PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen,
Germany

Temporal attention and comodulation in multisensory causal
inference
Virginie VAN WASSENHOVE1,2; Daria LA ROCCA2,3; Denis Alexander ENGEMANN2,3; Philippe
CIUCIU2,3
1

Cognitive Neuroimaging Unit, INSERM, Université Paris-Sud, 91191 Gif/Yvette, France
2

CEA/DRF/Joliot, Université Paris-Saclay, 91191, Gif-sur-Yvette, France

3

INRIA, Parietal team, Université Paris-Saclay, 91120 Palaiseau, France

ABSTRACT
Perception relies on inferences about the causal structure of the world provided by multiple sensory inputs.
In ecological settings, multisensory events that cohere in time and space contribute to such inferential
processes: the sound of flapping wings naturally paces the crowded vision of a flock of birds, and hearing
while seeing a speaker enhances speech comprehension. Psychophysical and magnetoencephalography(MEG) studies suggest that the Human brain may synthesize multisensory temporal comodulation in someabstract form, which may benefit (as top-down predictions) the feedforward analysis of incoming sensory
signals. For instance, recent findings have shown the emergence of large-scale phase synchronizations inhigh gamma (60-120 Hz) and beta (15-30 Hz) bands following the short experiencing of temporally
comodulated multisensory signals. The coordinated engagement of prefrontal, parietal, and visual cortices
suggests the possibility that a short experience of temporally comodulated stimuli signaling the same distal
information may functionally re-route unisensory processing.
Keywords: Audiovisual, Comodulation, Magnetoencephalography

1. INTRODUCTION
Textbook descriptions of perception have classically addressed the senses in isolation, eachconcerned with the transduction, coding, and analysis of different kinds of physical signals.
Increasingly however, empirical evidence from different fields of research have demonstrated thatmultisensory integration – i.e. the combination of multiple sensory signals – is the rule rather than the
exception (1-3). Indeed, a few seconds of life are filled with a multitude of sensory inputs. Somesensory events pertain to the same perceptual episode such as the smell, the sight, the sound, and theanticipated taste of wine being poured in a glass; others, although constitutive of the same experienced
scene, pertain to different experiential episodes such as the chattering crowd gathered around thewaiter, each with dyads entertaining distinct conversations. How then does the brain selectively
integrate and segregate multisensory information into a coherent, meaningful and intelligible story?
1.1 Causal Inference in Multisensory Perception
Since the seminal Fuzzy Logical Model of Perception (4-6) and the more recent Bayesian
approaches of multisensory integration in behavior and in neurosciences (e.g. 7-10), the resolution ofperceptual inference problems has been formulated as a weighted function of multisensory reliability
so that the most reliable information outweighs other sensory inputs. This model entails the existenceof multisensory priors shaped in the course of perceptual experiences (as posteriors). Hence, recent
working hypotheses posit that the brain can infer the causal structure of the sensory environment byintegrating multisensory signals originating from the same physical source and by segregating signals
originating from different causes (11-15). Audiovisual speech perception was conceived as a case13922

virginie.van.wassenhove@gmail.com

study for Bayesian inference (6) which likely follows similar Bayesian computational principles (16)mediated by predictive multisensory neural processes (17-20). Yet, what sensory cues are primordialin engaging multisensory integration is unclear, and whether multisensory integration is a default
operating mode or not is likely determined by selective attention mechanisms.
1.2 Selective Attention
The role of (selective) attention is omnipresent in multisensory research (2 1). Whileneuroanatomical connectivity demonstrates that feedforward integration could occur early at different
stages of the sensory hierarchy, the automaticity of behavioral integration effects is systematically put
into question.
For instance, in a seminal study (22), behavioral evidence for early feature-based multisensory
integration could not be found: using a visual search paradigm in which visual feature changes were
temporally coherent or co-modulated with acoustic changes, the authors found no evidence of parallel
search and concluded that audiovisual (AV) integration was mediated by attentional effects. To thecontrary, Van der Burg and colleagues (23-24) used a conjunction visual search in which a horizontal
or vertical bar (visual target) surrounded by distracters of various orientations changed colors (green,red) at a rate of 1 Hz. They showed that transient sounds synchronized with the visual target changes
fastened participants’ reaction times. As temporal coincidence was orthogonal to the feature space
relevant for the task (here, the orientation of the visual bar) and yet, perceptual facilitation effects wereobserved, the authors suggested that the synchrony between the transient sounds and the visualchanges was critical. This finding suggested that the feature of interest may have been the locus of
temporal attention as cued by the sound.
A follow-up study by an independent group tested whether behavioral facilitation in visual
conjunction search would show temporal selectivity in the predicted range of neurophysiologicalreports. Neurophysiological evidence had suggested that selective attention may be implemented as
the slow regulation of neuronal activity (25-27) in the delta band (1-2 Hz) across auditory and visualsensory modalities. More recent neurophysiological evidence further supports a selective
enhancement of sensory information processing in multisensory populations as well as sensory
cortices (28), and this can be observed for speech content as well (29-30).
The paradigm of Van der Burg and colleagues (23) was extended by measuring reaction times (RT)and performance at varying rates of AV presentation as well as insuring that no temporal correlation
survived between the sound and the visual target in a control condition (31). Below 2 Hz, faster RTs
and improved performances were observed for temporally congruent AV stimuli, whereas slower andworse performances were observed for temporally incongruent AV stimuli as compared to the visualcondition alone. These results provided evidence that AV interactions in visual sea rch were
temporally-dependent and that automaticity in multisensory integration was mediated by selective
temporal attention. In a similar vein, Parise and colleagues (32) presented a train of white noise clicks
and Gaussian blobs at different spatial locations. The authors showed that spatial localization was
more precise when AV stimuli were temporally correlated than when they were not; however,temporally uncorrelated AV stimuli were still more precise than the best unisensory condition. As
mentioned by the authors, even when stimuli were “uncorrelated”, they still presented somecorrelations at larger temporal scales which likely accounted for the lack of replication of thedifferences observed between correlated and uncorrelated effects reported in the previous study (31).
Altogether, these psychophysical studies suggest that the temporal comodulation of sensory inputs
is a crucial cue for engaging selective attention in multisensory integration by increasing the saliencein time of incoming sensory inputs. A recent model (15) relying on temporal coincidence further
suggested that temporal coherence may be sufficient to determine very early on in the sensory
hierarchy whether signals are causally related or temporally segregated.
1.3

Audiovisual Comodulation
The comodulation or temporal coherence of multisensory sources could be seen as a‘common-fate’ Gestalt principle so that features sharing the same dynamics in different sensory
modalities are inferred to belong to the same perceptual object (32), just as can be observed within
vision (33-35) and within audition (36, 37). The temporal comodulation of sensory signals helps
building the representation of complex perceptual scenes and in the context of multisensory
integration, may enable a feedforward volley of multisensory cueing. This is particularly relevant forspeech processing in which the acoustic envelope of the speech signal and the movements of the lips
show high temporal correlation or comodulation (38) in the spectral range that is relevant for speech3923

feature categorization i.e. the F2/F3 formants region (39,40). These correlations have been argued tosupport multisensory integration even when visual speech information is consciously suppressed (41).
Hence, an interlocutor's mouth movements temporally coherent with the envelope of the ac oustic
speech signals provide the listener with strong binding cues for predictive inferences (17, 18, 38, 42,43). These general considerations motivated a research program which explored whether AV
comodulation may also subsequently help visual perceptual discrimination (44, 45). We report below
a partial set of results from one study, which will be presented at the conference.

2. MATERIALS & METHODS
2.1 Participants
36 healthy human participants (16 females) were recruited for the study (22.1 +/- 2 y.o). All
participants were right-handed, had normal hearing and normal or corrected-to-normal vision. Before
the experiment, all participants provided a written informed consent in accordance with the
Declaration of Helsinki (2008) and the local Ethics Committee on Human Research at NeuroSpi n(Gif-sur-Yvette, France). Prior to the MEG acquisition, participants were randomly split into 3
experimental groups (V, AV, and CTRL) as detailed below.
2.2 Task
The behavioral task consisted in discriminating which of a red or green cloud of dots moved in t hemost coherent way (Figure 1). While being recorded with magnetoencephalography (MEG),
participants were tested with the visual task for 12 minutes before and after a short training (PRE,POST, respectively). The training was 20 minutes long (4 blocks, 5 minutes each) using visual stimuli
only (V group; N=12), congruent or incongruent audiovisual stimuli (AV and CTRL group,
respectively; each with N=12). In PRE and POST, individuals’ visual discrimination thresholds weredrawn from their individual psychophysical discrimination curve testing 7 strengths of motion
coherence (15%, 25%, 35%, 45%, 55%, 75% and 95%; 28 trials each). Each MEG recording block was
followed by a 5 minutes rest block.
2.3 Stimuli
Visual stimuli were isoluminant green and red clouds of dots (Figure 1A). Dots (0.2° radius) were
presented within an annulus of 4° to 15° of visual angle. The motion flow was 16.7 dots/deg 2/s with aspeed of 10°/s. The direction of motion was constrained to 45°-90° around the azimuth. Both colors(red, green) and directions (up, down) of the most coherent cloud were counterbalanced andpseudo-randomized across trials. The V group underwent training using visual only stimuli. The CTRL
group underwent training with acoustic noise fully uncorrelated with the cohe rent motion to test
specificity of AV associations. The AV group was tested with temporally comodulated AV associations(Figure 1B). We used parametrized acoustic textures (46) so that each visual dot was paired with alinear frequency-modulated acoustic sweep whose slope depended on the direction of the visual dot(see 44; Fig. 1A; fs = 44.1 kHz, 0.2 - 5 kHz). The maximal slope was 16 octaves/s corresponding tomotion directions of 82.9°-90°. A visual dot moving upwards (downwards) was associated with an
upward (downward) acoustic ramp. The duration of a ramp was identical to the life -time of a dot.
2.4 Magnetoencephalography (MEG)
Electromagnetic brain activity was recorded in a magnetically shielded room using a 306 MEG
system (Neuromag Elekta LTD, Helsinki). MEG signals were sampled at 2 kHz and band -pass filtered
between 0.03-600 Hz. Four head position coils (HPI) measured the head position of participants before
each block; three fiducial markers (nasion and pre-auricular points) were used during digitization as areference for coregistration of anatomical MRI (aMRI) immediately following MEG acquisition.
Electrooculograms (EOG) and electrocardiogram (ECG) were recorded simultaneously with MEG.
Five minutes of empty room recordings was acquired before each block for the computation of thenoise covariance matrix.

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Figure 1 – A: In a single trial of the main experimental task used for all participants (V, AV and CTRL
groups in the PRE and POST experimental blocks), a red and a green cloud of dots were intermixed.
All dots were fully incoherent for up to 0.6 s. Then, one of the clouds (here, red) became more coherent
for 1 s. Participants were asked to report the color of the most coherent cloud. B: During training, theAV group heard a coherent acoustic texture paired with the most coherent cloud (here, red). C:
Illustration of the source-estimated Human motion area (hMT+; left panel) activity in response to thepresentation of visual motion coherence of increasing strength (blue to red color). The stronger thecoherence of visual motion, the stronger the source amplitude estimated with MEG (right panel).
Samples of the video trials are provide as Movies S1 and S2 in Zilber et al (44).

2.5 Main Analyses
The analysis of the MEG used the MNE-python toolbox (47). After applying an anti-aliasing FIR
filter (low-pass cutoff frequency at 130 Hz), MEG data were down-sampled to 400 Hz, andpreprocessed to remove external and internal interferences. Signal Space Separation (SSS) wasapplied with MaxFilter to remove exogenous artifacts and noisy sensors (48). Ocular and cardiac
artifacts (eye blinks and heart beats) were removed using Independent Component Analysis (ICA) on
raw
signals(49;
https://github.com/mne-tools/mne-python/blob/master/tutorials/plot_artifacts_correction_ica.py).
The functional interaction between brain regions was assessed by evaluating the similarity of brain
activity across remote brain regions, namely functional connectivity (FC) of oscillatory activity using
the weighted Phase Lag Index (PLI) (50, 51). All statistics were corrected for multiple comparisons.

3. MAIN RESULTS
All participants improved their performance during training (Figure 2, left column). Performing
the visual discrimination task was accompanied by the activation of a large network of brain regions
encompassing sensory and multisensory cortices (44). Of interest, we found a selective implication of
prefrontal regions for those motion coherence strengths that improved after training (Figure 2, middle
column). Herein, we solely focused on the PRE and the POST functional networks, when participantsfrom all three groups performed the task on visual motion coherence only in the absence of any other3925

stimuli. 28 cortical regions were considered following source reconstruction. All statistical contrasts
were based on non-parametric permutation t-tests. Only phase coupling values showing significant
differences (p < 0.01) are being highlighted.
3.1 Alpha (8-12 Hz) Networks (De)Synchronization
The functional connectivity pattern observed in resting-state was subtracted from the PRE and
POST networks. A significant uncoupling of the alpha oscillatory network during both PRE and POST
was found as compared to resting-state. The observed alpha desynchronization was modulated by thetraining (i.e. the sensory history) experienced by participants. Consistent with the alphadesynchronization, a general task-related decrease of node degree from resting-state to PRE was foundin parietal, occipital, and temporal regions for the alpha network. Additionally, a relative increase insynchronization from PRE to POST was observed mainly in the V and CTRL groups (Figure 2).

Figure 2 – Perceptual Discrimination (left column): all three groups (V, AV and CTRL) significantly
improved their performance (significant decrease of perceptual threshold) after the short training. An
increased activity in ventro-lateral Prefrontal Cortices (vLPFC) was found in response to visualmotion coherence stimuli, which became discriminable after training (44; middle column). Thisobservation suggested that the temporal comodulation of sensory signals may be represented in an
abstract (sensory-independent) format. Post-training oscillatory networks showed the notable
emergence of beta and high-gamma oscillatory activity in the AV group (45; left column).
3.2 Beta (14-30 Hz) and High Gamma (> 60 Hz) Networks
A similar analysis performed for the beta and gamma oscillatory regimes showed an increase oflarge-scale coupling from resting-state to task (both PRE and POST; Figure 2, right column). The
beta network was found in all groups and implicated vlPFC, Intra-Parietal Sulcus (IPS) and hMT+. A
significant strengthening of the beta network following training was solely observed in the AV group,
who had experienced AV comodulated signals. A significant increase of functional connectivity inhigh gamma was also found in the AV group between auditory regions and pSTS. Further, the nodedegree value of beta oscillatory networks decreased with training in all three experimental groups.
Conversely, the right mSTS region increased following training in all three groups. This observationsuggested the implication of the mSTS during training, the synchronization of which got stronger andmore extensive following all trainings.

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3.3 Beta (14-30 Hz) network and perceptual decision-making
The functional network patterns emerged in a training-selective manner in the beta band for the AV
group, suggesting that comodulated AV stimuli affected the functional organization of corticalinteractions during visual discrimination task. Both beta and gamma long-range interactions in
POST-specific networks were found to predict an individual’s confidence rating trained with AV
comodulation (r=0.72, p=0.011, N=12; 45).

4. DISCUSSION
A series of psychophysical work and recent MEG data (44, 45) suggest that comodulated
audiovisual stimuli are crucial for multisensory perception. Comodulated sounds while performing a
motion coherence discrimination task seem to strengthen subsequent perceptual decision-making
through the regulation of large-scale oscillatory synchronizations in the Human brain. A functional
connectivity analysis quantifying the degree of oscillatory phase-coupling across brain regions
showed a global decrease of alpha (8-12 Hz) phase synchronization from to rest to task. This global
decrease was indicative of an enhanced attentional state when performing the task. Importantly, weobserved the selective emergence of the long-range beta (15-30Hz) and gamma (60-120 Hz)
synchronization networks in the AV group, that is the group having experienced coherent multisensory
stimulation. Altogether, our results suggest that temporal comodulation is an important ecological cue
for solving the seminal correspondence problem in multisensory research.

5. CONCLUSIONSIn sum, this body of work aims at exploring the interplay o f bottom-up (temporal comodulation)and top-down (abstraction) selectivity in the integration and segregation of multisensory information
during perception.

ACKNOWLEDGEMENTS
This work was supported by the ANR-16-CE33-0020 MultiFracs.

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