Publications

Abstract

Ook al hebben we het niet altijd door, onze zintuigen werken altijd samen. Als je iemand ziet praten, bijvoorbeeld, verwerken je hersenen automatisch tegelijkertijd het geluid van de woorden en de bewegingen van de lippen. Omdat onze zintuigen altijd samenwerken zijn onze hersenen erg gevoelig voor dingen die ‘samenhoren’ en goed bij elkaar passen. In dit hoofdstuk beschrijven we een project onderzoekend leren met als thema ‘Samenwerkende zintuigen’.

Abstract

In this chapter we introduce the CLARIN Knowledge Centre for Atypical Communication Expertise. The mission of ACE is to support researchers engaged in languages which pose particular challenges for analysis; for this, we use the umbrella term “atypical communication”. This includes language use by second-language learners, people with language disorders or those suffering from lan-guage disabilities, and languages that pose unique challenges for analysis, such as sign languages and languages spoken in a multilingual context. The chapter presents details about the collaborations and outreach of the centre, the services offered, and a number of showcases for its activities.

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.