Publications

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 term “event” is a controversial concept, and the “same” activity or situation can be linguistically encoded in many different ways. The aim of this task is to explore features of event representation in the language of study, in particular, multi-verb constructions, event typicality, and event complexity. The task consists of a description and recollection task using film stimuli, and a subsequent re-enactment of certain scenes by other participants on the basis of these descriptions. The first part of the task collects elaborate and concise descriptions of complex events in order to examine how these are segmented into macro-events, what kind of information is expressed, and how the information is ordered. The re-enactment task is designed to examine what features of the scenes are stereotypically implied.

Abstract

Field data are drawn from a particular speech community at a certain place and time. The intent of this survey is to enrich understanding of the various socio-cultural contexts in which linguistic and “cognitive” data may have been collected, so that we can explore the role which societal, cultural and contextual factors may play in this material. The questionnaire gives guidelines concerning types of ethnographic information that are important to cross-cultural and cross-linguistic enquiry, and will be especially useful to researchers who do not have specialised training in anthropology.

Abstract

How do languages encode different kinds of movement, and what features do people pay attention to when describing motion events? This document outlines topics concerning the investigation of “enter” and “exit” events. It helps contextualise research tasks that examine this domain (see 'Motion Elicitation' and 'Enter/Exit animation') and gives some pointers about what other questions can be explored.

Abstract

How do languages encode different kinds of movement, and what features do people pay attention to when describing motion events? This task investigates the expression of “enter” and “exit” activities, that is, events involving motion in(to) and motion out (of) container-like items. The researcher first uses particular stimuli (a ball, a cup, rice, etc.) to elicit descriptions of enter/exit events from one consultant, and then asks another consultant to demonstrate the event based on these descriptions. See also the related entries Enter/Exit Animation and Background Questions for Enter/Exit Research.

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.