# Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional
Contagion Evoked by Stereotype-Based Stressors

Jingyun Huang1, Rachel C. Amey 2, Mengting Liu3, Chad E. Forbes4
1School of Computer Science and Engineering, Sun Yat-sen University,
Guangzhou, China
2Army Research Institute for the Behavioral and Social Sciences, Fort Belvoir,
VA, USA
3School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
4Department of Psychology, Florida Atlantic University, Boca Raton, FL. 33431,
USA
Corresponding author

###### Abstract

This study delves into the intricacies of emotional contagion and its impact
on performance within dyadic interactions. Specifically, it focuses on the
context of stereotype-based stress (SBS) during collaborative problem-solving
tasks among female pairs. Through an exploration of emotional contagion, this
study seeks to unveil its underlying mechanisms and effects. Leveraging EEG-
based hyperscanning technology, we introduced an innovative approach known as
the functional Graph Contrastive Learning (fGCL), which extracts subject-
invariant representations of neural activity patterns from feedback trials.
These representations are further subjected to analysis using the Dynamic
Graph Classification (DGC) model, aimed at dissecting the process of emotional
contagion along three independent temporal stages. The results underscore the
substantial role of emotional contagion in shaping the trajectories of
participants’ performance during collaborative tasks in the presence of SBS
conditions. Overall, our research contributes invaluable insights into the
neural underpinnings of emotional contagion, thereby enriching our
comprehension of the complexities underlying social interactions and emotional
dynamics.

_K_ eywords Emotional Contagion $\cdot$ Graph Contrastive Learning $\cdot$
Stereotype-based stressor $\cdot$ Graph Classification $\cdot$ Graph
Representation Learning

##  1 Introduction

Emotional contagion refers to the sharing of emotional states between
individuals, and it has been observed in both animal and human models that the
infectivity of negative emotions is much greater than that of positive
emotions [1]. Negative emotional contagion has a powerful effect on our
relationships - family, friends, teams, etc. - and can lead, for example, to
depressive behavior in healthy people who live with depressed individuals. It
is urgent to understand the mechanism of emotional contagion, especially
negative emotional contagion. Emotional contagion has long been regarded as
reflecting a mimicry-based process, for which mimicry of emotional expressions
and its consequent feedback function are assumed and can be evoked by higher-
order social processes or by a simple emotion-to-action response as well as
the primary mimicry-based process [2]. At present, the emotional contagion
models mostly adopt behavioral analysis and questionnaires, which are often
affected by subjects’ subjective factors. They have mainly focused on
behavioral experiment such as analysing people’s posts containing emotional
information to extract affective evidence [3], using the Positive And Negative
Affective Schedule (PANAS) scale to measure positive and negative emotions as
a quantitive research [4] and the mathematical simulation model of emotional
contagion in crowd evacuation [5].

Although behavioral analysis and questionaires can provide valuable insights
into emotional contagion, they have limitations in terms of capturing the
neural mechanisms, timing, and subtleties of this phenomenon. To overcome
these limitations, researchers have turned to EEG-based hyperscanning, a
technology that records electroencephalographic (EEG) data from multiple
participants simultaneously. This approach complements traditional behavioral
analysis and questionnaires by providing a more direct and precise real-time
examination of the underlying brain activities associated with emotional
contagion. EEG-based hyperscanning technology has proven effective in
capturing brain states during affective communication. For instance, when an
individual experiences specific emotions like sadness, joy, or fear, their
brain activity may influence the brain activity of others they interact with,
thus bearing implications for emotional contagion [6].

However, the significant inter-subject variability of emotion-related EEG
signals poses a great challenge for cross-individual emotional representation
extraction [7]. In most cases, the accuracy of the intra-subject emotion
classification is higher than that of inter-subject classification with the
same classifier [8, 9]. This limitation in the generalization of emotion
classifiers may be attributed to individual differences in EEG-based emotional
representations, influenced by factors such as personality, dispositional
affect, and genotype [10]. Furthermore, individual variance in patterns of
brain connectivity reveals that the inter-subject contrast plays a significant
role in cognitive analysis [11]. These previous findings suggest that EEG
emotional representation should not be extracted without considering the
individual difference.

In this study, we investigated whether women who experienced social identity
threat could transmit their stress to women who were not under threat, using a
process known as Stereotype-based Stress (SBS) contagion and examined how this
collective stress affected women in a dyadic performance context. As previous
research revealed, SBS contexts typically engender a variety of behavioral and
physiological SBS responses including sustained vACC activation, unique neural
network configurations and enhanced connectivity between regions integral for
emotion (dACC, vACC, and mPFC) and saliency networks (IPL, insula, and STS) ,
this could collectively provide evidence for increased emotional processing
and the awareness of negatively arousing or stressful information [12, 13, 14,
15]. We sought to understand if threatened women can transmit their stress to
otherwise non-threatened partners, does it hurt or benefit the woman directly
under threat, and, to what extent can this come at a cost to their otherwise
non-threatened partners? To this end, we designed an experimental paradigm of
emotional contagion by using discussion and learning scenarios, and try to
understand the physiological mechanism of emotional contagion by using EEG-
based hyperscanning technology combined with a data-driven approach —
functional Graph Contrastive Learning (fGCL), which extracts the subject-
invariant emotional representations while preserving Functional Connectivity
(FC) information, with a downstream analysis to infer and explore its process
from outcomes of emotional contagion. The formulation of fGCL is grounded on
the assumption that the neural activities of the subjects are in a similar
state when they receive the same segment of emotional stimuli (i.e., the
displayed CORRECT or WRONG responses on the screen). Based on this fundamental
idea, we aim to learn subject-invariant representations of EEG signals in the
embedding space underlying similar mental activities. Specifically, fGCL
mainly consists of two components, i.e., the spectral-based graph
convolutional network (spectral GCN) encoder, and a two-layer multi-layer
perceptron (MLP). It maximizes the similarities of the representations in
response to identical emotional stimuli while minimizing the similarities
between signals corresponding to different stimuli. In the downstream
analysis, a classifier — Dynamic Graph Classification (DGC) utilize the
trained encoder extracted graph embeddings as input to identify brain state to
emotional stimuli after emotional contagion within dyads. Since the
representations are extracted based on semantically meaningful settings, they
are expected to be informative and generalizable in the downstream analysis.

The method presents three essential characteristics:

  * •

The presented model can extract EEG signal representations with inter-
individual commonality and remove the individual differences, which more
effectively summarize the internal neural activity pattern.

  * •

A deep learning model that is more effective than traditional statistical
analysis and behavioral analysis methods to investigate the emotional
contagion mechanism

  * •

The graph data structure based analysis is more aligned with functional brain
network structure and thus yield more intuitive and effective results

##  2 Method

###  2.1 Graph Construction and Analysis Procedure

As individual difference exists in inter-subject functional connectivity, a
statistical dependency quantifies the connection strengths between brain
region of interests (ROIs) [16], our aim is to preserve the FC information for
for more effective emotional analysis. To this end, we adopt graphs, which are
naturally suitable for modeling brain topology. In this approach, we project
ROIs onto the nodes of a graph and the weighted edges connect nodes, this
overcomes the limitation of traditional 2D grid-like structure, where models
might fail to explore and exploit the complex FC [17].

Given a dataset $\\{(\mathcal{G}_{i}^{j},y_{i})\\}^{N}_{i=1}$ with N
individuals, where $y_{i}=\\{0,1\\}$ denotes the label of $i$-th graph,
$\mathcal{G}_{i}^{j}=\\{\mathcal{V}_{i}^{j},\mathcal{E}_{i}^{j}\\}$ is the
$j$-th view in augmented views (see section 3.7) where Pearson correlations
for each ROI is calculated as node features
$X_{i}^{j}\in\mathcal{R}^{ROIs\times ROIs}$ where $x_{n}=X[n,:]^{T}$ is the
ROIs (the number of ROI in a graph) dimensional node feature of node
$v_{n}\in\mathcal{V}$ containing FC information and Partial correlations
between ROIs is used as edge features
$\mathcal{H}_{i}^{j}\in\mathcal{R}^{ROIs\times ROIs}$ where
$h_{nn^{\prime}}=\mathcal{H}[n,n^{\prime}]^{T}$ is the edge feature of edge
$e_{nn^{\prime}}\in\mathcal{E}$.

Figure 1: The workflow of our approach. (A) Experiment Setting. Subjects in
one dyad answer mathematical questions while recording their EEG signals
simultaneously. (B) Given a minibatch of graph data representing subjects in
the same dyad, the fGCL encoder is able to extract subject-invariant graph
embeddings. (C) The splitted graph embeddings are then fed into DGC classifier
to produce classification evaluation results at these three stages
respectively. (D) Donwstram analysis. Data are divided into early stage,
middle stage and late stage according to the senario of tasks to perform
further emotional contagion analysis.

As illustrated in Figure 1, the functional Graph Contrastive Learning (fGCL)
encoder learns to extract embedding of each graph as subject-invariant
representations (i.e., maximize the representational similarity of EEG signals
belonging to the similar scenario, and minimize that of others), by leveraging
this encoder, we construct a population graph where a collection of subject-
invariant embeddings of EEG signals as nodes. Given this, the Dynamic Graph
Classification (DGC) classifier is trained to perform classification task on
three stages (early, middle and late). The results can be further utilized for
emotional contagion analysis (see Secction 4.3).

Figure 2: The illustration of the construction of positive pair and negative
pair in graph contrastive learning. In a minibatch, embedding $z_{1}^{A}$
forms a positive pair with $z_{1}^{B}$, other embeddings forms negative pairs
with embedding $z_{1}^{A}$.

####  2.1.1 The functional Graph Contrastive Learning (fGCL) Encoder

The Graph contrastive learning encoder $f$ takes N-paired graphs (minibatch)
$\\{\mathcal{G}_{i}^{A}|i=1,..,N\\}$ and $\\{\mathcal{G}_{j}^{B}|j=1,..,N\\}$
as inputs to generate subject-invariant embeddings $\\{z_{i}^{A}|i=1,..,N\\}$
and $\\{z_{j}^{B}|j=1,..,N\\}$ of graphs for each type of trial (correct or
wrong feedback in our experiment). It adopts spectral graph convolution
network consisting of 2 Chebshev spectral graph convolution layers [18] each
followed by a TopK pooling layer [19] as model backbone to capture
representations and retain important nodes in the iterative aggregation,
global average pooling layer and global mean pooling layer are used to capture
the global information, the final two-layer multi-layer perceptron (MLP) is
used to output the graph embedding. Similar to SimCLR framework [20] with
InfoNCE loss, we adopt the contrastive loss function for the anchor embedding
$z_{i}^{A}$ defined as:

$L(z_{i}^{A})=-log(\frac{exp(sim(z_{i}^{A},z_{i}^{B})/\tau)}{\sum^{N}_{j=1}I_{j\neq
i}exp(sim(z_{i}^{A},z_{j}^{A})/\tau)+\sum^{N}_{j=1}exp(sim(z_{i}^{A},z_{j}^{B})/\tau)})$
(1)

where $I_{j\neq i}=\\{0,1\\}$ is an indicator, which is set to 1 when $j\neq
i$, $\tau$ is a temperature factor to adjust the attractiveness strength.
Overall, this loss function increases the attractiveness of positive pair
($z_{i}^{A}$ and $z_{i}^{B}$) and decrease the attractiveness of negative pair
($z_{i}^{A}$ and others) in the embeddding space. The similarity between two
embeddings is computed by

$sim(z_{i}^{A},z_{j}^{B})=\frac{z_{i}^{A}\cdot z_{j}^{B}}{\lvert\lvert
z_{i}^{A}\rvert\rvert\lvert\lvert z_{j}^{B}\rvert\rvert}$ (2)

Eventually, the accumulated loss of the minibatch is computed by

$L=\sum_{i=1}^{N}L(z_{i}^{A})+\sum_{i=1}^{N}L(z_{i}^{B})$ (3)

The spectral convolution blocks of fGCL comprise of Chebyshev spectral graph
convolutional operator (i.e., ChebConv layer), which is defined by:

$X^{\prime}=\sum_{k=1}^{K}\mathcal{Z}^{(k)}(X)\cdot\mathcal{\theta}^{(k)}$ (4)

where K is the Chebyshev filter size, $\mathcal{Z}^{(k)}(X)$ is computed
recursively by $\mathcal{Z}^{(1)}=X$, $\mathcal{Z}^{(2)}=\tilde{L}\cdot X$,
all the way to
$\mathcal{Z}^{(k)}=2\cdot\tilde{L}\cdot\mathcal{Z}^{(k-1)}-\mathcal{Z}^{(k-2)}$,
$\tilde{L}$ denotes the scaled and normalized Laplacian
$\frac{2L}{\lambda_{max}}-I$, where $\lambda_{max}$ is the largest eigenvalue
of $L$. $\mathcal{\theta}^{(k)}$ are learnable parameters. To prevent
overfitting, each ChebConv layer is followed by a TopK pooling layer, which
downsamples graphs and reduce their dimensionality while retaining the most
relevant nodes (i.e., top K nodes) leading the model to focus on meaningful
information. Afterwards, embeddings of graphs optimized by graph contrastive
learning are input to the downstream classifier to perform graph
classification task.

####  2.1.2 Downstream Analysis on the Outcome of Emotional Contagion

After graph contrastive learning phase, the subject difference has been
eliminated in the embedding space, that is, the representation extracted the
neural activity pattern of the group commonality. To fully utilize these
aligned representations, we adopt Dynamic Graph Classification (DGC) model to
perform brain emotional contagion state analysis, the trained classifier could
better classify the commonality. Initially, the DGC takes a population graph
with isolated nodes as the input, by iteratively projecting node features to a
new feature space, it is able to construct new edges based on the top-K
connection strengths definded as:

$v_{i}^{P\prime}=\sum_{m\in\mathcal{E}_{(i,\cdot)}^{P}}\phi(v_{i}^{P}\,||\,v_{m}^{P}-v_{i}^{P}),\quad\mathcal{E}^{P}=\text{KNN}(\mathcal{V}^{P})_{\text{topK}}$
(5)

The classification tasks are mainly carried out according to the early, middle
and late stages of the problem solving tasks. (1) At the early stage, we
hypothesized that there was no significant difference in the brain activity of
DMT-PST dyads, that is, the emotional contagion effect did not occur, so the
differentiation between DMT and PST was low. (2) At the middle stage, we
assume that the DMT in DMT-PST dyad begins to induce emotional contagion
effect, gradually transferring the negative emotions caused by pressure to
PST, so it can be seen that the differentiation between and will gradually
increase. (3) At the late stage, we assume that the emotional contagion effect
of the DMT-PST dyad has terminated, and the negative emotion of DMT is
transferred to PST. Therefore, there is a significant difference in brain
activity between DMT and PST, so the differentiation between them reaches the
highest. In general, if the distinguishable degree is described with time as
the independent variable, then according to our hypothesis, it can be found
that the distinguishable degree in the pair will show a positive correlation
trend. For the control group PST-PST dyad, we assume that these is no
significant discrepancy between subject exists and thus the distinguishable
degree displays a stable trend.

The algorithm of training the fGCL encoder and the downstream classifier DGC
are summarised in Algotithm 1.

0: Collection of N batched double view data $\mathcal{B}$, the learning rate
$\alpha_{l}$ of fGCL, the learning rate $\alpha_{g}$ of DGC, batch size $N$,
temperature $T$, ratio $r$, kernel size $K$, maximum number of training epochs
of the fGCL $MaxEpoch_{f}$, maximum number of training epochs of the DGC
$MaxEpoch_{d}$;

1: Initialize the parameters of ChebConv and TopKPooling blocks in fGCL with
$K$ and $r$ and set $Epochs_{f}\leftarrow 1$, $Epochs_{d}\leftarrow 1$;

2: while not converge and $Epochs_{f}\leq MaxEpoch_{f}$ do

3: for batch
$B_{b}=\\{\\{(\mathcal{G}_{1}^{1},y_{1}^{1}),(\mathcal{G}_{2}^{1},y_{2}^{1})\\},....,\\{\\{(\mathcal{G}_{1}^{N},y_{1}^{N}),(\mathcal{G}_{2}^{N},y_{2}^{N})\\}\\}\\}\in\mathcal{B}$
do

4: Obtain graph embeddings $\\{z_{i}^{b}|i=1,2,...,2N\\}$ by two ChebConv and
TopKPooling blocks and a two-layer MLP;

5: Calculate graph contrastive loss by (1) - (3);

6: $\theta_{f}\leftarrow\theta_{f}-\alpha_{l}\frac{\partial
L}{\partial\theta_{f}}$

7: $\alpha_{l}\leftarrow F_{l}(\alpha_{l})$ where $F_{l}(\cdot)$ is the multi
step learning rate scheduler;

8: end for

9: $Epochs_{f}\leftarrow Epochs_{f}+1$;

10: end while

11: Construct a population graph $\mathcal{G}_{p}$ on $\mathcal{B}$ with
isolated nodes using trained $fGCL_{\theta_{f}}$;

12: while not converge and $Epochs_{d}\leq MaxEpoch_{d}$ do

13: Dynamically reconstruct population graph $\mathcal{G}_{p}$ using (5);

14: Calculate focal loss by (6) - (7);

15: $\theta_{d}\leftarrow\theta_{d}-\alpha_{g}\frac{\partial
L}{\partial\theta_{d}}$;

16: $Epochs_{d}\leftarrow Epochs_{d}+1$;

17: $\alpha_{g}\leftarrow F_{g}(\alpha_{g})$ where $F_{g}(\cdot)$ is the step
learning rate scheduler;

18: end while

19: Output: fGCL encoder parameters $\theta_{f}$ and DGC classifier parameters
$\theta_{d}$;

Algorithm 1 The Training Algorithm of fGCL+DGC — Learning Graph Embedding &
Classification

##  3 Experiment

###  3.1 Participants

Eighteen white female students who granted written consent participated in the
study for payment. Participants were recruited for the study if they expressed
knowledge of the stereotype that men are better at math than women.
Specifically, all participants responded with a three or lower to the
following question during a pre-study screening: "Regardless of what you
think, what is the stereotype that people have about women’s and men’s math
ability" (1= Men are better than women; 7= Women are better than men).
Participants were paired into nine dyads. One participant was excluded from
EEG analyses due to a lack of valid trials. Thus one (PST) dyad was removed
from all dyadic analyses.

###  3.2 Procedure

Upon arrival to the lab, partners of each dyad met for the first time while
signing consent forms; they were then prepared for EEG recording. Each member
of the dyad was seated in their own soundproof chamber in front of a computer
screen and iPad tablet. Dyads were randomly assigned to either an
SBS/diagnostic math test condition (DMT, n=14 dyads) or a control/problem-
solving task condition (PST, n=15 dyads). In the DMT condition, one
participant (referred to as the “actor”) was exposed to SBS by being told they
would complete tasks that were diagnostic of their math intelligence. They
also completed demographic questions that included a gender query, had pre-
recorded instructions read aloud to them in a male voice through headphones,
and were prepped for EEG recording by at least one male experimenter. In
contrast, the DMT actor’s interaction partner (referred to as the “partner”)
and all participants in the PST condition were informed that they would be
completing tasks that would inform researchers about the different types of
problem-solving techniques they prefer [21, 22], completed demographic
questions that excluded the gender query, had prerecorded instructions read
aloud to them by a female voice through headphones, and were set up by female
experimenters. Thus, DMT partners and both participants in the PST condition
were always placed in stereotype neutral/stress-free contexts. That is, only
the condition of the actor varied across dyad conditions. After an initial set
of instructions, participants were connected via webcam on their iPad tablet
in order to facilitate face-to-face communication during the interactive math
task (described below). Participants were able to see one another through the
duration of the interactive math task. When the interactive math task was
completed, participants answered a series of questionnaires alone (iPads were
removed from the EEG chambers), were debriefed, and were compensated for their
participation with cash or course credit.

###  3.3 Interactive Math Task

Actors and partners simultaneously completed a 100 problem math task
consisting of standard multiplication and division problems (e.g., 10x20=)
that they solved both alone and together. Initial pilot tests confirmed that
the problems selected varied in degree of difficulty (easy, medium, and hard),
ensuring all participants would solve problems correctly and incorrectly, thus
exposing them to both positive and negative performance feedback. Actors and
partners were first presented with the same math problem to solve alone for 16
seconds. During this solo time, participants were given three answer choices
below each problem (A, B, or C), with the answer to each problem randomly
presented in one of the three answer positions. Participants mentally
completed all problems without scratch paper and made all answer selections
via a button box placed in their laps. This solo answer was used for all
performance outcomes in our analyses. After participants entered their solo
answer, they were prompted with a screen that said, “Please discuss the answer
to the problem with your partner.” At this time, participants were given 20
seconds to discuss their answer with their partners. Participants were then
given five seconds to change or confirm their answer to the math problem they
just solved alone. After submitting their final response, participants
received feedback for two seconds that indicated whether their final answer
was correct or incorrect (presented as the words CORRECT or WRONG written in
black on a white screen).

###  3.4 EEG Recording

Consistent with Forbes’s research [23], continuous EEG activity was recorded
from each member of the dyad using an ActiveTwo head cap and the ActiveTwo
Biosemi system (BioSemi, Amsterdam, Netherlands). Recordings were collected
from 64 Ag-AgCl scalp electrodes and from bilateral mastoids. Two electrodes
were placed next to each other 1 cm below the right eye to record eye-blink
responses. A ground electrode was established by BioSemi’s common Mode Sense
active electrode and Driven Right Leg passive electrode. EEG activity was
digitized with ActiView software (BioSemi) and sampled at 2048 Hz. Data was
downsampled post-acquisition and analyzed at 512 Hz. In our data-driven
analysis, we only consider the feedback trials (i.e., CORRECT or WRONG
responses) to better capture elicited emotional patterns when participants are
evoked by stimuli.

###  3.5 Data Preprocessing

For feedback analyses, the EEG signal was epoched and stimulus-locked from
500ms pre-feedback presentation to 2000ms post-feedback presentation. EEG
artifacts were removed via FASTER (Fully Automated Statistical Thresholding
for EEG artifact Rejection) [24], an automated approach to cleaning EEG data
that is based on multiple iterations of independent component and statistical
thresholding analyses. Specifically, raw EEG data was initially filtered
through a band-pass FIR filter between 0.3 and 55 Hz. The EEG channels with
significant unusual variance (absolute z score larger than 3 standard
deviations from the average), mean correlations with other channels, and Hurst
exponents were removed and interpolated from neighboring electrodes using a
spherical spline interpolation function. EEG signals were then epoched, and
baseline corrected. Epochs with significant unusual amplitude range, variance,
and channel deviation were removed. The remaining epochs were then transformed
through ICA. Independent components with significant unusual correlations with
EOG channels, spatial kurtosis, slope in the filter band, Hurst exponent, and
median gradient were subtracted and the EEG signal was reconstructed using the
remaining independent components. Finally, EEG channels within single epochs
with significant unusual variance, median gradient, amplitude range, and
channel deviation were removed and interpolated from neighboring electrodes
within the same epochs.

###  3.6 Source Reconstruction

All a priori sources used in network connectivity analyses were identified and
calculated via forward and inverse models utilized by MNE-python [25, 26]. The
forward model solutions for all source locations located on the cortical sheet
were computed using a 3-layered boundary element model [27], constrained by
the default average template of anatomical MNI MRI. Cortical surfaces
extracted with FreeSurfer were sub-sampled to approximately 10,240 equally
spaced vertices on each hemisphere. The noise covariance matrix for each
individual was estimated from the pre-stimulus EEG recordings after
preprocessing. The forward solution, noise covariance, and source covariance
matrices were used to calculate the dynamic statistical parametric mapping
(dSPM) estimated inverse operator [28, 29]. The inverse computation was done
using a loose orientation constraint (loose = 0.2, depth = 0.8) [30]. dSPM
inverse operators have been reported to help characterize distortions in
cortical and subcortical regions and improve the bias accuracy of neural
generators in deeper structures, e.g., the insula [31] by using depth
weighting and a noise normalization approach. The cortical surface was divided
into 68 anatomical regions (i.e., sources) of interest (ROIs; 34 in each
hemisphere) based on the Desikan–Killiany atlas [32] and signal within a seed
voxel of each region was used to calculate the power within sources and phase
locking (connectivity) between sources. All graph theory calculation
descriptions are located in the supplementary materials.

###  3.7 Implementation Details

After the data preprocessing, 18 female individuals formed 9 pairs consisting
of 7 DMT-PST dyads and 2 PST-PST dyads. We then applied a sliding window
technique to all basic views (the initial multi-ROI EEG time series without
augmentation), denoted as $\mathcal{X}$, which has 768 time points and 68
ROIs. The width and step size of the sliding window were set to 300 and 50,
respectively, resulting in 10 augmented views (68 * 300) for each basic view
(68 * 768). This augmentation increased the size of our dataset to 17,650
response graphs, where subjects received either WRONG or CORRECT feedback.
This augmentation allows the model to learn more general patterns. The dataset
was split into training, testing, and validation sets in a 7:2:1 ratio.

In the graph contrastive learning procedure, within a N-sized minibatch, given
a graph collection $\\{\mathcal{G}_{i}^{j}|j=1,2,3,...,K\\}$ representing i-th
mathematical question, where $j$ is an index denoting augmented views,
$i=1,2,....,N$. We enumerate all possible pairs within these graphs to form
positive pairs. Within a dyad $D_{m}$ consists of subject $D_{m}^{A}$ and
$D_{m}^{B}$, since K augmented views $\\{\mathcal{G}_{i}^{j}|j=1,2,3,...,K\\}$
of one of the members (e.g. $D_{m}^{A}$) represent the same mathematical
question and exhibit similar neural activity patterns. Given one view
$\mathcal{G}_{i}^{j}$ within these K augmented views, the views
$\\{\mathcal{G}_{i}^{k}|k=1,2,3,...,K,k\neq j\\}$ form positive pair with it
and negative pairs with views representing other mathematical questions (i.e.,
$\\{\mathcal{G}_{r}^{n}|n=1,2,3,...,K,r\neq i\\}$). Additionally, when two
subjects encounter the same mathematical question, one augmented view of
subject $D^{A}_{m}$ in the m-th dyad should form positive pairs with the other
K augmented views of subject $D^{B}_{m}$ in the same dyad. This process
results in a total of 2K * (2K - 1) / 2 positive pairs, which is 190 in our
experiments. We trained the model for 700 epochs with early stopping and
adopted a multi step learning rate scheduler and Adam optimizer. The initial
learning rate, the weight decay, the temperature and the batch size was
empirically set to 0.001, 0.02, 0.5 and 68 respectively. The filter size in
the Chebyshev spectral graph convolutional operator was set to 4, and the
ratio of the TopK pooling layer was set to 0.5.

After obtaining the encoder $f_{\theta}$, graph features can be extracted as a
embedding $z_{i}=f_{\theta}(\mathcal{G}_{i})\in R^{d}$ in the $d$ dimensional
embedding space forming isolated nodes in the population graph initially. In
the feedback type classification procedure, we use the raw dataset (without
sliding window augmentation) to train the DGC classifier for 100 epochs. We
adopted focal loss eq. (6) - eq. (7) for the classifier to mitigate the
aftermath of imbalanced data. We set $\alpha$ and $\gamma$ to 0.5 and 2
empirically.

$FL(p_{t})=-\alpha_{t}(1-p_{t})^{\gamma}log(p_{t})$ (6)

$p_{t}=\begin{cases}p&\text{if }y=1\\\ 1-p&\text{otherwise},\end{cases}$ (7)

All methods are implemented with Pytorch and trained with GPU
NVIDIA_GeForce_RTX_3090.

##  4 Experimental Result

###  4.1 Graph Embedding Effectiveness Analysis

We visualize the feature attraction of graph embeddings and raw features in
the dataset. Figure 3 and Figure 4 indicate that the positive pairs have
higher feature attraction than that of negative pairs. Besides, the feature
attraction of negative pairs is close to 0, which verifies the effectiveness
of fGCL in reducing the attraction of negative pairs and heightening the
attraction of positive pairs. Conversely, the feature attraction on raw
features display no significant differences, indicating identical similarity
between positive and negative pairs.

Figure 3: The feature attraction of graph contrastive features and raw
features. Figure 4: The probability density function of attractions on graph
contrastive features and raw features.

###  4.2 Impact of Emotional Contagion on Performance Over Time

Initial examinations of performance revealed a condition by time interaction,
$Wald_{\chi}^{2}(2)$ = 13.72, p =.001. Specifically, DMT partner’s performance
decreased over time (B = -.005 (SE = .002); $Wald_{\chi}^{2}(1)$ = 7.45, (95%
Wald LL CI = -.009; UL CI = -.002), p =.006; for every one unit increase in
time (trial number), the log odds of getting the question correct decreased by
.005 units. In contrast, DMT actors and PST dyad members exhibited no over-
time changes. Their performance remained stable (p’s > .11). Moreover, simple
contrasts between conditions revealed performance differences between DMT
actors and partners over the course of the task. At the beginning of the task,
DMT partners had a higher probability of getting a question correct in
comparison to the DMT actor (p=.002), and PST dyad members (p=.034). At the
end of the task, DMT partners had a lower probability of getting a question
correct in comparison to the partner (p=.004) and were largely comparable to
PST dyad members (p=.089). DMT actors did not differ from PST dyad members
(p=.35). Thus, at the end of the task, DMT partners underperformed in
comparison to the actors. These findings support the possibility that DMT
actors benefited from dyadic interactions at the expense of their partners.

###  4.3 Contagion Stage Analysis

In the analysis of the contagion stage, we divided the basic view dataset into
training, testing, and validation sets in a 7:2:1 proportion. Subsequently, we
extracted subject-invariant embeddings using the trained graph contrastive
encoder for the classification task.

Table 1 presents the results of ablation experiments conducted on various
models, including KNN, SVM, MLP, and DGC, aimed at classifying feedback types
within the basic view dataset. Notably, we employed embeddings extracted by
the fGCL encoder as inputs for the KNN, SVM, and MLP models. The experimental
results demonstrate that our model exhibits superior performance in the task
of feedback type classification while maintaining a balance between
sensitivity and specificity. However, it is worth noting that the absence of
the fGCL encoder leads to a noticeable degradation in the performance of the
DGC model.

Models | ACC | AUC | F1 Score | SEN | SPEC
---|---|---|---|---|---
fGCL + KNN | 63.80 $\pm$ 0.15% | 66.07 $\pm$ 0.55% | 62.93 $\pm$ 0.15% | 58.63 $\pm$ 0.43% | 63.96 $\pm$ 0.05%
fGCL + SVM | 59.11 $\pm$ 0.22% | 63.19 $\pm$ 0.39% | 52.32 $\pm$ 0.25% | 51.21 $\pm$ 0.53% | 55.96 $\pm$ 0.45%
fGCL + MLP | 58.11 $\pm$ 0.32% | 63.19 $\pm$ 0.39% | 58.61 $\pm$ 0.25% | 54.38 $\pm$ 0.53% | 58.13 $\pm$ 0.18%
DGC w/o encoder | 61.11 $\pm$ 0.12% | 63.19 $\pm$ 0.39% | 58.61 $\pm$ 0.25% | 57.00 $\pm$ 0.53% | 64.96 $\pm$ 0.45%
fGCL + DGC (ours) | 65.12 $\pm$ 0.45% | 69.79 $\pm$ 0.35% | 64.90 $\pm$ 0.06% | 64.71 $\pm$ 0.35% | 65.53 $\pm$ 0.05%

Table 1: The whole-brain level feedback type classification result in the
entire period

####  4.3.1 Individual-level Classification

We further examined our hypothesis that whether the emotional contagion occur
over the period of sustained interpersonal interaction. To this end, we
divided the trial sequence into three stages averagely: early stage ($0\sim
1/3$), middle stage ($1/3\sim 2/3$) and late stage ($2/3\sim 1$) and evaluate
them separately by our model. The result in Table 2 revealed that the
classification performance of DMT actors in DMT-PST dyad is better than DMT
partners in DMT-PST dyad in Table 3 entirely, and the accuracy and the F1
score of DMT actors in DMT-PST dyad represents an evident increasing trend
especially from the early stage to the middle stage and the late stage was
superior to the other stages. In contrast, that of DMT partners in DMT-PST
dyad displayed a relatively less increment in the accuracy and F1-score. This
may additionally reflect that the emotional contagion evolve cumulatively over
the sustained interpersonal communication, this in turn, affect the
neurological representation when encountering negative event, and thus
resulting in the poor classification result of recognizing the pattern of
neural response of DMT partners in DMT-PST dyad. On the contrary, DMT actors
in DMT-PST dyad release negative emotions by transmitting them to their
partners, this then gradually break the disordered response pattern resulting
in the improvement of classification performance.

Periods | ACC | AUC | F1 Score | SEN | SPEC
---|---|---|---|---|---
Entire | 68.28 $\pm$ 0.05% | 70.49 $\pm$ 0.05% | 70.50 $\pm$ 0.06% | 66.67 $\pm$ 0.08% | 67.63 $\pm$ 0.20%
Early | 60.66 $\pm$ 0.14% | 64.13 $\pm$ 0.06% | 64.21 $\pm$ 0.17% | 58.85 $\pm$ 0.29% | 65.62 $\pm$ 0.25%
Middle | 69.33 $\pm$ 0.25% | 64.35 $\pm$ 0.27% | 68.23 $\pm$ 0.31% | 64.51 $\pm$ 0.35% | 64.52 $\pm$ 0.34%
Late | 71.33 $\pm$ 0.17% | 73.31 $\pm$ 0.06% | 72.64 $\pm$ 0.22% | 72.83 $\pm$ 0.28% | 67.50 $\pm$ 0.31%
Table 2: The classification result of DMT in DMT-PST dyad at early, middle and late stage with entire testing. Periods | ACC | AUC | F1 Score | SEN | SPEC
---|---|---|---|---|---
Entire | 63.33 $\pm$ 0.05% | 67.87 $\pm$ 0.06% | 67.20 $\pm$ 0.08% | 66.67 $\pm$ 0.10% | 63.01 $\pm$ 0.13%
Early | 59.38 $\pm$ 0.25% | 62.12 $\pm$ 0.13% | 64.96 $\pm$ 0.35% | 60.00 $\pm$ 0.46% | 67.14 $\pm$ 1.17%
Middle | 64.21 $\pm$ 0.15% | 63.19 $\pm$ 01.39% | 68.59 $\pm$ 0.26% | 68.20$\pm$ 0.37% | 64.96 $\pm$ 01.45%
Late | 64.76 $\pm$ 0.12% | 68.16 $\pm$ 0.08% | 65.18 $\pm$ 0.14% | 67.70 $\pm$ 0.18% | 65.12 $\pm$ 0.21%
Table 3: The classification result of PST in DMT-PST dyad at early, middle and
late stage with entire period testing.

Our classifier was evaluated using leave-dyad-out cross validation, results of
DMT-PST dyad and PST-PST dyad are shown in Table 2 and Table 3 respectively,
which shed light on our aforementioned hypothesis. Regarding the result of DMT
actors inside DMT-PST dyads, our model achieved the classification accuracy as
60.66 $\pm$ 0.14% at the early stage, this accuracy than increased at the
middle stage of problem solving to 69.33 $\pm$ 0.25% than raised to 71.33
$\pm$ 0.17% at the late stage. Conversely, the results for DMT partners within
DMT-PST dyads displayed a distinct pattern. The accuracy increased from 59.38
$\pm$ 0.25% in the early stage to 64.21 $\pm$ 0.15% in the middle stage, with
marginal change observed in the late stage, yielding an accuracy of 64.76
$\pm$ 0.12%. Analyzing this pattern, it becomes apparent that in the early
stage of problem-solving, no substantial difference in brain activity existed
between DMT and PST pairs, indicating that the emotional contagion effect was
relatively dormant. As the problem-solving process advanced to the middle
stage, the emotional contagion effect commenced within DMT-PST pairs, with DMT
actors gradually transmitting the negative emotions stemming from pressure to
DMT partners. As a result, the differentiation between the two groups
demonstrated an increasing trend. In the final stage of problem-solving, the
emotional contagion effect within DMT-PST pairs subsided, with DMT actors
transferring their negative emotions to DMT partners. Consequently, the
discernible distinction in brain activity between DMT actors and DMT partners
became significant, accentuating the differentiation in neural pattern between
the two. Besides, the results of PST-PST dyads in Table 4 and Table 5 display
no significant changes in the accuracy, this might suggest that the emotional
contagion does not appear in PST-PST dyads and thus the neural pattern
differentiation in all stages is vague.

Periods | ACC | AUC | F1 Score | SEN | SPEC
---|---|---|---|---|---
Entire | 75.03 $\pm$ 0.01% | 85.54 $\pm$ 0.09% | 82.48 $\pm$ 0.01% | 72.06 $\pm$ 0.01% | 68.09 $\pm$ 0.05%
Early | 76.67 $\pm$ 0.05% | 80.62 $\pm$ 0.25% | 79.17 $\pm$ 0.15% | 67.31 $\pm$ 0.03% | 63.96 $\pm$ 0.05%
Middle | 75.11 $\pm$ 03.12% | 84.19 $\pm$ 01.39% | 84.35 $\pm$ 0.13% | 75.61 $\pm$ 0.25% | 74.96 $\pm$ 0.45%
Late | 74.12 $\pm$ 01.45% | 83.79 $\pm$ 0.35% | 83.21 $\pm$ 0.11% | 64.71 $\pm$ 0.35% | 63.53 $\pm$ 0.05%
Table 4: The classification result of PST1 in PST-PST dyad at early, middle and late stage with entire period testing. Periods | ACC | AUC | F1 Score | SEN | SPEC
---|---|---|---|---|---
Entire | 59.83 $\pm$ 0.03% | 66.38 $\pm$ 0.03% | 69.13 $\pm$ 0.02% | 58.46 $\pm$ 0.05% | 63.96 $\pm$ 0.05%
Early | 57.17 $\pm$ 0.31% | 61.69 $\pm$ 0.21% | 63.37 $\pm$ 0.22% | 56.19 $\pm$ 0.43% | 62.96 $\pm$ 0.05%
Middle | 56.31 $\pm$ 0.19% | 60.12 $\pm$ 0.14% | 59.37 $\pm$ 0.13% | 53.19 $\pm$ 0.21% | 55.36 $\pm$ 0.12%
Late | 55.97 $\pm$ 0.13% | 59.12 $\pm$ 0.13% | 58.13 $\pm$ 0.12% | 55.39 $\pm$ 0.13% | 51.96 $\pm$ 0.03%
Table 5: The classification result of PST2 in PST-PST dyad at early, middle
and late stage with entire testing.

Overall, the findings put forth the notion that DMT actors and those within
the PST dyad condition were processing performance feedback in a comparable
fashion, particularly when considering the overall span of the task. This
alignment is noteworthy, given that DMT actors underwent initial exposure to
SBS. Conversely, discernible distinctions emerge in the processing patterns of
DMT partners, especially during the middle and later phases. These outcomes
lend credence to the idea that SBS contagion unfolds progressively over time.
Notably, DMT partners appears to play a role in buffering the negative
emotions of DMT actors.

##  5 Discussion

Overall, our findings suggest SBS-based emotional contagion can occur within
female dyads in problem-solving contexts and has different consequences on
performance for each member of the dyad. While working together on a math
task, DMT partners performed worse over time, whereas DMT actors (i.e., those
under SBS), performed better and comparable to dyads working in SBS neutral
contexts. Importantly, DMT partners showed evidence of “catching” this initial
stress response from the threatened actor. This, in turn, had direct
ramifications for DMT partners’ performance. Contrastively, these
relationships were not evident within PST dyad members interacting in SBS
neutral contexts.

###  5.1 Inference of performance decrement in dyadic interaction

Results provide further insight into the dynamic relationship between two
individuals performing in domains where their common identity is devalued.
Although it seems conceivable that the performance of both DMT actors and
partners would suffer when solving problems together in a negatively
stereotyped domain, results provide further support for the notion that non-
threatened partners help buffer initially threatened actors from the
deleterious consequences of SBS over time, at their own expense. These
findings are consistent with past work showing that the presence of a female
role model or competent female partner alleviates performance decrements
otherwise typically evident in stereotype threatening contexts [33, 34, 35].
Results expand upon this work in several ways. Most notably, by demonstrating
that the transference of an individual’s stress response on to their partners
may be one important factor in buffering women from SBS during dyadic problem-
solving interactions, particularly during initial stages of the interaction.
Conversely, like past work demonstrating that increased emotional processing
of feedback in SBS contexts has a negative impact on individuals’ performance
when alone [22, 23], findings from this study demonstrate that this effect
extends to partners in a dyadic interaction, providing a potential mechanism
for underperformance effects among these individuals in group problem-solving
contexts moving forward.

###  5.2 Efficiency in timescale and Implications for Contagion Hypotheses

Present results provide novel insight into how contagion manifests on the
order of milliseconds, a much more rapid timescale than previously assumed, to
affect performance accordingly using neuroscience methodologies. Moreover, the
design of the present study provides a novel yet realistic platform to examine
emotion contagion phenomena via EEG or fMRI methodology in future studies. By
using iPads, it was possible to capture simultaneous EEG activity in a
controlled manner while still allowing participants to have real time face to
face interaction. This design also provides implications for contagion
hypotheses specific to the mimicry and proximity literature. Because
participants only communicated through an iPad webcam, participants were only
able to view their partner’s face and hear their voice through the webcam
during the interaction. This suggests that vocal patterns and facial
expressions may have played an integral role in facilitating contagion effects
[36, 37]. Findings provide a more nuanced understanding of the contagion
process while also providing a better understanding of a heretofore largely
unexamined question in the literature: how social identity threats and SBS
manifest in dyadic interactions to have paradoxical effects on performance.
More importantly, findings provide further insight into the many ways the
gender gap in STEM domains can be perpetuated but also one day nullified.

###  5.3 Graph-based Approach for Subject Invariant Emotional Representation
Extraction

Graph structures naturally align with the brain’s topology, allowing for the
effective modeling of anatomical regions of interest while preserving
functional connectivity (FC) information. The proposed fGCL model harnesses
semantically meaningful information i.e., graphs corresponding to the same
mathematical problem, to construct both positive and negative pairs. This
innovative approach significantly mitigates inter-subject variability in EEG
data and has been evaluated as optimal. The subject-invariant representations
extracted from FC graphs are well-aligned within the common embedding space.
Furthermore, the fGCL employs a spectral graph network capable of convolving
across the entire node set with FC connections, integrating valuable cognitive
information [38].

###  5.4 Limitations and Future Works

Regarding the limitations of the study, it is important to note that when
considering EEG data and the spatial limitations associated with the
methodology, conclusions based on precise brain locations should always be
interpreted with caution. Results should be replicated and expanded upon in
future fMRI studies, although given the temporal constraints of fMRI
methodologies with respect to findings in this study (i.e., these effects may
occur on the order of milliseconds), this approach could be problematic as
well. Besides, the proposed fGCL encoder was validated with EEG data of young
female students (mean age = 25.18 years) and optimized with semantical
auxiliary task. As is well known that age plays an important role in emotion
processing and thus might have different emotion patterns in different age
ranges [39], further studies should be conducted to cover different age ranges
for a more generalized encoder model.

##  6 Conclusion

In this study, we addressed a critical issue in previous emotional contagion
research: the neglect of subject-level difference. We hereby adopt a self-
supervised learning approach to eliminate the subject difference by increasing
the attraction of positive pairs and reducing the attraction of negative
pairs, which aligns the neurophysiologically meaningful representations of EEG
signals in the embedding space, this results in the removal of subject variant
information. Based on this substrate, we than employ a dynamic graph
classification model to analyze process of emotional contagion. The results
suggest that DMT actors and those in PST dyads processed feedback similarly,
but distinctions emerged in DMT partners’ processing patterns, supporting the
idea of gradual SBS contagion. DMT partners appeared to buffer negative
emotions of DMT actors.

## Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any
commercial or financial relationships that could be construed as a potential
conflict of interest.

## Author Contributions

J.H.: Conceptualization, Methodology, Formal Analysis, Software, Validation,
Writing - Original Draft, Writing - Review & Editing, Visualization; R. A.:
Data Collection; M.L.:  Conceptualization, Writing - Review & Editing; C.F.:
Funding Collection.

## Acknowledgments

All aspects of this study and article were supported by National Science
Foundation grant #1535414 awarded to Chad E. Forbes.

## Supplemental Data

None

## Data Availability Statement

Our native EEG data will be made available on request from any qualified
investigator who provides a practicable proposal, or for the purpose of
replicating procedures and results presented in the current study.

## References

  * [1] Amit Goldenberg and James J. Gross.  Digital emotion contagion.  Trends in Cognitive Sciences, 24(4):316–328, April 2020.
  * [2] Shushi Namba, Wataru Sato, Koyo Nakamura, and Katsumi Watanabe.  Computational process of sharing emotion: An authentic information perspective.  Frontiers in Psychology, 13, 2022.
  * [3] Adam D. I. Kramer, Jamie E. Guillory, and Jeffrey T. Hancock.  Experimental evidence of massive-scale emotional contagion through social networks.  Proceedings of the National Academy of Sciences, 111(24):8788–8790, 2014.
  * [4] Abdulaziz Alhubaishy and Luigi Benedicenti.  Toward a model of emotional contagion influence on agile development for mission critical systems.  In 2017 International Conference on High Performance Computing & Simulation (HPCS), pages 541–544, 2017.
  * [5] Guijuan Zhang, Dianjie Lu, and Hong Liu.  Strategies to utilize the positive emotional contagion optimally in crowd evacuation.  IEEE Transactions on Affective Computing, 11(4):708–721, 2020.
  * [6] Difei Liu, Shen Liu, Xiaoming Liu, Chong Zhang, Aosika Li, Chenggong Jin, Yijun Chen, Hangwei Wang, and Xiaochu Zhang.  Interactive brain activity: Review and progress on EEG-based hyperscanning in social interactions.  Frontiers in Psychology, 9, October 2018.
  * [7] Xinke Shen, Xianggen Liu, Xin Hu, Dan Zhang, and Sen Song.  Contrastive learning of subject-invariant EEG representations for cross-subject emotion recognition.  IEEE Transactions on Affective Computing, pages 1–1, 2022.
  * [8] Jinpeng Li, Shuang Qiu, Changde Du, Yixin Wang, and Huiguang He.  Domain adaptation for eeg emotion recognition based on latent representation similarity.  IEEE Transactions on Cognitive and Developmental Systems, 12(2):344–353, 2020.
  * [9] Nazmi Suhaimi, James Mountstephens, and Jason Teo.  Eeg-based emotion recognition: A state-of-the-art review of current trends and opportunities.  Computational Intelligence and Neuroscience, 2020:1–19, 09 2020\.
  * [10] Stephan Hamann and Turhan Canli.  Individual differences in emotion processing.  Current Opinion in Neurobiology, 14(2):233–238, 2004.
  * [11] Emily S Finn, Xilin Shen, Dustin Scheinost, Monica D Rosenberg, Jessica Huang, Marvin M Chun, Xenophon Papademetris, and R Todd Constable.  Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity.  Nature Neuroscience, 18(11):1664–1671, Oct 2015.
  * [12] Chad Forbes, Rachel Amey, Adam Magerman, Kelly Duran, and Mengting Liu.  Stereotype-based stressors facilitate emotional memory neural network connectivity and encoding of negative information to degrade math self-perceptions among women.  Social cognitive and affective neuroscience, 13, 06 2018.
  * [13] Rachel Amey, Jordan Leitner, Mengting Liu, and Chad Forbes.  Neural mechanisms associated with semantic and basic self-oriented memory processes interact moderating self-esteem.  iScience, 25:103783, 01 2022.
  * [14] Mengting Liu, Robert Backer, Rachel Amey, Eric Splan, Adam Magerman, and Chad Forbes.  Context matters: Situational stress impedes functional reorganization of intrinsic brain connectivity during problem-solving.  Cerebral Cortex, 31, 11 2020.
  * [15] Mengting Liu, Robert A. Backer, Rachel C. Amey, and Chad E. Forbes.  How the brain negotiates divergent executive processing demands: Evidence of network reorganization in fleeting brain states.  NeuroImage, 245:118653, 2021.
  * [16] Sophia Mueller, Danhong Wang, Michael D. Fox, B.T. Thomas Yeo, Jorge Sepulcre, Mert R. Sabuncu, Rebecca Shafee, Jie Lu, and Hesheng Liu.  Individual variability in functional connectivity architecture of the human brain.  Neuron, 77(3):586–595, February 2013.
  * [17] Andac Demir, Toshiaki Koike-Akino, Ye Wang, Masaki Haruna, and Deniz Erdogmus.  Eeg-gnn: Graph neural networks for classification of electroencephalogram (eeg) signals, 2021.
  * [18] Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst.  Convolutional neural networks on graphs with fast localized spectral filtering.  CoRR, abs/1606.09375, 2016.
  * [19] Cătălina Cangea, Petar Veličković, Nikola Jovanović, Thomas Kipf, and Pietro Liò.  Towards sparse hierarchical graph classifiers, 2018.
  * [20] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton.  A simple framework for contrastive learning of visual representations, 2020.
  * [21] Chad Forbes and Jordan Leitner.  Stereotype threat engenders neural attentional bias toward negative feedback to undermine performance.  Biological psychology, 102, 07 2014.
  * [22] Chad Forbes, Kelly Duran, Jordan Leitner, and Adam Magerman.  Stereotype threatening contexts enhance encoding of negative feedback to engender underperformance and anxiety.  Social Cognition, 33:605–625, 12 2015.
  * [23] Chad Forbes, Rachel Amey, Adam Magerman, Kelly Duran, and Mengting Liu.  Stereotype-based stressors facilitate emotional memory neural network connectivity and encoding of negative information to degrade math self-perceptions among women.  Social cognitive and affective neuroscience, 13, 06 2018.
  * [24] Hugh Nolan, Robert Whelan, and Richard Reilly.  Faster: Fully automated statistical thresholding for eeg artifact rejection.  Journal of neuroscience methods, 192:152–62, 09 2010.
  * [25] Alexandre Gramfort, Martin Luessi, Eric Larson, Denis Engemann, Daniel Strohmeier, Christian Brodbeck, Roman Goj, Mainak Jas, Teon Brooks, Lauri Parkkonen, and Matti Hämäläinen.  Meg and eeg data analysis with mne-python.  Frontiers in neuroscience, 7:267, 12 2013.
  * [26] Alexandre Gramfort, Martin Luessi, Eric Larson, Denis A. Engemann, Daniel Strohmeier, Christian Brodbeck, Lauri Parkkonen, and Matti Hämäläinen.  Mne software for processing meg and eeg data.  NeuroImage, 86(Feb):446–460, 2014.
  * [27] Matti S. Hämäläinen and Jukka Sarvas.  Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data.  IEEE Transactions on Biomedical Engineering, 36:165–171, 1989.
  * [28] Anders M Dale, Bruce Fischl, and Martin I Sereno.  Cortical surface-based analysis: I. segmentation and surface reconstruction.  Neuroimage, 9(2):179–194, 1999.
  * [29] Anders M Dale, Athena K Liu, Bruce R Fischl, Randy L Buckner, John W Belliveau, Jeffrey D Lewine, and Eric Halgren.  Dynamic statistical parametric mapping: Combining fmri and meg for high-resolution imaging of cortical activity.  Neuron, 26(1):55–67, 2000.
  * [30] Fa-Hsuan Lin, John Belliveau, Anders Dale, and Matti Hämäläinen.  Distributed current estimates using cortical orientation constraints.  Human brain mapping, 27:1–13, 01 2006.
  * [31] Yohan Attal and Denis Schwartz.  Assessment of Subcortical Source Localization Using Deep Brain Activity Imaging Model with Minimum Norm Operators: A MEG Study.  PLoS ONE, 8(3):e59856, March 2013.
  * [32] Rahul Desikan, Florent Ségonne, Bruce Fischl, Brian Quinn, Bradford Dickerson, Deborah Blacker, Randy Buckner, Anders Dale, Ralph Maguire, Bradley Hyman, Marilyn Albert, and Ronald Killiany.  An automated labeling system for subdiving the human cerebral cortex on mri scans into gyral based regions of interest.  NeuroImage, 31:968–80, 08 2006.
  * [33] David Marx and Jasmin Roman.  Female role models: Protecting women’s math test performance.  Personality and Social Psychology Bulletin, 28:1183–1193, 09 2002\.
  * [34] R.B. McIntyre, C.G. Lord, Dana Gresky, L.L. Eyck, G.D.J. Frye, and C.F. Jr.  A social impact trend in the effects of role models on alleviating women’s mathematics stereotype threat.  Curr Res Soc Psychol, 10:116–136, 02 2005.
  * [35] Katherine Thorson, Chad Forbes, Adam Magerman, and Tessa West.  Under threat but engaged: Stereotype threat leads women to engage with female but not male partners in math.  Contemporary Educational Psychology, 58, 03 2019.
  * [36] Elaine Hatfield, John T Cacioppo, and Richard L Rapson.  Emotional contagion.  Current directions in psychological science, 2(3):96–100, 1993\.
  * [37] Roland Neumann and Fritz Strack.  “mood contagion”: The automatic transfer of mood between persons.  Journal of personality and social psychology, 79(2):211, 2000.
  * [38] Jessica R. Cohen.  The behavioral and cognitive relevance of time-varying, dynamic changes in functional connectivity.  NeuroImage, 180:515–525, 2018.  Brain Connectivity Dynamics.
  * [39] Natalie Ebner and Håkan Fischer.  Emotion and aging: Evidence from brain and behavior.  Frontiers in Psychology, 5, 09 2014.