Displaying 1 - 5 of 5
Dingemanse, M., Blythe, J., & Dirksmeyer, T. (2018). Formats for other-initiation of repair across languages: An exercise in pragmatic typology. In I. Nikolaeva (
Ed.), Linguistic Typology: Critical Concepts in Linguistics. Vol. 4 (pp. 322-357). London: Routledge.
AbstractIn conversation, people regularly deal with problems of speaking, hearing, and understanding. We report on a cross-linguistic investigation of the conversational structure of other-initiated repair (also known as collaborative repair, feedback, requests for clarification, or grounding sequences). We take stock of formats for initiating repair across languages (comparable to English huh?, who?, y’mean X?, etc.) and find that different languages make available a wide but remarkably similar range of linguistic resources for this function. We exploit the patterned variation as evidence for several underlying concerns addressed by repair initiation: characterising trouble, managing responsibility, and handling knowledge. The concerns do not always point in the same direction and thus provide participants in interaction with alternative principles for selecting one format over possible others. By comparing conversational structures across languages, this paper contributes to pragmatic typology: the typology of systems of language use and the principles that shape them.
Hoey, E., & Kendrick, K. H. (2018). Conversation analysis. In A. M. B. De Groot, & P. Hagoort (
Eds.), Research methods in psycholinguistics and the neurobiology of language: A practical guide (pp. 151-173). Hoboken: Wiley.
AbstractConversation Analysis (CA) is an inductive, micro-analytic, and predominantly qualitative method for studying human social interactions. This chapter describes and illustrates the basic methods of CA. We first situate the method by describing its sociological foundations, key areas of analysis, and particular approach in using naturally occurring data. The bulk of the chapter is devoted to practical explanations of the typical conversation analytic process for collecting data and producing an analysis. We analyze a candidate interactional practice – the assessmentimplicative interrogative – using real data extracts as a demonstration of the method, explicitly laying out the relevant questions and considerations for every stage of an analysis. The chapter concludes with some discussion of quantitative approaches to conversational interaction, and links between CA and psycholinguistic concerns
Janssen, R., & Dediu, D. (2018). Genetic biases affecting language: What do computer models and experimental approaches suggest? In T. Poibeau, & A. Villavicencio (
Eds.), Language, Cognition and Computational Models (pp. 256-288). Cambridge: Cambridge University Press.
AbstractComputer models of cultural evolution have shown language properties emerging on interacting agents with a brain that lacks dedicated, nativist language modules. Notably, models using Bayesian agents provide a precise specification of (extra-)liguististic factors (e.g., genetic) that shape language through iterated learning (biases on language), and demonstrate that weak biases get expressed more strongly over time (bias amplification). Other models attempt to lessen assumption on agents’ innate predispositions even more, and emphasize self-organization within agents, highlighting glossogenesis (the development of language from a nonlinguistic state). Ultimately however, one also has to recognize that biology and culture are strongly interacting, forming a coevolving system. As such, computer models show that agents might (biologically) evolve to a state predisposed to language adaptability, where (culturally) stable language features might get assimilated into the genome via Baldwinian niche construction. In summary, while many questions about language evolution remain unanswered, it is clear that it is not to be completely understood from a purely biological, cognitivist perspective. Language should be regarded as (partially) emerging on the social interactions between large populations of speakers. In this context, agent models provide a sound approach to investigate the complex dynamics of genetic biasing on language and speech
Mamus, E., & Karadöller, D. Z. (2018). Anıları Zihinde Canlandırma [Imagery in autobiographical memories]. In S. Gülgöz, B. Ece, & S. Öner (
Eds.), Hayatı Hatırlamak: Otobiyografik Belleğe Bilimsel Yaklaşımlar [Remembering Life: Scientific Approaches to Autobiographical Memory] (pp. 185-200). Istanbul, Turkey: Koç University Press.
Piepers, J., & Redl, T. (2018). Gender-mismatching pronouns in context: The interpretation of possessive pronouns in Dutch and Limburgian. In B. Le Bruyn, & J. Berns (
Eds.), Linguistics in the Netherlands 2018 (pp. 97-110). Amsterdam: Benjamins.
AbstractGender-(mis)matching pronouns have been studied extensively in experiments. However, a phenomenon common to various languages has thus far been overlooked: the systemic use of non-feminine pronouns when referring to female individuals. The present study is the first to provide experimental insights into the interpretation of such a pronoun: Limburgian zien ‘his/its’ and Dutch zijn ‘his/its’ are grammatically ambiguous between masculine and neuter, but while Limburgian zien can refer to women, the Dutch equivalent zijn cannot. Employing an acceptability judgment task, we presented speakers of Limburgian (N = 51) with recordings of sentences in Limburgian featuring zien, and speakers of Dutch (N = 52) with Dutch translations of these sentences featuring zijn. All sentences featured a potential male or female antecedent embedded in a stereotypically male or female context. We found that ratings were higher for sentences in which the pronoun could refer back to the antecedent. For Limburgians, this extended to sentences mentioning female individuals. Context further modulated sentence appreciation. Possible mechanisms regarding the interpretation of zien as coreferential with a female individual will be discussed.