Speech perception is multisensory: humans combine visual information from the talker’s face with auditory information from the talker’s voice to aid in perception. The contribution of visual information to speech perception is influenced by two factors. First, if the auditory information is noisy or absent, visual speech is more important than if the auditory speech is clear. Current models of speech perception assume that top-down processes serve to incorporate this factor into multisensory speech perception. For instance, visual cortex shows enhanced responses to audiovisual speech containing a noisy or entirely absent auditory component (Schepers et al., 2015) raising an obvious question: since visual cortex presumably cannot assess the quality of auditory speech on its own, what is the origin of the top-down modulation that enhances visual speech processing ? Neuroimaging studies have shown that speech reading (perception of visual-only speech) leads to strong responses in frontal regions including the inferior frontal gyrus, premotor cortex, frontal eye fields and dorsolateral prefrontal cortex (Callan et al., 2014; Calvert and Campbell, 2003; Hall et al., 2005; Lee and Noppeney, 2011; Okada and Hickok, 2009). We predicted that these frontal regions serve as the source of a control signal, which enhances activity in visual cortex when auditory speech is noisy or absent.

Second, visual information about the content of speech is not distributed equally throughout the visual field. Some regions of the talker’s face are more informative about speech content, with the mouth of the talker carrying the most information (Vatikiotis-Bateson et al., 1998; Yi et al., 2013). If frontal cortex enhances visual responses during audiovisual speech perception, one should expect this enhancement to occur preferentially in regions of visual cortex which represent the mouth of the talker’s face.

Responses to three types of speech—audiovisual (AV), visual-only (Vis) and auditory-only (Aud) (Figure 1A) —were measured in 73 electrodes implanted over visual cortex (Figure 1B). Neural activity was assessed using the broadband response amplitude (Crone et al., 2001; Canolty et al., 2007; Flinker et al., 2015; Mesgarani et al., 2014). The response to AV and Vis was identical early in the trial, rising quickly after stimulus onset and peaking at about 160% of baseline (Figure 1C); there was no response to Aud speech. The talker’s mouth began moving at 200 ms after stimulus onset, with the onset of auditory speech in the AV condition occurring at 283 ms. Shortly thereafter (at 400 ms) the responses to AV and Vis diverged, with significantly greater responses to Vis than AV. Examining individual electrodes revealed large variability in the amount of Vis enhancement, ranging from −16 to 78% (Figure 1D).

Figure 1

Download asset Open asset

Enhanced visual cortex responses to Vis speech may reflect the increased importance of visual speech for comprehension when auditory speech is not available. However, visual speech information is not evenly distributed across the face: the mouth of the talker contains most of the information about speech content. We predicted that mouth representations in visual cortex should show greater enhancement than non-mouth representations. To test this prediction, we measured the receptive field of the neurons underlying each electrode by presenting small checkerboards at different visual field locations and determining the location with the maximum evoked response (Figure 2A). As expected, electrodes located near the occipital pole had receptive fields near the center of the visual field, while electrodes located more anteriorly along the calcarine sulcus had receptive fields in the visual periphery (Figure 2B). In the speech stimulus, the mouth subtended approximately 5 degrees of visual angle. Electrodes with receptive fields of less than five degrees eccentricity were assumed to carry information about the mouth region of the talker’s face and were classified as ‘mouth electrodes’ (N = 49). Electrodes with more peripheral receptive fields were classified as ‘non-mouth electrodes’ (N = 24). We compared the response in each group of electrodes using a linear mixed-effects (LME) model with the response amplitude as the dependent measure; the electrode type (mouth or non-mouth) and stimulus condition (AV, Vis or Aud) as fixed factors; electrode as a random factor; and the response in mouth electrodes to AV speech as the baseline (see Table 1 for all LME results).

Figure 2

Download asset Open asset

The model found a significant main effect of stimulus condition, driven by greater responses to Vis (95% vs. 77%, Vis vs. AV; p=10⁻⁶) and weaker responses to Aud (12% vs. 77%, Aud vs. AV; p=10⁻¹⁶). Critically, there was also a significant interaction between stimulus condition and electrode type. Mouth electrodes showed a large difference between the responses to Vis and AV speech while non-mouth electrodes showed almost no difference (26% vs. 2%, mouth vs. non-mouth Vis – AV; p=0.01; Figure 2C).

An alternative interpretation of this results is raised by the task design, in which subjects were instructed to fixate the mouth of the talker. Therefore, the enhancement could be specific to electrodes with receptive fields near the center of gaze (central vs. peripheral) rather than to the mouth of the talker’s face (mouth vs. non-mouth).

To distinguish these two possibilities, we performed a control experiment in which the task instructions were to fixate the shoulder of the talker, rather than the mouth, aided by a fixation crosshair superimposed on the talker’s shoulder (Figure 2D). With this manipulation, the center of the mouth was located at 10 degrees eccentricity. Electrodes with receptive field centers near the mouth were classified as mouth electrodes (N = 19); otherwise, they were classified as non-mouth electrodes (N = 13). This dissociated visual field location from mouth location: mouth electrodes had receptive fields located peripherally (mean eccentricity = 9.5 degrees). If the enhancement during Vis speech was restricted to the center of gaze, we would predict no enhancement for mouth electrodes in this control experiment due to their peripheral receptive fields. Contrary to this prediction, the LME showed a significant interaction between electrode type and stimulus condition (Table 2). Mouth electrodes (all with peripheral receptive fields) showed a large difference between the responses to Vis and AV speech while non-mouth electrodes showed little difference (50% vs. 9%, mouth vs. non-mouth Vis – AV; p=10⁻³).

Both experiments demonstrated a large enhancement for Vis compared with AV speech in visual cortex electrodes representing the mouth of the talker. However, the visual stimulus in the Vis and AV conditions was identical. Therefore, a control signal sensitive to the absence of auditory speech must trigger the enhanced visual responses. We investigated responses in frontal cortex as a possible source of top-down control signals (Corbetta and Shulman, 2002; Gunduz et al., 2011; Kastner and Ungerleider, 2000; Miller and Buschman, 2013).

Figure 3A shows the responses during Vis speech for a frontal electrode located at the intersection of the precentral gyrus and middle frontal gyrus (Talairach co-ordinates: x = −56, y = −8, z = 33) and a visual electrode located on the occipital pole with a receptive field located in the mouth region of the talker’s face (1.8° eccentricity). Functional connectivity was measured with a Spearman rank correlation of the trial-by-trial response power within each pair. This pair of electrodes showed strong correlation (ρ = 0.57, p=10⁻⁶): on trials in which the frontal electrode responded strongly, the visual electrode did as well.

Figure 3

Download asset Open asset

We predicted that connectivity should be greater for mouth electrodes than non-mouth electrodes. In each subject, we selected the single frontal electrode with the strongest response to AV speech (Figure 3B; average Talairach co-ordinates of the frontal electrode: x = 49, y = −2, z = 42) and measured the connectivity between the frontal electrode and all visual electrodes in that subject (Figure 3B).

An LME model (Table 3) was fit with the Spearman rank correlation (ρ) between each frontal-visual electrode pair as the dependent measure; the visual electrode type (mouth vs. non-mouth) and stimulus condition (AV, Vis or Aud) as fixed factors; electrode as a random factor; and connectivity of mouth electrodes during AV speech as the baseline. The largest effect in the model was a main effect of electrode type, driven by strong frontal-visual connectivity in mouth electrodes and weak connectivity in non-mouth electrodes (0.2 vs. −0.02; p=10⁻⁵). There was significantly weaker frontal-visual connectivity during the Aud condition in which no visual speech was presented (p=0.01).

Our results suggest a model in which frontal cortex modulates mouth regions of visual cortex in a task-specific fashion. Since top-down modulation is an active process that occurs when visual speech is most relevant, this model predicts that frontal cortex should be more active during the perception of Vis speech. To test this prediction, we compared the response to the different speech conditions in frontal cortex (Figure 4A). Frontal electrodes responded strongly to all three speech conditions, with a peak amplitude of 109% at 470 ms after stimulus onset. Following the onset of auditory speech (283 ms after stimulus onset), the responses diverged, with a larger response to Vis than AV or Aud speech (53% vs. 33%, Vis vs. AV; p=0.001; Table 6). Greater responses to Vis speech were consistent across frontal electrodes (Figure 4B).

Figure 4

Download asset Open asset

Both frontal and visual cortex showed enhanced responses to Vis speech. If frontal cortex was the source of top-down modulation that resulted in visual cortex enhancements, one might expect frontal response differences to precede visual cortex response differences. To test this idea, we examined the time courses of the response to speech in a sample frontal-visual electrode pair (Figure 4C). The increased response to Vis speech occurred earlier in the frontal electrode, beginning at 247 ms after auditory onset versus 457 ms for the visual electrode. To test whether the earlier divergence between Vis and AV speech in frontal compared with visual cortex was consistent across electrodes, for each electrode we calculated the first time point at which the Vis response was significantly larger than the AV response (Figure 4D). 27 out of 29 visual electrodes showed Vis response enhancement after their corresponding frontal electrode, with an average latency difference of 230 ms (frontal vs. visual: 257 ± 150 ms vs. 487 ± 110 ms vs. with respect to auditory speech onset, t₃₄ = 4, p=10⁻⁴).

While our primary focus was on frontal modulation of visual cortex, auditory cortex was analyzed for comparison. 44 electrodes located on the superior temporal gyrus responded to AV speech (Figure 5A). As expected, these electrodes showed little response to Vis speech but a strong response to Aud speech, with a maximal response amplitude of 148% at 67 ms after auditory speech onset. While visual cortex showed a greater response to Vis speech than to AV speech, there was no significant difference in the response of auditory cortex to Aud and AV speech, as confirmed by the LME model (p=0.8, Table 4). Next, we analyzed the connectivity between frontal cortex and auditory electrodes. The LME revealed a significant effect of stimulus, driven by weaker connectivity in the Vis condition in which no auditory speech was presented (p=0.02; Table 5). Overall, the connectivity between frontal cortex and auditory cortex was weaker than the connectivity between frontal cortex and visual cortex (0.04 for frontal-auditory vs. 0.23 for frontal-visual mouth electrodes; p=0.001, unpaired t-test).

Figure 5

Download asset Open asset

In visual cortex, a subset of electrodes (those representing the mouth) showed a greater response in the Vis-AV contrast and greater connectivity with frontal cortex. To determine if the same was true in auditory cortex, we selected the STG electrodes with the strongest response in the Aud-AV contrast (Figure 5B). Unlike in visual cortex, in which there was anatomical localization of mouth-representing electrodes on the occipital pole, auditory electrodes with the strongest response in the Aud-AV contrast did not show clear anatomical clustering (Figure 5C). Electrodes with the electrodes with the strongest response in the Aud-AV contrast had equivalent connectivity with frontal cortex as other STG electrodes (ρ = −0.04 vs. roh = 0.06, unpaired t-test = 0.9, p=0.3). This was the opposite of the pattern observed in visual cortex. In order to ensure that signal amplitude was not the main determinant of connectivity, we selected the STG electrodes with the highest signal-to-noise ratio in the AV condition. These electrodes had equivalent connectivity with frontal cortex as other STG electrodes (ρ = −0.002 vs. ρ = 0.05, unpaired t-test = 0.4, p=0.7).

Taken together, these results support a model in which control regions of lateral frontal cortex located near the precentral sulcus selectively modulate visual cortex, enhancing activity with both spatial selectivity—only mouth regions of the face are enhanced —and context selectivity—enhancement is greater when visual speech is more important due to the absence of auditory speech.

Our results link two distinct strands of research. First, fMRI studies of speech perception frequently observe activity in frontal cortex, especially during perception of a visual-only speech, a task sometimes referred to as speech reading (Callan et al., 2014; Calvert and Campbell, 2003; Hall et al., 2005; Lee and Noppeney, 2011; Okada and Hickok, 2009). However, the precise role of this frontal activity has been difficult to determine given the relatively slow time resolution of fMRI.

Second, it is well known that frontal regions in an around the frontal eye fields in the precentral sulcus modulate visual cortex activity during tasks that require voluntary control of spatial or featural attention (Corbetta and Shulman, 2002; Gregoriou et al., 2012; Gunduz et al., 2011; Kastner and Ungerleider, 2000; Miller and Buschman, 2013; Popov et al., 2017). However, it has not been clear how these attentional networks function during other important cognitive tasks, such as speech perception.

We suggest that frontal-visual attentional control circuits are automatically engaged during speech perception in the service of increasing perceptual accuracy for the processing of this very important class of stimuli. This allows for precise, time-varying control: as the quality of auditory information fluctuates as auditory noise in the environment increases or decreases, frontal cortex can up or down-regulate activity in visual cortex accordingly. It also allows for precise spatial control: as the mouth of the talker contains the most speech information, frontal cortex can selectively enhance visual cortex activity that is relevant for speech perception by enhancing activity in subregions of visual cortex that represent the talker’s mouth. A possible anatomical linkage supporting this processing is the inferior fronto-occipital fasciculus connecting frontal and occipital regions, found in human but not non-human primates (Catani and Mesulam, 2008). Most models of speech perception focus on auditory cortex inputs into parietal and frontal cortex (Hickok and Poeppel, 2004; Rauschecker and Scott, 2009). Our findings suggest that visual cortex should also be considered an important component of the speech perception network, as it is selectively and rapidly modulated during audiovisual speech perception.

The analysis of auditory cortex responses provides an illuminating contrast with the visual cortex results. While removing auditory speech increased visual cortex response amplitudes and frontal-visual connectivity, removing visual speech did not change auditory cortex response amplitudes or connectivity. A simple explanation for this is that auditory speech is easily intelligible without visual speech, so that no attentional modulation is required. In contrast, perceiving visual-only speech requires speechreading, a difficult task that demands attentional engagement. An interesting test of this idea would be to present auditory speech with added noise. Under these circumstances, attentional engagement would be expected in order to enhance the intelligibility of the noisy auditory speech, with a neural manifestation of increased response amplitudes in auditory cortex and increased connectivity with frontal cortex. The interaction of stimulus and task also provides an explanation for the frontal activations in response to the speech stimuli. The human frontal eye fields show rapid sensory-driven responses, with latencies as short as 24 ms to auditory stimuli and 45 ms to visual stimuli (Kirchner et al., 2009). Following initial sensory activation, task demands modulate frontal function, with demanding tasks such as processing visual-only or noisy auditory speech or resulting in enhanced activity (Cheung et al., 2016; Cope et al., 2017).

1. 1. Argall BD
   2. Saad ZS
   3. Beauchamp MS

   (2006) Simplified intersubject averaging on the cortical surface using SUMA

   Human Brain Mapping 27:14–27.

   https://doi.org/10.1002/hbm.20158

   • PubMed
   • Google Scholar
2. Preprint

   1. Bates D
   2. Mächler M
   3. Bolker B
   4. Walker S

   (2014) Fitting linear mixed-effects models using lme4

   arXiv.

   https://arxiv.org/abs/1406.5823

   • Google Scholar
3. 1. Callan DE
   2. Jones JA
   3. Callan A

   (2014) Multisensory and modality specific processing of visual speech in different regions of the premotor cortex

   Frontiers in Psychology 5:389.

   https://doi.org/10.3389/fpsyg.2014.00389

   • PubMed
   • Google Scholar
4. 1. Calvert GA
   2. Campbell R

   (2003) Reading speech from still and moving faces: the neural substrates of visible speech

   Journal of Cognitive Neuroscience 15:57–70.

   https://doi.org/10.1162/089892903321107828

   • PubMed
   • Google Scholar
5. 1. Canolty RT
   2. Soltani M
   3. Dalal SS
   4. Edwards E
   5. Dronkers NF
   6. Nagarajan SS
   7. Kirsch HE
   8. Barbaro NM
   9. Knight RT

   (2007) Spatiotemporal dynamics of word processing in the human brain

   Frontiers in Neuroscience 1:185–196.

   https://doi.org/10.3389/neuro.01.1.1.014.2007

   • PubMed
   • Google Scholar
6. 1. Catani M
   2. Mesulam M

   (2008) The arcuate fasciculus and the disconnection theme in language and aphasia: History and current state

   Cortex 44:953–961.

   https://doi.org/10.1016/j.cortex.2008.04.002

   • Google Scholar
7. 1. Cheung C
   2. Hamiton LS
   3. Johnson K
   4. Chang EF

   (2016) The auditory representation of speech sounds in human motor cortex

   eLife 5:e12577.

   https://doi.org/10.7554/eLife.12577

   • PubMed
   • Google Scholar
8. 1. Cope TE
   2. Sohoglu E
   3. Sedley W
   4. Patterson K
   5. Jones PS
   6. Wiggins J
   7. Dawson C
   8. Grube M
   9. Carlyon RP
   10. Griffiths TD
   11. Davis MH
   12. Rowe JB

   (2017) Evidence for causal top-down frontal contributions to predictive processes in speech perception

   Nature Communications 8:2154.

   https://doi.org/10.1038/s41467-017-01958-7

   • Google Scholar
9. 1. Corbetta M
   2. Shulman GL

   (2002) Control of goal-directed and stimulus-driven attention in the brain

   Nature Reviews Neuroscience 3:201–215.

   https://doi.org/10.1038/nrn755

   • Google Scholar
10. 1. Cox RW

    (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages

    Computers and Biomedical Research 29:162–173.

    https://doi.org/10.1006/cbmr.1996.0014

    • Google Scholar
11. 1. Crone NE
    2. Boatman D
    3. Gordon B
    4. Hao L

    (2001) Induced electrocorticographic gamma activity during auditory perception

    Clinical Neurophysiology 112:565–582.

    https://doi.org/10.1016/S1388-2457(00)00545-9

    • Google Scholar
12. 1. Dale AM
    2. Fischl B
    3. Sereno MI

    (1999) Cortical surface-based analysis. I. Segmentation and surface reconstruction

    NeuroImage 9:179–194.

    https://doi.org/10.1006/nimg.1998.0395

    • PubMed
    • Google Scholar
13. 1. Fischl B
    2. Sereno MI
    3. Dale AM

    (1999) Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system

    NeuroImage 9:195–207.

    https://doi.org/10.1006/nimg.1998.0396

    • PubMed
    • Google Scholar
14. 1. Flinker A
    2. Korzeniewska A
    3. Shestyuk AY
    4. Franaszczuk PJ
    5. Dronkers NF
    6. Knight RT
    7. Crone NE

    (2015) Redefining the role of Broca’s area in speech

    PNAS 112:2871–2875.

    https://doi.org/10.1073/pnas.1414491112

    • PubMed
    • Google Scholar
15. 1. Foster BL
    2. Rangarajan V
    3. Shirer WR
    4. Parvizi J

    (2015) Intrinsic and task-dependent coupling of neuronal population activity in human parietal cortex

    Neuron 86:578–590.

    https://doi.org/10.1016/j.neuron.2015.03.018

    • Google Scholar
16. 1. Gregoriou GG
    2. Gotts SJ
    3. Desimone R

    (2012) Cell-type-specific synchronization of neural activity in FEF with V4 during Attention

    Neuron 73:581–594.

    https://doi.org/10.1016/j.neuron.2011.12.019

    • Google Scholar
17. 1. Gunduz A
    2. Brunner P
    3. Daitch A
    4. Leuthardt EC
    5. Ritaccio AL
    6. Pesaran B
    7. Schalk G

    (2011) Neural correlates of visual?spatial attention in electrocorticographic signals in humans

    Frontiers in Human Neuroscience 5:89.

    https://doi.org/10.3389/fnhum.2011.00089

    • Google Scholar
18. 1. Hall DA
    2. Fussell C
    3. Summerfield AQ

    (2005) Reading fluent speech from talking faces: typical brain networks and individual differences

    Journal of Cognitive Neuroscience 17:939–953.

    https://doi.org/10.1162/0898929054021175

    • PubMed
    • Google Scholar
19. 1. Hickok G
    2. Poeppel D

    (2004) Dorsal and ventral streams: a framework for understanding aspects of the functional anatomy of language

    Cognition 92:67–99.

    https://doi.org/10.1016/j.cognition.2003.10.011

    • Google Scholar
20. 1. Hipp JF
    2. Hawellek DJ
    3. Corbetta M
    4. Siegel M
    5. Engel AK

    (2012) Large-scale cortical correlation structure of spontaneous oscillatory activity

    Nature Neuroscience 15:884–890.

    https://doi.org/10.1038/nn.3101

    • Google Scholar
21. 1. Jacques C
    2. Witthoft N
    3. Weiner KS
    4. Foster BL
    5. Rangarajan V
    6. Hermes D
    7. Miller KJ
    8. Parvizi J
    9. Grill-Spector K

    (2016) Corresponding ECoG and fMRI category-selective signals in human ventral temporal cortex

    Neuropsychologia 83:14–28.

    https://doi.org/10.1016/j.neuropsychologia.2015.07.024

    • Google Scholar
22. 1. Kastner S
    2. Ungerleider LG

    (2000) Mechanisms of visual attention in the human cortex

    Annual Review of Neuroscience 23:315–341.

    https://doi.org/10.1146/annurev.neuro.23.1.315

    • PubMed
    • Google Scholar
23. 1. Kirchner H
    2. Barbeau EJ
    3. Thorpe SJ
    4. Regis J
    5. Liegeois-Chauvel C

    (2009) Ultra-rapid sensory responses in the human frontal eye field region

    Journal of Neuroscience 29:7599–7606.

    https://doi.org/10.1523/JNEUROSCI.1233-09.2009

    • Google Scholar
24. 1. Lee H
    2. Noppeney U

    (2011) Physical and perceptual factors shape the neural mechanisms that integrate audiovisual signals in speech comprehension

    Journal of Neuroscience 31:11338–11350.

    https://doi.org/10.1523/JNEUROSCI.6510-10.2011

    • PubMed
    • Google Scholar
25. 1. Mesgarani N
    2. Cheung C
    3. Johnson K
    4. Chang EF

    (2014) Phonetic feature encoding in human superior temporal gyrus

    Science 343:1006–1010.

    https://doi.org/10.1126/science.1245994

    • PubMed
    • Google Scholar
26. 1. Miller EK
    2. Buschman TJ

    (2013) Cortical circuits for the control of attention

    Current Opinion in Neurobiology 23:216–222.

    https://doi.org/10.1016/j.conb.2012.11.011

    • Google Scholar
27. 1. Mukamel R
    2. Gelbard H
    3. Arieli A
    4. Hasson U
    5. Fried I
    6. Malach R

    (2005) Coupling between neuronal firing, field potentials, and fmri in human auditory cortex

    Science 309:951–954.

    https://doi.org/10.1126/science.1110913

    • Google Scholar
28. 1. Nir Y
    2. Fisch L
    3. Mukamel R
    4. Gelbard-Sagiv H
    5. Arieli A
    6. Fried I
    7. Malach R

    (2007) Coupling between neuronal firing rate, gamma lfp, and bold fmri is related to interneuronal correlations

    Current Biology 17:1275–1285.

    https://doi.org/10.1016/j.cub.2007.06.066

    • Google Scholar
29. 1. Okada K
    2. Hickok G

    (2009) Two cortical mechanisms support the integration of visual and auditory speech: a hypothesis and preliminary data

    Neuroscience Letters 452:219–223.

    https://doi.org/10.1016/j.neulet.2009.01.060

    • PubMed
    • Google Scholar
30. 1. Oostenveld R
    2. Fries P
    3. Maris E
    4. Schoffelen JM

    (2011) FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

    Computational Intelligence and Neuroscience 2011:1–9.

    https://doi.org/10.1155/2011/156869

    • PubMed
    • Google Scholar
31. 1. Popov T
    2. Kastner S
    3. Jensen O

    (2017) FEF-controlled alpha delay activity precedes stimulus-induced gamma-band activity in visual cortex

    The Journal of Neuroscience 37:4117–4127.

    https://doi.org/10.1523/JNEUROSCI.3015-16.2017

    • Google Scholar
32. 1. Rauschecker JP
    2. Scott SK

    (2009) Maps and streams in the auditory cortex: nonhuman primates illuminate human speech processing

    Nature Neuroscience 12:718–724.

    https://doi.org/10.1038/nn.2331

    • Google Scholar
33. 1. Ray S
    2. Maunsell JHR

    (2011) Different origins of gamma rhythm and high-gamma activity in macaque visual cortex

    PLoS Biology 9:e1000610.

    https://doi.org/10.1371/journal.pbio.1000610

    • Google Scholar
34. 1. Schepers IM
    2. Yoshor D
    3. Beauchamp MS

    (2015) Electrocorticography reveals enhanced visual cortex responses to visual Speech

    Cerebral Cortex 25:4103–4110.

    https://doi.org/10.1093/cercor/bhu127

    • PubMed
    • Google Scholar
35. 1. Vatikiotis-Bateson E
    2. Eigsti IM
    3. Yano S
    4. Munhall KG

    (1998) Eye movement of perceivers during audiovisual speech perception

    Perception & Psychophysics 60:926–940.

    https://doi.org/10.3758/BF03211929

    • PubMed
    • Google Scholar
36. 1. Yi A
    2. Wong W
    3. Eizenman M

    (2013) Gaze patterns and audiovisual speech enhancement

    Journal of Speech Language and Hearing Research 56:471–480.

    https://doi.org/10.1044/1092-4388(2012/10-0288)

    • Google Scholar
37. 1. Yoshor D
    2. Bosking WH
    3. Ghose GM
    4. Maunsell JHR

    (2007) Receptive fields in human visual cortex mapped with surface electrodes

    Cerebral Cortex 17:2293–2302.

    https://doi.org/10.1093/cercor/bhl138

    • Google Scholar

Author details

1. Muge Ozker

   Department of Neurosurgery, Baylor College of Medicine, Texas, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft

   For correspondence

   mozker@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7472-4528

2. Daniel Yoshor

   1. Department of Neurosurgery, Baylor College of Medicine, Texas, United States
   2. Michael E. DeBakey Veterans Affairs Medical Center, Texas, United States

   Contribution

   Resources, Supervision, Funding acquisition, Writing—review and editing

   Competing interests

   No competing interests declared

3. Michael S Beauchamp

   Department of Neurosurgery, Baylor College of Medicine, Texas, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   michael.beauchamp@bcm.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7599-9934

• 1,356

  views

• 184

  downloads

• 15

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.