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Neural mechanisms of eye contact when listening to another person talking

Abstract Eye contact occurs frequently and voluntarily during face-to-face verbal communication. However, the neural mechanisms underlying eye contact when it is accompanied by spoken language remain unexplored to date. Here we used a novel approach, ﬁxation-based event-related functional magnetic resonance imaging (fMRI), to simulate the listener making eye contact with a speaker during verbal communication. Participants’ eye movements and fMRI data were recorded simultaneously while they were freely viewing a pre-recorded speaker talking. The eye tracking data were then used to define events for the fMRI analyses. The results showed that eye contact in contrast to mouth fixation involved visual cortical areas (cuneus, calcarine sulcus), brain regions related to theory of mind/intentionality processing (temporoparietal junction, posterior superior temporal sulcus, medial prefrontal cortex) and the dorsolateral prefrontal cortex. In addition, increased effective connectivity was found between these regions for eye contact in contrast to mouth fixations. The results provide first evidence for neural mechanisms underlying eye contact when watching and listening to another person talking. The network we found might be well suited for processing the intentions of communication partners during eye contact in verbal communication.


Introduction
Eye contact is a powerful visual cue for building social links between communicating partners. It has strong influences on several perceptual processes in communication, such as face detection, gender perception and facial expression recognition (for reviews, see Itier and Batty, 2009;Senju and Johnson, 2009;Madipakkam et al., 2015;Schilbach, 2015). Impairments in eye contact are common and might significantly contribute to communication difficulties, e.g. in autism spectrum disorder (ASD) (Dalton et al., 2005;Pelphrey et al., 2005a), in schizophrenia (Choi et al., 2010;Tso et al., 2012) or in social anxiety disorder (Horley et al., 2003;Schneier et al., 2011).
Eye contact is an important means of non-verbal communication (for reviews, see Itier and Batty, 2009;Senju and Johnson, 2009;Schilbach, 2015), but also occurs very frequently during verbal communication (Lewkowicz and Hansen-Tift, 2012;Macdonald and Tatler, 2013). Two important features characterize eye contact in verbal communication. First, eye contact occurs voluntarily while the partners are listening or talking to each other (Macdonald and Tatler, 2013). Second, eye contact is intermittent and of varying duration (Argyle and Dean, 1965), because communication partners freely and actively fixate on and scan between different regions of the partner's face. The two salient fixation regions are the eyes and the mouth (Vatikiotis-Bateson et al., 1998;Jack et al., 2009;Lusk and Mitchel, 2016). To our knowledge, currently no study has investigated the neural mechanisms underlying eye contact in scenarios that contain these two major features of eye contact in verbal communication. Thus, the first aim of this study was to identify brain regions and networks involved in eye contact with an approach that simulates eye contact when watching and listening to a speaker talking.
Currently there are three accounts of the neural mechanisms of eye contact (for a review, see Senju and Johnson, 2009) (Figure  1). The first account, the 'affective arousal model' (Figure 1A), postulates that eye contact elicits responses in brain areas involved in arousal, particularly the amygdala (Kawashima et al., 1999; for a review, see Senju and Johnson, 2009;Conty et al., 2010;von dem Hagen et al., 2014). The second account, the 'communicative intention detector model' (Figure 1B), assumes that eye contact signals the intention to communicate with others and involves the theory-of-mind (ToM) network (Kampe et al., 2003; for reviews, see Senju and Johnson, 2009;von dem Hagen et al., 2014), including posterior superior temporal sulcus and/or temporoparietal junction (pSTS&TPJ), medial prefrontal cortex (mPFC) and temporal pole (TP). The most recent account is the 'fast-track modulator model' ( Figure 1C) (for a review, see Senju and Johnson, 2009). It proposes that a rapid subcortical visual processing route [including superior colliculus (SC), pulvinar (Pulv) and amygdala (Amy)] and a slow cortical visual processing route [including lateral occipital cortex (LOC) and inferior temporal cortex (ITC)] interact with brain regions of the so-called 'social brain network'. The social brain network comprises the Amy and orbitofrontal cortex (OFC) for emotion, pSTS and mPFC for intentionality, right anterior STS for gaze direction, and fusiform gyrus (FG) for face identity. In addition, the regions of the social brain network are modulated by the dorsolateral prefrontal cortex (dlPFC) according to task demands and social context. There is solid evidence that these accounts can explain neural mechanisms involved in eye contact in non-verbal situations in which participants view faces with averted or direct gaze (Calder et al., 2002;Kampe et al., 2003;Pelphrey et al., 2004;von dem Hagen et al., 2014;Cavallo et al., 2015;Oberwelland et al., 2016). To date, it is unknown whether the three accounts are also suitable to explain neural mechanisms underlying eye contact in verbal communication. Therefore, the second aim of the present study was to test the hypothesis that eye contact in verbal communication may rely at least partly on the mechanisms proposed in the three models.
We developed a novel experimental paradigm to simulate eye contact when listening to a speaker talking. We recorded videos of speakers talking about daily life topics with direct gaze at the camera. Participants were instructed to listen to the pre-recorded speakers and freely look at different regions of the speaker's face as they would do naturally. At random intervals the videos were stopped and participants were asked to report the last word they heard. We recorded functional magnetic resonance imaging (fMRI) and eye tracking data from the participants simultaneously. The fixations obtained from the eye tracking data were used to define the events for the fMRI data analysis. The feasibility of this fixation-based event-related (FIBER) fMRI approach has been successfully shown in recent studies on natural viewing behavior in visual scene perception (Marsman et al., 2012;Henderson and Choi, 2015). In our study, this approach allowed us to distinguish when participants made voluntary eye contact with the speaker (i.e. fixated on the eyes region of the speaker whose gaze was directed towards the participant, 'Eyes' events) from when they looked at the mouth region ('Mouth' events) or other regions of the video ('Off' events). We assumed that the natural gaze patterns of viewing a speaker, fixation shifts between different regions of the speaker's face and variable fixation durations (Argyle and Dean, 1965;Henderson, 2011), would meet basic rules for rapid eventrelated designs (Burock et al., 1998;Friston et al., 1999). The affective arousal model postulates that eye contact elicits responses in the brain arousal system and/or emotional system, especially in the amygdala (Amy) (Kawashima et al., 1999;Hooker et al., 2003;Sato et al., 2004). (B) The communicative intention detector model assumes that eye contact signals the intention to communicate with others and involves cerebral cortex regions of the ToM network. They are the pSTS&TPJ, the medial mPFC and the TP. This model (for review, see Senju and Johnson, 2009) is based on an earlier model proposed by Baron-Cohen (1997). (C) The fast-track modulator model (for review, see Senju and Johnson, 2009) proposes that eye contact is processed via a rapid and a slow information processing route. The rapid route (blue arrows) corresponds to a subcortical pathway involving SC, Pulv and Amy. Information processed in this route modulates processing in different regions of a so-called 'social brain network' including regions processing emotion (Amy; OFC), intentionality (pSTS and mPFC), gaze direction (right anterior STS, aSTS) and face identity (FG). At the same time these regions are modulated via the dlPFC according to task demands and context (green arrows). The slow information route is a visual cortical route including LOC and ITC that projects to regions analysing gaze direction and face identity (black arrows). Figure 1C is adapted from the model figure in Senju and Johnson (2009).

Participants
In total, 30 healthy volunteers [15 female, 15 male; 27.5 6 3.6 SD year-old; all right handers (Edinburgh questionnaire; Oldfield, 1971)] who reported normal vision without correction participated in this study. Written informed consent was provided by all participants. The study protocol was approved by the Research Ethics Committee of the University of Leipzig (AZ: 192-14-14042014). Nine participants were excluded due to difficulties with obtaining eye tracking data (e.g. difficulties with calibration before the experiment or eye tracking during the experiment). Two participants were excluded because of head movement in the MRI scanner (>3 mm). Furthermore, one subject's behavioral data were excluded because of technical problems with the response box. Therefore, eye tracking and fMRI data analyses were based on 19 subjects and behavioral data analyses were based on 18 subjects. All participants filled in the Autism-Spectrum Quotient questionnaire  before the fMRI experiment. All participants scored below the cut-off value (32) that is indicative of a manifestation of autistic traits typical for ASD (17.95 6 4.99 SD).

Stimuli
We recorded 8 monologue videos from 2 female and 2 male German speakers (20, 22, 24 and 24-years-old, respectively). Each monologue lasted $6 min and was about daily life topics, such as a description of a typical week or how to learn a foreign language. All speakers had received speaking training in their study or career. They were asked to speak in a natural and emotionally neutral manner. Their naturalness and emotional content were rated by another five native German speakers (see Supplementary Materials). Eye contact is an event in which two partners look at each other's eyes at the same time (also called mutual gaze) (Schilbach, 2015). Here, we simulated such a situation by pre-recording the speakers in front view with their gaze constantly directed towards a person behind the camera (for recording details, see Supplementary Materials).
Our daily communications occur either in relatively quiet situations or noisy situations. Therefore, we made two types of videos corresponding to two conditions: the original recordings with high signal-to-noise ratio (SNR) ('Normal' condition) and videos with low SNR ('Noise' condition) by mixing the audio tracks of the videos with background noise, (i.e. people talking and the clatter of dishes in a cafeteria). The Noise condition also allowed us to obtain balanced fixations between eyes and mouth. Because when watching and listening to speakers, listeners fixate more on the eyes. However, if the speech signal is noisy, fixations on the mouth of the speaker increase (Vatikiotis-Bateson et al., 1998;Yi et al., 2013). For video postprocessing procedures, see Supplementary Materials.

Experimental procedure
The fMRI experiment consisted of four sessions. In each session one 'Normal' and one 'Noise' video were shown ( Figure 2A). These two videos were from two different speakers of different genders. The orders of speaker gender, speaker identity and conditions were counterbalanced across sessions and participants. At the beginning of each video, the speaker's mouth was closed for 3 s. Between videos, a black screen was presented for 4 s to indicate a short pause. We instructed the participants to watch the speaker and to listen carefully. To make sure participants attended to what the speaker said, they performed a speech recognition task: The videos were stopped at random intervals and participants were asked 'Which is the last word you heard?' (Figure 2A). They chose the answer from 3 words listed on the screen by pressing one of three corresponding buttons on a response-box. The video continued once the participant pressed a button or after 4 s if no button was pressed. In total, 40 questions were asked, i.e. 5 in each monologue. Participants performed the speech recognition task with high accuracy in both conditions (see Supplementary Materials). Participants were instructed outside the MRI-scanner before the experiment. Between sessions, the participants were asked to close their eyes and rest for 1 min. The experiment was implemented in Presentation software (version 14.5, Neurobehavioral Systems Inc., USA).

Eye tracking
During fMRI scanning, participants' eye movements were recorded using a 120 Hz monocular MR-compatible eye tracker (EyeTrac 6, ASL, USA) simultaneously. The optical path was reflected over a mirror placed on top of the head coil in order to capture the eye image of the participant. Prior to the fMRI experiment, the eye tracking system was calibrated using a standard nine-point calibration procedure for each participant ( Figure 2A). Before each session, the accuracy of eye tracking was checked. If necessary, the eye tracking system was recalibrated (ca. 1 time per participant).

fMRI data acquisition
Functional images and structural T1-weighted images were obtained using a 3 T Siemens Tim Trio MR scanner (Siemens Healthcare, Erlangen, Germany), equipped with a 12 channel head coil. A gradient-echo EPI (echo planar imaging) sequence was used for the functional MRI (TE 30 ms, flip angle 90 , TR 2.79 s, whole brain coverage with 42 slices, acquisition bandwidth 116 kHz, 2 mm slice thickness, 1 mm inter-slice gap, in-plane resolution 3 Â 3 mm). Geometric distortions were characterized by a B0 field-map scan. For further details, see Supplementary Materials.

Eye tracking analysis
We used EyeNal software (ASL, USA) and customized MATLAB scripts for the eye tracking data analysis. A fixation was defined as having a minimum duration of 100 ms and a maximum visual angle change of 1 . Natural speaking is accompanied by head movements of the speaker. We therefore corrected the position of the participants' fixations based on the head movements of the speaker in the videos (for details see Supplementary Materials). This correction was necessary, because the eye tracker captures positions on the screen rather than real positions of the speaker's head. We defined two areas of interest (AOIs), i.e. eyes region and mouth region (see definition in Figure 2B). We labeled fixations falling inside the AOIs of eyes and mouth as 'Eyes' and 'Mouth' respectively, and fixations outside these AOIs as 'Off'. Fixations occurring consecutively within the same AOI were concatenated into one fixation, resulting in one event trial for the fMRI analyses. The event onset was the start time of the first fixation falling into the respective AOI.
Eye gaze patterns. To examine whether our design was suitable for fMRI analysis as a rapid event-related design, we checked the number of events (NE), the duration of the interevent-interval (IEI) and the event order. First, we calculated the NE for each event type, to ensure there were a sufficient number of events to get a good and stable estimate of the hemodynamic response (!25) and to obtain sufficient power (!100) in the fMRI analyses (Huettel and McCarthy, 2001;Desmond and Glover, 2002). Second, we calculated the IEI (the time interval between the onset of one event and that of the next event), to check whether the mean IEI was of suitable length, i.e. around 2 s, which has been reported as optimal mean interval for rapid event-related designs (Dale and Buckner, 1997). Additionally, we checked whether the NE and IEI were balanced across conditions (Normal and Noise) and event types (Eyes, Mouth, Off) by performing a 2 Â 3 repeated measures ANOVA for each index and post-hoc t-tests. The P values were Bonferroni-corrected for multiple comparisons between event types (n ¼ 3). Finally, we tested whether the gaze patterns met two further recommendations for rapid event-related design, i.e. a jittered IEI and a variable event order (Burock et al., 1998;Friston et al., 1999). We computed the IEI distribution for each event type to illustrate that the IEI was jittered rather than fixed. We also checked whether the events occur in a variable order by calculating the transition probability of every possible combination of event order.
Pupil diameter. In addition, we measured pupil dilation as a measure of arousal (Libby et al., 1973;Kampe et al., 2003). To examine whether eye contact leads to larger pupil diameter than mouth or off fixations, we computed pupil diameter for each participant and compared it between event types using a one-way ANOVA.
Potential confounding variables. The different shapes and sizes of the AOIs (see Figure 2B) might influence the saccade distance between sub-fixations and the number of saccades (NS) within each event, thus confounding the fMRI results. To test whether this was the case, we calculated the distance between subfixations as inter-fixation distance (IFD) in degrees of visual angle and the NS during each event. We then compared IFD and NS between event types using a one-way ANOVA.

fMRI analysis
Analyses of BOLD responses. All fMRI analyses were performed using Statistical Parametric Mapping software (SPM8, Wellcome Trust Centre for Neuroimaging, UCL, UK, http://www.fil.ion.ucl. ac.uk/spm). We performed standard pre-processing procedures (see Supplementary Materials). At first-level, the general linear model (GLM) analysis included six events of interest that were defined by the eye tracking events (Eyes_normal, Eyes_noise, Mouth_normal, Mouth_noise, Off_normal, Off_ noise) and seven regressors of no interest (speech recognition task, 6 spatial movement parameters estimated during realignment). The onset of the speech recognition task regressor corresponded to when the question started to appear (Figure 2A). All events were modeled with a duration of 0 s (referred to as 'standard GLM').
Because there were significant differences between event types on both IFD (Eyes ¼ 1.20 , Mouth ¼ 0.52 , Off ¼ 0.27 ; F(2,17) ¼ 57.95, P < 0.001) and NS (Eyes ¼ 2.86, Mouth ¼ 1.39, Off ¼ 0.21; F (2,17) ¼ 22.19, P < 0.001), we conducted a 'control' GLM analysis. In this GLM, IFD and NS were entered as two additional parametric modulators for each event type into the same design matrix as that of the standard GLM. Each event had one corresponding IFD value and one corresponding NS value.
To identify the brain regions involved in eye contact when watching another person talking, we computed at the first-level the main effect 'Eyes vs Mouth' for both 'standard' and 'control GLM'. In addition, we conducted the simple main effects 'Eyes_noise vs Mouth_noise' and 'Eyes_normal vs Mouth_normal' and other contrasts (Eyes vs Off and Mouth vs Off) for 'standard GLM'. The contrast maps obtained from the first-level analysis were entered into second-level random-effects analyses using one-sample t-tests. The SPM statistical maps were thresholded at voxel-wise P < 0.01 (for main effects) or P < 0.05 (for simple main effects) with a cluster-wise familywise error (FWE) correction of P < 0.05 for the whole brain. We labeled the cluster locations based on anatomical information provided by the Brodmann areas (BAs) and Automated Anatomical Labeling atlas (Tzourio-Mazoyer et al., 2002) and/or probabilistic cytoarchitectonic maps (Eickhoff et al., 2005).
Region of interest-based psychophysiological interactions analysis. The fast-track modulator model ( Figure 1C) makes not only predictions about regions involved in eye contact processing, but also about the connectivity between these regions. To test such connectivity in our data set, we conducted psychophysiological interaction (PPI) analyses (Friston et al., 1997) with SPM8. We extracted the physiological variable (first Eigenvariate) from all regions included in the fast-track modulator model (see Figures 1C and 3). In addition, we included the cuneus in the PPI analyses; it is not part of the fast track modulator model, but showed highly significant responses for the Eyes vs Mouth contrast in the present study (see 'Results' section).We defined these regions ( Figure 3) either functionally, or if that was not possible, anatomically (see Supplementary  Materials).
The first-level GLM analysis included the interaction term (physiological Â psychological variable), the physiological variable and the psychological variable (Eyes vs Mouth contrast) and was conducted for each source region, i.e. the region of Eigenvariate extraction. To test whether these regions showed enhanced connectivity with each other during eye contact in contrast to mouth fixation, we performed region of interest (ROI) analyses for all regions in the fast track modulator model and the cuneus (target ROIs). The target ROIs were again defined either functionally or anatomically (see Supplementary Materials). They were used to correct for multiple comparisons at the voxel-wise FWE of P < 0.05 using small-volume corrections in the second-level analyses.   Table S1). The mean IEI across participants was 2.44s (Eyes), 1.65s (Mouth), 0.62s (Off) ( Figure 4B, Supplementary Table S2). There were significant main effects of the event types on both NE [F (2,36) ¼ 11.27, P < 0. 001] and IEI [F (2,36) ¼ 21.84, P < 0.001). Both Eyes and Mouth were significantly more and longer than the Off events (NE: Eyes > Fig. 3. Regions of interest. SC, superior colliculus; Pulv, pulvinar; LOC, lateral occipital cortex; OFC, orbitofrontal cortex; mPFC, medial prefrontal cortex; FFA, fusiform face area; dlPFC, dorsolateral prefrontal cortex; Amy, amygdala; a/pSTS, anterior/posterior superior temporal sulcus; ITC, inferior temporal cortex. The figure displays the target regions at which we conducted small volume correction for the PPI analyses. Of these, cuneus, pSTS, mPFC and dlPFC were 10-mmradius spheres centered at maximum peak coordinates in group GLM analysis and were restricted to the gray matter within the brain; others were standard anatomical maps or customized anatomical masks (see Supplementary Materials). For source regions, we used the same regions as above, except that for cuneus, pSTS, mPFC and dlPFC, we used the subject-specific coordinates in the GLM analysis (Supplementary Table S1). Off, t ¼ 4.15, P ¼ 0.002; Mouth > Off, t ¼ 2.85, P ¼ 0.032; IEI: Eyes > Off, t ¼ 8.42, P < 0.001, Mouth > Off, t ¼ 4.94, P < 0.001) ( Figure  4A and B). However, there was no significant difference between Eyes and Mouth in the NE (t ¼ 2.21, P ¼ 0.120) or the IEI (t ¼ 2.13, P ¼ 0.139), indicating these two indexes were roughly balanced between event types. NE and IEI within condition. The average NE across participants was again well above 100 for all event types in the noise and normal condition ( Figure 4C, Supplementary Table S1). There were significant event types Â conditions interactions [NE: F (2,36) ¼ 7.21, P ¼ 0.002, Figure 4C; IEI: F (2,36) ¼ 20.83, P < 0. 001, Figure 4D]. Eyes and Mouth events differed significantly in the Normal condition (NE: t ¼ 3.39, P ¼ 0.010; IEI: t ¼ 4.68, P < 0. 001) ( Figure 4C and D, Supplementary Table S2), but were balanced in the Noise condition as expected (NE: t ¼ 1.11, P ¼ 0.852; IEI: t ¼ 0.11, P ¼ 1.000) ( Figure 4C and D, Supplementary  Table S2).

Eye gaze patterns
IEI distribution and event order. The IEI distribution showed a jittered duration ranging from 0.1 to 80.1 s for all the events. The main part of the range (0.1-20 s) was plotted in Figure 4E. The order of events (fixations on different regions) was variable. All combinations of event orders were used and the transition probability for each combination ranged from 10 to 25%, which is close to 16.7% (equal chance for a strict randomized order) ( Figure 4F).
Taken together, the gaze patterns showed that our design met the requirements for a rapid event-related design: There were sufficient NE and suitably long IEI for the Eyes and Mouth events and both indices were roughly balanced between event types, particularly across conditions and in the Noise condition.
In addition the IEI was jittered and the events occurred in a variable order.

Pupil diameter
We did not find significant differences in average pupil diameter in response to Eyes fixation and Mouth fixation (P ¼ 0.892) or Off fixation (P ¼ 0.95), indicating no increased arousal during eye contact as compared to fixating on the mouth when people watch a speaker talking.

Brain regions involved in eye contact vs Mouth fixation
When participants fixated on the eyes of the speaker in contrast to the mouth (Eyes > Mouth), we found increased responses in a large brain network ( Figure 5A, Table 1): (i) visual cortices including cuneus (Cun, BA 17/18), bilateral calcarine sulcus (Cal, BA 17/18) covering V1, V2 and V3 and extending to bilateral precuneus (Prec, BA 7), (ii) ToM-related brain regions: the right temporal-parietal junction including angular gyrus and supramarginal gyrus and extending into the posterior superior temporal sulcus (TPJ&pSTS, BA 39/40), mPFC including the anterior cingulate cortex extending to medial orbital frontal cortex (BA 10/24/32) and (iii) dlPFC (BA 9/46) (FWE cluster-wise corrected, P < 0.05). Results for the 'standard GLM' ( Figure 5A) and 'control GLM' ( Figure 5B) were qualitatively the same, indicating little effect of the distance or NS within the event on the fMRI results.
To check whether the differential responses between Eyes and Mouth events were caused by a higher NE and longer duration of eye contact during the normal condition, we analysed itional regressors). Hot colors (red to yellow) indicate brain areas showing higher response to Eye contact than mouth fixation. Cold colors (blue to cyan) showed higher response to Mouth fixations than eye contact. L, left hemisphere, R, right hemisphere. For visualization purposes only, voxels surviving with a voxel-level threshold of P < 0.05 and 160 voxels are shown. MNI coordinates of significant brain regions are listed in Table 1. Cun, cuneus; Cal, calcarine; Prec, precuneus; TPJ, temporoparietal junction; STS/G, superior temporal sulcus/gyrus; vmPFC, ventral medial prefrontal cortex; dlPFC, dorsolateral prefrontal cortex; MOG, middle occipital cortex; IFG, inferior frontal gyrus; PCG, precentral and/or postcentral gyrus. the simple main effect of eye contact in the normal condition and the noise condition separately. The response patterns for eye contact in the two conditions (Supplementary Figure S1A and B, Supplementary Table S3) were similar to that obtained in the main effects, except that the response in the right dlPFC was absent in the noise condition after FWE correction, but was present at the threshold of P ¼ 0.001 uncorrected. Results for the contrasts Eyes vs Off are displayed in Supplementary Figure S1C and listed in Supplementary Table S4.
Brain regions involved in mouth fixation vs Eye contact. For completeness, we here also report regions that were engaged in mouth fixation in contrast to eye fixation. These included (i) bilateral middle occipital gyrus (MOG, BA 18), (ii) bilateral superior temporal gyrus/sulcus (STG/S, BA 21/22), (iii) left inferior frontal gyrus (IFG, BA 44/45) and (iv) left precentral and postcentral gyrus (PCG, BA4/6) (FWE cluster-wise corrected, P < 0.05) ( Figure  5 and Table 1). This network of brain regions overlapped well with previous reports on brain mechanisms supporting audiovisual speech perception and lip reading (Skipper et al., 2007;Blank and von Kriegstein, 2013; for a review, see Erickson et al., 2014). Results for the contrast Mouth vs Off are displayed in Supplementary Figure S1D and listed in Supplementary  Table S4.

Effective connectivity (EC) between brain regions
Our PPI results were consistent with several of the predictions of the fast track modulator model (Figure 6, Supplementary  Table S5). There were significant EC increases for eye contact vs mouth fixation (i) between the rapid (SC, Pulv, Amy) and slow (LOC, ITC) routes of visual processing and parts of the social brain network; (ii) between the dlPFC and regions involved in intentionality processing (pSTS and mPFC); and (iii) within the social brain network ( Figure 6A and B, Supplementary Table S5). However, some of the predicted connectivity was not found (see Figure 6B, dashed arrows). For example, the dlPFC did not show EC increase to emotion, gaze direction, and face identity processing regions. In addition, we also found EC not predicted by the fast track modulator model: First, there was strong connectivity between the slow visual processing systems and the fast visual processing systems, the dlPFC, and other parts of the social brain network ( Figure 6B, cyan solid arrows). Second, we also found that the visual cortex (i.e. the cuneus) that was not part of the fast-track modulator model showed EC with most of the brain regions in the fast-track modulation model ( Figure 6B, pink solid arrows). Note that the effective connectivity here did not reflect real direction of influence between regions.

Discussion
We used a novel paradigm to explore the neural mechanisms of eye contact when listening to another speaker talking. We identified a network of brain regions that showed higher responses when looking at the eyes than the mouth of a speaker. The network included visual cortices, brain regions that have been related to ToM/intentionality processing (TPJ&pSTS and mPFC), and the dlPFC. Effective connectivity was enhanced between regions within this network, as well as further regions mainly involved in visual processing and regions that serve functions in social settings, i.e. the so-called social brain network ( Figures 1C and 6B).
Several of the findings were in agreement with the present models of eye contact, particularly the communicative intention detector model and the fast-track modulator model (for a review, see Senju and Johnson, 2009), which both assume that the ToM/intentionality processing network (TPJ&pSTS and mPFC) is involved in eye contact. The validity of the predictions of the models in the context of verbal communication was not self-evident. The way of eliciting eye contact in the current study differed in multiple aspects from the ways in previous studies (Conty et al., 2007;Ethofer et al., 2011;von dem Hagen et al., 2014;Cavallo et al., 2015;Oberwelland et al., 2016) on which the models are largely based. First, in our study eye contact occurred in a verbal listening context. Second, it was elicited by the participants themselves rather than by stimuli with averted and direct gaze. These differences between our approach and designs in previous studies might explain several findings in our study that differed from the models' predictions.
As predicted by the fast-track modulator model, the dlPFC was involved in eye contact in contrast to mouth fixation and had particularly strong connectivity with the pSTS and mPFC, which have been implicated in ToM/intentionality processing (Saxe et al., 2004;Gao et al., 2012), but not with the other regions of the social brain network. We speculate that this pattern of results can be explained by the specific nature of listening to the speaker in an emotionally neutral audiovisual verbal context. Our design emphasized the understanding of what was said in a relatively long listening situation, but not face identity, emotion or gaze direction processing. The fast-track modulator model assumes that the dlPFC has the role of modulating the social brain network (see Figure 1C) depending on the context. Our findings suggest that the dlPFC might be used to modulate regions involved in ToM/intentionality processing, when we look into the eyes of a speaker we are listening to.
Some studies have suggested that eye contact elicits arousal/emotional responses (Kawashima et al., 1999;Conty et al., 2010;von dem Hagen et al., 2014), an important feature of the affective arousal model (for a review, see Senju and Johnson, 2009). However, in what situations an arousal/emotional response to eye contact occurs is currently unclear (Mormann et al., 2015; for a review, see Hamilton, 2016). We found no differences in average pupil diameter, which is considered a reliable measure of arousal (Libby et al., 1973;Kampe et al., 2003). There was also no amygdala response to eye contact. These results suggest that eye contact initiated by the listener may not act as an arousal cue in the context of listening to someone talking about a neutral topic in a neutral manner.
A major difference between the predictions of current neuroscientific models of eye contact processing and our findings was the strong responses in visual cortices, i.e. responses in cuneus/calcarine sulcus to eye contact and in MOG to mouth fixation. It is difficult to explain these responses as the result of larger IFDs or NS within the eye-region, because the control GLM (with IFD and NS as parametric modulators) still showed qualitatively the same results as the standard GLM. The differential responses in early visual areas might be explained by the different information present at foveal vision. The features (eyes, mouth) and the amount of movement (constant gaze, mouth movement) naturally differ at foveal vision for the two event types. We speculate that the feature difference might not play a major role, because previous fMRI studies comparing responses to eyes and mouth stimuli did not report similar responses in early visual areas as in this study (Puce et al., 1998;Pelphrey et al., 2005b;Liu et al., 2010;Arcurio et al., 2012). In contrast, studies investigating responses to moving vs static faces/ lips have revealed similar MOG (BA18) responses as the Mouth vs Eyes contrast in the present study (Calvert and Campbell, 2003;Schultz et al., 2012). This might be a first indication that it is the difference between movement information at the fovea that leads to the differential responses in cuneus and MOG.
Eye tracking and fMRI have primarily been utilized as independent techniques to investigate the gaze patterns or neural mechanisms involved in eye contact processing (for a review, see Schilbach et al., 2012). The current study provides evidence that the FIBER-fMRI method (Marsman et al., 2012;Henderson and Choi, 2015) is an innovative and promising technique balanced between ecological validity and methodological constraints for investigating eye contact in verbal communication. This technique can shed light on the relatively unexplored question of how our brain 'sees' the complex and dynamic world in a voluntary manner. Our experimental paradigm involved several important features of eye contact during verbal communication. However, it missed some other features that are often present in genuinely reciprocal face-to-face communication between partners, such as eye gaze shifts of the talker, verbal turn-taking and the knowledge of interacting with a real partner (Vertegaal et al., 2001;Wilson and Wilson, 2005;Jiang et al., 2012Jiang et al., , 2015. We expect that our experimental paradigm together with other interactive approaches (e.g. Oberwelland et al., 2016) will be a solid foundation for integrating these features in future studies.
In this study, FIBER-fMRI allowed us to investigate the neural mechanisms underlying eye contact with a paradigm that is uncorrected P values. Note that, since the findings for right and left cuneus as source region were qualitatively similar, only the findings of right one are displayed here. In addition, for all the target regions, only the hemisphere with the highest P value is shown here. For corresponding coordinates see Supplementary Table S4. b, bilateral; r, right; l, left. B, Comparison of effective connectivity found in the current study and that predicted in the fast-track modulator model. For easier comparison, the colors of arrows here are the same as those in Figure 1C. Solid arrows represent connectivity found in the PPI analyses that were predicted by the fast-track modulator model. Dashed arrows represent connectivity predicted in the model but not found in our results. Pink and cyan solid arrows represent new connectivity found in this study, but not predicted by the model. Brain areas marked with yellow are the areas found to be significantly more responsive to eye contact vs mouth fixation in the standard GLM analysis. ecologically valid for situations in which we listen to another person talking. The network that we found for eye contact might be well suited for processing the intentions of communication partners when listening to them during face-to-face verbal communication.

Funding
This work was supported by a Max Planck Research Grant to K.v.K and a China Scholarship Council (CSC) and German Academic Exchange Service (DAAD) scholarship to J.J.