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Research Articles, Behavioral/Cognitive

Balancing Prediction and Sensory Input in Speech Comprehension: The Spatiotemporal Dynamics of Word Recognition in Context

Anastasia Klimovich-Gray, Lorraine K. Tyler, Billi Randall, Ece Kocagoncu, Barry Devereux and William D. Marslen-Wilson
Journal of Neuroscience 16 January 2019, 39 (3) 519-527; DOI: https://doi.org/10.1523/JNEUROSCI.3573-17.2018
Anastasia Klimovich-Gray
Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, United Kingdom
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  • ORCID record for Anastasia Klimovich-Gray
Lorraine K. Tyler
Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, United Kingdom
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Billi Randall
Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, United Kingdom
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Ece Kocagoncu
Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, United Kingdom
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Barry Devereux
Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, United Kingdom
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William D. Marslen-Wilson
Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, United Kingdom
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    Figure 1.

    Schematic overview of RSA multivariate analysis procedures. (1) RDMs are derived for each ROI and each subject across the length of the analysis epoch. These data RDMs summarize the differences between the activation patterns (1 − r) between trials in a given time window (20 ms in width) every 5 ms across all vertices of a given ROI. (2) Each data RDM is compared with model RDMs (Spearman r), derived separately for each cognitive measure. To produce group-level statistics, a one-sample t test is taken for each ROI and each time point across subjects. The resulting t maps were thresholded at the p < 0.01 level. To correct for multiple comparisons, values surviving the primary threshold were entered into a cluster-permutation analysis (1000 permutations) (Maris and Oostenveld, 2007; Su et al., 2012). Only clusters that survived the p < 0.05 cluster-correction threshold are reported (red bar).

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    Figure 2.

    Spatiotemporal coordinates of RSA model fits for the W1 modifier-based constraints. A–C, Left, The ROIs (in green on the inflated MNE brain) producing significant model fit for the four models. A, Right, Subject-averaged model fit r value in left BA45 across the analysis epoch (−200 ms to 300 ms relative to W2 onset) for the Entropy (green) and the Semantic Similarity (pink) models. B, Semantic Blend (blue) model fit in left middle and posterior MTG. C, Entropy Change (orange) model fit in left HG. Shaded areas represent the SE of the model-fit means. Thick lines below the r curves indicate the significant t value model-fit clusters (corrected for multiple comparisons at p < 0.05). Vertical broken red line indicates W2 onset. D, Left, Key specifying the mean and SD of the critical time points overlaid (right) on the auditory waveform of a sample test phrase.

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The Journal of Neuroscience: 39 (3)
Journal of Neuroscience
Vol. 39, Issue 3
16 Jan 2019
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Balancing Prediction and Sensory Input in Speech Comprehension: The Spatiotemporal Dynamics of Word Recognition in Context
Anastasia Klimovich-Gray, Lorraine K. Tyler, Billi Randall, Ece Kocagoncu, Barry Devereux, William D. Marslen-Wilson
Journal of Neuroscience 16 January 2019, 39 (3) 519-527; DOI: 10.1523/JNEUROSCI.3573-17.2018

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Balancing Prediction and Sensory Input in Speech Comprehension: The Spatiotemporal Dynamics of Word Recognition in Context
Anastasia Klimovich-Gray, Lorraine K. Tyler, Billi Randall, Ece Kocagoncu, Barry Devereux, William D. Marslen-Wilson
Journal of Neuroscience 16 January 2019, 39 (3) 519-527; DOI: 10.1523/JNEUROSCI.3573-17.2018
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Keywords

  • language
  • prediction
  • RSA
  • speech
  • time-course
  • word recognition

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