Publications

Abstract

Co-speech gestures embody a form of manual action that is tightly coupled to the language system. As such, the co-occurrence of speech and co-speech gestures is an excellent example of the interplay between language and action. There are, however, other ways in which language and action can be thought of as closely related. In this paper we will give an overview of studies in cognitive neuroscience that examine the neural underpinnings of links between language and action. Topics include neurocognitive studies of motor representations of speech sounds, action-related language, sign language and co-speech gestures. It will be concluded that there is strong evidence on the interaction between speech and gestures in the brain. This interaction however shares general properties with other domains in which there is interplay between language and action.

Abstract

Brain processing depends on the interactions between neuronal groups. Those interactions are governed by the pattern of anatomical connections and by yet unknown mechanisms that modulate the effective strength of a given connection. We found that the mutual influence among neuronal groups depends on the phase relation between rhythmic activities within the groups. Phase relations supporting interactions between the groups preceded those interactions by a few milliseconds, consistent with a mechanistic role. These effects were specific in time, frequency, and space, and we therefore propose that the pattern of synchronization flexibly determines the pattern of neuronal interactions.

Abstract

This auditory lexical decision study shows that cohort entropies, conditional root uniqueness points, and morphological family size all contribute to the dynamics of the auditory comprehension of prefixed words. Three entropy measures calculated for different positions in the stem of Dutch prefixed words revealed facilitation for higher entropies, except at the point of disambiguation, where we observed inhibition. Morphological family size was also facilitatory, but only for prefixed words in which the conditional root uniqueness point coincided with the conventional uniqueness point. For words with early conditional disambiguation, in contrast, only the morphologically related words that were onset-aligned with the target word facilitated lexical decision.

Abstract

This article provides a first overview of some striking grammatical structures in Türk Idotscedilaret Dili (Turkish Sign Language, TID), the sign language used by the Deaf community in Turkey. The data are described with a typological perspective in mind, focusing on aspects of TID grammar that are typologically unusual across sign languages. After giving an overview of the historical, sociolinguistic and educational background of TID and the language community using this sign language, five domains of TID grammar are investigated in detail. These include a movement derivation signalling completive aspect, three types of nonmanual negation — headshake, backward head tilt, and puffed cheeks — and their distribution, cliticization of the negator NOT to a preceding predicate host sign, an honorific whole-entity classifier used to refer to humans, and a question particle, its history and current status in the language. A final evaluation points out the significance of these data for sign language research and looks at perspectives for a deeper understanding of the language and its history.

Abstract

This article reports on results from a broad crosslinguistic study based on data from thirty-five signed languages around the world. The study is the first of its kind, and the typological generalizations presented here cover the domain of interrogative structures as they appear across a wide range of geographically and genetically distinct signed languages. Manual and nonmanual ways of marking basic types of questions in signed languages are investigated. As a result, it becomes clear that the range of crosslinguistic variation is extensive for some subparameters, such as the structure of question-word paradigms, while other parameters, such as the use of nonmanual expressions in questions, show more similarities across signed languages. Finally, it is instructive to compare the findings from signed language typology to relevant data from spoken languages at a more abstract, crossmodality level.

Abstract

This article presents a typology of negative constructions across a substantial number of sign languages from around the globe. After situating the topic within the wider context of linguistic typology, the main negation strategies found across sign languages are described. Nonmanual negation includes the use of head movements and facial expressions for negation and is of great importance in sign languages as well as particularly interesting from a typological point of view. As far as manual signs are concerned, independent negative particles represent the dominant strategy, but there are also instances of irregular negation in most sign languages. Irregular negatives may take the form of suppletion, cliticisation, affixing, or internal modification of a sign. The results of the study lead to interesting generalisations about similarities and differences between negatives in signed and spoken languages.

Abstract

Genome-wide association studies using thousands to hundreds of thousands of single nucleotide polymorphism (SNP) markers and region-wide association studies using a dense panel of SNPs are already in use to identify disease susceptibility genes and to predict disease risk in individuals. Because these tasks become increasingly important, three different data sets were provided for the Genetic Analysis Workshop 15, thus allowing examination of various novel and existing data mining methods for both classification and identification of disease susceptibility genes, gene by gene or gene by environment interaction. The approach most often applied in this presentation group was random forests because of its simplicity, elegance, and robustness. It was used for prediction and for screening for interesting SNPs in a first step. The logistic tree with unbiased selection approach appeared to be an interesting alternative to efficiently select interesting SNPs. Machine learning, specifically ensemble methods, might be useful as pre-screening tools for large-scale association studies because they can be less prone to overfitting, can be less computer processor time intensive, can easily include pair-wise and higher-order interactions compared with standard statistical approaches and can also have a high capability for classification. However, improved implementations that are able to deal with hundreds of thousands of SNPs at a time are required.