Stephen C. Levinson

Publications

Displaying 1 - 8 of 8
  • Bögels, S., Kendrick, K. H., & Levinson, S. C. (2019). Conversational expectations get revised as response latencies unfold. Language, Cognition and Neuroscience. Advance online publication. doi:10.1080/23273798.2019.1590609.

    Abstract

    The present study extends neuro-imaging into conversation through studying dialogue comprehension. Conversation entails rapid responses, with negative semiotics for delay. We explored how expectations about the valence of the forthcoming response develop during the silence before the response and whether negative responses have mainly cognitive or social-emotional consequences. EEG-participants listened to questions from a spontaneous spoken corpus, cross-spliced with short/long gaps and “yes”/“no” responses. Preceding contexts biased listeners to expect the eventual response, which was hypothesised to translate to expectations for a shorter or longer gap. “No” responses showed a trend towards an early positivity, suggesting socio-emotional consequences. Within the long gap, expecting a “yes” response led to an earlier negativity, as well as a trend towards stronger theta-oscillations, after 300 milliseconds. This suggests that listeners anticipate/predict “yes” responses to come earlier than “no” responses, showing strong sensitivities to timing, which presumably promote hastening the pace of verbal interaction.

    Supplementary material

    plcp_a_1590609_sm4630.docx
  • Casillas, M., Brown, P., & Levinson, S. C. (2019). Early language experience in a Tseltal Mayan village. Child Development. Advance online publication. doi:10.1111/cdev.13349.

    Abstract

    Daylong at-home audio recordings from 10 Tseltal Mayan children (0;2–3;0; Southern Mexico) were analyzed for how often children engaged in verbal interaction with others and whether their speech environment changed with age, time of day, household size, and number of speakers present. Children were infrequently directly spoken to, with most directed speech coming from adults, and no increase with age. Most directed speech came in the mornings, and interactional peaks contained nearly four times the baseline rate of directed speech. Coarse indicators of children's language development (babbling, first words, first word combinations) suggest that Tseltal children manage to extract the linguistic information they need despite minimal directed speech. Multiple proposals for how they might do so are discussed.

    Supplementary material

    Tseltal-CLE-SuppMat.pdf
  • Enfield, N. J., Stivers, T., Brown, P., Englert, C., Harjunpää, K., Hayashi, M., Heinemann, T., Hoymann, G., Keisanen, T., Rauniomaa, M., Raymond, C. W., Rossano, F., Yoon, K.-E., Zwitserlood, I., & Levinson, S. C. (2019). Polar answers. Journal of Linguistics, 55(2), 277-304. doi:10.1017/S0022226718000336.

    Abstract

    How do people answer polar questions? In this fourteen-language study of answers to questions in conversation, we compare the two main strategies; first, interjection-type answers such as uh-huh (or equivalents yes, mm, head nods, etc.), and second, repetition-type answers that repeat some or all of the question. We find that all languages offer both options, but that there is a strong asymmetry in their frequency of use, with a global preference for interjection-type answers. We propose that this preference is motivated by the fact that the two options are not equivalent in meaning. We argue that interjection-type answers are intrinsically suited to be the pragmatically unmarked, and thus more frequent, strategy for confirming polar questions, regardless of the language spoken. Our analysis is based on the semantic-pragmatic profile of the interjection-type and repetition-type answer strategies, in the context of certain asymmetries inherent to the dialogic speech act structure of question–answer sequences, including sequential agency and thematic agency. This allows us to see possible explanations for the outlier distributions found in ǂĀkhoe Haiǁom and Tzeltal.
  • Holler, J., & Levinson, S. C. (2019). Multimodal language processing in human communication. Trends in Cognitive Sciences, 23(8), 639-652. doi:10.1016/j.tics.2019.05.006.

    Abstract

    Multiple layers of visual (and vocal) signals, plus their different onsets and offsets, represent a significant semantic and temporal binding problem during face-to-face conversation. Despite this complex unification process, multimodal messages appear to be processed faster than unimodal messages. Multimodal gestalt recognition and multilevel prediction are proposed to play a crucial role in facilitating multimodal language processing. The basis of the processing mechanisms involved in multimodal language comprehension is hypothesized to be domain general, coopted for communication, and refined with domain-specific characteristics. A new, situated framework for understanding human language processing is called for that takes into consideration the multilayered, multimodal nature of language and its production and comprehension in conversational interaction requiring fast processing.
  • Levinson, S. C. (2019). Natural forms of purposeful interaction among humans: What makes interaction effective? In K. A. Gluck, & J. E. Laird (Eds.), Interactive task learning: Humans, robots, and agents acquiring new tasks through natural interactions (pp. 111-126). Cambridge, MA: MIT Press.
  • Levinson, S. C., & Toni, I. (2019). Key issues and future directions: Interactional foundations of language. In P. Hagoort (Ed.), Human language: From genes and brain to behavior (pp. 257-261). Cambridge, MA: MIT Press.
  • Levinson, S. C. (2019). Interactional foundations of language: The interaction engine hypothesis. In P. Hagoort (Ed.), Human language: From genes and brain to behavior (pp. 189-200). Cambridge, MA: MIT Press.
  • Thomaz, A. L., Lieven, E., Cakmak, M., Chai, J. Y., Garrod, S., Gray, W. D., Levinson, S. C., Paiva, A., & Russwinkel, N. (2019). Interaction for task instruction and learning. In K. A. Gluck, & J. E. Laird (Eds.), Interactive task learning: Humans, robots, and agents acquiring new tasks through natural interactions (pp. 91-110). Cambridge, MA: MIT Press.

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