Publications

Displaying 201 - 214 of 214
  • Sumer, B., Perniss, P. M., & Ozyurek, A. (2017). A first study on the development of spatial viewpoint in sign language acquisition: The case of Turkish Sign Language. In F. N. Ketrez, A. C. Kuntay, S. Ozcalıskan, & A. Ozyurek (Eds.), Social Environment and Cognition in Language Development: Studies in Honor of Ayhan Aksu-Koc (pp. 223-240). Amsterdam: John Benjamins. doi:10.1075/tilar.21.14sum.

    Abstract

    The current study examines, for the first time, the viewpoint preferences of signing children in expressing spatial relations that require imposing a viewpoint (left-right, front-behind). We elicited spatial descriptions from deaf children (4–9 years of age) acquiring Turkish Sign Language (TİD) natively from their deaf parents and from adult native signers of TİD. Adults produced these spatial descriptions from their own viewpoint and from that of their addressee depending on whether the objects were located on the lateral or the sagittal axis. TİD-acquiring children, on the other hand, described all spatial configurations from their own viewpoint. Differences were also found between children and adults in the type of linguistic devices and how they are used to express such spatial relations.
  • Takashima, A., & Bakker, I. (2017). Memory consolidation. In H.-J. Schmid (Ed.), Entrenchment and the Psychology of Language Learning: How We Reorganize and Adapt Linguistic Knowledge (pp. 177-200). Berlin: De Gruyter Mouton.
  • 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.
  • Trujillo, J. P., Levinson, S. C., & Holler, J. (2021). Visual information in computer-mediated interaction matters: Investigating the association between the availability of gesture and turn transition timing in conversation. In M. Kurosu (Ed.), Human-Computer Interaction. Design and User Experience Case Studies. HCII 2021 (pp. 643-657). Cham: Springer. doi:10.1007/978-3-030-78468-3_44.

    Abstract

    Natural human interaction involves the fast-paced exchange of speaker turns. Crucially, if a next speaker waited with planning their turn until the current speaker was finished, language production models would predict much longer turn transition times than what we observe. Next speakers must therefore prepare their turn in parallel to listening. Visual signals likely play a role in this process, for example by helping the next speaker to process the ongoing utterance and thus prepare an appropriately-timed response.

    To understand how visual signals contribute to the timing of turn-taking, and to move beyond the mostly qualitative studies of gesture in conversation, we examined unconstrained, computer-mediated conversations between 20 pairs of participants while systematically manipulating speaker visibility. Using motion tracking and manual gesture annotation, we assessed 1) how visibility affected the timing of turn transitions, and 2) whether use of co-speech gestures and 3) the communicative kinematic features of these gestures were associated with changes in turn transition timing.

    We found that 1) decreased visibility was associated with less tightly timed turn transitions, and 2) the presence of gestures was associated with more tightly timed turn transitions across visibility conditions. Finally, 3) structural and salient kinematics contributed to gesture’s facilitatory effect on turn transition times.

    Our findings suggest that speaker visibility--and especially the presence and kinematic form of gestures--during conversation contributes to the temporal coordination of conversational turns in computer-mediated settings. Furthermore, our study demonstrates that it is possible to use naturalistic conversation and still obtain controlled results.
  • Van Berkum, J. J. A., & Nieuwland, M. S. (2019). A cognitive neuroscience perspective on language comprehension in context. In P. Hagoort (Ed.), Human language: From genes and brain to behavior (pp. 429-442). Cambridge, MA: MIT Press.
  • Van Gijn, R., Hammarström, H., Van de Kerke, S., Krasnoukhova, O., & Muysken, P. (2017). Linguistic Areas, Linguistic Convergence and River Systems in South America. In R. Hickey (Ed.), The Cambridge Handbook of Areal Linguistics (pp. 964-996). Cambridge: Cambridge University Press. doi:10.1017/9781107279872.034.
  • Van Valin Jr., R. D. (1994). Extraction restrictions, competing theories and the argument from the poverty of the stimulus. In S. D. Lima, R. Corrigan, & G. K. Iverson (Eds.), The reality of linguistic rules (pp. 243-259). Amsterdam: Benjamins.
  • Vernes, S. C., Janik, V. M., Fitch, W. T., & Slater, P. J. B. (Eds.). (2021). Vocal learning in animals and humans [Special Issue]. Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences, 376.
  • Vernes, S. C. (2019). Neuromolecular approaches to the study of language. In P. Hagoort (Ed.), Human language: From genes and brain to behavior (pp. 577-593). Cambridge, MA: MIT Press.
  • Von Stutterheim, C., & Klein, W. (1989). Referential movement in descriptive and narrative discourse. In R. Dietrich, & C. F. Graumann (Eds.), Language processing in social context (pp. 39-76). Amsterdam: Elsevier.
  • Zavala, R. (2000). Multiple classifier systems in Akatek (Mayan). In G. Senft (Ed.), Systems of nominal classification (pp. 114-146). Cambridge University Press.
  • Zhang, Y., Chen, C.-h., & Yu, C. (2019). Mechanisms of cross-situational learning: Behavioral and computational evidence. In Advances in Child Development and Behavior; vol. 56 (pp. 37-63).

    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.
  • De Zubicaray, G., & Fisher, S. E. (Eds.). (2017). Genes, brain and language [Special Issue]. Brain and Language, 172.
  • Zuidema, W., & Fitz, H. (2019). Key issues and future directions: Models of human language and speech processing. In P. Hagoort (Ed.), Human language: From genes and brain to behavior (pp. 353-358). Cambridge, MA: MIT Press.

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