Displaying 1 - 12 of 12
  • Karaca, F., Brouwer, S., Unsworth, S., & Huettig, F. (2021). Prediction in bilingual children: The missing piece of the puzzle. In E. Kaan, & T. Grüter (Eds.), Prediction in Second Language Processing and Learning (pp. 116-137). Amsterdam: Benjamins.

    Abstract

    A wealth of studies has shown that more proficient monolingual speakers are better at predicting upcoming information during language comprehension. Similarly, prediction skills of adult second language (L2) speakers in their L2 have also been argued to be modulated by their L2 proficiency. How exactly language proficiency and prediction are linked, however, is yet to be systematically investigated. One group of language users which has the potential to provide invaluable insights into this link is bilingual children. In this paper, we compare bilingual children’s prediction skills with those of monolingual children and adult L2 speakers, and show how investigating bilingual children’s prediction skills may contribute to our understanding of how predictive processing works.
  • Mani, N., Mishra, R. K., & Huettig, F. (2018). Introduction to 'The Interactive Mind: Language, Vision and Attention'. In N. Mani, R. K. Mishra, & F. Huettig (Eds.), The Interactive Mind: Language, Vision and Attention (pp. 1-2). Chennai: Macmillan Publishers India.
  • Mitterer, H., Brouwer, S., & Huettig, F. (2018). How important is prediction for understanding spontaneous speech? In N. Mani, R. K. Mishra, & F. Huettig (Eds.), The Interactive Mind: Language, Vision and Attention (pp. 26-40). Chennai: Macmillan Publishers India.
  • Smith, A. C., Monaghan, P., & Huettig, F. (2016). Complex word recognition behaviour emerges from the richness of the word learning environment. In K. Twomey, A. C. Smith, G. Westermann, & P. Monaghan (Eds.), Neurocomputational Models of Cognitive Development and Processing: Proceedings of the 14th Neural Computation and Psychology Workshop (pp. 99-114). Singapore: World Scientific. doi:10.1142/9789814699341_0007.

    Abstract

    Computational models can reflect the complexity of human behaviour by implementing multiple constraints within their architecture, and/or by taking into account the variety and richness of the environment to which the human is responding. We explore the second alternative in a model of word recognition that learns to map spoken words to visual and semantic representations of the words’ concepts. Critically, we employ a phonological representation utilising coarse-coding of the auditory stream, to mimic early stages of language development that are not dependent on individual phonemes to be isolated in the input, which may be a consequence of literacy development. The model was tested at different stages during training, and was able to simulate key behavioural features of word recognition in children: a developing effect of semantic information as a consequence of language learning, and a small but earlier effect of phonological information on word processing. We additionally tested the role of visual information in word processing, generating predictions for behavioural studies, showing that visual information could have a larger effect than semantics on children’s performance, but that again this affects recognition later in word processing than phonological information. The model also provides further predictions for performance of a mature word recognition system in the absence of fine-coding of phonology, such as in adults who have low literacy skills. The model demonstrated that such phonological effects may be reduced but are still evident even when multiple distractors from various modalities are present in the listener’s environment. The model demonstrates that complexity in word recognition can emerge from a simple associative system responding to the interactions between multiple sources of information in the language learner’s environment.
  • Hintz, F., & Huettig, F. (2015). The complexity of the visual environment modulates language-mediated eye gaze. In R. Mishra, N. Srinivasan, & F. Huettig (Eds.), Attention and Vision in Language Processing (pp. 39-55). Berlin: Springer. doi:10.1007/978-81-322-2443-3_3.

    Abstract

    Three eye-tracking experiments investigated the impact of the complexity of the visual environment on the likelihood of word-object mapping taking place at phonological, semantic and visual levels of representation during language-mediated visual search. Dutch participants heard spoken target words while looking at four objects embedded in displays of different complexity and indicated the presence or absence of the target object. During filler trials the target objects were present, but during experimental trials they were absent and the display contained various competitor objects. For example, given the target word “beaker”, the display contained a phonological (a beaver, bever), a shape (a bobbin, klos), a semantic (a fork, vork) competitor, and an unrelated distractor (an umbrella, paraplu). When objects were presented in simple four-object displays (Experiment 2), there were clear attentional biases to all three types of competitors replicating earlier research (Huettig and McQueen, 2007). When the objects were embedded in complex scenes including four human-like characters or four meaningless visual shapes (Experiments 1, 3), there were biases in looks to visual and semantic but not to phonological competitors. In both experiments, however, we observed evidence for inhibition in looks to phonological competitors, which suggests that the phonological forms of the objects nevertheless had been retrieved. These findings suggest that phonological word-object mapping is contingent upon the nature of the visual environment and add to a growing body of evidence that the nature of our visual surroundings induces particular modes of processing during language-mediated visual search.
  • Huettig, F., Srinivasan, N., & Mishra, R. (2015). Introduction to 'Attention and vision in language processing'. In R. Mishra, N. Srinivasan, & F. Huettig (Eds.), Attention and vision in language processing. (pp. V-IX). Berlin: Springer.
  • Huettig, F. (2015). Literacy influences cognitive abilities far beyond the mastery of written language. In I. van de Craats, J. Kurvers, & R. van Hout (Eds.), Adult literacy, second language, and cognition. LESLLA Proceedings 2014. Nijmegen: Centre for Language Studies.

    Abstract

    Recent experimental evidence from cognitive psychology and cognitive neuroscience shows that reading acquisition has non-trivial consequences for cognitive processes other than reading per se. In the present chapter I present evidence from three areas of cognition: phonological processing, prediction in language processing, and visual search. These findings suggest that literacy on cognition influences are far-reaching. This implies that a good understanding of the dramatic impact of literacy acquisition on the human mind is an important prerequisite for successful education policy development and guidance of educational support.
  • Huettig, F. (2014). Role of prediction in language learning. In P. J. Brooks, & V. Kempe (Eds.), Encyclopedia of language development (pp. 479-481). London: Sage Publications.
  • Smith, A. C., Monaghan, P., & Huettig, F. (2014). Modelling language – vision interactions in the hub and spoke framework. In J. Mayor, & P. Gomez (Eds.), Computational Models of Cognitive Processes: Proceedings of the 13th Neural Computation and Psychology Workshop (NCPW13). (pp. 3-16). Singapore: World Scientific Publishing.

    Abstract

    Multimodal integration is a central characteristic of human cognition. However our understanding of the interaction between modalities and its influence on behaviour is still in its infancy. This paper examines the value of the Hub & Spoke framework (Plaut, 2002; Rogers et al., 2004; Dilkina et al., 2008; 2010) as a tool for exploring multimodal interaction in cognition. We present a Hub and Spoke model of language–vision information interaction and report the model’s ability to replicate a range of phonological, visual and semantic similarity word-level effects reported in the Visual World Paradigm (Cooper, 1974; Tanenhaus et al, 1995). The model provides an explicit connection between the percepts of language and the distribution of eye gaze and demonstrates the scope of the Hub-and-Spoke architectural framework by modelling new aspects of multimodal cognition.
  • Huettig, F. (2013). Young children’s use of color information during language-vision mapping. In B. R. Kar (Ed.), Cognition and brain development: Converging evidence from various methodologies (pp. 368-391). Washington, DC: American Psychological Association Press.
  • Mishra, R. K., Olivers, C. N. L., & Huettig, F. (2013). Spoken language and the decision to move the eyes: To what extent are language-mediated eye movements automatic? In V. S. C. Pammi, & N. Srinivasan (Eds.), Progress in Brain Research: Decision making: Neural and behavioural approaches (pp. 135-149). New York: Elsevier.

    Abstract

    Recent eye-tracking research has revealed that spoken language can guide eye gaze very rapidly (and closely time-locked to the unfolding speech) toward referents in the visual world. We discuss whether, and to what extent, such language-mediated eye movements are automatic rather than subject to conscious and controlled decision-making. We consider whether language-mediated eye movements adhere to four main criteria of automatic behavior, namely, whether they are fast and efficient, unintentional, unconscious, and overlearned (i.e., arrived at through extensive practice). Current evidence indicates that language-driven oculomotor behavior is fast but not necessarily always efficient. It seems largely unintentional though there is also some evidence that participants can actively use the information in working memory to avoid distraction in search. Language-mediated eye movements appear to be for the most part unconscious and have all the hallmarks of an overlearned behavior. These data are suggestive of automatic mechanisms linking language to potentially referred-to visual objects, but more comprehensive and rigorous testing of this hypothesis is needed.
  • Huettig, F. (2011). The role of color during language-vision interactions. In R. K. Mishra, & N. Srinivasan (Eds.), Language-Cognition interface: State of the art (pp. 93-113). München: Lincom.

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