11affiliationtext: Department of Electrical and Computer Engineering,
University of Rochester, NY, USA22affiliationtext: Haskins Laboratories and
Department of Psychology, Yale University, CT, USA33affiliationtext:
Department of Physics and Astronomy, University of Rochester, NY,
USA44affiliationtext: Department of Computer Science, University of Rochester,
NY, USA**affiliationtext<EMAIL_ADDRESS>
# Cues to gender and racial identity reduce creativity in diverse social
networks

Raiyan Abdul Baten  Richard Aslin  Gourab Ghoshal  Ehsan Hoque

###### Abstract

The characteristics of social partners have long been hypothesized as
influential in guiding group interactions. Understanding how demographic cues
impact networks of creative collaborators is critical for elevating creative
performances therein. We conducted a randomized experiment to investigate how
the knowledge of peers’ gender and racial identities distorts people’s
connection patterns and the resulting creative outcomes in a dynamic social
network. Consistent with prior work, we found that creative inspiration links
are primarily formed with top idea-generators. However, when gender and racial
identities are known, not only is there (1) an increase of $82.03\%$ in the
odds of same-gender connections to persist (but not for same-race
connections), but (2) the semantic similarity of idea-sets stimulated by these
connections also increase significantly compared to demography-agnostic
networks, negatively impacting the outcomes of divergent creativity. We found
that ideas tend to be significantly more homogeneous within demographic groups
than between, taking away diversity-bonuses from similarity-based links and
partly explaining the results. These insights can inform intelligent
interventions to enhance network-wide creative performances.

###### keywords:

Creativity, Dynamic Social Network, Diversity

### Introduction

> “Outstanding discoveries, insights and developments do not happen in a
> vacuum.”—Nobel Laureate Tom Steitz, on the impacts of discussions with
> collaborators on his research

Creativity, although an individual strength, benefits strongly from
interactions with others. Online/offline networks of creative collaborators,
e.g., among researchers, designers, or marketers, can socially stimulate novel
ideas in people [1]. The network dynamics of who seeks creative inspiration
from whom can therefore critically impact the creative outcomes of a
population. In such social settings, people inevitably and implicitly respond
to the demographic cues of their peers [2, 3, 4]. Whether the knowledge of
peers’ demographic identities distorts the dynamics of the network ties and,
consequently, the associated creative outcomes, remain important questions for
both scientific and practical interests. While there exists a body of
literature examining the diversity effects on creativity at individual and
group levels [5, 6, 7, 8, 9], such studies typically overlook the temporally
dynamic nature of real social networks, limiting our scientific understanding
of its implications. Previous work shows that the dynamic nature of social
networks can influence human cooperation [10], collective intelligence [11],
and the development of public speaking skills [12], among other examples, but
the effects in the creativity context remain unclear. From a practical
perspective, it is desirable to elevate the creative capabilities of human
networks. As automation takes away manual and repetitive jobs, the future of
work will increasingly require offline/online ensembles of people to be adept
in social-cognitive avenues of soft-skills such as creativity [13, 14, 15].
Understanding the effects of demographic cues on networked creativity is
critical for designing informed interventions that can promote creative
outcomes in both offline (e.g., large-scale creative teams) and online (e.g.,
in work-from-home settings, social media) dynamic networks alike.

There are two competing hypotheses on how social networks might adapt to
demographic signals when people seek creative stimulation from peers. On the
one hand, there is the notion of a diversity bonus that can help creative
ideation [6]. People from different demographic identities (e.g., gender/race)
come with different concerns, perspectives, and life experiences. This can
impact the knowledge domains they have access to, influencing the ideas they
generate [5]. Furthermore, humans are known to be exceptionally adept at
employing cues of skill/competence, success, prestige, age, health, and self-
similarity to assess who most likely possess information useful to them, and
form social links accordingly [16, 17, 18, 19]. Following this, it seems
promising for (rational) humans to selectively form links with people from
different demographic identities, so as to cash in on the diversity bonuses to
generate novel ideas.

On the other hand, the social network literature has extensively documented
homophily, where people tend to form and sustain links with peers similar to
themselves [20]. It can then be argued that people might forego potential
diversity bonuses in favor of self-similarity-based network connections.
Previous work suggests that if multiple people draw inspirations from similar
stimuli, then even their independently stimulated ideas can become similar to
each other [21]. This way, homophily-guided network dynamics can limit the
variety of ideas stimulated—leading to counter-productive outcomes in
divergent creativity [22]. An implicit conflict thus results from the trade-
off between the diversity bonus and homophily—choosing to affiliate with
others based on self-similarity attenuates creativity while the collective
effect of group diversity enhances creativity.

Unfortunately, despite its scientific and practical importance, our
understanding of the impact of demographic cues on networked creativity is
limited, because such a test requires independently controlling for the
exposure of the social-identity cues within an evolving social system.
Furthermore, it is challenging to identify a dataset in the wild that allows
for traceable links between the generation of creative ideas and the social
context that facilitates them, gives agency to the participants for choosing
their own inspiration sources, captures temporal evolution information of the
dynamic network, and offers a way of revealing demographic information without
introducing confounding effects. We therefore design and conduct a randomized
controlled experiment within the virtual laboratory. Using a custom social-
media-like web-interface for the interactions, we are able to unambiguously
track creative stimulation of ideas in a dynamic social network, while the
challenges of ensuring an unconfounded exposure to demographic cues are
overcome with the use of avatars.

Figure 1: Experimental Setup. (A) The bipartite network used as the initial
configuration. Pre-recorded ideas of the alters were shown to the egos of both
study conditions. Each ego was connected to $2$ alters. (B) The study protocol
for each of the $5$ rounds. In turn-$1$, the egos generated ideas
independently. Turn-$2$ involved an additional idea-generation task on the
same object. In this turn, the control egos were shown only the ideas of their
alters for inspiration, while both the ideas and the demographic information
of the alters were shown to the treatment egos. The egos could add inspired
ideas to their lists. Finally, the egos rated the ideas of all the alters in
the trial, and optionally updated the $2$ followee-alters at the end of each
round. In the illustration, the ego dissolved the red dashed link to form the
green link, while the grey link persisted.

### Experimental Setup

Participants recruited from Amazon Mechanical Turk took part in $5$ rounds of
text-based creative tasks, where they generated alternative use ideas for a
given common object (e.g., a brick) in each round. We adopt a bipartite
network design that involved two kinds of roles for the participants: as
alters ($N=12$) and as egos ($N=180$). The alters’ ideas were pre-recorded to
be used as stimuli for the egos. The egos were randomly placed into either of
two conditions: (1) Control ($N=90$, demographic cues not shown) and (2)
Treatment ($N=90$, demographic cues shown). We ran two trials of the study.
Each trial consisted of $6$ alters, whose ideas were shown to both the control
and treatment egos in that trial.

Initially, each ego was randomly assigned to ‘follow’ $2$ alters (out of $6$
in the trial). In each round, the egos first generated ideas independently
(turn-1). In the control condition, the egos were then shown the ideas of the
$2$ alters they were following. However, in the treatment condition, the egos
were additionally shown the demographic information (gender and race) of their
followee alters using avatars. If the egos got inspired with new alternate use
ideas on the same object, they could add those ideas to their own lists
(turn-2). Then, the egos were shown the ideas of all $6$ alters, which they
rated on novelty. Finally, they were allowed to optionally follow/unfollow
alters to have an updated list of $2$ followee alters each. In turn-2 of the
following round, they were shown the ideas of their newly chosen alters
(Figure 1; Methods). Importantly, the only difference between the two
conditions was that the treatment egos had access to the gender and race cues
of the alters. Thus, any difference in the connectivity dynamics and network-
level creative outcomes between the two conditions can be attributed to the
availability of demographic cues.

### Results

#### Same-gender links are highly stable in the presence of demographic cues

We employ Separable Temporal Exponential Random Graph Models [23] to capture
the link dynamics in the temporal network data. Two separate models capturing
link (1) Formation and (2) Persistence patterns are fitted for each study
condition (Figure 2). As exogenous features, we choose attributes that the
treatment egos were likely to consider in making connectivity decisions: (a)
round-wise creative performances of the alters (measured by non-redundant idea
counts), (b) gender-based homophily, and (c) race-based homophily.
Additionally, we employ one endogenous feature of edge-counts, to control for
network density (see Methods for details).

In the control condition, we find that both the link formation and persistence
patterns are significantly guided by the creative performances of the alters
(formation model: $\beta=0.324$, $Z$-value = $5.51$, $P<10^{-4}$; persistence
model: $\beta=0.417$, $Z$-value = $6.95$, $P<10^{-4}$). The positive $\beta$
values suggest that better performing alters are more likely to be followed by
the egos and that these links are significantly stable across rounds. The
gender and race features do not show any significant effect once the
performance-based link dynamics are accounted for ($P>0.05$ for both features
in both models). This is intuitive, as the egos did not have any information
about the alters’ gender and race, and could only see their ideas.

In the treatment condition, the link formation model once again shows only the
creative performances of the alters to be a significant predictor
($\beta=0.197$, $Z$-value = $3.93$, $P<10^{-4}$), and not the demographic
features ($P>0.05$ for both). However, we observe a notably different trend in
the link persistence model. We find that link persistence depends
significantly on both non-redundant idea counts ($\beta=0.355$, $Z$-value =
$6.08$, $P<10^{-4}$) and gender-based homophily ($\beta=0.599$, $Z$-value =
$3.74$, $P<10^{-3}$). In other words, if a link exists between participants of
the same gender, its odds of persisting increases by $82.03\%$, after
controlling for merit-based persistence. No significant effect is observed for
the race feature ($P>0.05$).

In summary, the availability of demographic cues in the treatment condition is
observed to be associated with a significant stability in same-gender links,
unlike what is seen in the control condition (see SI Tables S1-S4). When
presented with multiple modes of demographic information at the same time
(e.g., both gender and race), humans in many contexts make choices based on
the mode they find more salient [24]. This intuition can partly explain why we
observe gender-based but not race-based link stability in our experiment.

Figure 2: Intuition behind STERGMs. To capture the formation and persistence
dynamics of the links, two separate models are fitted for the temporal
networks of each study condition. The formation model tracks links that do not
exist in time $t$, but exist in time $t+1$, e.g., the green link between ego
$e_{1}$ and alter $a_{4}$. The persistence model considers links that exist in
both time instances, e.g., all grey links. Since the egos had to follow a
constant number of two alters, the red link $e_{1}$-$a_{2}$ needed to dissolve
to allow the green link to form. However, if a link does not persist, it must
dissolve—thus, the dissolution effects are captured in the persistence model
and we need not fit a separate model for dissolution.

#### Inter-ego semantic similarity increases when the alters’ demographic cues
are known

To assess the creative outcomes in the networks, we use natural language
processing tools for computationally capturing the semantic qualities of
ideas. Divergent thinking/creativity leads individuals to generate numerous
and varied responses to a given prompt [22]. Two crucial dimensions of
creativity, flexibility and originality, explicitly take into consideration
the semantic qualities of ideas: flexibility is the number of distinct
semantic categories that a person accesses in his/her ideas, while originality
is the extent to which a solution is semantically novel [25]. If the network
processes systematically make the stimulated ideas of the participants
semantically similar, it hurts the divergence purposes. We estimate the
semantic nature of the egos’ idea-sets using neural word embeddings [26]. To
compute the semantic similarities between idea-sets, we employ the cosine
similarity metric [27].

Previous work suggests that following the same people, i.e., having the same
stimuli, can introduce semantic similarities among independently stimulated
idea-sets [21]. Therefore, from each round, we collect pairs of egos who share
(a) $2$ common alters (i.e., exactly the same stimuli), (b) $1$ common alter
and (c) no common alter. Within these subgroups, we compute the semantic
similarities between every ego-pair’s stimulated ideas in turn-2 (see
Methods).

Figure 3: Inter-ego semantic similarities under various conditions. Cosine
similarities between the idea-sets of egos-pairs are shown across three sub-
groups: ego-pairs who share $0$, $1$ and $2$ common alters between them. With
the increase in the number of common alters (i.e., increase in stimuli
similarity), the inter-ego semantic similarity typically increases.
Importantly, the semantic similarities are significantly higher in the
treatment condition compared to the control condition in all three sub-groups.
Whiskers denote $95\%$ C.I. ***$P<0.0001$, corrected for multiple comparisons.

Using a $3\times 2$ factorial design ($3$ levels in the number of common
alters and $2$ study conditions), we find significant main effects for both
factors (Aligned Rank Transform procedure [28], $P<10^{-15}$ for both), and a
significant interaction between them ($P<10^{-7}$; Figure 3). Post-hoc
analyses reveal that in both the treatment and control conditions, the inter-
ego similarities increase significantly as the number of common alters
increase from $0$ to $1$ and also from $1$ to $2$ ($P<10^{-4}$ for all of
these comparison cases). These trends intuitively follow that inter-follower
similarities can stem from having common stimuli.

Notably, we observe that the inter-ego semantic similarities are significantly
higher in the treatment condition compared to their control counterparts, in
all of the common-alter-based subgroups ($P<10^{-4}$ in each). In addition,
the inter-ego semantic similarities are found to increase with time
(treatment: Pearson’s $r=0.13$, $P<10^{-4}$; control: $r=0.12$, $P<10^{-4}$).
These results mark systematically negative impacts of demographic similarity
on the treatment networks’ creative outcomes. Supplementary Text and Tables
S5-S10 provide full details of the statistical analyses.

#### Homophily can make one’s stimuli set less diverse

To explain these results, we test whether the well established intuition of
ideas being more homogeneous within demographic groups than between [6, 29]
holds true for the alters’ ideas. We find that idea-pairs within gender/race
are indeed significantly more similar to each other than idea-pairs between
gender/race ($2$-tailed tests; gender: $t(4633)=11.66$, $P<10^{-30}$; race:
$t(4870)=5.73$, $P<10^{-7}$; Figure 4; Methods). Thus, it follows logically
that homophily-guided network dynamics can make a follower’s stimuli idea-set
uniform and similar. This can deprive the follower of possible diversity
bonuses, partly explaining the increased inter-ego semantic similarity
observed in the treatment condition that can stem from having similar stimuli.

Figure 4: Homogeneity of ideas within demographic-groups. (A) shows gender-
based partitioning of the alters’ ideas. Some of the ideas were submitted by
both males and non-male alters, while others were submitted uniquely by either
gender categories. We only consider ideas from the latter case in the
similarity analysis. Pairwise comparisons of ideas within gender show a
significantly higher similarity than idea-pairs between genders. (B) shows the
same insights in case of race-based partitioning of the alters’ ideas.
Whiskers denote $95\%$ C.I. ***$P<0.0001$.

### Discussion

Our study design afforded several benefits over observational data. Crucially,
in this design, the egos could dynamically update connections to alters every
round, the links between ideas and their inspiration sources were
unambiguously traceable, and the revelation of gender and race cues was
controlled using avatars. This dynamic network approach is at a stark contrast
to traditional diversity/group dynamics exploration setups, where the groups
are typically treated as black boxes and the intra-group ties are seldom
tracked explicitly [30]. Furthermore, our choice of a bipartite network
structure ensured uniform stimuli-sets for the egos. However, like in all
experiments, there were limitations, too. The unidirectional nature of the
networks prohibited us from assessing the effects of natural bidirectional
interaction settings. The avatars we used could potentially have differential
effectiveness in indicating demographic contrasts between the gender and race
of the alters, and the effectiveness could also vary for different egos. We
nevertheless chose to use avatars over more realistic photos to standardize
visual depictions and to remove confounding factors stemming from facial,
personality, or other visual cues. We stopped collecting data for trial-2 when
results consistent with trial-1 were achieved, thereby supporting our initial
findings. However, future research should use a pre-specified sample size for
trial-2 to provide a stronger replication of the trial-1 effects.

Diversity, creativity and homophily are complex bodies of knowledge, each
driven by multidimensional mechanisms. For instance, a large body of work has
examined how exposure to gender and race cues can bias people’s behavior in
various settings. Descriptions of identical medical symptoms from men and
women can be perceived differently by physicians [31], even leading to cases
where men patients are more comprehensively investigated than women patients
with the same symptoms [32]. The perception of one’s creativity can be biased
based on identity as well: in their highly creative craft, male florists can
be perceived as ‘naturally’ and ‘truly’ creative and thus held to higher
regards than their female counterparts [3]. The power relations among various
demographic identities have widely been documented and are known to dictate
such biases. For example, the production of knowledge in academia can be
racialized [33], where the contributions by minorities can be systematically
neglected [33, 5]. Such identity-based realities can influence the occurrence
of homophilic ties in many domains, for example in engineering, where
knowledge can be perceived to be highly accessible when women seeks it from
other women [2].

Naturally, our work did not encompass every possible combination of contexts
and scenarios that can emerge in such highly complex systems. Rather, we
showed one set of empirical evidences in support of our arguments linking the
interdisciplinary components, towards filling an important void in literature
with regards to identity-informed creative inspiration seeking behavior in a
temporal social network setting. We found evidence that demographic cues can
indeed bias the connectivity dynamics in creativity-centric social networks,
and systematically influence the creative outcomes therein. The more
genetically diverse a bee hive, the higher is its survival probability [34].
Likewise, a diversified pool of creative influencers can help people generate
non-redundant ideas by allowing them to draw on diverse stimuli sets,
something that is likely stifled in homophily-driven and highly centralized
networks.

Both of our study conditions had explicit instructions and incentives to
generate creative ideas (elaborated in Methods), while only the treatment
condition received implicit demographic cues. Previous literature suggests
that such implicit priming can activate demography-specific schemas in
people’s memory [35], potentially transferring to how one approaches a
creative task [36]. Surface-level cues on gender or race can trigger the
Social Categorization process [37], whereby people differentiate themselves on
the basis of demographic characteristics, e.g., race-based categorization can
make the differences between White and Black more prominent. Our study
revealed both gender and race cues simultaneously, which can trigger the
Multiple Social Categorization process [24]. Under this process, shared
identities can cut across the dichotomies of group identities. For example, if
members belonging to Black and White categories are also female, then Black
women and White women can be perceived as more similar compared to men [38],
reducing the race-based ‘us’ versus ‘them’ distinction. This process can
partly explain why we observed a significant link persistence pattern in
gender but not in race. Moreover, in the social science literature, both
gender and race are known to be interwoven with questions of power and
context. These identities often come with certain assumptions and stereotypes
about social roles, including assumptions and stereotypes about knowledge:
regarding who can know what and how the knowledge of different people are to
be accessed, received, evaluated and prioritized [39, 40, 41]. Such
assumptions and stereotypes could also have played a role in guiding the egos’
rewiring decisions, as the same-gender connections became significantly stable
across time.

The Social Comparison process posits that people self-identifying to belong to
different social categories can compete over identity and resources [42].
Given this, a potential sense of conflict can arise if the egos find their
alter-sets to comprise entirely of ‘out-group’ members, and feel compelled to
choose creative stimulations from them. We guarded against this possibility by
ensuring that we had at least two alters from every demographic dimension in
each trial.

In the teamwork literature, such an ‘us-them’ distinction is known to
potentially reduce information-sharing among diverse members [43], often
leading to fractures within the team [44]. Conversely, there can be benefits
of the process, since the subgroups may enjoy reduced conflict, greater
psychological safety, ease of communication [45], and greater accessibility to
knowledge [2]. The egos in our study merely viewed the stimuli and formed no
proper ‘team’ with the alters. Moreover, the ideas of the alters of various
identities were equally accessible for the egos. This setting is rather
similar to social media and academic networks of researchers, for example,
where unidirectional and unreciprocated inspiration ties are common. Given
this setting, the self-similarity based benefits, e.g., reduced conflict and
psychological safety, were not on offer for the egos. We still saw a tendency
of the egos to maintain links based on self-similarity of gender, thereby
foregoing some values in diversity. It is not uncommon for people to report
unreciprocated ties with creative others [46]; and we find that such ties
extend to homophily-guided dynamics as well. In behavioral economics, it is
well known that humans are not always perfectly logical, rational and informed
actors, and can often diverge from optimal ‘rational’ behavior (i.e., behavior
that maximizes personal utility) in various social-psychological contexts
[47]. Although maintaining ties with alters of different identities than one’s
own would seem to be the logical approach for the egos in our exploration, we
observed the opposite trend of behavior.

In summary, our results provide insights on how demography-driven behavior can
influence creativity, a soft-skill with accelerated demand in an automation-
driven world. We found that in a creativity-centric social network, same-
gender links persisted significantly. Such behavior was not observed if the
gender and race cues were not available to the participants, everything else
held constant. Moreover, we found people’s ideas to be more homogeneous within
demographic groups than between—reinforcing the intuition that diversity
bonuses might get compromised if one systematically maintains connections
based on demographic identity. We indeed found that in the presence of
demographic cues, the inter-ego semantic similarity increased significantly
compared to the control condition where no such cue was shown, thus hurting
divergent creativity.

These insights can help inform interventions/scaffolding in social systems
where superior creative outcomes are sought after. For instance, due to the
COVID-19 pandemic, many teams are brainstorming using remote-collaboration
tools. While many face-to-face interaction benefits may go missing in such
platforms, there can be upsides on offer, too. Following our findings,
masking/modifying people’s identities online can potentially help to elevate
network-level creative outcomes. In social media, people often follow highly
creative peers for novel ideas. Their choices of who to follow can be biased
by demographic considerations. Algorithmic interventions can then be made to
help people diversify their creative stimulation sources. Such measures can
proactively guard against the inter-follower semantic similarities to enhance
network-wide creative performances. In our future work, we intend to explore
more nuanced research questions about the differential effects of identity
attributes in epistemic burden, impacts and privileges in creativity-centric
social interactions. In addition, we intend to extend the scope of the study
from the binary classification of gender and race to a multi-class
perspective, using higher-resolution data from a larger set of gender and race
categories.

### Methods

#### Ethics Statement

The study was reviewed and approved by the Institutional Review Board of the
University of Rochester, NY, USA. All participants provided informed consent.
All methods were carried out in accordance with relevant guidelines and
regulations for involvement of human participants.

#### Participants

There were two trials in the study. In the first trial, the ideas of $6$
alters were used as stimuli for $72$ egos each in the control and treatment
conditions. In the second trial, $6$ different alters acted as the stimulation
sources for $18$ egos each in the two conditions. The second trial helped to
ensure that our results do not overfit to the alters of the first trial, and
gave results consistent with the first trial. Our reported results are derived
from the full dataset from both of the trials together, to help reduce noise
and increase the statistical power of the results. All of the participants
were located in the United States. The participants chose for themselves
pseudo usernames as per their liking, and were assigned their alter/ego roles
and treatment/control conditions randomly.

Among the $180$ egos, $95$, $84$ and $1$ ego(s) respectively self-identified
to be of male, female and other gender. $134$ of the egos self-identified as
White, and $46$ of them as non-White. The age distribution was as follows:
18-24: 16, 25-34: 69, 35-44: 48, 45-54: 30, 55+: 17. As for the alters, in
trial 1, $4$ of them identified as male and $2$ as female; while there were
$3$ alters from each of the White and non-White race categories. In trial 2,
there were $3$ alters from each of male and female genders; while $4$ of them
identified as White and $2$ as non-White. The age distribution was: 18-24: 2,
25-34: 8, 35-44: 2. There is precedence in scientific literature of using
binary splitting of data along gender (male vs. non-male) and race (White vs.
non-White) dimensions, especially when the data availability is limited for
many of the actual non-majority identity subgroups [48, 5]. We adopted this
binary-splitting strategy to make the statistical analysis feasible.

#### Measure of Divergent Creativity

In this experiment, we are interested in divergent creativity, which deals
with a person’s ability to come up with or explore many possible solutions to
a given problem [49]. We use a customized version of Guilford’s Alternate Uses
Test [50], the canonical approach for quantifying divergent creative
performances111Guilford’s Alternate Uses Test is Copyright @ 1960 by Sheridan
Supply Co., all rights reserved in all media, and is published by Mind Garden,
Inc, www.mindgarden.com.. In each of the $5$ rounds, the participants were
instructed to consider an everyday object (e.g., a brick), whose common use
was stated (e.g., a brick is used for building). The participants were asked
to come up with novel and useful alternative uses for the object: uses that
are different from each other, different than the given common use, and are
appropriate and feasible. We choose the first $5$ objects from the Form B of
Guilford’s test as the prompt objects in the $5$ rounds.

#### Procedure

Each of turn-1 and turn-2 allowed the egos $3$ minutes to generate their ideas
on the same object. In turn-2, the egos in the control condition were shown
only the pseudo usernames and the lists of ideas of their followee alters. The
egos in the treatment condition were additionally shown the gender (male and
female) and race (White and non-White) information of the alters using text
and avatars (see SI Figure S1). The avatars were used to ensure uniform visual
depiction for all of the alters of the same demographic group, so as not to
bias the egos by any facial, personality or other visual cues. The egos were
instructed not to resubmit any of the alters’ exact ideas, and told that only
non-redundant ideas would contribute to their performance. They were also told
that there will be a short test at the end of the study, where they will need
to recall the ideas shown to them. This was to ensure that the participants
paid attention to the stimuli ideas, which has been shown to positively impact
ideation performances [51, 52, 53, 54]. After turn-2, the egos rated all the
ideas of the $6$ alters in their trial on a 5-point Likert scale (1: not
novel, 5: highly novel) [55, 56]. As the egos optionally rewired their network
connections to have an updated list of which alters to follow, they were
required to submit the rationale behind their choices of updating/not updating
links in each round. This was in place to make the egos accountable for their
choices, which has been shown to raise epistemic motivation and improve
systematic information processing [56, 57]. The participants were paid $10
upon the completion of the tasks, as well a bonus of $5 if they were among the
top 5 performers in groups of 18. SI Figures S2-S4 show the user interfaces.

#### Analysis Strategy

##### Quantifying Creativity

Against the pool of ideas submitted by one’s peers, the number of non-
redundant ideas that a participant comes up with is a widely accepted marker
of his/her creativity [58, 59]. The intuition being, to be creative, an idea
has to be statistically rare. First, we filtered out inappropriate submissions
that did not meet the requirements of being feasible and different from the
given use. Then, all the ideas submitted in a given round by all the
participants were organized so that the same ideas are binned or collected
together. We followed the coding rules described by Bouchard and Hare [60] and
the rules specified in the scoring key of Guilford’s Alternate Uses test, Form
B, for binning the ideas. On average, the egos submitted $5.43$ and $4.33$
ideas respectively in turn-1 and turn-2 of every round. These counts did not
change significantly over time.

Once all the ideas were binned, we computed the non-redundant idea counts by
looking at the statistical rarity of the ideas submitted by the participants.
Namely, an idea was determined to be non-redundant if it was given by at most
a threshold number of participants in a given pool of ideas. For the alters,
the threshold was set to $1$, and the pools were set to be the round-wise
idea-sets of the $6$ alters in the given trial.

The first author and two undergraduate research assistants independently coded
the data to bin similar ideas together. The first author coded all of the
ideas in the dataset, while the two research assistants binned ideas from a
random split of $50\%$ participants each. The coders were shown the anonymized
ideas in a random order. Based on their coding, the total non-redundant idea
counts of the participants in all $5$ rounds were computed separately and the
agreements were calculated. Between the first and second coder, the intra-
class correlation coefficient was $ICC(3,2)=0.92$, $P<10^{-19}$, $95\%$ C.I. =
$[0.88,0.95]$, and the Pearson’s correlation coeffient was $r=0.87$,
$P<10^{-20}$, $95\%$ C.I.=$[0.79,0.92]$. Between the first and third coder,
the agreements were again high: intra-class correlation coefficient
$ICC(3,2)=0.88$, $P<10^{-15}$, $95\%$ C.I. = $[0.83,0.92]$; Pearson’s
$r=0.83$, $P<10^{-19}$, $95\%$ C.I.$=[0.74,0.89]$). The coded data from the
first coder were then used in the analyses.

##### Capturing Link Formation and Persistence Dynamics: Separable Temporal
ERGM

In the classic framework of the Exponential Random Graph Model (ERGM), the
observed network (i.e., the data collected by the researcher) is regarded as
one realization out of a set of possible networks originating from an unknown
stochastic process we wish to understand. The range of possible networks, and
their probability of occurrence under the model, is represented by a
probability distribution on the set of all possible graphs with the same
number of nodes as the observed network. Against these possible networks, we
can then ask whether the observed network shows strong tendencies for
structural characteristics that cannot be explained by random chance alone
[61].

The basic expression for the classic (static) ERGM model can be written as,

$P(\mathbf{Y}=\mathbf{y}|\mathbf{X}=\mathbf{x})=\Big{(}\frac{1}{\kappa}\Big{)}\text{exp}\\{\mathbf{\beta}^{T}\mathbf{g}(\mathbf{y,x})\\}$
(1)

Here, $\mathbf{Y}$ is the random variable for the state of the network
(adjacency matrix), with a particular realization $\mathbf{y}$. $\mathbf{X}$
denotes the vector of exogenous attribute variables, while $\mathbf{x}$ is the
vector of observed attributes. $\mathbf{\beta}\in\mathbb{R}^{p}$ is a $p\times
1$ vector of parameters. $\mathbf{g}(\mathbf{y},\mathbf{x})$ is a
$p$-dimensional vector of model statistics for the corresponding network
$\mathbf{y}$ and attribute vector $\mathbf{x}$. $\kappa$ is a normalizing
quantity which ensures that Eq. 1 is a proper probability distribution.
Unfortunately, evaluating $\kappa$ exactly is non-trivial. Therefore, we need
to resort to numerical methods to approximate the coefficients
$\hat{\mathbf{\beta}}$. Namely, we use Markov Chain Monte Carlo methods to
simulate draws of $\mathbf{Y}$, and from those draws we estimate the
coefficients using Maximum Likelihood Estimation (MCMC-MLE method). Such
estimation methods make it convenient to transform Eq. 1 to the following
equivalent conditional log-odds form:

$\text{log}\Bigg{[}\frac{P(Y_{ij}=1|\mathbf{y}^{C}_{ij},\mathbf{X})}{P(Y_{ij}=0|\mathbf{y}^{C}_{ij},\mathbf{X})}\Bigg{]}=\mathbf{\beta}^{T}\Delta_{ij}(\mathbf{y,x})$
(2)

Here, $\mathbf{y}^{C}_{ij}$ denotes all the observations of ties in
$\mathbf{y}$ except $y_{ij}$. $\Delta_{ij}(\mathbf{y,x})$ is the change
statistic, which denotes the change in the value of the network statistic
$\mathbf{g}(\mathbf{y,x})$ when $y_{ij}$ changes from $1$ to $0$. This
emphasizes the log-odds of an individual tie conditional on all other ties.

For our temporal network data, we employed an extension of the static ERGM
that deals with dynamic networks in discrete time: the Separable Temporal ERGM
(STERGM). In contrast to static ERGMs, here we fitted two models: one for the
underlying relational formation, and another for the relational persistence.
In going from a network $\mathbf{Y}^{t}$ at time $t$ to a network
$\mathbf{Y}^{t+1}$ at time $t+1$, the formation and persistence of ties are
assumed to occur independently of each other within each time step (hence
‘separable’), to be captured by the two models respectively. The governing
equations for the formation and persistence models, analogous to Eq. 2, are
then written respectively as:

$\text{log}\Bigg{[}\frac{P(Y_{ij,t+1}=1|\mathbf{y}^{C}_{ij},\mathbf{X},Y_{ij,t}=0)}{P(Y_{ij,t+1}=0|\mathbf{y}^{C}_{ij},\mathbf{X},Y_{ij,t}=0)}\Bigg{]}=\beta_{f}^{T}\Delta_{ij,f}(\mathbf{y,x})$
(3)

$\text{log}\Bigg{[}\frac{P(Y_{ij,t+1}=1|\mathbf{y}^{C}_{ij},\mathbf{X},Y_{ij,t}=1)}{P(Y_{ij,t+1}=0|\mathbf{y}^{C}_{ij},\mathbf{X},Y_{ij,t}=1)}\Bigg{]}=\beta_{p}^{T}\Delta_{ij,p}(\mathbf{y,x})$
(4)

Here, time indices have been added to the equations unlike before, as well as
new conditionals. In the formation model in Eq. 3, the expression is
conditional on the tie not existing at the previous time step, whereas in the
persistence model in Eq. 4, it is conditional on the tie existing. Figure 2
summarizes these intuitions. There are separate coefficient vectors
$\beta_{f}$ and $\beta_{p}$ for the formation and persistence models
respectively, as well as separate change statistics
$\Delta_{ij,f}(\mathbf{y,x})$ and $\Delta_{ij,p}(\mathbf{y,x})$ for the two
models. Note that in the literature, it is common to refer to the persistence
model as the ‘dissolution’ model instead. However, given how Eq. 4 is set up,
and given that positive coefficients in this model indicate link persistence
rather than dissolution, we take the liberty to refer to the model as the
persistence model.

Since our network is bipartite, we considered links to form between ‘actors’
$i$ (egos) and ‘events’ $j$ (alters). To that end, we employed one endogenous
and three exogenous features. Namely, we used the number of edges as the
endogenous feature, which controls for network density:
$g_{1}(\mathbf{y},\mathbf{x})=\sum_{ij}y_{ij}=N_{e}$. As exogenous features,
we included:

  1. 1.

The alters’ creative performances (i.e., non-redundant idea counts,
$x^{\text{(score)}}$):
$g_{2}(\mathbf{y},\mathbf{x})=\sum_{ij}y_{ij}x^{\text{(score)}}_{j}$

  2. 2.

Gender-based homophily between the egos and alters:
$g_{3}(\mathbf{y},\mathbf{x})=\sum_{ij}y_{ij}\mathbb{I}\\{x^{\text{(gender)}}_{i}=x^{\text{(gender)}}_{j}\\}$

  3. 3.

Race-based homophily between the egos and alters:
$g_{4}(\mathbf{y},\mathbf{x})=\sum_{ij}y_{ij}\mathbb{I}\\{x^{\text{(race)}}_{i}=x^{\text{(race)}}_{j}\\}$

where $\mathbb{I}\\{\cdot\\}$ denotes the indicator function. These four
features constitute the network statistic $\mathbf{g}(\mathbf{y},\mathbf{x})$,
which is then used in computing the change statistics in the Eqns. 3 and 4.
Note that the fitted coefficients $\beta_{f}$ and $\beta_{p}$ are conditional
log-odds ratios, so their exponentials can intuitively be interpreted as the
factors by which the odds of the formation and persistence of the network ties
change respectively. For our implementation, we used the tergm package
available within the statnet suite in R, and fitted both formation and
persistence models for both of the study conditions.

##### Capturing Inter-ego Semantic Similarity

From each round, we collected pairs of egos who shared (a) $2$ common alters
(i.e., exactly the same stimuli), (b) $1$ common alter and (c) no common
alter. Within these subgroups, we computed the semantic similarities between
every ego-pair’s stimulated ideas in turn-2.

To semantically compare the idea-sets of the egos, we first removed stop words
and punctuation marks to convert the idea-sets to bag-of-words documents. We
represented each document by taking the Word2Vec embeddings of all of the
words in the document, and computing the centroid of those embedded vectors.
The centroid of a set of vectors is defined as the vector that has the minimum
sum of squared distances to each of the other vectors in the set. This
centroid is then used as the final document vector representation of the given
idea-set [27]. Word2Vec is a popular word-embedding algorithm, which employs
skip-gram with negative sampling to train $300$-dimensional embeddings of
words [26].

Given two idea-sets, we computed their document vectors $\mathbf{u}$ and
$\mathbf{v}$, and estimated the similarity between the two vectors by taking
their cosine similarity,

$\text{cosine}(\mathbf{u},\mathbf{v})=\frac{\mathbf{u\cdot
v}}{|\mathbf{u}||\mathbf{v}|}$ (5)

##### Capturing Homogeneity of Ideas within Demographic groups

We first considered the sets of ideas that were uniquely submitted by the
alters of male and non-male gender identities, but not both. We created vector
representations for each of the distinct ideas in the two sets as follows. The
same idea can be phrased differently by different people. Therefore, we made
use of the manual binnings of ideas described in the Quantifying Creativity
subsection, where all the different phrasings of the same idea were collected
under a common bin ID. We collected the bin IDs of ideas that were submitted
uniquely by males and non-males. Under each bin ID, all the different
phrasings of the idea were collected in a bag-of-words document, with all
stop-words and punctuation marks removed. Similarly as before, we took the
Word2Vec embeddings of the words in this document and computed their centroid
to be the final vector representation of the idea.

We then considered pairs of ideas from alters of the same gender, and computed
their cosine similarities. However, we only considered idea-pairs from the
same round, and if an idea-pair came uniquely from a single person, we ignored
that pair. Similarly, we computed the cosine similarities between idea-pairs
from alters of different genders, and run statistical tests to confirm
homogeneity of ideas within demographic-groups. We ran the race-based idea-
homogeneity analysis exactly the same way.

### Data Availability

Please see https://github.com/ROC-HCI/demography-creativity-networks for the
data and code. Due to the copyright protection of the creativity test, we
provide processed data of the participants’ ideas.

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### Acknowledgements

This work was supported by funding from the National Science Foundation
(IIS-1750380), US Army Research Office (W911NF-18-1-0421), and National
Institutes of Health (HD-037082).

### Author contributions statement

R.A.B. designed the study, collected, annotated, and analyzed the data, and
authored the manuscript. R.A. contributed to the result interpretation and
preparation of the manuscript. G.G. and E.H. oversaw the study design,
interpretation of results, and preparation of the manuscript.

### Ethics statement

The study was reviewed and approved by the Institutional Review Board of the
University of Rochester, NY, USA. All participants provided informed consent.
All methods were carried out in accordance with relevant guidelines and
regulations for involvement of human participants.

### Additional information

Competing interests The authors declare no competing interest.

## Supplementary Information

Cues to gender and racial identity reduce creativity in diverse social
networks

Raiyan Abdul Baten, Richard Aslin, Gourab Ghoshal, and Ehsan Hoque

Corresponding Author: Ehsan Hoque<EMAIL_ADDRESS>
#### Capturing Inter-ego Semantic Similarity: Statistical Test Details

From each round, we collected pairs of egos who shared (a) $2$ common alters
(i.e., exactly the same stimuli), (b) $1$ common alter and (c) no common
alter. Within these subgroups, we computed the semantic similarities between
every ego-pair’s stimulated ideas in turn-2.

We adopted a $3\times 2$ factorial design to analyze the data, with $3$ levels
in the number of common alters (i.e., the a, b and c subgroups above) and $2$
levels in the study condition factor (i.e., control and treatment). In doing
so, we employed the Aligned Rank Transform (ART) procedure, which is a linear
mixed model-based non-parametric test. Figure 3 in the main manuscript
visualizes the results. We found significant main effects for both of the
factors (Number of common alters: $F(2,25964)=135.94$, $P<10^{-15}$; Study
condition: $F(1,25964)=369.98$, $P<10^{-15}$). We also found a significant
interaction between the two factors ($F(2,25964)=17.81$, $P<10^{-7}$).

Post-hoc analysis on the ART-fitted model revealed that the semantic
similarity between ego-pairs increases as their number of common alters
increases ($0$ vs $1$ common alter: $t(25964)=-10.18$, $P<10^{-4}$; $1$ vs $2$
common alter(s): $t(25964)=-9.01$, $P<10^{-4}$). Further pairwise comparisons
using $2$-tailed tests showed that this trend holds individually in both of
the control and treatment conditions. In the control condition, the inter-ego
similarities increased significantly as the number of common alters increased
from 0 to 1 and also from 1 to 2 (0 vs. 1 common alter: $t(25964)=-10.61$,
$P<10^{-4}$; 1 vs. 2 common alters: $t(25964)=-8.55$, $P<10^{-4}$). The same
held for the treatment condition (0 vs. 1 common alter: $t(25964)=-4.71$,
$P<10^{-4}$; 1 vs. 2 common alters: $t(25964)=-5.15$, $P<10^{-4}$). These
trends intuitively follow the argument that inter-follower similarities can
stem from having common stimulation sources.

Notably, we observed that the inter-ego semantic similarities are
significantly higher in the treatment condition compared to their control
counterparts, as revealed by post-hoc analysis on the study condition factor
in the ART-fitted model ($t(25964)=19.24$, $P<10^{-4}$). Further pairwise
comparisons using $2$-tailed tests revealed that this result holds for all of
the three common-alter-based subgroups (treatment vs. control; 2 common
alters: $t(25964)=5.27$, $P<10^{-4}$; 1 common alter: $t(25964)=13.79$,
$P<10^{-4}$; 0 common alter: $t(25964)=17.87$, $P<10^{-4}$). In addition, we
observed that with time, the inter-ego semantic similarities in both of the
control and treatment conditions show increasing trends (Pearson’s
correlation, $r$=$0.13$, $P<10^{-4}$ in the treatment condition; $r$=$0.12$,
$P<10^{-4}$ in the control condition). All of the $P$-values reported here
have been corrected for multiple comparisons using Holm’s sequential
Bonferroni procedure whenever needed. SI Tables S5-S10 capture these results.

### Supplementary Figures and Tables

The supplementary figures and tables are listed below.

Figure S1: Avatars used for depicting demographic information. Top row: Non-
White female and Non-White male; Bottom row: White female and White male.

Figure S2: Study interface: Initial (turn-1) idea submission interface for
egos in both of the study conditions. This interface is used for recording the
alters’ ideas as well.

Figure S3: Study interface: Turn-2 interface for the egos in the treatment
condition. The alters’ ideas are shown on the left-side cards. In the control
condition, only the usernames and ideas of the alters are shown.

Figure S4: Study interface: Rating and rewiring interface for the egos in the
treatment condition. In the control condition, only the usernames and ideas of
the alters are shown.

Table S1: Link formation dynamics in the control condition. Summary results from the Monte Carlo Maximum Likelihood Estimation fit in the STERGM model. ***$P<0.001$ | $\beta$ | Std. Error |  $Z$ value | Pr($>|Z|$) |
---|---|---|---|---|---
Edges | $-4.496$ | $0.295$ | $-15.220$ | $<1e-04$ | ***
Alters’ non-redundant idea count | $0.324$ | $0.059$ | $5.505$ | $<1e-04$ | ***
Gender-based homophily | $-0.014$ | $0.145$ | $-0.094$ | $0.925$ |
Race-based homophily | $0.056$ | $0.144$ | $0.389$ | $0.698$ |

Table S2: Link persistence dynamics in the control condition. Summary results from the Monte Carlo Maximum Likelihood Estimation fit in the STERGM model. ***$P<0.001$, *$P<0.05$ | $\beta$ | Std. Error |  $Z$ value | Pr($>|Z|$) |
---|---|---|---|---|---
Edges | $-0.577$ | $0.290$ | $-1.989$ | $0.0467$ | *
Alters’ non-redundant idea count | $0.417$ | $0.060$ | $6.952$ | $<1e-04$ | ***
Gender-based homophily | $-0.202$ | $0.167$ | $-1.207$ | $0.2275$ |
Race-based homophily | $0.179$ | $0.166$ | $1.077$ | $0.2815$ |

Table S3: Link formation dynamics in the treatment condition. Summary results from the Monte Carlo Maximum Likelihood Estimation fit in the STERGM model. ***$P<0.001$ | $\beta$ | Std. Error |  $Z$ value | Pr($>|Z|$) |
---|---|---|---|---|---
Edges | $-3.740$ | $0.246$ | $-15.230$ | $<1e-04$ | ***
Alters’ non-redundant idea count | $0.197$ | $0.050$ | $3.933$ | $<1e-04$ | ***
Gender-based homophily | $-0.117$ | $0.134$ | $-0.879$ | $0.379$ |
Race-based homophily | $0.096$ | $0.134$ | $0.714$ | $0.475$ |

Table S4: Link persistence dynamics in the treatment condition. Summary results from the Monte Carlo Maximum Likelihood Estimation fit in the STERGM model. ***$P<0.001$, **$P<0.01$ | $\beta$ | Std. Error |  $Z$ value | Pr($>|Z|$) |
---|---|---|---|---|---
Edges | $-0.751$ | $0.290$ | $-2.588$ | $0.0097$ | **
Alters’ non-redundant idea count | $0.355$ | $0.058$ | $6.075$ | $<1e-04$ | ***
Gender-based homophily | $0.599$ | $0.160$ | $3.743$ | $0.0002$ | ***
Race-based homophily | $-0.067$ | $0.158$ | $-0.425$ | $0.671$ |

Table S5: Omnibus test results for analyzing the inter-ego semantic similarities under various conditions. The cosine similarity between idea-sets of pairs of egos is the response variable. The analysis of variance of Aligned Rank Transformed data is run on a model with two factors: the number of popular alters of the egos ($3$ levels) and the study condition ($2$ levels). The degrees of freedom are specified using the Kenward-Roger method. | Df | Df.res | $F$ | Pr($>F$)
---|---|---|---|---
Number of Popular Alters | $2$ | $25964$ | $135.944$ | $<2.22e-16$
Condition | $1$ | $25964$ | $369.983$ | $<2.22e-16$
NumPopularAlters:Condition | $2$ | $25964$ | $17.811$ | $1.86e-8$

Table S6: Post-hoc contrast analysis among the three levels in the ‘number of popular alters’ factor from the fitted model reported in Table S5. The degrees of freedom are specified using the Kenward-Roger method. The $P$-values are adjusted using Holm’s sequential Bonferroni procedure. Contrast | SE | df | $t$ | $p$
---|---|---|---|---
0 common alter-1 common alter | $102$ | $25964$ | $-10.176$ | $<0.0001$
0 common alter-2 common alters | $141$ | $25964$ | $-15.999$ | $<0.0001$
1 common alter-2 common alters | $136$ | $25964$ | $-9.014$ | $<0.0001$

Table S7: Post-hoc contrast analysis among the two levels in the ‘study condition’ factor from the fitted model reported in Table S5. The degree of freedom is specified using the Kenward-Roger method. The $P$-value is adjusted using Holm’s sequential Bonferroni procedure. Contrast | SE | df | $t$ | $p$
---|---|---|---|---
control-treatment | $104$ | $25964$ | $-19.235$ | $<0.0001$

Table S8: Comparisons of cosine similarities among the three levels of ‘number of popular alters’ factor in the control condition. $2$-tailed tests. The $P$-values are adjusted using Holm’s sequential Bonferroni procedure. Contrast | SE | df | $t$ | $P$
---|---|---|---|---
0 common alter-1 common alter | $0.00225$ | $25964$ | $-10.614$ | $<0.0001$
0 common alter-2 common alters | $0.00302$ | $25964$ | $-16.067$ | $<0.0001$
1 common alter-2 common alters | $0.00288$ | $25964$ | $-8.551$ | $<0.0001$

Table S9: Comparisons of cosine similarities among the three levels of ‘number of popular alters’ factor in the treatment condition. $2$-tailed tests. The $P$-values are adjusted using Holm’s sequential Bonferroni procedure. Contrast | SE | df | $t$ | $P$
---|---|---|---|---
0 common alter-1 common alter | $0.00216$ | $25964$ | $-4.709$ | $<0.0001$
0 common alter-2 common alters | $0.00312$ | $25964$ | $-8.274$ | $<0.0001$
1 common alter-2 common alters | $0.00304$ | $25964$ | $-5.152$ | $<0.0001$

Table S10: Comparisons of cosine similarities between the two study conditions. $2$-tailed tests. The $P$-values are adjusted using Holm’s sequential Bonferroni procedure. Contrast | SE | df | $t$ | $P$
---|---|---|---|---
control-treatment; 2 common alters | $0.00365$ | $25964$ | $-5.271$ | $<0.0001$
control-treatment; 1 common alter | $0.00205$ | $25964$ | $-13.786$ | $<0.0001$
control-treatment; 0 common alter | $0.00235$ | $25964$ | $-17.865$ | $<0.0001$