Publications

Abstract

No idea is older in the history of linguistics than the thought that there is, somehow hidden underneath the surface of sentences, a form or a structure which provides a semantic analysis and lays bare their logical structure. In Plato’s Cratylus the theory was proposed, deriving from Heraclitus’ theory of explanatory underlying structure in physical nature, that words contain within themselves bits of syntactic structure giving their meanings. The Stoics held the same view and maintained moreover that every sentence has an underlying logical structure, which for them was the Aristotelian subject- predicate form. They even proposed transformational processes to derive the surface from the deep structure. The idea of a semantically analytic logical form underlying the sentences of every language kept reappearing in various guises at various times. Quite recently it re-emerged under the name of generative semantics.

Abstract

Computational models can reflect the complexity of human behaviour by implementing multiple constraints within their architecture, and/or by taking into account the variety and richness of the environment to which the human is responding. We explore the second alternative in a model of word recognition that learns to map spoken words to visual and semantic representations of the words’ concepts. Critically, we employ a phonological representation utilising coarse-coding of the auditory stream, to mimic early stages of language development that are not dependent on individual phonemes to be isolated in the input, which may be a consequence of literacy development. The model was tested at different stages during training, and was able to simulate key behavioural features of word recognition in children: a developing effect of semantic information as a consequence of language learning, and a small but earlier effect of phonological information on word processing. We additionally tested the role of visual information in word processing, generating predictions for behavioural studies, showing that visual information could have a larger effect than semantics on children’s performance, but that again this affects recognition later in word processing than phonological information. The model also provides further predictions for performance of a mature word recognition system in the absence of fine-coding of phonology, such as in adults who have low literacy skills. The model demonstrated that such phonological effects may be reduced but are still evident even when multiple distractors from various modalities are present in the listener’s environment. The model demonstrates that complexity in word recognition can emerge from a simple associative system responding to the interactions between multiple sources of information in the language learner’s environment.

Abstract

Previous studies show that children do not become adult-like in learning to set the scene and introduce referents in their narrations until 9 years of age and even beyond. However, they investigated spoken languages, thus we do not know much about how these skills are acquired in sign languages, where events are expressed in visually similar ways to the real world events, unlike in spoken languages. The results of the current study demonstrate that deaf children (3;5–9;10 years) acquiring Turkish Sign Language, and hearing children (3;8–9;11 years) acquiring spoken Turkish both acquire scene-setting and referent introduction skills at similar ages. Thus the modality of the language being acquired does not have facilitating or hindering effects in the development of these skills.

Abstract

When we want to describe a path through space, but do not share a common perceptual field with a conversation partner, language has to work doubly hard. This task investigates how people communicate the navigation of space in the absence of shared visual cues, as well as collecting data on motion verbs and the roles of symmetry and landmarks in route description. Two speakers (separated by a curtain or other barrier) are each given a model of a landscape, and one participant describes standard routes through this landscape for the other to match.

Abstract

The encoding of apparently ‘simple’ movement concepts such as ‘COME’ and ‘GO’ can differ widely across languages (e.g., in regard to specifying direction of motion relative to the speaker). This questionnaire is used to identify the range of use of basic motion verbs in a language, and investigate semantic parameters that are involved in high frequency ‘COME’ and ‘GO’-like terms.