In this article we use a qualitative method, conversation analysis, to examine videos of caregivers interacting with their young autistic children who are in the early phases of language learning. Conversation analysis involves preparation of detailed transcripts of video data, which are then analyzed together to understand how interactional moves (e.g. talk, gestures, and physical conduct) are linked with prior and subsequent interactional moves. We analyzed data from 15 participants, and focused on instances when caregivers made a proposal about something the child was playing with. In previous research, similar instances have been referred to as "follow-in directives." We found that these proposals were embedded in sequences that had a similar structure, and were prefaced with a 'pre-proposal'; where the caregiver established the child's interest in a joint activity and signaled the upcoming proposal. The caregiver's talk was also provided in such a way that there was a clear "slot" for the child's turn, which made it easy for the child's actions to become part of an interactional sequence. In addition, proposal sequences were very negotiable-the caregivers do not usually insist that the child follow through on the proposal, only that they produce an action that could be taken as a response. Finally, there were some instances where the child's turn was very precisely timed to occur right at the end of a caregiver's proposal; this precise timing could signal the child's understanding of how interactional turn-taking works. We suggest that this method of examining caregiver-child interactions provides new insights into how interactions proceed, which could be useful for future intervention research.
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http://dx.doi.org/10.1177/13623613211046799 | DOI Listing |
JMIR AI
January 2025
Faculty of Social Science, Ruhr University Bochum, Bochum, Germany.
Background: Conversational agents (CAs) are finding increasing application in health and social care, not least due to their growing use in the home. Recent developments in artificial intelligence, machine learning, and natural language processing have enabled a variety of new uses for CAs. One type of CA that has received increasing attention recently is smart speakers.
View Article and Find Full Text PDFDev Psychol
January 2025
School of Communication, Ohio State University.
We investigated the impact of parents' open-ended questions during collaborative science activities. Specifically, we randomly assigned 116 parents (69.8% mothers; 89.
View Article and Find Full Text PDFPsychol Aging
January 2025
Department of Psychology, National Taiwan University.
The Socioemotional Selectivity Theory (SST) posits that older and younger adults have different life goals due to differences in perceived remaining lifetime. Younger adults focus more on future-oriented knowledge exploration and forming new friendships, while older adults prioritize present-focused emotional regulation and maintaining close relationships. While previous research has found these age differences manifest in autobiographical textual expressions, their presence in verbal communication remains unexplored.
View Article and Find Full Text PDFJ Child Lang
January 2025
Center for Data Science in Humanities, Institute of Humanities, Chosun University, Gwangju, Korea.
We investigated the dynamics of communicative initiation in infant-caregiver interactions across ages and language abilities. Analyses of 228 Language ENvironment Analysis (LENA) recordings from 141 Korean adult-child dyads (60 girls; aged 7-30 months) replicated the initiator effect reported in North American populations. This effect, demonstrated by longer utterances, more frequent speech, and shorter response times in self-initiated interactions for both children and adults, suggests potential cross-cultural consistency in this conversational dynamic and remained consistent across ages in most conversational measures.
View Article and Find Full Text PDFFront Psychol
December 2024
Institute for Logic, Language and Computation, University of Amsterdam, Amsterdam, Netherlands.
The key function of storytelling is a meeting of hearts: a resonance in the recipient(s) of the story narrator's emotion toward the story events. This paper focuses on the role of gestures in engendering emotional resonance in conversational storytelling. The paper asks three questions: Does story narrators' gesture expressivity increase from story onset to climax offset (RQ #1)? Does gesture expressivity predict specific EDA responses in story participants (RQ #2)? How important is the contribution of gesture expressivity to emotional resonance compared to the contribution of other predictors of resonance (RQ #3)? 53 conversational stories were annotated for a large number of variables including Protagonist, Recency, Group composition, Group size, Sentiment, and co-occurrence with quotation.
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