Remarkably, the GRBS theory as an equilibrium-based reconceptualization and unification of truth-based local reality with quantum nonlocality provides logically definable causality in regularity for quantum emergence or submergence of spacetime—a key for revealing the ubiquitous effects of quantum entanglement with mind–light–matter unity. Notably, GRBS does not exclude local reality but reveals it—the opening of a new research direction in science and philosophy. While the compatibility of GRBS with the Standard Model and the intertwining of different quantum gravity theories are left open for further research efforts, some unifying properties have been examined and illustrated in complete background-independent logical, algebraic, and physical terms. Thus, GRBS forms a real-world relativistic causal–logical quantum gravity theory with unexpected but much-needed simplification.

As a formal unified logical/physical theory, GRBS makes Nature–human unity/harmony a scientific topic for global environment protection, global economy regulation, and mind–light–matter unity QCQB. It is hoped that GRBS can help humanity in dealing with global climate change with sustainable scientific research/development. While this work has been focused on GRBS with illustrations in entangled causal–logical machine thinking/imagination, hopefully, as a basis for QCQB it has opened a new door toward human-level AI&QI in physical, logical, mental, biological, and social-economical quantum gravity terms.

It should be further clarified that GRBS is reached by following the quantum information science path instead of the one-dimensional string theory path in physics. It is dramatic but also normal because GRBS as an axiomatization of physics and quantum information science is supposed to be logician's work. As the first and only background-independent theory of its kind, GRBS happened to provide a reconceptualization and unification of string theory, loop quantum gravity, and M-theory—the three roads to quantum gravity [65]—with unexpected but much-needed simplification. Alternatively, GRBS could have been named as *global realism with bipolar dynamic equilibria (GRBDE)*. It can be observed that GRBS holds the advantage over GRBDE in terms of bipolar entangled quantum graph representation, information visualization, reconceptualization, and causal–logical unification for real-world applications.

Finally, with a formal logical and physical basis, GRBS is expected to be free from the "Not Even Wrong", "Trouble with Physics", and "Lost in Math" problems. While it is a matter of debate whether GRBS would be able to inject new life into the faded TOE, this work has shown the potential of the equilibrium-based theory for major scientific advances, especially for the sustainable development of mind–light–matter unitary AI/QI machinery toward human-level intelligence and beyond. Now, with entangled machine thinking and imagination, our AI&QI humanoid robots should keep the hope alive for the miracle of reaching human-level intelligence someday as string theory was once lost in the beauty of the truth-based singularity but found in the harmony of equilibrium-based bipolar relativity.

**Funding:** No funding for this research. It is purely curiosity-driven.

**Data Availability Statement:** The article is logical and theoretical in nature, not experimental. Illustrations are included as examples and figures. No additional data are available.

**Conflicts of Interest:** The author declares that the research was conducted in the absence of any commercial or financial relationship that can be construed as a potential competing interest of a financial or personal nature.

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Article

# Inter-Brain Hemodynamic Coherence Applied to Interoceptive Attentiveness in Hyperscanning: Why Social Framing Matters

Michela Balconi<fnref n="1" />,<fnref n="2" /> and Laura Angioletti <fnref n="1" />,<fnref n="2" />,*

**Abstract:** Grossberg's classification of adaptive resonance mechanisms includes the cognitive-emotional resonances that support conscious feelings and recognition of them. In this regard, a relevant question concerns the processing of signals deriving from the internal body and their contribution to interpersonal synchronization. This study aims to assess hemodynamic inter-subject coherence in the prefrontal cortex (PFC) through functional near-infrared spectroscopy (fNIRS) hyperscan recording during dyadic synchronization tasks proposed with or without a social frame and performed in two distinct interoceptive conditions: focus and no focus on the breathing condition. Individuals' hemodynamic data (oxygenated and de-oxygenated hemoglobin (O2Hb and HHb, respectively)) were recorded through fNIRS hyperscanning, and coherence analysis was performed. The findings showed a significantly higher O2Hb coherence in the left PFC when the dyads performed the synchronization tasks with a social frame compared with no social frame in the focus condition. Overall, the evidence suggests that the interoceptive focus and the presence of a social frame favor the manifestation of a left PFC interpersonal tuning during synchronization tasks.

**Keywords:** interoceptive attentiveness; hyperscanning; fNIRS; lateralization; inter-brain coherence; interpersonal synchronization; social frame

## 1. Introduction

The term “social interoception” was introduced in the literature to describe the link and the potential influence of interoception (the process by which our brain receives and processes information derived from our body [1]) on a number of social processes, such as self-other differentiation [2], social cognition, social isolation and connectedness [3], and emotional experience [4–6].

The ability to intentionally focus attention on one's body signal for a determined span of time is defined as “interoceptive attentiveness” (IA) [7,8]. Weng and colleagues [9] showed that it is possible to modulate IA to observe positive outcomes in an individual's emotional and cognitive health. However, there has been little neuroscientific research conducted to date on how manipulating interoception can impact the interpersonal synchronization processes. Turn-taking, mimicry, and non-verbal social communication [10], as well as time and content synchronization [11], can be included in interpersonal synchronization processes.

Previously, single-brain studies were carried out to investigate the neural correlates of IA manipulation (operationalized as focused on breathing) on the inter-personal synchronization necessary for performing joint tasks [12–15]. In two recent studies, functional near-infrared spectroscopy (fNIRS) was used to record the oxygenated hemoglobin (O2Hb) changes during joint tasks involving motor and cognitive synchronization, while the participants were required to concentrate on their breathing to better understand the hemodynamic correlates of IA manipulation in interpersonal synchronization [12,14]. In the

**Citation:** Balconi, M.; Angioletti, L. Inter-Brain Hemodynamic Coherence Applied to Interoceptive Attentiveness in Hyperscanning: Why Social Framing Matters. *Information* **2023**, *14*, 58. https://doi.org/10.3390/info14020058

Academic Editors: Birgitta Dresp-Langley and Luiz Pessoa

Received: 21 December 2022

Revised: 12 January 2023

Accepted: 14 January 2023

Published: 17 January 2023

<figure><img src="image_122.png" /></figure>

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

<footnote n="1">¹ International Research Center for Cognitive Applied Neuroscience (IrcCAN), Università Cattolica del Sacro Cuore, 20123 Milan, Italy</footnote>

<footnote n="2">² Research Unit in Affective and Social Neuroscience, Department of Psychology, Università Cattolica del Sacro Cuore, 20123 Milan, Italy</footnote>

<footnote>* Correspondence: laura.angioletti1@unicatt.it; Tel.: +39-2-7234-5929</footnote>
first study, the induction of explicit focus on breathing during a socially framed motor task requiring synchronization increased the responsiveness of the prefrontal cortex (PFC), which is involved in sustained attention, reorientation of attention, social responsiveness, and synchronization. In the absence of a broader and more explicit social frame, this effect was not significant in the motor task [14].

The PFC was shown to play a significant role in high-order functions, including social and cognitive functions, motor control, and attention. Grossberg [16] recently proposed an increasingly comprehensive attentive brain architecture in his predictive adaptive resonance theory (pART) and assigned to the PFC the control of high-order functions, including working memory, learned plans, predictions, and optimized action. As a part of a larger interoceptive network [17], the PFC is also crucial in initiating and maintaining focused attention on a target while regulating internal and external interferences [18]. Furthermore, the PFC supports sustained attention to breathing by increasing individuals' awareness of the mind wandering and enables them to bring their attention back to breathing [19]. Additionally, it has been associated with social functions, such as fully aware motor control and the ability to adapt to shifting rhythmic patterns [20], and also interpersonal coordination and cooperative interactions [21,22].

In the second fNIRS study, hemispheric lateralization was reported with an increase in O2Hb in the right PFC when intentional attention toward breathing was induced during a cognitive synchronization task, namely a linguistic task [12]. According to previous studies, the right PFC appears to support the execution of IA tasks [6,23,24] and sustained and goal-directed attention [18].

Although the above-mentioned fNIRS studies were the first to describe the effect of IA manipulation on individuals completing motor and cognitive tasks in synchronization with another partner and aiding in identifying the role of the PFC in this phenomenon at the intraindividual level, one obvious shortcoming consisted of the lack of assessments of the interactional dynamics between the two members of the dyad.

With the development of the hyperscanning paradigm [25], numerous works have calculated inter-agent synchronization and inter-brain coupling metrics that reflect the degree of social attunement based on the simultaneous recording of behavioral and hemodynamic responses from various agents involved in a joint task or a social exchange [22,26]. For instance, this made it possible to investigate how dyads' inter-individual brain synchronization changes depending on whether the participants are collaborating or competing [27,28].

In addition, hyperscanning studies were extensively used for deepening synchronization mechanisms during motor [29–31], linguistic, and cognitive tasks. Naturalistic paradigms including verbal collaboration and turn-taking have revealed a lateralization effect, with right-sided activations of the dorsolateral PFC and temporal areas [32].

However, it has also been established that the setting of the interaction affects inter-brain synchrony. For example, facing the interacting partner appears to improve inter-brain synchrony, as evidenced by more simultaneous increases in activity within the left inferior frontal cortex and the right temporal parietal junction (TPJ) in subjects who were singing [33] or playing interactive games while facing each other as opposed to a wall [34]. Additionally, face-to-face interactions showed increased inter-brain synchrony, but back-to-back ones did not [35].

Furthermore, inter-brain synchronization in the left PFC was discovered to predict the effectiveness of teaching, highlighting the significance of shared attention for the accomplishment of shared objectives [36]. Greater inter-brain synchrony in the left frontopolar region was also found in dyads with different social experience [37]. In regions connected to the social alignment loop, such as the left inferior frontal cortex, behavioral alignment was found to be mediated by inter-brain synchrony [38].

Nonetheless, these hyperscanning studies did not manipulate IA when individuals performed the tasks in synchrony. Moreover, the social framing was not explicitly emphasized. Therefore, it is also interesting to determine the impact of these two variables on intercerebral coherence in terms of lateralization.
With reference to the influence of a social frame on synchronization performance, before it was shown how, during a real-person, joint-tapping hyperscanning experiment, interpersonal sensorimotor performance and interbrain synchrony in the left TPJ was greater in a bidirectional than in a unidirectional condition, indicating the social effect of a more cooperative condition [39] or suggesting a potential neural mechanism for selective tuning in to a target speaker while tuning out others [40]. Additionally, as stated above, the presence of an explicit social frame during a motor synchronization task, executed while paying attention to breathing, augmented the O2Hb values in the PFCs of individuals [14]. Thus, it may be argued that even basic exercises of synchronization, if explicitly socially framed, may differently impact an individual's inter-brain coherence. This research used neural coherence indices to explore the hemodynamic correlates of between-brain interconnectivity by using a two-person neuroscience paradigm. In former fNIRS hyperscanning experiments, coherence indices were used to examine the synchronization of brain rhythms during cooperative and competitive joint activities [22, 26, 41, 42], social exchanges [43], and gesture observation and reproduction [44].

Therefore, the primary aim of the present study is to assess hemodynamic inter-subject coherence through fNIRS hyperscan recording during dyadic synchronization tasks, proposed with and without a social frame and performed in two distinct interoceptive conditions. Specifically, the experimental design examines two distinct conditions of presence and absence of interoceptive focus (i.e., when the attention of the participants is focused on breathing versus not focused on breathing), the specific synchronization task performed by the participants (cognitive versus motor), as well as the social frame applied at the beginning of the synchronization task (that could be socially framed or not).

Given previous evidence, we hypothesized observed higher inter-brain coherence in the PFC of the dyads during focusing on breathing compared with not focusing on the breathing condition for both synchronization tasks [12,14].

Secondly, regarding social frame manipulation, we expect to observe an increase in the inter-brain coherence effect for socially framed synchronization compared with non-socially framed synchronization. It is also supposed that an increased effect in response to the motor compared with the linguistic synchronization task will be present, given the effect of the social frame we previously observed specifically for the motor task in our previous research [14].

Thirdly, as indicated in the literature reported above, we aim to observe a potential lateralization effect even in terms of inter-brain coherence, with a potential right hemisphere lateralization effect connected to interoceptive focus [12, 23, 24] and a left hemispheric activation predominance for positive emotions derived from the synchronization.

Finally, taking into account that coherence indices were used before in fNIRS hyper-scanning experiments to investigate PFC synchrony, we also intend to deepen whether inter-subject hemodynamic coherence indices can be exploited as a reliable indicator of dyads' neural synchronization in this context (i.e., when the interoceptive attention to breathing is manipulated).

## 2. Materials and Methods

### 2.1. Participants

A total of 32 university students were enlisted for the current fNIRS experiment using a non-probabilistic convenience sampling technique (14 females; age mean = 27.1; standard deviation = 3.19). Each dyad was composed of two individuals of the same sex matched for age, and they did not meet before the experiment. We have previously estimated the adequate sample size to detect medium effects via inferential statistics (f = 0.25), with the α error probability set at 0.05 and with 0.80 power (G*Power 3.1 software [45]). The analysis suggested that a total of 15 observations (i.e., in our case, dyads) would be sufficient. All participants were right-handed and had normal or corrected-to-normal eyesight. The criteria for exclusion included pregnancy, past meditative experience, severe physical and chronic illnesses, convulsions, persistent pain, and any mental or neurological disorders.
They signed written informed consent forms and willingly agreed to participate in the study after being advised they would not receive payment for their contributions. The Ethics Committee of the Department of Psychology (Catholic University of the Sacred Heart in Milan, Italy) gave its approval for this study (2020 TD-a.a.2020–2021), which was conducted in conformity with the Declaration of Helsinki.

## 2.2. *Joint Synchronization Tasks Description*

Two basic motor and cognitive synchronization tasks were adopted as joint tasks in the current study.

The participants in the motor synchronization task had to synchronize and coordinate their finger-tapping motion for three minutes with the other person in their dyad. The participants were instructed to sit in a chair with their elbows resting on a table and their dominant hand's fingers spread about a centimeter apart. They were instructed to use all of the fingers on their dominant hand to tap the table. They were not told to move at a certain speed or to extend their fingers as wide as they could. All they had to do was ensure their finger movements matched those of the participant sitting in front of them. The average number of loops—measured as the total number of times a finger-tapping pattern was repeated—was 60.

A modified version of the human-to-human alternating speech task was utilized for the cognitive synchronization test, requiring the participants to syllabicate in unison with the other participant in the dyad for a total of three minutes. The four syllables of “LA”, “BA”, “CA”, and “DA” were to be spoken consecutively and alternately by the participants. To pronounce a syllable at the same time, for example, when one member of the dyad said “LA”, the other member should have paired the syllable by saying “LA”, and so on. The speech patterns were not chosen in advance. Without any breaks, each language synchronization task session lasted three minutes. The number of repetitions from “LA” to “DA” in each loop throughout the course of the three minutes was at least 45.

These tasks were employed in prior single-brain investigations [12,14] and were used for this hyperscanning study to ensure consistency in the experimental design.

## 2.3. *Procedure and Experimental Manipulations*

Each dyad was positioned such that the participants could comfortably interact with each other face to face. The participants received procedural instructions before the experiment started. They were informed that they were required to execute two joint synchronization tasks following different experimental conditions in which IA was manipulated.

In the first condition, IA was purposefully controlled by instructing the participants to concentrate on their breathing. The following directions were given in this focus on breathing: “During this task, we ask you to concentrate on your breathing. Try to pay attention to how you feel and whether your breathing changes as you complete the activity.” The participants were not instructed to breathe at a certain pace. In contrast, no specific instructions were given in the no attention to breathing condition, which was regarded as the control condition, in which interoception was not manipulated, and the participants were only instructed to complete the joint tasks. The same interoceptive manipulation was used in earlier investigations to preserve the procedure’s reliability, and it was shown to have an impact on the hemodynamic neural correlates [12,46].

For the social framing manipulation, we asked the participants to perform the same motor and cognitive synchronization tasks previously described, but they were socially framed by specifying that they needed to synchronize in order to develop greater teamwork skills. In this way, the absence of a social frame resulted from not emphasizing the sharing of intention, whereas stressing the shared intentionality explicitly served to introduce the social frame [14].

A 120 s baseline of each dyad member’s hemodynamic resting state was gathered before the synchronization tasks began. The order in which the condition and the synchronization tasks were performed were randomized and counterbalanced to prevent any
potential biases brought on by sequence effects. After completing the activities, there was a debriefing phase in which the participants rated their attention to their breathing, the other person, and the task on a scale of 0 to 10. The entire experimental procedure took one hour to be completed (Figure 1A,B).

<figure><img src="image_123.png" /><figcaption>**Figure 1.** (A) Experimental procedure representing the setting for the joint task and (B) the fNIRS hyperscanning acquisition from the dyad. To avoid an order effect, the task execution was randomized and counterbalanced for the type of the task and the condition.</figcaption></figure>

## 2.4. fNIRS Data Recording and Biosignal Data Analysis

The hemodynamic signal was recorded using a six-channel optode matrix of an NIRScout System (NIRx Medical Technologies, LLC, Los Angeles, CA, USA). This system measures fluctuations in the concentrations of oxygenated hemoglobin (O2Hb) and deoxygenated hemoglobin (HHb). Using an fNIRS cap, four light sources or emitters and four detectors were placed over the scalp in line with the worldwide standard 10/5 system [47].

For the montage, four emitters were installed at AF3, AF4, F5, and F6, and four detectors were installed at AFF1h, AFF2h, F3, and F4. The emitter-detector distance for consecutive optodes was kept at 30 mm, and two wavelengths of near-infrared light were used (760 and 850 nm). Six channels were acquired using this optode configuration: Ch1 (AF3-F3), Ch2 (AF3-AFF1h), and Ch3 (F5-F3), which corresponded to the left PFC, and Ch4 (AF4-F4), Ch5 (AF4-AFF2h), and Ch6 (F6-F4), which corresponded to the right PFC [6,48] (Figure 2). The sources, detectors, and space between them were placed in relation to the underlying functional region and the most appropriate Brodmann area according to online atlases [49,50].

## 2.5. Hemodynamic Data Reduction

NIRStar Acquisition Software (NIRx Medical Technologies LLC, 15 Cherry Lane, Glen Head, NY, USA) was used to continuously record the fluctuations in O2Hb and HHb concentrations during an initial 120 s resting baseline and the tasks. The signals from the six channels were collected at a sample rate of 6.25 Hz and then extracted and converted with nirsLAB software (v2014.05; NIRx Medical Technologies LLC, 15 Cherry Lane, Glen Head, NY, USA) based on their wavelengths and positions, producing mmol mm values that corresponded to the variations in the concentration of O2Hb and HHb per channel. Each channel's acquired raw O2Hb and HHb data were digitally band-pass filtered at 0.01–0.3 Hz [41,43].
<figure><img src="image_124.png" /><figcaption>**Figure 2.** Head rendering with the fNIRS montage layout. Emitters and detectors are indicated in red and blue, respectively.</figcaption></figure>

Both during the experimental phase and the signal analysis, raw time series were visually reviewed subject by subject to identify noisy channels caused by motion artifacts or amplitude variations (criterion for rejection: amplitude of hemoglobin (Hb) signal above or below ±5 SD; visual inspection). Artifacts caused 3% of the data to be removed. Channels with poor optical coupling and lack of heartbeat oscillations at 1 Hz were disregarded during this visual evaluation. Additionally, a linear-phase FIR filter on respiration was applied (0.3 Hz), which produced a symmetric impulse response [51,52].

Both during the experimental phase and the signal analysis, raw time series were visually inspected subject by subject to identify noisy channels caused by motion artifacts or amplitude variations. Here, 3% of the data were removed for artifacts.

The mean concentration of each channel for the tasks was determined following biosignal analysis. Based on the mean concentrations in the time series for each channel and subject, the effect size in each condition was calculated.

The effect sizes (Cohen's d) were calculated by dividing the difference between the baseline and trial means by the baseline standard deviation (SD) such that $D = (m1 - m2)/s$, where $m1$ and $m2$ are the mean concentration levels for the baseline and trial, respectively, and $s$ is the baseline SD. The effect sizes from the six channels were averaged in order to increase the signal-to-noise ratio. While raw fNIRS data were initially relative values that could not be directly averaged across people or channels, and the normalized effect sizes were averaged regardless of the unit, since the effect size is unaffected by the differential pathlength factor (DPF).

For the statistical analysis of the fNIRS data, the channels were grouped to compose the lateralization factor for the left (Ch1, Ch2, and Ch3) and right (Ch4, Ch5, and Ch6) hemispheres, corresponding to the left and right PFCs.

## 2.6. Coherence Value Analysis

A first analysis was conducted to obtain the inter-brain coherence by computing the partial correlation coefficient $\Pi_{ij}$ for each dyad, applied to each channel and to the lateralization factor for both the O2Hb and the HHb. These indices were obtained by normalizing the covariance matrix's inverse:

$$ \Gamma = \Sigma - 1 $$

$$ \Gamma = (\Gamma_{ij}) = \Sigma - 1 \text{ inverse of the covariance matrix} $$
This analysis permits evaluating the relationship between two signals (i, j) independent of one another [53], and it was previously applied often in earlier fNIRS hyperscanning research [22,54].

## 2.7. Statistical Analysis

A second step of analysis was applied to the coherence values, considered as dependent measures of a repeated measures ANOVA with independent within the factors of condition (two: focus on breathing or no focus on breathing) × task (two: motor or cognitive) × lateralization (two: left or right) × frame (two: not social or social). For this analysis, IBM SPSS Statistics (version 25) was used. For all ANOVA tests, in case of significant effects, pairwise comparisons were conducted to explore the significant interactions between simple effects, and the Bonferroni correction was applied to lessen the possible bias of repeated comparisons. The degrees of freedom for all ANOVA tests were adjusted using the Greenhouse-Geisser epsilon where required. Using partial eta squared (η²) indices, the magnitudes of the statistically significant effects were calculated.

# 3. Results

Two sets of results corresponding to the two analyses performed on the hemodynamic dependent measures will be described below. The first step of analysis included the application of coherence analysis for each dyad. The second step concerned the application of an inferential statistical ANOVA test to the coherence values considered dependent measures.

## 3.1. First Step: Coherence Results

For the first step of analysis, we found the computed coherence values for each fNIRS channel in each experimental condition for both the O2Hb and HHb. A successive coherence analysis was applied to the lateralization factor for the left and right hemispheres, which were calculated as the average of the homologous channels for both the O2Hb and HHb, respectively. However, due to the limited significant values of coherence for the HHb, only the results for the O2Hb were considered and reported. In the graphs below, we have reported for this first step the mean trend of the coherence index for each dyad of participants (Figure 3A–D).

<figure><img src="image_125.png" /><figcaption>**Figure 3.** fNIRS coherence values for O2Hb. Trend of the coherence indices for the motor synchronization task in the focus (A) and no focus conditions (B) and for the cognitive synchronization task in the focus (C) and no focus conditions (D) in each dyad.</figcaption></figure>
## 3.2. Second Step: ANOVA Results

The ANOVAs applied to the inter-brain coherence indices as dependent variables for each dyad revealed significant effects for the D values of the O2Hb hemoglobin. The following paragraphs report the significant results obtained for the ANOVAs.

A first significant main effect was observed for the lateralization (F [1, 15] = 7.09, *p* = 0.01, η² = 0.421), for which higher coherence for the O2Hb values was observed in the left compared with the right hemisphere.

Secondly, an interaction effect was detected for condition × task × lateralization (F [1, 43] = 8.09, *p* = 0.01, η² = 0.498). The pairwise comparison revealed an increase in coherence of the O2Hb values in the focus condition for both tasks (motor and cognitive) in the left compared with the right hemisphere (motor: F [1, 15] = 8.56, *p* = 0.01, η² = 0.442; linguistic; F [1, 15] = 8.11, *p* = 0.01, η² = 0.432).

Then, a third interaction effect was found for condition × task × lateralization × frame (F [1, 82] = 7.89, *p* = 0.01, η² = 0.432). A pairwise comparison revealed an increase in coherence in the left hemisphere when the participants in the focus condition performed the motor synchronization task with a social frame compared with no social frame (F [1, 15] = 8.98, *p* = 0.01, η² = 0.408) (Figure 4A). According to the pairwise comparison, greater coherence was observed in the left hemisphere when the participants in the focus condition performed the cognitive synchronization task with a social frame compared with no social frame (F [1, 15] = 8.04, *p* = 0.01, η² = 0.391) (Figure 4B).

<figure><img src="image_126.png" /><figcaption>**Figure 4.** Mean coherence indices for the cognitive and motor synchronization tasks. All asterisks (*) mark statistically significant differences, with *p* ≤ 0.05.</figcaption></figure>

## 4. Discussion

In the present work, the hemodynamic interpersonal attunement of healthy participants during synchronization tasks with and without a social frame was explored and executed in two different experimental conditions. In particular, these tasks were executed in the presence or absence of explicit focus on breathing as interoceptive (IA) manipulation conditions. A social neuroscientific hyperscanning approach by fNIRS was applied to allow the recording of participants' hemodynamic responses related to the motor and cognitive synchronization tasks presented with or without a social frame. For the fNIRS signal, an analysis of the coherence indices and a comparison of the fNIRS coherence's strength for the conditions, tasks, and homologous PFC channels were performed.

The coherence analysis was computed to check the inter-subject neural hemodynamic coherence between the dyads for the left and right hemispheres considered in each experimental condition.

First, it was chosen to report the main significance results in graphs to describe the trend of synchronization within the dyads. In former fNIRS hyperscanning experiments, coherence indices were used to examine the synchronization of the hemodynamic signal during cooperative and competitive joint activities [22, 26, 41, 42], social exchanges [43], and gesture observation and reproduction [44]. At the methodological level, the results of the present study proved that inter-subject hemodynamic coherence indices can be exploited as a reliable indicator of dyads' PFC tuning in this context, specifically when there is a focus on breathing and the social frame is manipulated.
Moreover, some relevant and significant outcomes were detected. In general, higher coherence for the O2Hb values was observed in the left compared with the right hemisphere when the participants were executing both synchronization tasks (motor and cognitive). This effect was especially observed during the condition of focusing on breathing (i.e., the interoceptive condition). Additionally, greater coherence for the O2Hb values was observed in the left hemisphere when the participants in the focus condition performed both synchronization tasks with a social frame compared with when they executed the same tasks without an explicit social frame.

The higher inter-brain coherence in the left compared with the right PFC observed for both synchronization tasks in the interoceptive condition can be explained by taking into consideration the role of the left PFC in synchronization. In fact, former studies reported a right hemisphere lateralization effect connected to interoceptive focus [12,23,24]. However, they did not apply IA to social synchronization tasks. Therefore, a possible explanation for this apparently counterintuitive result may consider the left PFC's role in relation to the positive impact of synchronization.

Past hyperscanning research on cooperation exploited fNIRS to assess subjects' brain activity during a cooperative dual task [41] which adopted the same computation for neural coupling. The fNIRS results revealed increased brain PFC activity and higher synchronization over the left PFC after feedback, in line with the perception of a positive dynamic of social synchronization and compatible with positive emotions and approach-related motivations. This finding can be explained by considering the left PFC's "emotional relevance" in comparison with the right PFC. As was previously noted, approach motivation, the capacity to control negative emotions, and general well-being are all correlated with the frontal cortical asymmetry favoring the left hemisphere [55-58]. According to converging data from evolutionary psychology and developmental research, cooperation is psychologically gratifying for the individual, and as a result, during cooperative conditions, frontal reward-processing regions may be more active than other cortical areas.

Therefore, it may be plausible that the synchronization per se, together with IA, may promote the left PFC's neural synchrony in dyads. Nonetheless, the effect of interoception on PFC lateralization, particularly during social processes, needs to be further explored in future studies.

Furthermore, this PFC left-lateralized synchrony was not only associated with the interoceptive condition but also with the social framework (compared with no social framework) for both synchronization tasks (motor and cognitive).

The PFC, together with the TPJ, has been previously considered a neuroanatomical region belonging to the so-called "mutual attention system", whose main characteristic is the mutual and synchronized activation aspect [40]. In particular, inter-brain synchrony in the left PFC was also found in several previous hyperscanning studies during simultaneous singing [33], face-to-face conversations [34], shared attention for the achievement of mutual goals [36], and also during a real-person, joint-tapping hyperscanning experiment in a bidirectional rather than unidirectional condition, indicating the social effect of a more cooperative condition [39] or suggesting a potential neural mechanism for selective tuning in to a target speaker while tuning out others [40]. Therefore, the present study adds to the existing body of knowledge evidence that even basic exercises of synchronization, which are also explicitly socially framed, may increase an individual's inter-brain coherence in the left PFC.

Interestingly, different from what was hypothesized in our second hypothesis, no differences were observed between the motor and cognitive synchronization tasks, since the increased effect of coherence in the left PFC was detected for both tasks in the condition of focusing on breathing when the tasks were socially framed compared with not being socially framed. This result suggests that the effect of the synchronization and the social frame affects both the motor [14] and cognitive synchronization.

Such evidence is also crucial from a neuroanatomical perspective, since the involvement of the left prefrontal areas has been associated with interoception during social synchronization.
While this study demonstrated the relevance of coherence indices as a marker of the focus on breathing as an interoceptive condition and of social frame manipulation in synchronization tasks, it also presents some limitations. In fact, given the focus of this study on the PFC, fNIRS channels were applied on the frontal locations only without covering the whole scalp, which would have included somatosensory cortical regions and other relevant structures, such as the TPJ. Future research should focus on these structures' roles in interoceptive processing [7] and social synchronization [39], respectively.

Furthermore, this basic research adopted some simplistic synchronization tasks that just required finger tapping or alternate syllable pronunciation. Future studies could (1) utilize more ecological and complex joint activities, such as dancing, playing an instrument, or communicating in a live interactive speech, as previously performed in prior hyper-scanning studies, and (2) integrate multi-level measurements of the empathic resonance mechanisms, such as facial behavior analysis (to explore participants' facial feedback) [59] and self-reported measures, such as a scale to measure empathic behavior (e.g., the Balanced Emotional Empathy Scale) and individual differences in approach or avoidance motivational tendencies (e.g., the Behavioral Inhibition and Activation Scale) [60], to gather the complexity of this phenomenon. Moreover, an impfigureortant effect may be suggested for the gender factor in relation to the social synchronization. Indeed, as shown by Cheng, Li, and Hu (2015), “the presence of a homogender of a heterogender dyad” may modulate interbrain activity, and future enquiries could be formulated about the role of gender on the joint cognitive and brain strategies.

Finally, to increase the ecological validity of the findings, participants could receive less artificial and more contextually related instructions for the social frame.

**5. Conclusions**

Overall, this hyperscanning research shows how manipulating IA, which is attained by concentrating on breathing, and the social frame in a dyadic condition enhances the emergence of hemodynamic indicators of interpersonal tuning in the left PFC during basic synchronization tasks. The increase in coherence in the left PFC can be considered a neuroanatomic marker of the combination of IA and social synchronization. This evidence opens the way to also considering in Grossberg's pART model the contribution of interoception on high-level cognitive and emotional functions and on resonance mechanisms.

To the best of our knowledge, this is the first time that the effect of IA and the social frame on inter-brain neural synchrony has been explored during an interactive social dynamic involving two individuals. This experiment contributes to increasing the knowledge relating to those studies within the category of “social interoception”, which intends to investigate the impact of interoception on social dynamics and processes.

**Author Contributions:** Conceptualization, M.B. and L.A.; methodology, M.B. and L.A.; software, L.A.; validation, M.B. formal analysis, M.B.; investigation, M.B.; resources, M.B.; data curation, M.B. and L.A.; writing—original draft preparation, L.A.; writing—review and editing, M.B. and L.A.; visualization, M.B.; supervision, M.B.; project administration, M.B.; funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** The datasets generated and analyzed for this study are available from the corresponding author upon reasonable request.

**Acknowledgments:** The authors acknowledge Carlotta Acconito and Katia Rovelli for their assistance with data curation.

**Conflicts of Interest:** The authors declare no conflict of interest.
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Article

# "We Will Let You Know": An Assessment of Digital vs. Face-to-Face Job Interviews via EEG Connectivity Analysis

Michela Balconi<sup>1,2</sup>, Davide Crivelli<sup>1,2</sup> and Federico Cassioli<sup>1,2,*</sup>

**Abstract:** We focused on job interviews as critical examples of complex social interaction in organizational contexts. We aimed at investigating the effect of face-to-face vs. computer-mediated interaction, of role (candidate, recruiter), and of the interview phase (introductory, attitudinal, technical, conclusive) on intra-brain and inter-brain connectivity measures and autonomic synchronization. Twenty expert recruiters and potential candidates took part in a hyperscanning investigation. Namely, electroencephalography (delta, theta, alpha, beta bands) and autonomic (skin-conductance, heart-rate) data were collected in candidate-recruiter dyads during a simulated job interview and then concurrently analyzed. Analyses highlighted a link between face-to-face condition and greater intra-/inter-brain connectivity indices in delta and theta bands. Furthermore, intra-brain and inter-brain connectivity measures were higher for delta and theta bands in the final interview phases compared to the first ones. Consistently, autonomic synchronization was higher during the final interview phases, specifically in the face-to-face condition. Finally, recruiters showed higher intra-brain connectivity in the delta range over frontal and temporoparietal areas, while candidates showed higher intra-brain connectivity in the theta range over frontal areas. Findings highlight the value of hyperscanning investigations in exploring social attunement in professional contexts and hint at their potential to foster neuroscience-informed practices in human resource management processes.

**Keywords:** job interview; remote vs. face-to-face; EEG hyperscanning; brain connectivity; autonomic synchronization

## 1. Introduction

Social neuroscience investigates the neural mechanisms involved in the functioning of interpersonal behavior. The social brain comprehends the neurophysiological basis of interpersonal behavior and social cognition [1], mediated by certain neural networks that connect the limbic regions to the prefrontal cortex (PFC). For instance, the dorsal (DLPFC) and ventral portion of the lateral PFC support the components of social interaction and cooperative behavior [2–4]. These networks allow effective interpersonal interchanges.

In an organization, the social dimension appears critical for value and innovation developments, and in this light, cognitive neuroscience elicits the comprehension of the workers’ cognitive and affective systems. Ongoing neuroscientific research refers to leadership, motivation, job assessment, and interviewing [5,6]. Its framework is referred to as neuromanagement, defined as that interdisciplinary approach that explores internal mechanisms of management by applying knowledge derived from neuroscience and cognitive sciences [7].

In this work, we focused on the job interview for the following reasons. First, psychologically, it represents the main social interaction between the organization and the candidate. Then, strategically, it was shown to be a good predictor of employability [8]

**Citation:** Balconi, M.; Crivelli, D.; Cassioli, F. "We Will Let You Know": An Assessment of Digital vs. Face-to-Face Job Interviews via EEG Connectivity Analysis. *Information* **2022**, *13*, 312. https://doi.org/10.3390/info13070312

Academic Editors: Khalid Sayood, Luiz Pessoa and Birgitta Dresp-Langley

Received: 6 May 2022

Accepted: 18 June 2022

Published: 27 June 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_127.png" /></figure>

Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

<footnote n="1">¹ International Research Center for Cognitive Applied Neuroscience (IrcCAN), Università Cattolica del Sacro Cuore, 20123 Milano, Italy; michela.balconi@unicatt.it (M.B.); davide.crivelli@unicatt.it (D.C.)</footnote>

<footnote n="2">² Research Unit in Affective and Social Neuroscience, Department of Psychology, Università Cattolica del Sacro Cuore, 20123 Milano, Italy</footnote>

<footnote>* Correspondence: federico.cassioli@unicatt.it</footnote>
and a value-maximizer, as it limits cases of bad hiring and loss of virtuous workers. Lastly, according to the authors, available studies have never properly considered its social complexity. A job interview represents a subcomponent of the selection process, where a candidate is invited to meet and interact with a company representative, who aims at selecting the best-fitting candidate for a position [9].

Dimensions such as anxiety, self-efficacy, its predictive validity, performance outcome, and the impact of age, accent, age, and gender, were previously investigated [10,11]. Moreover, it was shown that anchoring-and-adjustments heuristics and associated motivational mechanisms drive bias against stigmatized people in interview decisions [12]. Stigmatized applicants receive lower interview judgments and have a lower probability to be selected for the job.

Through methodological lenses, research has adopted psychometric methods, via questionnaires, qualitative-verbal methods (i.e., focus groups, in-depth interviews), or behavioral approaches [12]. These methods are extremely valuable for investigating associations of meaning, concepts, events, or complex attitudinal constructs on a general scale, but they present weaknesses if covert cognitive and affective processes are the research object. In fact, they are mediated by the person's language, cognitive bias, and self-awareness. Social neuroscience's interest in the job interview appears evident as it represents a pivotal social moment, which can be easily stressful and demanding [5].

Furthermore, due to COVID-19, the employment of videoconferencing platforms proliferated. Therefore, the rise of digital forms is considered in this work. Historically telephone interviews were the first step toward technologized job interviews. Via the distribution of laptops, equipped with cameras, and a broadband internet connection, job interviews were firstly carried out in remote settings. In this sense, COVID-19 forced companies to switch to digitalized forms. Nowadays, many companies also include the use of machine learning approaches in the selection process, in particular for initial curricula screening purposes.

As many studies related to human-computer interaction have shown, a new medium generally determines different social responses, which are embodied at a neurophysiological and cognitive level. Communication platforms shape social relationships [13]. Unfortunately, existing evidence is ambiguous [14]. Computer-mediated interaction could act as a social connector or a separator. Remote communication has been linked to diminished levels of empathy [15], and factors such as age and technology habits are known to be significant mediators [14]. Conversely, face-to-face interaction is sometimes perceived as challenging and stressful [16].

Other approaches have focused on the behavioral dimension. The detection of non-verbal behaviors of the interviewee (gaze, facial expression, and posture) was investigated [17] aiming at signaling possible improvements to the candidate for training purposes [18]. Another study on job interviews found different gaze patterns between recruiters and candidates [19], in particular, the interviewer made more frequent and longer gaze contacts compared to the interviewee. Furthermore, automated video interview (AVI) analysis, via machine learning, using verbal, paraverbal, and nonverbal behavior from audio-video data was employed to assess personality traits [20]. Moreover, the positive effect of combined cognitive reappraisal (CCR) on stress levels, in terms of heart rate variability was considered, with the CCR group presenting less physiological stress, a speech better perceived by others, and more affiliative smile and hand gestures [21]. Besides these findings, it should be pointed out that the metrics used here are indirect proxies for cognitive and affective processes and only a few studies consider both interacting agents.

The complexity that arises in the social dimension can be efficiently approached only if all the agents are simultaneously considered [22]. For these reasons, among the available methods, hyperscanning is a proposed paradigm that allows the exploration of interpersonal brain mechanisms, generated by social interaction, via the consideration of inter-brain—besides intra-brain—connectivity [23]. In fact, hyperscanning consists of the simultaneous recording of cerebral activity of two or more subjects involved in a task.
EEG hyperscanning extends the research application in the domain of the social brain. Examples of applications were carried out and an electrophysiological synchronization occurs between the agents in terms of inter-brain connectivity [24] and intra-brain connectivity. Intra-brain connectivity refers to the synchronization and co-occurrence of neural activity between an individual's brain regions and is a proxy of the functional specialization of an individual's brain activity [25]. In contrast, inter-brain connectivity can be understood as functional connectivity between the individual's brain related to inter-personal coupling mechanisms during social exchanges [26]. The effects of increased inter-personal coupling could also result in a certain degree of synchronization of autonomic responses in interacting dyads [27].

Augmented portability, improved devices, and signal processing techniques are major factors that allowed the development of neuroscientific protocols for the organization [28]. Common techniques aim at assessing a stimulus (communication or advertising-related) or a medium (user experience of a product), training an individual (neuroempowering and neuro- and bio-feedback protocols), or assessing specific cognitive or affective dimensions within a group. An employed tool is non-invasive electroencephalography (EEG), which has been applied to investigate workers' mental workload [29], risk management [30], workers' trust [31], and emotion detection [32]. The power density data is extracted via Fourier's transformation [33] and then considered within specific spectral boundaries. The most commonly signal subcomponents are alpha (8-13 Hz), beta (13.5-30 Hz), delta (0.5-3.5 Hz), and theta (4-7.5 Hz). Each frequency wave is associated with a functional significance, which also depends on the activated cortical region where the pattern is detected. Concerning low-frequency waves, theta is considered correlated to long-term memory and emotional processing [34], and delta is linked to declarative, explicit memories, and other affective states [35]. Instead, alpha and beta are associated with attentional and conscious processing towards specific stimuli, or a general environment e.g., [36].

Together with central electrophysiological activity, peripheral autonomic parameters have been employed to determine the positive and negative emotional activation, the arousal, and the stress response in individuals and therefore they proved to be valuable in the assessment of cognitive-affective dimensions in social interactions [37]. Electrodermal activity (EDA) corresponds to the resistance and electrodermal potential that provoke alteration in the electrical characteristics of the skin and reflects a person's autonomic responses to internal and external stimuli. EDA is composed of skin conductance level (SCL) and skin conductance response (SCR). While the latter reflects short-lasting fluctuations, associated with attentional processing e.g., [38] SCL corresponds to the tonic activity of EDA and is linked to workload and arousal [39].

Given the methodological issues we underlined, and the known synergetic interaction between brain, body, and behaviors, composed of complex feedback and feedforward processes, we advocate and propose an integrated multi-method approach to study social dynamics during a job interview, adopting a dual hyperscanning paradigm [40]. Thus, central and peripheral parameters were applied to a qualitative evaluation of the job interview session to address set questions, where role (recruiter, candidate), setting (face-to-face vs. remote), and job interview phases (introductory, attitudinal, technical, and conclusive) are together considered.

Therefore, the following research questions were set. Is there a difference in intrapersonal and interpersonal connectivity levels depending on the face-to-face vs. computer-mediated setting of the job interview? Is electrophysiological connectivity in the dyads detected during a job interview? Does the connectivity emerge in specific interview phases?

Electrophysiological and autonomic parameters are simultaneously gathered during the task, in both participants. We hypothesized to observe specific patterns in the EEG and autonomic correlates, based on the considered factors. First, we expected to observe higher intra-brain connectivity in those job interview phases, which presented particular cognitive and emotional relevance such as the attitudinal and technical phases, where soft and hard skills are tested on the candidate. Conversely, regarding inter-brain connectivity,
we expected to detect significantly increased activity in the last phases of the job interview
process, due to a deeper social understanding within the dyad. Furthermore, we conjec-
tured that the face-to-face condition might be an eliciting factor for both these predicted
patterns we just introduced. Ultimately, as for the synchronization of autonomic indices
(electrodermal activity and heart rate), we believed that it could mirror the trend expected
for central activity, thus increasing during the final phases of the interview, resulting in it
being higher in the face-to-face condition.

**2. Method**

2.1. *Participants*

Participants joined the experiment after signing the written informed consent. The research was carried out following the principles of the Helsinki Declaration and was further approved by the Ethical Committee, where the work was designed. The populations of interest were, on one side, human resource professionals, on the other, potential candidates who were actively seeking a job. A total of 20 subjects (n = 20, M<sub>age</sub> = 27.3, SD = 9.17), 10 recruiters and 10 candidates, were successfully recruited. All subjects were right-handed, presenting normal/corrected-to-normal visual acuity. For all participants, the following exclusion criteria were considered: (i) history of neurology or psychiatry disorders; (ii) being involved in therapies with psychoactive drugs; (iii) presenting clinically relevant distress or burnout history.

Participants were then randomly divided into dyads while considering their role, and
then randomly assigned to the condition (face-to-face vs. remote). For ethical reasons,
the candidates were not real contenders, and have not been involved in other previous
job interviews for this position. The following ad hoc inclusion criteria were considered:
(i) being older than 18 years old; (ii) proficiency in online communication technologies;
(iii) currently looking for a similar/equal job position in the experiment. For human re-
source professionals, the following ad hoc inclusion criteria were also considered: (a) being
employed; (b) being regularly involved in the recruitment process and personally carrying
out job interviews; (c) five or more years of professional experience in job interviewing.

## 2.2. *Procedure*

For the data acquisition, a dual-EEGs and autonomic activity hyperscanning paradigm was designed. In this work, simulated job interviews were carried out by participants. Every subject signed the written informed consent and was randomly assigned to one of the two experimental conditions: remote vs. face-to-face. Every dyad, composed of one recruiter and one candidate, underwent one interview. Every interview presented four different phases. Participants, one recruiter, and one candidate were introduced in a laboratory room by a researcher.

In the remote condition, two independent rooms were used. In one condition a face-to-
face job interview was carried out, while in the other condition the task was conducted via
the video teleconferencing software program *Microsoft Teams*, through personal computers.

A member of the research team previously verified that every subject was familiar with the software. EEG and autonomic system devices were installed on both the subjects and a 2-min resting-state baseline was recorded. Data were simultaneously gathered from both agents. The research team previously briefed the recruiters regarding the conduction of the job interview to limit the impact of the interviewing style and maximize the standardization. All candidates were motivated to participate in the research protocol, as it represented a learning occasion for their professional future.

In line with the interviewers' experience, four phases were established, each one with
an approximate duration of ~5 min. The interview lasted about 25 min, with the following
phases: (i) introductory; (ii) attitudinal; (iii) technical; and (iv) conclusive. In the *introductory*
phase, both actors shortly described themselves. The candidate's curriculum is discussed.
Recruiters presented the company's profile, the aim of the process, and the job offer. In
the *attitudinal* phase, candidates were questioned regarding their motivation and attitude.
A description of their soft skills was discussed. In the technical stage, recruiters investigated the candidate's hard skills, discussing how they would fit the job position and the overall company needs. Finally, in the conclusive phase, feedback was furnished, eliciting strengths and weaknesses. Once the job interview was concluded, tools were detached. A debriefing phase was conducted to assure that participants understood the research aim. Job interview phases are reported in Figure 1.

<figure><img src="image_128.png" /><figcaption>**Figure 1. The four phases of the job interview.** The four phases carried out during the experiment task by the dyads: introductory, attitudinal, technical, and conclusive.</figcaption></figure>

To maximize the relevancy of the social interactions we checked each session to highlight not-pertinent time intervals, using audio-visual materials.

## 2.3. *Signal Recording and Processing*

### 2.3.1. EEG Signal

Recruiters' and candidates' EEG activity was recorded, according to the hyperscanning specifics, via two lean 15-channels EEG systems. Electrodes were placed in correspondence to F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P3, Pz, P4, O1, and O2 electrode sites (SI10) [41], using Ag/AgCl sensors with physical reference in the two earlobes. vEOG was monitored for subsequent signal processing. Electrodes impedance was kept below 5 kΩ. Data were sampled at 1000 Hz, with a 0.01–200 Hz bandpass input filter and a 50 Hz notch filter. During offline processing, a 0.5–50 Hz bandpass filter was applied to the raw data. Average reference was computed to limit the effect of situational biases on recorded data and improve the comparability between EEGs. Furthermore, to lower ocular movements and blinks noise, a regression-based ocular correction algorithm was applied. Data were then segmented according to the internal structure of the assessment interview (four phases) and manually screened for residual ocular and movement artifacts. Finally, power spectra were computed, via Fast Fourier Transform algorithm (resolution: 0.5 Hz; window length: 2 s) to extract power density data for the standard EEG bands, defined as follows: delta (0.5–3.5 Hz), theta (4–7.5 Hz), alpha (8–13 Hz), and beta (13.5–30 Hz). The average EEG power profile was computed and extracted for both resting and for each of the four phases (introductory, attitudinal, technical, and conclusive) of the interview plot, normalized based on the time lengths, and considering four main Regions of Interest (ROI): frontal (F7, F3, F4, F8), central (C3, C4), temporoparietal (T7, T8, P3, P4), and occipital (O1, O2).

### 2.3.2. Autonomic Activity

The autonomic activity was recorded in the dyads during the task through multi-use units. Recording sensors were positioned on subjects in correspondence with the distal phalanx of the second finger, on the non-dominant hand. The sensor monitored electrodermal and cardiovascular activity, in terms of skin conductance level (SCL), skin conductance response (SCR), and heart rate (HR). An accommodation phase took place before the recording session began. Data were sampled at 40 Hz. Moreover, to maximize accuracy, artifact rejection and data filtering were offline applied when needed. Heart Rate (HR, measured as beats per minute) was extracted via photoplethysmography, detecting blood volume changes in the microvascular tissues. Phasic SCR was computed using tonic EDA activity, via moving average. Mean HR, mean SCL, and SCR count were computed
for each of the four phases of the job interview plot (introductory, attitudinal, technical, and conclusive).

## 2.4. Data Analysis

Here we described the data analysis process for the considered variables. For all ANOVA models, degrees of freedom were corrected by Greenhouse-Geisser epsilon when needed. No outliers were observed in that sample or subgroup. Post-hoc analysis (contrast analysis for ANOVA, with Bonferroni corrections for multiple comparisons) was successfully applied. The size of statistically significant effects has been estimated via partial eta squared (η²) indices.

### 2.4.1. EEG

In the first part of the analysis, we calculated the intra-brain and inter-brain connectivity index for every dependent variable (delta, theta, beta, and alpha power). The phase synchronization approach was not implemented in the preliminary analysis as it does not allow the further computation of correlation coefficients. Specifically, for the calculation of the functional intra- and inter-brain connectivity indices, the partial correlation coefficient $\Pi_{ij}$ was computed, by normalizing the inverse of the covariance matrix $\Gamma = \Sigma^{-1}$:

$$ \Pi_{ij} = \vdash (\Box \{-\Gamma_{ij}\} \{\sqrt{\vdash (\Gamma_{ii}\Gamma_{jj} \vdash)}\} \vdash) \text{ Partial correlation matrix} \\ \Gamma = (\Gamma_{ij}) = \Sigma - 1 \text{ Inverse of the covariance matrix} $$

This procedure [42] can quantify the relationship between two independent signals (i, j), for example within a dyad, known as inter-brain connectivity, and the neural connectivity between an individual's brain regions, defined as intra-brain connectivity.

For intra-brain connectivity, we applied a set of four ANOVA, one per every frequency band intra-brain connectivity index, considering the following factors: Role (2: recruiter, candidate) and Condition (2: face-to-face, remote) as between factors, and Interview phase (4: introductory, attitudinal, technical, and conclusive) and Region of Interest (ROI; 4: frontal, central, temporoparietal and occipital) as within factors.

Similarly, for inter-brain connectivity, we applied a set of four ANOVA, one per every frequency range inter-brain connectivity index, considering Condition (2: face-to-face, remote) as between factor, and Interview phase (4: introductory, attitudinal, technical, and conclusive) and Region of Interest (ROI; 4: frontal, central, temporoparietal and occipital) as within factors.

### 2.4.2. Autonomic Activity

In the first step of the analysis, we computed the synchronization indices using Pearson correlation coefficients [43] for each autonomic measure (HR, SCL, and SCR), with the same mathematical procedure previously described in the EEG section.

Then a set of three repeated-measures ANOVAs, one per each dependent variable, was run considering Condition (2: face-to-face, remote) as between factor and Interview phase (4: introductory, attitudinal, technical, and conclusive) as within factor.

## 3. Results

### 3.1. EEG

#### 3.1.1. Intra-Brain Connectivity

Delta. Interview was found to be significant ($F[3,57] = 7.09, p \le 0.05, \eta^2 = 0.43$). From post hoc analyses, higher intra-brain connectivity was found in the attitudinal phase compared to introductory phase ($F[1,19] = 7.13, p \le 0.05, \eta^2 = 0.43$), and in the technical phase compared to the introductory phase ($F[1,19] = 8.07, p \le 0.05, \eta^2 = 0.44$). Data is reported in Figure 2a. Moreover, Condition was found significant ($F[1,19] = 9.33, p \le 0.05, \eta^2 = 0.48$), with higher intra-brain connectivity in the face-to-face compared to the remote condition. Data is reported in Figure 2b. Finally, the interaction effect Role*ROI resulted significant ($F[3,54] = 6.78, p \le 0.05, \eta^2 = 0.40$). Post hoc analyses highlighted higher intra-
brain connectivity for recruiters compared to candidates in the frontal region (F[1,19] = 6.70, *p* ≤ 0.05, *η*² = 0.40) and in the temporoparietal region (F[1,19] = 6.78, *p* ≤ 0.05, *η*² = 0.40). Data is reported in Figure 2c.

<figure><img src="image_129.png" /><figcaption>**Figure 2.** Delta intra-brain connectivity results. (a) Bar graph shows differences in Interview. Bars represent ±1 SE. Stars mark statistically significant pairwise comparisons. (b) Bar graph shows differences for Condition. Bars represent ±1 SE. Stars mark statistically significant pairwise comparisons. (c) Bar graph shows differences for Condition*Region of interest (ROI). Bars represent ±1 SE. Stars mark statistically significant pairwise comparisons.</figcaption></figure>

Theta. The condition factor was found significant (F[1,19] = 8.45, *p* ≤ 0.05, *η*² = 0.45), with increased intra-brain connectivity for the face-to-face condition compared to the remote one. Data is reported in Figure 3a. Furthermore, the interaction effect Role*ROI was found to be significant (F[3,54] = 6.70, *p* ≤ 0.05, *η*² = 0.40). Post hoc analyses highlighted higher intra-brain connectivity in the candidates compared to the recruiters (F[1,19] = 6.16, *p* ≤ 0.05, *η*² = 0.38) in the frontal region. Data is reported in Figure 3b. Moreover, the interaction effect Interview*ROI*Role was also found to be significant (F[9,162] = 7.65, *p* ≤ 0.05, *η*² = 0.44). The post hoc analyses highlighted increased intra-brain connectivity in the frontal region for the candidates during the technical phase compared to the introductory (F[1,9] = 6.78, *p* ≤ 0.05, *η*² = 0.40) and attitudinal (F[1,9] = 6.98, *p* ≤ 0.05, *η*² = 0.40) phases, as well as during the conclusive phase compared to the introductory (F[1,9] = 9.14, *p* ≤ 0.05, *η*² = 0.46) and attitudinal (F[1,9] = 7.74, *p* ≤ 0.05, *η*² = 0.40) phases. Data is reported in Figure 3c.

No other significant results for intra-brain connectivity were detected.

### 3.1.2. Inter-Brain Connectivity

Delta. The factor Interview resulted in being significant (F[3,27] = 9.32, *p* ≤ 0.05, *η*² = 0.49). Post hoc analyses highlighted higher inter-brain connectivity for the conclusive phase compared to the introductory phase (F[1,9] = 10.98, *p* ≤ 0.05, *η*² = 0.46). Data is reported in Figure 4a. Furthermore, Condition resulted in being significant (F[1,9] = 9.32, *p* ≤ 0.05, *η*² = 0.48), with higher inter-brain connectivity for the face-to-face condition compared to the remote condition. Data is reported in Figure 4b.
<figure><img src="image_130.png" /><figcaption>**Figure 3.** Theta intra-brain connectivity results (a) Bar graph shows differences in Condition (ROI). Bars represent ±1 SE. Stars mark statistically significant pairwise comparisons. (b) Bar graph shows differences considering Condition*ROI. Bars represent ±1 SE. Stars mark statistically significant pairwise comparisons. (c) Bar graph shows differences considering the interaction Interview*ROI*Role. Here are reported only candidate's data where significant differences were detected. Bars represent ±1 SE. Stars mark statistically significant pairwise comparisons.</figcaption></figure>

<figure><img src="image_131.png" /><figcaption>**Figure 4.** Delta inter-brain connectivity results. (a) Bar graph shows differences considering the factor Interview. Bars represent ±1 SE. Stars mark statistically significant pairwise comparisons. (b) Bar graph shows differences considering the Condition factor. Bars represent ±1 SE. Stars mark statistically significant pairwise comparisons.</figcaption></figure>

Theta. The factor Condition was found to be significant ($F[1,9] = 8.80, p \le 0.05, \eta^2 = 0.45$), with increased inter-brain connectivity in the face-to-face condition compared to the remote setting. Data is reported in Figure 5a.

<figure><img src="image_132.png" /><figcaption>**Figure 5.** Theta inter-brain connectivity results. (a) Bar graph shows differences considering Condition. Bars represent ±1 SE. Stars mark statistically significant pairwise comparisons. (b) Bar graph shows differences considering Interview*ROI. Bars represent ±1 SE. Stars mark statistically significant pairwise comparisons.</figcaption></figure>
The interaction effect Interview\*ROI was also found to be significant ($F[9,81] = 6.78, p \le 0.05, \eta^2 = 0.40$). Post hoc comparisons detected significant differences with increased inter-brain connectivity in the frontal area for technical and conclusion phases compared to introductory (respectively: $F[1,9] = 6.04, p \le 0.05, \eta^2 = 0.37$; and $F[1,9] = 6.11, p \le 0.05, \eta^2 = 0.36$) and attitudinal (respectively: $F[1,9] = 7.13, p \le 0.05, \eta^2 = 0.41$; and $F[1,9] = 6.93, p \le 0.05, \eta^2 = 0.37$) phases. Data is reported in Figure 5b.

No other significant results for inter-brain connectivity were detected from the statistical analysis.

## 3.2. Autonomic Data

HR. The interaction effect Interview\*Condition was found to be significant ($F[3,27] = 6.16, p \le 0.05, \eta^2 = 0.39$), revealing higher synchronization in the technical and conclusive phases compared to introductory (respectively: $F[1,9] = 6.21, p \le 0.05, \eta^2 = 0.40$; and $F[1,19] = 6.78, p \le 0.05, \eta^2 = 0.40$) and attitudinal (respectively: $F[1,9] = 7.12, p \le 0.05, \eta^2 = 0.41$; and $F[1,9] = 8.90, p \le 0.05, \eta^2 = 0.41$) phases in the face-to-face condition. Data is reported in Figure 6a.

<figure><img src="image_133.png" /><figcaption>**Figure 6.** Heart rate (HR) and skin conductance level (SCL) significant results. (a) Bar graph shows the difference in HR considering the Condition\*Interview interaction. Bars represent ±1 SE. Stars mark statistically significant pairwise comparisons. (b) Bar graph shows the difference in SCL considering the Condition\*Interview interaction. Bars represent ±1 SE. Stars mark statistically significant pairwise comparisons.</figcaption></figure>

SCL. The interaction effect Interview\*Condition was found to be significant ($F[3,27] = 8.23, p \le 0.05, \eta^2 = 0.42$). As revealed by post hoc analyses, synchronization was generally higher in the technical and conclusive phases compared to the introductory (respectively: $F[1,9] = 8.90, p \le 0.05, \eta^2 = 0.44$; and $F[1,9] = 8.98, p \le 0.05, \eta^2 = 0.42$) and attitudinal (respectively: $F[1,9] = 6.09, p \le 0.05, \eta^2 = 0.40$; and $F[1,9] = 9.04, p \le 0.05, \eta^2 = 0.40$) phases in the face-to-face condition. Data is reported in Figure 6b.

# 4. Discussion

In the present study, we focused on the job interview as a critical example of complex social interaction in organizational contexts. The research aimed at investigating the effect of face-to-face vs. remote computer-mediated interaction, of the phase of the job interview (introductory, attitudinal, technical, and conclusive), and of the role (candidate vs. recruiter) on neurophysiological and autonomic markers of inter-personal coupling between candidates and recruiters, as well as on intra-brain connectivity measures. Data analysis highlighted the influence of the investigated factors on both intra- and inter-brain connectivity measures and on autonomic synchronization of interlocutors.

Face-to-face interactions have systematically elicited higher intra-brain connectivity in delta and theta frequency ranges, mirroring a peculiarly coherent activation of neural networks likely involved in supporting the understanding of interlocutors' behavior [44]. It is worth noting that slower components of the EEG spectra are known to be linked to emotional processing and responsiveness, in particular when social reinforcements are present [45]. Greater intra-brain connectivity in lower frequency bands during face-to-face interviews, compared to remote ones, might hint at the greater information-processing demand imposed on social understanding and regulation processes by such a complex
form of interaction. Namely, social exchanges occurring in face-to-face conditions are intrinsically connoted by richer social information deriving from the full set of verbal and non-verbal communication channels available for the dyadic exchange. Conversely, computer-mediated communication, due to setting and technical limitations, allows conveying only a part of non-verbal communication cues (e.g., degraded information on body posture, proxemics, and gestures in case a webcam is used, or loss of relevant information apart from verbal and vocal ones if no webcam is used). The greater presence of social cues in face-to-face interaction, while likely allowing the understanding of the interlocutor's motives and communication and fostering adaptive social regulation, increases processing effort. Furthermore, their multimodal nature plausibly requires the combined involvement of different neural structures.

Intra-brain connectivity in slower EEG bands changed with the progress of the job interview and was modulated by the phases and content of the interaction. In particular, the interview phases with higher delta connectivity indices appear to be the attitudinal and the technical ones. Those two phases were the most cognitively demanding during the interview and plausibly required peak attention from both the candidates—who were trying to provide the best presentation of their skills—and the recruiters—who were involved in checking matches and discrepancies in candidates' skills. The different observed levels of intra-brain connectivity might reflect the mediation of broad cortical networks for cognitive elaboration, with a more relevant impact and greater “echo effect” on the recruiter role e.g., [46]. At the same time, the peculiar pattern of increased intra-brain connectivity in the theta band specifically shown by candidates in the last phases of the interview compared to the first ones might mirror the progressive increase in cohesion among frontal structures involved in cognitive-affective control and behavior regulation in complex situations [47].

Then, higher intra-brain connectivity indices were observed in frontal and temporoparietal areas for the recruiters in the delta frequency range, while candidates showed higher intra-brain connectivity in frontal areas in the theta range. Intra-brain connectivity in the frontal and posterior regions is generally involved in mirroring mechanisms during action execution and the observation of similar actions and may be evidence of an interdependent synchronization at a cognitive level [48]. In fact, increased delta intra-brain connectivity was observed in the temporoparietal area while decoding informative gestures [23] and linked to the involvement of mirroring mechanisms e.g., [49]. Complementarily, delta intra-brain connectivity in frontal areas was associated with social-and affective- gesture encoding [23] and could be linked to the ability to respond to relational and social situations [50]. Building on such evidence, present findings might reflect the greater expertise of senior recruiters in decoding and understanding non-verbal language and socially relevant cues, compared to candidates. Yet, we acknowledge that this interpretation would benefit from additional testing and corroboration via focused investigations. The higher theta connectivity indices shown by candidates over the frontal region, instead, likely mark, as above mentioned, the shared neural effort imposed by the socially-salient situation in terms of cognitive-affective control and self-regulation.

Regarding inter-brain connectivity data, connectivity indices in both theta and delta ranges were higher in the face-to-face than in the remote condition. Such a pattern is consistent with the one emerging from the analysis of intra-brain connectivity measures. Decoding and encoding of affective non-verbal communication were previously linked to modulations of delta and theta frequency waves [51]. The response in slower EEG components might suggest a greater focus of the dyads on social processing and on the regulation of interpersonal interaction and its affective correlates during the face-to-face condition [52]. As above noted, the observed difference in neurophysiological indices of inter-brain coupling between face-to-face and remote computer-mediated interactions might plausibly reflect the intrinsic limitations in properly conveying part of non-verbal communication cues in remote interaction settings. The use of web-based communication platforms, indeed, leads to a greater focus on linguistic content and, among non-verbal communication channels, on non-verbal vocal cues (e.g., prosody), paralinguistic contents,
eye behavior, and facial expressions, while neglecting other unavailable cues. The difference in the amount and in the source of information used to guide interpersonal regulation and sense-making processes during the interviews likely provides different socio-psychological contexts for engagement and interpersonal coupling.

In addition, inter-brain connectivity changed with the progress of the interview, with higher inter-brain connectivity measures in the delta range during the final phase than in the initial phase of social interaction. A similar pattern, though localized in frontal areas, for the theta frequency range was detected. Consistently, we observed greater autonomic synchronization during the final phases of the task, specifically in the face-to-face condition. This gradually increased interpersonal coupling pattern is consistent with the hypothesis that attunement in dyads increases by building on the progressive shared communication experience and fine-tuning processes aimed at optimizing the exchange and communication efficacy. Specifically, activity in the theta frequency range has been related to emotional tuning and encoding/recall processes [53], which might have happened more intensively in the last part of the job interview. Slow EEG activity predicts working memory, navigation, and encoding [54] during the wake. Furthermore, the implicit/explicit exchange of feedback during social interaction enhances emotional elaboration, shapes adaptation, and self-regulation mechanisms, and boosts long-term memory formation, modulating the degree of syntonization between inter-agents. Again, recent perspectives see inter-brain synchronization correlated to the degree of the sense of joint agency in communicative and shared actions e.g., [55]. Previous studies found that brain activities in the frontal region were synchronized in dyads engaged in joint actions [56] and, in particular, that inter-brain theta synchronization in the prefrontal cortex was especially associated with collaborative tasks between individuals that strongly involve executive functions [57].

In addition, being involved in a collaborative or a social communication task proved to elicit greater autonomic synchronization between the involved individuals, with a greater inter-personal correlation between cardiovascular and electrodermal activities.

**5. Conclusions**

This study explored the benefits of applying neuroscience methods in the investigation of human resource management (HRM), with a focus on job interviews. Given their strategic value and their psychological salience for both candidates and company representatives, such a form of social interaction typically elicits remarkable recruitment of cognitive resources and requires high levels of engagement in both inter-agents, which in turn modulate social attunement and interactional dynamics. Present findings highlight the value of hyperscanning investigations as a methodology to explore the quality of social dynamics and attunement even in real-life social exchanges, point out the feasibility of such investigations at the workplace, and hint at their potential to foster the development of neuroscience-informed evidence-based practices in HRM processes.

Indeed, some initial guidelines, in the form of practical implications can be derived from the acquired data. Firstly, job interviewing is a two-way process, but recruiters are more emotionally in control of the situation. Therefore, they should foster an emphatic connection with the candidate. For a reliable assessment, a successful tuning in the first phases of the interaction before proceeding into technical assessment is advisable. Secondly, the setting condition is relevant. Especially for positions that require social skills, available data suggests the employment of a face-to-face interview, in which the richness of social cues allows for a deeper attunement between recruiter and candidate and, therefore, for a more reliable evaluation.

The study is, however, not exempt from limitations. Firstly, the strength of current results would benefit from replication and investigation with bigger samples. Furthermore, interviews were simulated and the use of research tools might have impacted the setting and the individuals' interaction (i.e., observer bias) in terms of ecological validity. Psychometric data (e.g., personality traits) were not gathered in this work. Further research could use
them to confront self-report measures and neurophysiological correlates. In replications studies, other factors could also be employed as covariates to ensure higher internal validity such as the recruiter's experience, interviewing style, and job position. Furthermore, in the future, other potentially relevant factors modulating the quality and efficacy of mediated vs. presence communication could be considered in addition. For instance, individual and socio-demographic factors play a role in acceptance and user experience with technology-mediated communication and should be explored. Moreover, the use of a deep-learning-based job interview solution was recently proposed [58], based on voice and video data. Personality, aptitude, and neurophysiological data could be further fed into the algorithm.

**Author Contributions:** M.B., D.C. and F.C. designed the research paradigm, carried out the data collection and analysis, and wrote the article. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by "D.3.2. Fund (ricerche di particolare interesse per l'Ateneo) 2020" at Università Cattolica del Sacro Cuore.

**Institutional Review Board Statement:** Research was conducted following the principles and guidelines of the Helsinki Declaration and was approved by the local Ethical Committee institution where the work was carried out.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author, [F.C.], upon reasonable request. The data are not publicly available due to privacy reasons.

**Conflicts of Interest:** The authors declare no conflict of interest.

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