Publications

Abstract

The widely prevailing view that standard bivalent logic is the only possible sound logical system, imposed by metaphysical necessity, has been shattered by the development of multivalent logics during the 20th century. It is now clear that standard bivalent logic is merely the minimal representative of a wide variety of viable logics with any number of truth values. These viable logics can be subdivided into families. In this article, the Kleene family and the PPCn family are subjected to special examination, as they appear to be most relevant for the study of the logical properties of human language.

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

The Papuan languages of the central Solomon Islands are a negatively defined areal grouping: They are those four or possibly five languages in the central Solomon Islands that do not belong to the Austronesian family. Bilua (Vella Lavella), Touo (Rendova), Lavukaleve (Russell Islands), Savosavo (Savo Island) and possibly Kazukuru (New Georgia) have been identified as non-Austronesian since the early 20th century. However, their affiliations both to each other and to other languages still remain a mystery. Heterogeneous and until recently largely undescribed, they present an interesting departure from what is known both of Austronesian languages in the region and of the Papuan languages of the mainland of New Guinea.

Abstract

Although sign language-using communities exist in all areas of the world, few sign languages have been documented in detail. Sign languages occur in a variety of sociocultural contexts, ranging from sign languages used in closed village communities to officially recognized national sign languages. They may be grouped into language families on historical grounds or may participate in various language contact situations. Systematic cross-linguistic comparison reveals both significant structural similarities and important typological differences between sign languages. Focusing on information from non-Western countries, this article provides an overview of the sign languages of the world.

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.