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. 2025 Aug 13;62(8):e70124. doi: 10.1111/psyp.70124

Functional Connectivity Is Stronger Between Unisensory and Multisensory Regions for Nonsimultaneous Judgments of Visual‐Tactile Stimuli

M K Huntley 1,✉, A Nguyen 1, M A Albrecht 2, W Marinovic 1,✉
PMCID: PMC12351210  PMID: 40808337

ABSTRACT

Multisensory integration is an automatic process that occurs across unisensory and multisensory areas of the brain. Although multisensory integration is often quantified using the simultaneity judgment task, which measures the temporal binding window for multisensory integration, little is known about the neural processes associated with the task. In 26 participants, we used electroencephalography to measure functional connectivity between parietal–occipital, parietal–central, and central–occipital regions during the simultaneity judgment task. Our aim was to compare connectivity patterns associated with simultaneous and nonsimultaneous perception, which is important for calculating the temporal binding window. Our results show that functional connectivity in the beta frequency was stronger between parietal–occipital, parietal–central, and central–occipital regions when individuals perceived visual‐tactile stimuli as nonsimultaneous than simultaneous. However, connectivity was only stronger in the theta and alpha frequencies for parietal–central and central–occipital regions. Stronger connectivity in the theta and alpha frequencies is likely associated with detecting and encoding changes in temporal dynamics between cross‐modal stimuli, and in the beta frequency, stronger connectivity is likely related to a violation of expectancy for simultaneous stimuli. Overall, our findings demonstrate that functional connectivity between unisensory and multisensory neural regions occurring during and immediately following stimulus presentation is important for the perception of simultaneity.

Keywords: connectivity analysis, debiased weighted phase lag index, multisensory integration, simultaneity judgment, visual‐tactile

Short abstract

Our research deepens the existing knowledge on the neural mechanisms underlying multisensory integration by investigating functional connectivity during the simultaneity judgment task. The findings reveal distinct patterns of connectivity in theta, alpha, and beta frequencies between parietal, central, and occipital brain regions, highlighting their roles in detecting temporal dynamics and expectancy violations. By exploring the relationship between the temporal binding window and sensory traits, this study advances our understanding of multisensory perception and its neural correlates, offering novel insight into sensory processing.

Our perceptual experience is shaped by the integration of various sensory inputs, such as visual, auditory, and tactile, which are processed, integrated, and interpreted by the central nervous system. The formation of our perceptual experience is largely facilitated within an optimal temporal binding window (TBW) for multisensory integration (Ernst and Bülthoff 2004). The TBW is a period in which multisensory stimuli are bound together (i.e., integrated) into a single percept and attributed to an event in the environment (Vroomen and Keetels 2010). When TBWs are wider (i.e., multisensory integration occurs over a longer period), there is more opportunity for irrelevant information to be bound together with relevant information (Wallace and Stevenson 2014), which in turn can distort our perceptual experiences and make it difficult to effectively interact with the environment (Iarocci and McDonald 2006). In clinical populations, wider TBWs have been associated with social and communication differences in autism spectrum disorder (ASD) and hallucinations in schizophrenia (Stevenson et al. 2017, 2014; Zhou et al. 2021). In both clinical and nonclinical populations, the TBW for multisensory integration is typically measured using the simultaneity judgment task. In this task, participants are presented with two stimuli from different modalities, either simultaneously or at varying stimulus onset asynchronies (SOAs), and they are asked to judge whether the stimuli occurred simultaneously. The width of the TBW is taken from the time point at which participants display ambiguity in their judgments about whether stimuli are simultaneous or nonsimultaneous.

Behavioral studies have used the simultaneity judgment task to measure the audio‐visual, visual‐tactile, and olfaction‐gustation TBWs (Chen et al. 2018; Costantini et al. 2016; Gotow and Kobayakawa 2023; Hillock et al. 2011; Ikeda and Morishita 2020; Migliorati et al. 2020; Opoku‐Baah and Wallace 2021; Stevenson, Fister, et al. 2012; Stevenson and Wallace 2013; Stevenson, Zemtsov, and Wallace 2012). To understand mechanisms underlying the TBW, research using electroencephalography (EEG) has primarily focused on the neural activity that occurs prior to the onset of the first stimulus in cross‐modal simultaneity judgment tasks, that is, the activity that occurs prior to multisensory binding. Simultaneity perception, which refers to the ability to distinguish between simultaneous and nonsimultaneous stimuli, has been associated with prestimulus individual alpha frequency and prestimulus alpha power, such that individuals with shorter (i.e., higher frequency) alpha oscillations and stronger alpha power can more easily distinguish between simultaneous and nonsimultaneous audio‐visual and visual‐tactile stimuli (Bastiaansen et al. 2020; Migliorati et al. 2020). Whereas individuals with longer (i.e., lower frequency) prestimulus alpha oscillations and weaker prestimulus alpha power perceive stimuli as occurring simultaneously at longer SOAs. Simultaneity perception of audio‐visual stimuli is also influenced by prestimulus beta (14–28 Hz) and gamma (55–80 Hz) oscillatory power (Bastiaansen et al. 2020; Ikumi et al. 2019; Yuan et al. 2016). In the beta (14–28 Hz) and gamma (55–80 Hz) frequency bands, prestimulus power across parietal‐occipital areas was stronger when audio‐visual stimuli were perceived as occurring simultaneously than nonsimultaneously, and weaker when visual–auditory stimuli were perceived as simultaneous rather than nonsimultaneous (Yuan et al. 2016). Similarly, functional connectivity measured during an audio‐visual simultaneity judgment task showed stronger prestimulus connectivity across auditory and visual regions in the beta‐high frequency (21–28 Hz) when stimuli were perceived as occurring simultaneously than nonsimultaneously, but this pattern of connectivity was not replicated for visual–auditory stimuli (Jiang et al. 2023). This prior research is important as it shows that the state of the brain prior to stimulus onset influences whether individuals perceive stimuli as occurring simultaneously or nonsimultaneously. However, given that judgments about simultaneity can only be made after presentation of the stimuli, the perception of simultaneity is likely also influenced by neural activity occurring during and immediately following stimulus presentation.

When recording neural activity with EEG during an audio‐visual simultaneity judgment task, individuals with narrower audio‐visual TBWs showed stronger connectivity across distributed populations of neurons in various regions of the brain, particularly in the temporal and frontal regions, than in localized populations of neurons (Johnston et al. 2022). By comparison, individuals with wider TBWs—who integrate multisensory information across a longer period—showed stronger connectivity in localized populations of neurons than in distributed populations of neurons. Thus, efficient integration of multisensory information involves strong connections between various neural regions activated during the simultaneity judgment task with audio‐visual stimuli. However, it is unclear whether specific areas associated with the processing of unisensory and multisensory stimuli during the simultaneity judgment task show increased functional connectivity, particularly for visual‐tactile information. Gaining an understanding about the connectivity patterns between neural regions associated with the processing of visual‐tactile stimuli during, and immediately following, stimulus presentation in the simultaneity judgment will improve our understanding about multisensory integration in general.

In the current study, we examine functional connectivity between somatosensory (tactile), occipital (visual), and parietal (multisensory) regions during temporal binding in a visual‐tactile simultaneity judgment task. Our study focuses on connections between neural regions associated with unisensory and multisensory processing of visual and tactile information. As multisensory interactions and the transfer of sensory information between cortices induce changes in oscillatory activity in the theta, alpha, and beta frequencies (Bauer et al. 2020; Göschl et al. 2015; Senkowski et al. 2008), we have focused on functional connectivity within these frequency bands. We hypothesize that functional connectivity between parietal–occipital, parietal–central, and central–occipital regions will be stronger when individuals perceive visual‐tactile stimuli as simultaneous than nonsimultaneous in the simultaneity judgment task.

1. Method

1.1. Participants

Twenty‐six participants were recruited from Curtin University undergraduate student population to participate in the study (M = 20.88 years old, SD = 3.14, 20 female). All volunteers received credit points towards their course as compensation for their participation in the study. We determined our sample size based on previous research investigating multisensory interactions with EEG (Bastiaansen et al. 2020; Göschl et al. 2015; Hipp et al. 2011; Ikumi et al. 2019; Migliorati et al. 2020; Wang et al. 2019), exceeding the typically observed sample sizes in comparable studies within this research area. All participants were right‐handed, reported an absence of color blindness, and had no known neurological condition. Written informed consent was obtained from the participants prior to conducting the experiments. The study was approved by Curtin University Human Research Ethics Committee (HRE2018‐0257). Participants completed a demographics questionnaire and the simultaneity judgment task in one session.

1.2. Materials

1.2.1. Stimuli

A visual stimulus and a tactile stimulus were used in the experiment. The visual stimulus consisted of a 5 mm green light‐emitting diode (LED; 10,000 mcd), and the tactile stimulus was a 10 × 3.4 mm shaftless vibration motor (Pololu Corporation, Las Vegas, NV; Pololu item #1636). The tactile stimulus was a high‐frequency vibration motor operating at approximately 242 Hz. A high‐speed video analysis was conducted to measure the latency between the control signal and vibration onset, which confirmed a consistent mechanical delay of ~7 ms. The visual and tactile stimuli were placed beside each other on the participant's right index finger under a semitransparent fabric elastic band that was secured with micropore tape. The participant's hand was then placed onto a raised piece of foam with the index finger resting at a similar level to the instruction on the computer monitor. This placement of the hand on the foam aimed to reduce excessive eye muscle activity from the participant moving their eyes between the monitor and the stimuli on their finger. Both the visual and tactile stimuli were presented for a 50 ms duration in each trial. Timing triggers for presentation of the stimuli were sent direct from the computer via a parallel port connection. The experiment was programmed in MATLAB version 2015b, and instructions for the task were displayed on a 19″ Dell LCD computer monitor (60 Hz refresh rate) using Psychtoolbox (version 3.0.8).

1.3. Procedure

In the simultaneity judgment task, participants were presented with two sensory stimuli, one light as the visual stimulus and one vibration as the tactile stimulus. Participants were instructed to attend to both stimuli: the words “Both stimuli” were presented on the screen at the beginning of each trial, prior to stimulus onset (see Figure 1 for trial design) to prompt participants to attend to both stimuli. Stimuli were presented either together (i.e., simultaneously) or at various stimulus onset asynchronies (SOAs) ranging from ±25, 50, 75, 100, 125, and 150 ms. Positive SOAs indicate that the visual stimulus preceded the tactile stimulus (“visual‐tactile”), and negative SOAs indicate that the tactile stimulus preceded the visual stimulus (“tactile‐visual”). There were four target‐positive SOAs: 0, 50, 75, and 100 ms, three nontarget‐positive SOAs: 25, 125, and 150 ms, and six nontarget‐negative SOAs: 25, 50, 75, 100, 125, and 150 ms. “Target” SOAs refer to the SOAs we wanted to obtain enough stimuli for EEG analysis. The “nontarget” visual‐tactile and tactile‐visual SOAs were used to reduce the likelihood of participants learning which trials were simultaneous and nonsimultaneous, which may have happened if they had repeated exposure to only a few SOAs. The visual‐tactile target positive SOAs were the primary interest for this study as our previous research shows that the width of the VT‐TBW is reliably narrower for each participant than the TV‐TBW (Huntley et al. 2023). In contrast, the higher variability in the size of the TV‐TBW precludes it from group‐level analysis with EEG. The target SOAs are based on our previous findings that show at the SOA 0 ms, individuals perceive visual‐tactile stimuli occurring simultaneously for approximately 90% of trials and at SOA 100 ms individuals perceive the stimuli as occurring simultaneously for approximately 50% of the trials. Following presentation of the cross‐modal stimuli in each trial, participants were asked “Were the stimuli simultaneous?”, and participants responded either “yes” by pressing the left mouse button or “no” by pressing the right mouse button. After the participant responded, the next trial started automatically. There were 410 trials in total presented in random order: 80 trials presented for each target SOA and 10 trials presented for each nontarget SOA. There was one designated break during the task, lasting a minimum of 10 s. Participants completed practice trials before commencing the experiment. The practice trials and experimental task were completed in a dimly lit room. Following completion of the simultaneity judgment task, participants completed the three questionnaires: AQ, GSQ and MUSEQ.

FIGURE 1.

FIGURE 1

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Graphical representation of the trial design for visual‐tactile stimuli presented in the simultaneity judgment task. Green circle represents the green LED used as the visual stimulus and the white star represents the vibration motor that was used as the tactile stimulus. Stimuli were presented at these SOAs: ±0, 25, 50, 75, 100, 125, and 150 ms.

1.4. Electroencephalography (EEG) Processing

1.4.1. EEG Acquisition and Preprocessing

EEG data were recorded continuously throughout the simultaneity judgment task using a 64‐channel Biosemi Active Two EEG system and ActiView (version 7.07). Data were sampled at 2048 Hz with a DC‐100 Hz online filter. The 64‐channel electrodes were positioned on the scalp in accordance with the 10–20 system, and additional electrodes were placed adjacent to the outer canthi of both eyes and on the left infraorbital region to record eye muscle activity. The EEG data were preprocessed offline using MATLAB (version 2018b) and EEGLAB (version 14.1.2, Delorme and Makeig 2004), with Cz as the reference electrode. Prior to filtering the data, electrode channels recorded from the outer canthi of both eyes and on the left infraorbital region were removed from the dataset. Each data set was visually inspected to identify channels that showed excessive noise or drift, which was not typical of neural activity, and these channels were interpolated. A high‐pass filter of 0.5 Hz and a low‐pass filter of 45 Hz were applied to the data and then subsequently down‐sampled to 256 Hz. The epoch length was initially set to −1600 ms to 2500 ms relative to the onset of the first stimulus, and baseline amplitudes were corrected based on the 100 ms period preceding the onset of the first (visual) stimulus. After baseline correction, data were time‐locked to the second (tactile stimulus) in the cross‐modal pair. Independent components analysis was performed using adaptive mixture independent component analysis (AMICA, version 1.5, Palmer et al. 2012), and components were visually inspected for each participant. Components that were identified as artifacts, such as eye blinks and muscle activity, were subtracted from each participant's data. Artifact rejection was determined by removing activity that was less than −75 mV or exceeded 75 mV, and Surface Laplacian was applied to the processed EEG data to minimize the impact of volume conduction contaminating the signal (Carvalhaes and de Barros 2015; Perrin et al. 1989).

1.4.2. Time–Frequency Decomposition

After preprocessing, time–frequency decomposition was performed on the EEG signal to extract information about both the temporal and spectral characteristics of the signal in preparation for connectivity analysis. The data for time–frequency decomposition were organized based on experimental condition with SOA and response type (either “yes” for simultaneous and “no” for nonsimultaneous) used as sorting variables. Morlet wavelet convolution was performed for time‐frequency decomposition with a frequency range of 1–50 Hz, 30 frequencies, and a cycle range of 4–13 cycles (increasing in frequency) in MATLAB. Trials were split by the target SOA—SOA0, SOA50, SOA75, and SOA100—and then split by response into “yes” for simultaneous and “no” for nonsimultaneous. Data were converted to FieldTrip format using the FieldTrip toolbox (version 2019; Oostenveld et al. 2011) to calculate debiased weighted phase lag index (dWPLI).

1.5. Data Analysis

1.5.1. Measuring the Temporal Binding Window

The percentage of simultaneity responses (i.e., “yes”, “no” to the question “were the stimuli simultaneous?”) were averaged for each participant at each SOA and fitted with a model‐free line across all SOAs using the modelfree package in RStudio (Model‐Free Estimation of a Psychometric Function, version 1.2) (Zychaluk and Foster 2009). The model‐free function is a parameter free method that has no assumptions about the shape of the data. This function is an alternative to a Gaussian or sigmoid function, which are commonly used methods for fitting the rate of perceived simultaneity data in simultaneity judgment tasks (Costantini et al. 2016; Hillock‐Dunn and Wallace 2012; Hillock et al. 2011; Migliorati et al. 2020; Moro and Steeves 2018; Noel et al. 2016, 2017; Powers et al. 2009; Stevenson et al. 2018, 2014; Stevenson and Wallace 2013; Venskus et al. 2021). To estimate the width of the TBW for each participant, we measured the half‐way point between 0% (no perceived simultaneity) and the maximal peak of simultaneity perception on the y‐axis and where this half‐way point intersects the model‐free fitted line on the x‐axis. The distance (in ms) between where this halfway point on the y‐axis intersects with the model‐free fitted line on the x‐axis is taken as the width of the TBW (see Figure 2 from Huntley et al. (2023)).

FIGURE 2.

FIGURE 2

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Graphical representation with example data from one participant showing how the model‐free line was fitted to the subject‐level data for each participant.

1.5.2. Debiased Weighted Phase Lag Index (dWPLI)

Debiased weighted phase lag index (dWPLI) is a functional connectivity measure that is an extension of previous phase synchronization methods, imaginary component of the coherency (IMC) (Nolte et al. 2004), and phase lag index (PLI, Stam et al. 2007). These methods measure synchronization between neural regions while reducing the potential for capturing spurious connectivity. We used the dWPLI method for measuring connectivity over other methods of phase synchronization (i.e., weighted PLI and PLI) as it is robust against volume conduction and is not limited by sample size (Vinck et al. 2011). Using FieldTrip in MATLAB, dWPLI was averaged across frequency ranges from 2 to 30 Hz in steps of 1 Hz, and across a time range of 0 to 400 ms, in steps of 5 ms, from the onset of the second stimulus. Then, dWPLI was calculated between all channel pairs (4489 electrode pairs), across SOAs (SOA50, SOA75, SOA100), response (“yes” = simultaneous, “no” = nonsimultaneous perception) and time ranges (0–100 ms, 100–200 ms, 200–300 ms, and 300–400 ms) in four frequency bands: theta (4–7 Hz), alpha (7–11 Hz), beta‐low (12–20 Hz), and beta‐high (20–30 Hz) (see Figure 3). dWPLI values range from −1 to +1, with 1 indicating maximum connectivity; negative values typically occur due to reduced sampling and therefore were corrected to zero values prior to statistical analysis (Yusuf et al. 2021). We analyzed dWPLI between P5‐Oz, C5‐Oz, and C5‐P5 as these electrode pairs are the most relevant for our study measuring connectivity between unisensory areas and between unisensory and multisensory areas. Note that due to insufficient nonsimultaneous responses at the SOA 0 ms across participants (likely due to the perception of simultaneity being at maximum), we have only presented results for simultaneous responses at SOA 0 ms in Figures 5, 6, 7 and did not include SOA 0 ms in the statistical analysis.

FIGURE 3.

FIGURE 3

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Left image shows the timing of stimuli presentation for SOA50, SOA75 and SOA100 relative to the onset of the tactile stimulus. For statistical analysis, data were time‐locked to the tactile stimulus, which is depicted in this figure as the tactile stimuli are all aligned at 0 ms. Green circles and arrows show the onset and duration of the visual stimulus. Black star shows the onset and duration of the tactile stimulus. The incremental shading for 0–100, 100–200, 200–300, and 300–400 ms shows the time ranges that were examined in the connectivity analysis. The right image shows the three electrode pairs used for connectivity analysis (P5‐Oz, C5‐Oz and C5‐P5).

FIGURE 5.

FIGURE 5

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Parietal–Central dWPLI values for “yes” (simultaneous) and “no” (nonsimultaneous) responses by SOA across theta, alpha, beta‐low, and beta‐high frequencies. The electrode pair—C5 and P5—represents the tactile and multisensory areas of the cortex, respectively. Error bars represent the standard error of the model estimates.

FIGURE 6.

FIGURE 6

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Parietal–Occipital dWPLI values for “yes” (simultaneous) and “no” (nonsimultaneous) responses by SOA across theta, alpha, beta‐low, and beta‐high frequencies. The electrode pair—P5 and Oz—represents the multisensory and visual areas of the cortex, respectively. Error bars represent the standard error of the model estimates.

FIGURE 7.

FIGURE 7

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Central–Occipital dWPLI values for “yes” (simultaneous) and “no” (nonsimultaneous) responses by SOA across theta, alpha, beta‐low, and beta‐high frequencies. The electrode pair—C5 and Oz—represents the visual and tactile areas of the cortex, respectively. Error bars represent the standard error of the model estimates.

Linear mixed models (LMM) were conducted using the glmmTMB function in the glmmTMB package (version 1.1.7; Brooks et al. 2017; Kristensen et al. 2015) in R (R version 4.3.2; RStudioTeam 2015). Results from the LMMs are reported as F‐values, obtained using the “anova” function with Satterthwaite method applied for correcting degrees of freedom. LMMs were conducted to analyze the fixed effects of Response (“yes” or “no”), Time‐Range (0–100 ms, 100–200 ms, 200–300 ms, 300–400 ms) and SOA (50, 75, 100) on dWPLI, with Participant as the random factor.

The LMMs were specified using the following equation:

1.5.2.

where dWPLIij is the connectivity value for trial i from participant j; the β terms represent the fixed‐effects coefficients for the main effects and interactions; u0j is the random intercept for each participant j, assumed to be normally distributed with a mean of 0; and ϵij is the residual error for each trial. LMMs were performed for each electrode pair (P5‐Oz, C5‐Oz, C5‐P5) and frequency band (theta, alpha, beta‐low and beta‐high). As our initial analysis was hypothesis‐driven, we did not correct for multiple comparisons.

2. Results

2.1. Behavioral Data

The visual‐tactile TBW was significantly narrower (VT; M = 93 ms, SD = 33 ms) than the tactile‐visual TBW (TV; M = 129 ms, SD = 48 ms) (t(25) = 17.1, p < 0.001) (See Figure 4). However, for 17 participants, the mean width of their TV‐TBW was set at the maximum SOA tested in our experiment of 150 ms, and their width may extend beyond 150 ms. Therefore, these results may underestimate the difference between VT and TV TBWs. Furthermore, data from three participants were removed from the analysis as the uniformity of their responses limited the applicability of the model‐free fitting, resulting in an estimated TV‐TBW of 0 ms.

FIGURE 4.

FIGURE 4

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Combined group averaged data of perceived simultaneity for visual‐tactile (black) and tactile‐visual (red) stimuli with model‐free line fitted to the data. Error bars represent standard error.

2.2. Connectivity Using dWPLI

2.2.1. Parietal—Central (P5‐C5)

There were significant responses by SOA interactions for all frequencies (Table 1), thus indicating that the simultaneity of stimuli could be determined when stimuli were presented at SOAs of 50, 75, and 100. Specifically, at SOA50, dWPLI connectivity was stronger when individuals perceived visual‐tactile stimuli as nonsimultaneous compared to simultaneous for all frequency bands. At SOA75, dWPLI connectivity was stronger when individuals perceived visual‐tactile stimuli as nonsimultaneous compared to simultaneous for theta, beta‐low, and beta‐high. While at SOA100, theta connectivity was increased for nonsimultaneous perception (see Figure 5).

TABLE 1.

Parietal–Central ANOVA tables for theta, alpha, beta‐low, and beta‐high frequencies.

df F p
Theta
Response 1, 575 48.346 < 0.001*
Time 3, 575 1.265 0.285
SOA 2, 575 11.564 < 0.001*
Response:Time 3, 575 0.983 0.400
Response:SOA 2, 575 13.179 < 0.001*
Time:SOA 6, 575 0.857 0.526
Response:Time:SOA 6, 575 0.802 0.568
Alpha
Response 1, 575 22.064 < 0.001*
Time 3, 575 1.024 0.381
SOA 2, 575 1.941 0.144
Response:Time 3, 575 1.805 0.145
Response:SOA 2, 575 9.286 0.0001*
Time:SOA 6, 575 0.791 0.577
Response:Time:SOA 6, 575 1.607 0.142
Beta‐low
Response 1, 575 47.463 < 0.001*
Time 3, 575 1.742 0.157
SOA 2, 575 3.154 0.043*
Response:Time 3, 575 1.237 0.295
Response:SOA 2, 575 6.5082 0.001*
Time:SOA 6, 575 0.7860 0.581
Response:Time:SOA 6, 575 1.0475 0.393
Beta‐high
Response 1, 600 52.6354 < 0.001*
Time 3, 600 0.2011 0.895
SOA 2, 600 7.3139 < 0.001*
Response:Time 3, 600 0.3936 0.757
Response:SOA 2, 600 8.7380 < 0.001*
Time:SOA 6, 600 1.5625 0.155
Response:Time:SOA 6, 600 1.2865 0.261
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Note: Significance of * indicates statistical significance level for * when p < 0.05.

2.2.2. Parietal—Occipital (P5‐Oz)

There was a significant response by SOA interaction for beta‐high and main effects for response, time, and SOA for beta‐low (see Table 2), thus indicating that activity in the beta‐high frequency contributes to the accuracy of perceiving stimuli as nonsimultaneous when they occur at SOA50. In both cases, dWPLI was generally stronger for nonsimultaneous perception than simultaneous perception across all time ranges, with the largest difference for beta‐high at SOA50. For theta and alpha, there were only main effects of time, with dWPLI decreasing in strength from 0 ms to 400 ms for all SOAs (Figure 6).

TABLE 2.

Parietal–Occipital ANOVA tables for theta, alpha, beta‐low, and beta‐high frequencies.

df F p
Theta
Response 1, 575 2.3309 0.127
Time 3, 575 19.752 < 0.001*
SOA 2, 575 0.638 0.529
Response:Time 3, 575 0.343 0.794
Response:SOA 2, 575 0.643 0.526
Time:SOA 6, 575 0.204 0.975
Response:Time:SOA 6, 575 0.373 0.896
Alpha
Response 1, 575 2.079 0.149
Time 3, 575 19.454 < 0.001*
SOA 2, 575 0.356 0.700
Response:Time 3, 575 0.496 0.685
Response:SOA 2, 575 0.530 0.589
Time:SOA 6, 575 0.544 0.775
Response:Time:SOA 6, 575 0.514 0.798
Beta‐low
Response 1, 575 12.492 0.0004*
Time 3, 575 4.587 0.003*
SOA 2, 575 4.074 0.017*
Response:Time 3, 575 0.346 0.791
Response:SOA 2, 575 1.190 0.304
Time:SOA 6, 575 1.394 0.214
Response:Time:SOA 6, 575 0.780 0.585
Beta‐high
Response 1, 575 59.046 < 0.001*
Time 3, 575 0.351 0.7887
SOA 2, 575 5.629 0.003*
Response:Time 3, 575 1.023 0.382
Response:SOA 2, 575 4.750 0.009*
Time:SOA 6, 575 0.684 0.662
Response:Time:SOA 6, 575 0.576 0.749
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Note: Significance of * indicates statistical significance level for * when p < 0.05.

2.2.3. Central–Occipital (Oz‐C5)

There were significant responses by SOA interactions for all frequencies (Table 3), and an additional time by SOA interaction in the alpha frequency. Broadly, these results indicated that connectivity was stronger for nonsimultaneous perception at SOA50 and SOA75, but not SOA100 (Table 3). In the theta, alpha, and beta‐low frequencies, there were main effects of response, and an additional main effect of SOA in the theta frequency, thus indicating connectivity was stronger for nonsimultaneous perception across these frequencies, particularly at SOA50 and SOA75 in the theta frequency. For beta‐high, the main effect of time reflects stronger connectivity for nonsimultaneous perception at early time points (0–200 ms) followed by a decrease in connectivity strength over time (see Figure 7).

TABLE 3.

Central–Occipital ANOVA tables for theta, alpha, beta‐low, and beta‐high frequencies.

df F p
Theta
Response 1, 575 67.371 < 0.001*
Time 3, 575 0.680 0.564
SOA 2, 575 14.250 < 0.001*
Response:Time 3, 575 0.164 0.920
Response:SOA 2, 575 8.620 0.0002*
Time:SOA 6, 575 0.649 0.690
Response:Time:SOA 6, 575 0.672 0.672
Alpha
Response 1, 575 40.523 < 0.001*
Time 3, 575 1.047 0.371
SOA 2, 575 2.386 0.092
Response:Time 3, 575 0.553 0.646
Response:SOA 2, 575 5.958 0.002*
Time:SOA 6, 575 2.216 0.040*
Response:Time:SOA 6, 575 1.384 0.218
Beta‐low
Response 1, 575 59.244 < 0.001*
Time 3, 575 2.494 0.059
SOA 2, 575 1.484 0.227
Response:Time 3, 575 1.074 0.359
Response:SOA 2, 575 3.576 0.028*
Time:SOA 6, 575 0.973 0.442
Response:Time:SOA 6, 575 1.462 0.189
Beta‐high
Response 1, 575 40.360 < 0.001*
Time 3, 575 0.382 0.766
SOA 2, 575 1.940 0.144
Response:Time 3, 575 0.852 0.465
Response:SOA 2, 575 3.243 0.039*
Time:SOA 6, 575 0.420 0.865
Response:Time:SOA 6, 575 0.576 0.749
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Note: Significance of * indicates statistical significance level for * when p < 0.05.

3. Discussion

Our study measured functional connectivity between brain regions associated with unisensory and multisensory processing to understand differences in the strength of connectivity between simultaneous and nonsimultaneous judgments of visual‐tactile stimuli pairs. Generally, our results show stronger functional connectivity between central–occipital and parietal–central regions when individuals perceive visual‐tactile stimuli as nonsimultaneous compared to simultaneous across theta, alpha, and beta frequencies, but for parietal–occipital this effect was only observed in the beta frequencies. The strength of connectivity was largely similar across regions, SOAs, and time ranges, which suggests temporal binding of multisensory stimuli occurs automatically, thus giving rise to the perception of simultaneity. This pattern of results was in contrast to what we predicted, and previous research showing prestimulus connectivity was stronger for simultaneous than nonsimultaneous responses (Jiang et al. 2023).

It is likely that stronger connectivity for nonsimultaneous responses in the theta and alpha frequencies for central–occipital and parietal–central reflects the detection and encoding of temporal differences of each stimulus in the cross‐modal pair (Kayser et al. 2012) and the shifting of attention between multisensory stimuli (Keil and Senkowski 2018; Keller et al. 2017). For example, at SOA50 the visual stimulus is presented for a duration of 50 ms, and then the tactile stimulus is presented for 50 ms. This timing of stimuli is difficult within the constraints of our perceptual system. Since these stimuli are presented immediately following each other, greater attention to, and encoding of, the temporal onset and offset of the stimuli is required to make a judgment on whether the stimuli occurred simultaneously or nonsimultaneously. The increase in attention and encoding of temporal differences between stimuli is reflected by increased connectivity for nonsimultaneous perception as these stimuli are actually occurring nonsimultaneously. Supporting this idea, connectivity was strongest primarily at SOA50 and SOA75, where attention and encoding of the temporal differences are most crucial due to the ambiguity of judging simultaneity at these short SOAs compared to longer SOAs (e.g., SOA100), when it is arguably easier to correctly identify stimuli as simultaneous or nonsimultaneous. Activity in the theta and alpha frequency connecting parietal and occipital areas to a central site likely plays a specific role in maintaining attention (Keller et al. 2017), explaining why we observe stronger connectivity between these regions than for parietal–occipital connectivity.

In the beta frequency (12–30 Hz), stronger connectivity for nonsimultaneous responses likely reflects a violation of expectation and the feed forward/back of information about this violation. Generally, the brain is conditioned to expect multiple stimuli in the environment to occur simultaneously when they appear in close temporal proximity to each other. Therefore, when stimuli are presented with marginally longer SOAs, they are perceived as nonsimultaneous, which likely violates an expectancy/prediction. Beta oscillations have been suggested to facilitate both feed‐forward and feedback flow of information (Alais et al. 2010; Keil and Senkowski 2018), and are involved in making predictions about our environment and updating predictions with new information following violations of expectancy (Arnal and Giraud 2012; Meindertsma et al. 2018). Therefore, the violation of expectancy of simultaneous cross‐modal stimuli is transferred between unisensory and multisensory regions to update expectations of stimulus‐simultaneity in the current environment for future predictions. These findings in the theta, alpha, and beta frequencies are supported by previous research showing synchronization of neural oscillations (i.e., functional connectivity) in different frequency bands across unisensory and multisensory brain regions are involved in processing multisensory information (Alais et al. 2010; Hipp et al. 2011; Keil and Senkowski 2018; Senkowski et al. 2008; Wang et al. 2019).

Our results extend previous functional connectivity research by demonstrating that connectivity between visual and tactile regions during stimulus presentation in the simultaneity judgment task is important for determining whether stimuli occur simultaneously and whether they are subsequently integrated into multisensory percepts. Our findings add to the research examining prestimulus activity and functional connectivity across alpha and beta frequencies in the simultaneity judgment task (Bastiaansen et al. 2020; Hipp et al. 2011; Jiang et al. 2023; Johnston et al. 2022; Migliorati et al. 2020; Yuan et al. 2016) by demonstrating that neural activity occurring during and immediately following stimulus presentation contributes to the perception of simultaneity. Our results build upon previous research measuring visual‐tactile connectivity across large‐scale brain networks (Wang et al. 2019) as we have demonstrated that functional connectivity is strongest between unisensory and multisensory areas when neural resources are needed to discriminate the separation of visual‐tactile stimuli and encode the timing differences between the stimuli.

3.1. Limitations and Future Directions

Functional connectivity measures, such as dWPLI, are limited to measuring nondirectional connectivity between two neural regions. The benefit of using directional connectivity measures is that they indicate the direction of information flow between neural regions—that is, whether the signal travels from point A to point B or vice versa. Examining the direction of information flow during the presentation of cross‐modal stimuli (and immediately after) in the simultaneity judgment task would aid in understanding the neural mechanisms associated with the task specifically and multisensory integration more broadly. Further, as multisensory integration involves various cortical and subcortical regions, measuring connectivity between two regions at a time (i.e., bivariate connectivity) may result in missing some key information. Measuring connectivity between more than two neural regions (i.e., multivariate connectivity; occipital—central—frontal connectivity) is especially important for multisensory integration as the combined signals from unisensory regions (i.e., parietal and occipital) may be sent to high‐order areas for processing and there may be contributions from other regions that perceive, process, and integrate unimodal stimuli or the combined multisensory signal. Therefore, future research may benefit from using effective connectivity measures with EEG data, such as Granger Causality and Transfer Entropy that can measure the direction of connectivity between multiple neural regions to determine cause–effect relationships and further our understanding of the neural mechanisms underlying the TBW (Cao et al. 2022; Chiarion et al. 2023).

Further, it is important for future research to consider specific aspects of the experimental protocol that are used to measure the width of the TBW. Specifically, there are two important factors to consider: (1) SOAs need to be long enough to capture the width of the TV‐TBW sufficiently, and (2) including unimodal trials (in addition to cross‐modal trials) likely influences the width of the cross‐modal TBW. Our findings, combined with our prior research (Huntley et al. 2023) indicate that the SOAs need to be > 250 ms to obtain the full width of the TV‐TBW, particularly when participants have no prior experience judging pairs of unimodal stimuli. When participants are not exposed to unimodal stimuli (i.e., tactile‐tactile) there is limited opportunity to form representations of the stimuli, which likely assist in facilitating efficient integration. Therefore, when designing experiments using the simultaneity judgment task, it is important to consider whether to measure the TBW with prior experience (i.e., including unimodal trials) or without prior experience (i.e., excluding unimodal trials) as the inclusion of unimodal trials seems to influence the width of the TBW. For example, including unimodal trials provides an opportunity for participants to judge the simultaneity of two unimodal stimuli, which forms their prior experience for other trials when they are required to judge the simultaneity of multisensory stimuli. In the current study, we observed large differences between the widths of the curves on the percentage of simultaneity for TV‐TBW and VT‐TBW, which is in contrast to our previous study that showed a similar pattern of percentage of simultaneity for both VT‐TBWs and TV‐TBWs (Huntley et al. 2023). In short, including tactile‐tactile trials sharpens the temporal precision of tactile‐visual judgments of synchronicity, which results in narrower TV‐TBWs. The inclusion of visual–visual trials seems to have little effect on the temporal accuracy used to decide on the simultaneity of visual‐tactile stimuli, as shown by the curves for the percentage of simultaneity in both studies.

Finally, since previous behavioral research shows people with ASD and schizophrenia have wider TBWs (Stevenson et al. 2014, 2017; Zhou et al. 2021), future research should consider adapting the current protocol for these clinical populations to examine neural connectivity associated with multisensory integration. Comparing differences in connectivity between clinical and nonclinical populations may provide insight into the differences in sensory processing and integration that contribute to atypical perceptual experiences in ASD and schizophrenia.

4. Conclusion

Functional connectivity between unisensory (central, occipital) and multisensory (parietal) regions was stronger when individuals perceived cross‐modal stimuli as nonsimultaneous compared to simultaneous. Stronger connectivity between unisensory and multisensory areas in the theta and alpha frequencies was likely associated with processing the temporal dynamics of the cross‐modal stimuli. Whereas stronger connectivity in the beta frequency reflected violations of expectancy in the timing of stimuli and the feed forward/back of information flow between unisensory and multisensory regions. Our study provides a foundation for future research to investigate visual‐tactile connectivity in the simultaneity judgment task in clinical populations, such as ASD and schizophrenia, to determine whether connectivity during the task contributes to the wider TBWs typically found in these populations.

Author Contributions

M. K. Huntley: conceptualization, methodology, software, validation, formal analysis, investigation, resources, writing – original draft, writing – review and editing, visualization, project administration. A. Nguyen: conceptualization, methodology, software, formal analysis, writing – review and editing, supervision. M. A. Albrecht: conceptualization, methodology, software, formal analysis, resources, writing – review and editing, supervision. W. Marinovic: conceptualization, methodology, software, formal analysis, writing – review and editing, visualization, supervision.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Appendix S1: psyp70124‐sup‐0001‐AppendixS1.docx.

PSYP-62-e70124-s001.docx (21.2KB, docx)

Huntley, M. K. , Nguyen A., Albrecht M. A., and Marinovic W.. 2025. “Functional Connectivity Is Stronger Between Unisensory and Multisensory Regions for Nonsimultaneous Judgments of Visual‐Tactile Stimuli.” Psychophysiology 62, no. 8: e70124. 10.1111/psyp.70124.

Contributor Information

M. K. Huntley, Email: m.k.huntley@outlook.com.

W. Marinovic, Email: welber.marinovic@curtin.edu.au.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix S1: psyp70124‐sup‐0001‐AppendixS1.docx.

PSYP-62-e70124-s001.docx (21.2KB, docx)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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