Spacecraft Radiation Shielding by a Dispersed Array of Superconducting Magnets

Two studies examined how lexical information contained in words affects people’s category representations. Some words are lexically suggestive regarding the taxonomic position of their referent (e.g., bumblebee, starfish). However, this information differs from language to language (e.g., in Dutch the equivalent words hold no taxonomic information: hommel, vlinder). Three language groups, Dutch, English, and Indonesian speakers, were tested in similarity and typicality judgment tasks. The results show that the lexical information affects only the users of the language (e.g., Dutch speakers rated Dutch-informative items, both in similarity and typicality tasks, higher than English and Indonesian speakers). Results are discussed in light of theories of concept representation and the language relativity hypothesis.status: publishe

Extensions and intensions of semantic categories

As Murphy (2002) said, “concepts are the glue that hold our mental world together” (p. 1). People carve up the world into concepts that capture what things are and what properties they have. So in order to understand a novel object, one could compare it with objects that have similar properties, and group them together. For instance, a never-seen object that has fur, four legs, a tail, and the tendency to bark, can be compared to similar exemplars and classified into the category of dogs. This example shows that concepts have two important aspects that play a role in categorization: the intension (i.e., the properties that define concepts) and the extension (i.e., the set of category members). Both theoretical constructs have been the topic of much research, however, there are still some crucial issues that remain unsolved. In this PhD project, three research lines were developed that focus on the category extension, category intension, and how these two aspects are measured and connected. A first research line focused on the measurement of category extension and intension. Chapter 2 describes an alternative method to measure category extension in young children. Crucially, the method, which was called the typicality ranking task, does not require advanced numerical or linguistic knowledge. Furthermore, Chapter 3 comprises a detailed investigation of category intensions across different age groups. More specifically, I examined the kind of properties that people generated and how these properties predict their category extension. In a second research line, the relationship between extensions and intensions was investigated. It has been a long-held belief that category extension and intension have a direct relationship, until a recent study by Hampton and Passanisi (2016) challenged it. Two experiments, described in Chapter 4, were conducted to test this relationship and the results actually provided evidence in favour of a link between extension and intension at the subject level. To further examine when this relationship begins to occur, Chapter 5 was devoted to investigating its developmental trajectory. The aim was to test to what extent children’s category extensions are based on their own category intensions, and how this relationship evolves during language learning. Finally, the last research line looked into other factors, besides a list of properties (i.e., intensions), that can affect category extension. Concept representation is arguably more than a list of properties or memories of previously encountered instances. Other factors such as the contexts in which words figure, the relation to other words in the semantic network, the actual words that are used for particular referents, and pragmatics can also play an important role (Hollis & Westbury, 2016; Spalding & Gagné, 2015). Chapter 6 and 7 look into these factors, specifically focussing on the role of vocabulary across different languages.status: publishe

Exemplar versus Prototype Models in Predicting Children’s Category Membership

This research compared two classical models of semantic concepts, prototype and exemplar-based models, in predicting category membership. Heit and Barsalou’s (1996) exemplar-based instantiation model was compared to a prototype predictor model based on Rosch and Mervis’ (1975) family resemblance. For three semantic categories (clothes, fruit, and vehicles), category judgements were collected for 25 possible exemplars. Four age groups (5-, 7-, 10-year-olds, and adults) performed a category membership judgement task and either a feature generation task or an exemplar generation task. The category judgement scores were correlated with the family resemblance measure based on the generated features to assess the validity of the prototype-based model. The exemplar-based model was tested by correlating category judgement scores with rated similarity of the 25 possible exemplars towards generated category exemplars. Results of the model comparison and the specific tendencies across age groups will be discussed in the light of the development of category representations.status: accepte

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