Ojemann 1991). With the help of modern neuroimaging techniques, researchers have been able to track language pathways that involve more brain structures than are in the classical model, and relate them to certain language functions. In such a background, a large part of this thesis made a contribution to the study of the topology of the language networks. It revealed that the language networks form a topographical functional connectivity pattern in the left hemisphere for the right-handers. This thesis also revealed the importance of structural hubs, such as Broca’s and Wernicke’s areas, which have more connectivity to other brain areas and play a central role in the language networks. Furthermore, this thesis revealed both functionally and structurally lateralized language networks in the brain. The consistency between what is found in this thesis and what has been known from previous functional studies seems to suggest, that the human brain is optimized and ‘ready’ for the language function even when there is currently no explicit language-processing running.

Abstract

Word learning happens in everyday contexts with many words and many potential referents for those words in view at the same time. It is challenging for young learners to find the correct referent upon hearing an unknown word at the moment. This problem of referential uncertainty has been deemed as the crux of early word learning (Quine, 1960). Recent empirical and computational studies have found support for a statistical solution to the problem termed cross-situational learning. Cross-situational learning allows learners to acquire word meanings across multiple exposures, despite each individual exposure is referentially uncertain. Recent empirical research shows that infants, children and adults rely on cross-situational learning to learn new words (Smith & Yu, 2008; Suanda, Mugwanya, & Namy, 2014; Yu & Smith, 2007). However, researchers have found evidence supporting two very different theoretical accounts of learning mechanisms: Hypothesis Testing (Gleitman, Cassidy, Nappa, Papafragou, & Trueswell, 2005; Markman, 1992) and Associative Learning (Frank, Goodman, & Tenenbaum, 2009; Yu & Smith, 2007). Hypothesis Testing is generally characterized as a form of learning in which a coherent hypothesis regarding a specific word-object mapping is formed often in conceptually constrained ways. The hypothesis will then be either accepted or rejected with additional evidence. However, proponents of the Associative Learning framework often characterize learning as aggregating information over time through implicit associative mechanisms. A learner acquires the meaning of a word when the association between the word and the referent becomes relatively strong. In this chapter, we consider these two psychological theories in the context of cross-situational word-referent learning. By reviewing recent empirical and cognitive modeling studies, our goal is to deepen our understanding of the underlying word learning mechanisms by examining and comparing the two theoretical learning accounts.

Abstract

Classifiers (currently also called 'depicting handshapes'), are observed in almost all signed languages studied to date and form a well-researched topic in sign language linguistics. Yet, these elements are still subject to much debate with respect to a variety of matters. Several different categories of classifiers have been posited on the basis of their semantics and the linguistic context in which they occur. The function(s) of classifiers are not fully clear yet. Similarly, there are differing opinions regarding their structure and the structure of the signs in which they appear. Partly as a result of comparison to classifiers in spoken languages, the term 'classifier' itself is under debate. In contrast to these disagreements, most studies on the acquisition of classifier constructions seem to consent that these are difficult to master for Deaf children. This article presents and discusses all these issues from the viewpoint that classifiers are linguistic elements.