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Beauty and Wellness in the Semantic Memory of the Beholder

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Beauty and wellness are terms used often in common parlance, however their meaning and relation to each other is unclear. To probe their meaning, we applied network science methods to estimate and compare the semantic networks associated with beauty and wellness in different age generation cohorts (Generation Z, Millennials, Generation X, and Baby Boomers) and in women and men. These mappings were achieved by estimating group-based semantic networks from free association responses to a list of 47 words, either related to Beauty, Wellness, or Beauty + Wellness. Beauty was consistently related to Elegance, Feminine, Gorgeous, Lovely, Sexy, and Stylish. Wellness was consistently related Aerobics, Fitness, Health, Holistic, Lifestyle, Medical, Nutrition, and Thrive. In addition, older cohorts had semantic networks that were less connected and more segregated from each other. Finally, we found that women compared to men had more segregated and organized concepts of Beauty and Wellness. In contemporary societies that are preoccupied by the pursuit of beauty and a healthy lifestyle, our findings shed novel light on how people think about beauty and wellness and how they are related across different age generations and by sex.
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ORIGINAL RESEARCH
published: 05 August 2021
doi: 10.3389/fpsyg.2021.696507
Edited by:
Vassilis Cutsuridis,
University of Lincoln, United Kingdom
Reviewed by:
Naveen Kashyap,
Indian Institute of Technology
Guwahati, India
Charles Fox,
University of Lincoln, United Kingdom
Anca Doloc-Mihu,
Georgia Gwinnett College,
United States
*Correspondence:
Yoed N. Kenett
yoedk@technion.ac.il
Specialty section:
This article was submitted to
Cognitive Science,
a section of the journal
Frontiers in Psychology
Received: 16 April 2021
Accepted: 12 July 2021
Published: 05 August 2021
Citation:
Kenett YN, Ungar L and
Chatterjee A (2021) Beauty
and Wellness in the Semantic
Memory of the Beholder.
Front. Psychol. 12:696507.
doi: 10.3389/fpsyg.2021.696507
Beauty and Wellness in the Semantic
Memory of the Beholder
Yoed N. Kenett1,2*, Lyle Ungar3and Anjan Chatterjee1
1Penn Center for Neuroaesthetics, University of Pennsylvania, Philadelphia, PA, United States, 2Faculty of Industrial
Engineering & Management, Technion–Israel Institute of Technology, Haifa, Israel, 3Department of Computer and Information
Science, University of Pennsylvania, Philadelphia, PA, United States
Beauty and wellness are terms used often in common parlance, however their meaning
and relation to each other is unclear. To probe their meaning, we applied network
science methods to estimate and compare the semantic networks associated with
beauty and wellness in different age generation cohorts (Generation Z, Millennials,
Generation X, and Baby Boomers) and in women and men. These mappings
were achieved by estimating group-based semantic networks from free association
responses to a list of 47 words, either related to Beauty,Wellness, or Beauty +Wellness.
Beauty was consistently related to Elegance,Feminine,Gorgeous,Lovely,Sexy, and
Stylish.Wellness was consistently related Aerobics,Fitness,Health,Holistic,Lifestyle,
Medical,Nutrition, and Thrive. In addition, older cohorts had semantic networks that
were less connected and more segregated from each other. Finally, we found that
women compared to men had more segregated and organized concepts of Beauty
and Wellness. In contemporary societies that are pre-occupied by the pursuit of beauty
and a healthy lifestyle, our findings shed novel light on how people think about beauty
and wellness and how they are related across different age generations and by sex.
Keywords: beauty, wellness, semantic networks, word2vec, aging
INTRODUCTION
Beauty seems to be a pervasive human pre-occupation (Grammer et al., 2003;Zangwill, 2005,
2018;Scruton, 2011, 2018;Sartwell, 2017). We aspire to be beautiful, surround ourselves with
beautiful things, and create beautiful artifacts (Bloch and Richins, 1992;Chatterjee, 2014;Redies,
2014). Beauty is attributed to natural objects (e.g., faces landscapes, animals, etc.) as well as to
cultural artifacts (e.g., art and architectural design; Menninghaus et al., 2019). Despite this pre-
occupation with beauty, we have little clarity on what beauty means, and how it is linked to different
kinds of valuation.
Examining this elusive concept based on its associations could provide some sense of how people
conceptualize beauty (Chatterjee, 2014;Chatterjee and Vartanian, 2014;Menninghaus et al., 2019).
According to the aesthetic triad framework (Chatterjee and Vartanian, 2014, 2016), an aesthetic
experience, such as beauty, emerges from three components: A sensory-motor component, an
emotional-evaluative component, and a meaning-knowledge component. Cognitive theory and
research have linked the meaning of concepts to their semantic neighborhoods, e.g., concepts that
are directly linked to it (Klimesch, 1987;Landauer and Dumais, 1997;Günther et al., 2019). Thus,
a conceptual space of beauty may be inferred from the concepts to which it is linked.
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In the current study, we focus on the meaning-knowledge
component of the aesthetic triad, by analyzing the natural
language of participants. Beauty is also linked to other kinds of
valuation, such as judgments of good (Dion et al., 1972;Eagly
et al., 1991), and ideas of wellness (Koskinen et al., 2017). In
this study, we also query the concept of wellness and its relation
to beauty. We apply computational network science methods to
natural language to determine how people’s concepts of beauty
and wellness are similar or vary by age cohorts and by sex.
Evolutionary psychology research highlights notions of beauty
as reflecting a preference for attractive people over unattractive
people when choosing mates (Grammer et al., 2003). Valuation of
beauty extend into other kinds of valuation, such as in judgments
of morality, an extension often referred to as the “Beauty is
good” (Dion et al., 1972;Eagly et al., 1991) stereotype. According
to this stereotype, attractive people are judged to have positive
personal characteristics; people who look good also are good.
In a complementary line of research, faces with anomalies,
such as scars, are rated by participants as having negative
characteristics compared to non-anomalous faces (Hartung et al.,
2019;Workman et al., 2020). This research suggests that our
valuation systems are imprecise. In addition to conflating beauty
and good, valuations of beauty also extend to wellness. Consistent
with claims from evolutionary psychology, attractiveness signals
mates who are healthy, to assure healthy off-springs (Grammer
et al., 2003). Thus, the link between beauty and wellness may have
deep evolutionary roots.
Concerns for wellness goes back many years. For example,
Aristotle discussed how wellness is related to hedonia (the search
for pleasure) and to eudaimonia (the cultivation of virtue; Huta
and Waterman, 2014). In contemporary materially developed
societies (Chen et al., 2015;Koskinen et al., 2017), wellness often
refers to holistic health that emphasizes healthy self-interest,
self-awareness, and self-improvement (Mueller and Kaufmann,
2001). Seeking physical and mental health is part of a meaningful
lifestyle (Chen et al., 2015;Valentine, 2016;Lee et al., 2019).
However, like beauty, wellness is challenging to define, and its
meaning is malleable (Corbin and Pangrazi, 2001;Koskinen
et al., 2017). Beyond health and medicine, wellness is associated
with proactive practices such as meditation and exercise (Chen
et al., 2015), a focus on self-realization, and the pursuit of
pleasure (Ryan and Deci, 2001;Diener and Biswas-Diener, 2011;
Ryff, 2018). This cultural trend toward wellness is reflected in
a booming wellness industry (Lee et al., 2019), valued globally
at 4.2 trillion dollars in 2017 (Global Wellness Institute, 2017).
Thus, wellness is also a target of consumption (Featherstone,
2010). Given the range of associations of wellness to virtue,
health, and leisure (Koskinen et al., 2017), how do people think
about this concept as expressed in natural language?
The beauty industry often markets its products as reflecting
health and inner beauty, suggesting that they capitalize on a
psychological link between the two concepts. The logic of this
marketing strategy, as mentioned above, receives support from
evolutionary psychology. Sexual selection theories propose that
an attractive, beautiful face and body signal healthy and fertile
potential mates, with the most attractive individual expected
to be the healthiest and most fertile (Buss and Schmitt, 1993;
Grammer et al., 2003;Hönekopp et al., 2007;Coetzee et al.,
2009;Stephen and Tan, 2015). Under these theories, beauty
and wellness are both connected to physical health and
reproductive success. This relationship may still be linked in
our brains, even though the original factors that signaled health
(e.g., immunocompetence, or susceptibility to parasites) and
reproductive success, may be not be as relevant in the 21st
Century (Chatterjee, 2014).
Given the potential overlap in how the concepts beauty and
wellness relate to physical health, and the malleability of their
meaning, how do they vary by age and sex? As people age they
accumulate more experience and thus the knowledge component
of these constructs may continue to develop (Park et al., 2002;
Chatterjee and Vartanian, 2014). However, we know little about
the effects of aging on the perception of beauty and wellness.
For example, Koskinen et al. (2017) interviewed middle aged
people (aged 50–65 years) on the topic of wellness. The authors
found that wellness related activities were not aimed at directly
combating ill-effects of aging, but were focused on sustaining a
happy and fulfilled dally life (Koskinen et al., 2017).
To study how the conceptual space of thoughts about beauty
and wellness vary by age, we need either longitudinal or cross-
sectional research (Wulff et al., 2019). One approach is cross-
sectional research on groups that differ in age and relate to
different generations (Costanza et al., 2012, 2017;Rudolph
et al., 2020a). While age cohorts refer to groups of individuals
who are pooled together based on shared year of birth or
specific significant events, age can also reflect variation between
individuals associated with aging caused by physical and cultural
maturation (Costanza et al., 2012).
To examine potential differences in the representation of these
concepts across different age groups, we adopted an age cohort
approach. Specifically, we targeted four cohorts, as defined by
the Pew Research Center (Dimock, 2019): Generation Z (people
born 1997–2012; 9–24 year old); Millennials (people born 1981–
1996; 25–40 years old); Generation X (people born 1965–1980;
41–56 year old); and Baby Boomers (people born 1946–1964;
57–75 year old). Although individuals vary across generations,
these generations are considered internally similar and distinct
from each other (Costanza and Finkelstein, 2015;Rickes, 2016;
Rudolph et al., 2020b). While individuals can certainly differ from
their assigned group generalizations, typically Baby Boomers are
described as idealistic optimists and educated; Generation X
as cynical, disconnected, and practical skeptics; Millennials as
realists, sheltered, and technological-savvy; and Generation Z as
adaptive and social-media savvy (Rickes, 2016).
Finally, any difference in the meaning of beauty and wellness
may also be related to sex differences, i.e., how men and women
comprehend these constructs, and their relation, differently. For
example, women rate physical beauty as less important than men
do when ranking important attributes in attraction. The beauty
market is also reshaping gender stereotypes and how men relate
to beauty products. Contemporary men are more concerned
with their own physical appearance and are more open to using
cosmetics than were men who adopted traditional male gender
stereotypes (Ostapenko, 2015;Khan et al., 2017). Such cultural
changes raise questions about sex differences in the current
perception of beauty and wellness. Taken together, the constructs
of beauty and wellness are not well understood, and their
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meaning may vary by age and sex. We query the associations of
beauty and wellness, their relationship, and by analyzing natural
language examine how age and sex influence these concepts.
Our strategy of using natural language to examine beauty and
wellness is motivated by the hypothesis that knowledge plays
a role in aesthetic experiences (Chatterjee and Vartanian, 2014,
2016). Computationally analyzing verbal responses can measure,
differentiate, and describe subjective experiences of psychological
constructs (John et al., 1988;Kjell et al., 2019;Coburn et al.,
2020;Hayn-Leichsenring et al., 2020). Several studies have used
lexical corpora to analyze the semantics of aesthetics (Kuehnast
et al., 2014;Hosoya et al., 2017;Menninghaus et al., 2019). Lexical
corpus analysis—identifying the “semantic spaces” of concepts—
is an indirect way of revealing associations of concepts, by
representing conceptual spaces that make up such constructs
(Günther et al., 2019). The “semantic neighborhood” of a concept
can shed light on its meaning, even when the concept is hard to
define directly. Kuehnast et al. (2014) analyzed free-association
responses to emotional terms related to the concept of being-
moved. The authors identified joy and sadness as key emotions
related to the experience of being-moved, and showed how
mapping the semantic space of being-moved, provided insight
into components of this space (Kuehnast et al., 2014). In a recent
study, Menninghaus et al. (2019) investigated how Beauty related
to three similar aesthetics concepts: elegance, grace, and sexiness.
Using free associations, questionnaires and semantic differential
ratings (Menninghaus et al., 2019), they showed that elegance and
grace differ from sexiness, but that all three concepts overlap in
the broader category of beauty (Menninghaus et al., 2019). Their
report is an example of the growing interest in representing the
semantic space of aesthetic concepts such as beauty.
An approach to estimate semantic spaces from linguistic data
is network science (Siew et al., 2019). Network science provides
quantitative methods to investigate complex systems as networks
(Newman, 2010;Barabási, 2016). A network is comprised of
nodes, that represent the basic unit of the system (e.g., concepts
in semantic memory) and links, or edges, that signify the
relations between them (e.g., semantic similarity). Over the past
two decades, several studies have used computational science
to represent and study cognitive systems as networks (Borge-
Holthoefer and Arenas, 2010;Baronchelli et al., 2013;Karuza
et al., 2016;Siew et al., 2019). Specifically, such methods have been
applied to investigate semantic memory—the cognitive system
that stores facts and knowledge—as semantic networks (Siew
et al., 2019;Kumar, 2021). For example, network science has
tested psychological hypotheses that highly creative individuals
have a more flexible semantic memory structure (Kenett et al.,
2018;Kenett and Faust, 2019), identified mechanisms of language
development (Steyvers and Tenenbaum, 2005;Hills et al., 2009),
shed light on statistical learning (Karuza et al., 2017), shown how
specific linguistic network properties influences memory retrieval
(Vitevitch et al., 2012, 2014), and provided insight into the
structure of semantic memory of second language in bilinguals
(Borodkin et al., 2016).
In addition to these studies directed at cognition, network
science can examine the effect of age cohorts on semantic
memory (Zortea et al., 2014;Dubossarsky et al., 2017;Wulff
et al., 2018, 2019;Cosgrove et al., 2021). These studies find that
concepts in older adults’ semantic memory are more organized
(i.e., concepts have sparser semantic neighborhoods, which
means that concepts in the network are less connected) and
more segregated (any pair of concepts in the network is “further
apart”) than those of younger adults. For example, Dubossarsky
et al. (2017) analyzed the network structure of free associations
obtained from a cross-sectional cohort sample to estimate
semantic networks for groups of young, middle-aged, and older
adults. The authors found a U-shape change in semantic memory
properties across the lifespan: Semantic memory starts off sparse,
increases in density toward midlife, followed by increasing
sparseness toward older ages (Dubossarsky et al., 2017).
We applied a semantic network approach to examine the
following questions: (1) What are the semantic neighborhoods
of the concepts Beauty and Wellness? (2) How are concepts of
Beauty and Wellness linked to each other? (3) Do the concepts
of Beauty and Wellness change across different age generations?
And, as an exploratory question. (4) Do the concepts of Beauty
and Wellness differ between men and women?
To address these questions, we conducted an online study
in which we collected free-association responses to cue words
related to Beauty,Wellness, and Beauty +Wellness (described
later) from people of four age cohorts (Generation-Z, Millennials,
Generation-X, and Baby Boomers). As mentioned earlier, free
associations have been used to empirically study aesthetic
concepts (Kuehnast et al., 2014;Hosoya et al., 2017;Menninghaus
et al., 2019). This task taps into natural conceptual structures
in semantic memory (Deese, 1965;Nelson et al., 2000;De
Deyne et al., 2016a). Free associations reflect lexical knowledge
acquired through experience and thus can be used to represent
the organization of concepts in semantic networks (Siew et al.,
2019). As a secondary, exploratory analysis, we also examine sex
differences, by collapsing data across the age generation cohorts
and dividing the data by sex (male and female).
If Beauty and Wellness are distinct concepts, we expect distinct
and separate “semantic neighbors” surrounding each concept.
If these concepts overlap, we expect to find less separated
neighborhoods, and that both concepts would be mostly related
to terms that are members of the Beauty +Wellness category.
Additionally, based on previous studies on the “aging lexicon”
(Wulff et al., 2019), we expected to find increased segregation
and decreased connectivity in the semantic networks of older
generations cohorts. Finally, if men and women represent
differently the concepts Beauty and Wellness, these differences
would be evident in their corresponding semantic networks.
For example, do men and women weight physical attributes
differently when thinking of beauty or wellness?
MATERIALS AND METHODS
Study Design
Our study has a between-subject age generation cohort design.
We collected free association responses to estimate the semantic
network of the concepts beauty and wellness and how they
relate to each other across the different groups. We used
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computational linguistic methods (Mikolov et al., 2013) to
identify candidate terms (using 100 million words from a decade
of published Google News) that relate to the concepts Beauty
and Wellness (top 15 most frequent terms for each concept).
To examine the relation of Beauty and Wellness, we identified
terms that related to a category that combined both concepts
(Beauty +Wellness, 15 terms). Such computational methods
have been used widely in psycholinguistic research (Mandera
et al., 2015, 2017) as a method to identify category related
terms. We then collected free-association responses to these
45 terms as well as to the terms Beauty and Wellness from
samples of participants of our targeted four age generations
cohorts. Next, we applied a computational approach developed
by Kenett et al. (2011) to represent semantic networks derived
from these free associations. We estimated a general semantic
network of all terms, collapsed across conditions (to address
question 1 and 2); the semantic networks for each of the age
cohorts (to address question 3); and the semantic network of
these terms for men and women, collapsed across all conditions
(to address question 4). We then measured different properties
of these networks, focusing on the semantic neighbors of the
concepts Beauty and Wellness and their level of integration
with each other.
Participants
Participants were recruited online via Amazon Mechanical
Turk (AMT; Buhrmester et al., 2011). We aimed to recruit
participants from our four age cohorts, but not to overly
constrain random selection of participants. As such, participants
were assigned to approximate Generation Z (GenZ; 20–
30 years old), Millennials (Mill; 31–40 years old), Generation
X (GenX; 41–50 years old), and Baby Boomers (Boomers; 61–
70 years old) cohorts. Data were collected in batches of 50
participants at a time and were constrained to specific age
groups after a specific age group reached 100 participants. All
participants were United States citizens and had English as
their native language. To address potential issues with AMT
data quality (Chmielewski and Kucker, 2020), we manually
scrutinized the raw associative responses data (see below).
Exclusion criterions were: (1) Participants who did not respond
to more than 10% of the cue words in the free association
task were excluded; (2) Participants who generated, on average,
a number of associative responses per cue word that was
two standard deviations below the group mean were excluded;
and (3) Participants who entered non-words as associative
responses were excluded. Eight GenZ and three Boomers
participants were excluded for generating a low number
of associative responses per cue-word (e.g., one associative
response). Four additional GenZ participants were excluded
because they did not comply with task instructions. Table 1
lists demographic information of the participants in all groups.
No statistical differences were found across the cohort groups
for years of education or the average number of associations
generated by the groups across the different cue words.
This study was approved by the University of Pennsylvania
Institutional Review Board.
TABLE 1 | Demographic information on the three groups.
GenZ Mill GenX Boomers
N 88 118 151 97
%M/%F 63/37 63/37 50/50 38/62
Age 26.5 (2.1) 33.0 (2.2) 46.4 (4.4) 60.1 (5.4)
Education 15 (1.7) 15 (1.9) 15 (2.4) 15 (2.1)
Associations 6.67 (2.66) 7.06 (2.50) 7.45 (2.65) 7.37 (2.16)
N–number of participants. %M/%/F–percentage of male and female participants.
Age–average age in years. Education–average number of education years
(standard deviation in parentheses). Associations–averaged number of
associations generated across over all cue words.
Measures
Stimuli Construction
We started with terms that relate to Beauty and Wellness and
terms that bridge the two concepts. To identify such terms,
we examined the patterns of natural language use in English
(Chowdhury, 2003), using the computational semantic approach,
word2vec (Mikolov et al., 2013). Word2vec focuses specifically
on word-level textual corpora, trained on a corpus of 100 billion
words scraped from Google News over 10 years. The model
contained 300-dimensional vectors for 3 million unique words
and phrases. Word2vec focuses on the context of individual
words, and through a deep neural network predictive modeling
approach predicts a target word from a sample of closely co-
occurring words (i.e., context words). Previous work reports
that this method outperforms traditional computational semantic
models, such as latent semantic analysis (Mikolov et al., 2013).
We implemented word2vec via the Natural language Toolkit
(Edward and Steven, 2002) and the Gensim (Rehurek and Sojka,
2010) python packages. We computed the top 10% (300K)
most frequently appearing words and then computed 700
word-vectors from this list most similar (cosine similarity) to
the word-vector Beauty, the word-vector Wellness, and the
word-vector obtained by adding the word vectors of Beauty
and Wellness. From these lists of terms, we selected 15 terms
that appeared in only one list. We did so by selecting terms
based on descending frequency of occurrence in the corpus,
in relation to each of these vectors. Word2vec vectors are
based on 10 years of Google news articles, and it has been
argued that it captures mental thought in a broad sense
(Mikolov et al., 2013;Mandera et al., 2017). As such, we
assume that these terms capture our multidimensional vectors
of interest (Beauty,Wellness, and Beauty +wellness). Finally,
these vector related terms as corpus-based categories offer
a benchmark against which to compare to our empirically
collected data. Cue words were checked for concreteness using
norms of concreteness ratings for 40,000 known English lemmas
(Brysbaert et al., 2014). No significant differences were found
for the rated concreteness of the words across the three groups
(all ps ns).
Continuous Free Associations Task
Participants were presented with a cue word and had 1 min
to generate as many associative responses as possible they
could for that cue word. Participants generated single word
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free associations to the list of 47 cue words. These 47 cue
words consisted of the top 15 words related to Beauty,
Wellness, and Beauty +Wellness, with the additional concepts
Beauty and Wellness as core words (Appendix Table A1). The
Continuous free association task has been shown to be superior
to discrete free association tasks in revealing weaker associative
relations (Hahn, 2008;Kenett et al., 2011;De Deyne et al.,
2013). Continuous free association tasks vary in the amount
of time or number of responses required from participants
(Kenett et al., 2011). Here, we follow previous studies in
having participants generate unrestricted number of associative
responses for 1 min per cue word (Kenett et al., 2011;Kenett
and Thompson-Schill, 2020). The data were preprocessed to
standardize responses and to fix spelling mistakes, e.g., plural
responses were converted to singular form, non-words were
removed, and capital letters were changed to lower letters.
Furthermore, any of the cue words generated as associative
responses were also removed.
Semantic Network Estimation
The various semantic networks were computed and compared
using a computational approach developed by Kenett et al.
(2011). The core idea of this method is the definition of
connections between concepts in the semantic network as an
overlap of associative responses generated to these concepts.
This notion accords with Collins and Loftus (1975) definition
of semantic similarity (Kenett et al., 2011). Currently, growing
evidence points to a strong coupling between associative and
semantic relations (McRae et al., 2012). Our estimated semantic
networks consisted of 47 nodes (cue words from the free
association task), that map to the three corpus-based categories.
The associative correlation between any pair of cue words
was calculated using Pearson’s correlation. This resulted in a
47 ×47 matrix where each cell denotes the association correlation
between node iand node j. Many edges have small values
or weak associations, representing noise in the network. To
minimize noise and remove possible spurious associations, we
applied a planar maximally filtered graph filter (Tumminello
et al., 2005;Kenett et al., 2014;Christensen et al., 2018).
This approach retains the same number of edges between the
groups and avoids the confound of different network structures
arising from different number of edges (van Wijk et al., 2010;
Christensen et al., 2018). Thus, networks constructed by this
approach can be compared directly because they have the same
number of nodes and edges. To examine the structure of the
networks, the edges are binarized so that all edges are converted
to a uniform weight (i.e., 1). Although the networks could
be analyzed using weighted edges (weights equivalent to the
similarity strength), this approach potentially adds noise to the
structure of the network. Furthermore, Abbott et al. (2015)
found that weighted and unweighted semantic network analysis
leads to similar results. Thus, the networks are analyzed as
unweighted (all weights are treated as equal) and undirected
(bidirectional relations between nodes) networks. Estimating
semantic networks for different groups (age generation semantic
networks) that are comprised from the same nodes (47 cue
words) and with an equal number of edges (270 edges) allows
comparison between them. Furthermore, the average degree, the
average amount of edges per node in all networks, was equal
(average of 5.74 edges per node).
Network analyses were performed with the Brain Connectivity
Toolbox for Matlab (Rubinov and Sporns, 2010). The clustering
coefficient (CC) (measuring network connectivity) and the
average shortest path length (ASPL) (measuring global distances)
were calculated (Siew et al., 2019). Lastly, the modularity (Q)
measure was calculated (Newman, 2006). CC refers to the extent
that two neighbors of a node will themselves be neighbors
(i.e., a neighbor is a node ithat is connected through an edge
to node j), averaged across all nodes in the network. Thus, a
higher CC relates to higher overall connectivity in the network.
In semantic networks, such connectivity denotes the similarity
between concepts. ASPL refers to the average shortest number
of steps (i.e., edges) needed to traverse between any pair of
nodes, e.g., the higher the ASPL, the more spread out a network
is. Previous research at the semantic level have shown that
ASPL between concepts in semantic networks corresponds to
participants judgments whether two concepts are related to each
other (Kenett et al., 2017;Kumar et al., 2020). The network’s
CC and ASPL were evaluated qualitatively against the equivalent
parameters in a random network with the same number of nodes
and edges (CCrand and ASPLrand, respectively). Q estimates how
a network breaks apart (or partitions) into smaller sub-networks
or communities (Newman, 2006;Fortunato, 2010). It measures
the extent to which a network has dense connections between
nodes within a community and sparse (or few) connections
between nodes in different communities. Thus, the higher Q
is, the more the network sorts into subcommunities. Such
subcommunities can be thought of as subcategories within
a semantic memory network. Previous research has shown
that modularity in semantic networks is inversely related to a
network’s flexibility (Kenett et al., 2016, 2018).
The group-based network analysis computes a single value for
each network measure for the different networks. In order to
statistically compare the semantic networks across time points
and across groups, we applied a bootstrap method (Efron, 1979)
to simulate a large distribution of the network measures from
the empirical data and compare partial networks for each of
the conditions. The bootstrapping procedure involves a random
selection of half of the nodes comprising the networks. Partial
networks were constructed for each semantic network separately
for these selected nodes. This method is known as the without-
replacement bootstrap method (Bertail, 1997). Finally, for each
partial network, the CC, ASPL, and the Q measures were
computed. This procedure was simulated with 1,000 realizations.
The difference between the bootstrapped partial networks on
each network measure was then tested using either t-test (for
the sex networks) or one-way ANOVA (for the generation
networks) analyses.
To examine differences in the organization of concepts
related to Beauty and Wellness in our networks, we conducted
several complementary analyses: First, we qualitatively identified
and compared the neighbors (directly connected nodes) of
the Beauty and Wellness nodes for each of the networks.
This allows examining key terms associated with each of
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these concepts across each of the networks. Second, we
conducted a community detection analysis to examine how
the terms (nodes) in the network cluster into well-defined
communities (Hayn-Leichsenring et al., 2020). To do so, we use
a data driven approach to determine community assignment
of each node in all of the networks (Betzel et al., 2017). We
applied a modularity maximization approach to partition a
network into communities. This approach uses the Louvain
modularity method, a greedy stochastic method (Lancichinetti
and Fortunato, 2009). Given the stochasticity of this method,
the application of the Louvain modularity method is reiterated
1,000 times (Bassett et al., 2011). To resolve the variability across
the 1,000 iterations of the community partitions, a consensus
analysis was used to identify the optimal community partition
that summarizes the commonalities across the entire distribution
of partitions (Lancichinetti and Fortunato, 2012;Betzel et al.,
2017). The results of this process are data-driven consensus-
based identified communities for each of the networks and
community assignment of each of the nodes in the network
to a specific community. These data-driven partitions are then
compared to our corpus-based defined categories, based on the
word2vec analysis.
We conducted two types of analysis on these data driven
partitions: First, we computed the Rand similarity index between
the corpus-based and each of the data-driven partition separately
(Rand, 1971;Traud et al., 2011). The Rand similarity index
measures the similarity between two partitions, corresponding
to the fraction of node pairs identified the same way by both
partitions (either together in both or separate in both partitions).
Second, we measured the distribution of the different cue-word
categories into the different data-driven identified communities
for each network separately. We then computed the standard
deviation of these distributions as an indicator for how much they
differ (Hayn-Leichsenring et al., 2020). A low SD indicates that
the word-cue category is largely distributed across the different
communities. A high SD indicates that the word-cue category
is clustered in one or a small number of communities. These
analyses allow examining how well the corpus-based and data-
driven partitions align.
Procedure
Participants signed a consent form and then completed all tasks
using Qualtrics1. Participants were instructed to generate, in
1 min, as many different single word associative responses they
could think of to a cue word. In each trial, the cue word
was presented in the center of the screen with a response box
below it, where participants typed their responses. Below the
response box appeared a timer, counting down from 60 s. After
60 s elapsed, a new trial immediately began. Cue words were
presented randomly and after 25 cue words participants had a
short break. Finally, participants were asked a few demographic
questions. Participants were asked to self-identify their age
(“What is your age?”), sex (“What sex were you assigned at
birth?”), and years of education (“How many years of education
have you completed?”).
1www.qualtrics.com
RESULTS
To address our four research questions, we conducted three
analyses: Global network analysis, semantic neighborhood
analysis, and semantic communities’ analysis. These analyses
were conducted across three different types of networks:
A general network (including data from all participants),
generation age cohort groups based semantic networks, and sex
based semantic networks.
Global Network Analysis
We began by estimating a semantic network based on the entire
collected data. Such an estimated general semantic network
provides us a baseline for comparison with the different
group-based networks we further analyze (sex and generation
networks). Next, we computed the network measures of this
general network (Table 2). To visualize the networks (Figure 1),
we applied the force-directed layout (Fruchterman and Reingold,
1991) of the Cytoscape software (Shannon et al., 2003). In
these 2D visualizations, nodes are represented by the respective
images and edges between them are represented by lines.
Since these networks are undirected and weighted, the edges
convey symmetrical (i.e., bidirectional) similarities between two
nodes. Visual inspection of the networks shows that the cue
words Beauty and Wellness are separated from each other, each
surrounded by cue-words in their corresponding category, while
cue-words from the Beauty +Wellness category are distributed
across both “semantic neighborhoods.”
Interim Observations
The conceptual space of Beauty and Wellness segregate into two
communities. The terms that linked Beauty +Wellness based
in the word2vec selection criteria do not appear to bridge the
concepts. These presumptively combination terms appear to link
more closely to the concept of Beauty. Finally, terms such as
Talent,Exotic,Uniqueness, and Individuality, contrary to the
word2vec analysis, were more closely related to Wellness than to
Beauty.
Age-Generation Cohort-Based Networks
Next, we estimated the semantic networks for the GenZ, Mill,
GenX, and Boomers generation cohorts. Similarly, to the general
network, we computed the network measures (Table 3) and
visualized the networks with the Cytoscape software (Figure 2A).
TABLE 2 | Network measures for the different networks.
General
CC 0.72
ASPL 2.94
Q 0.47
CCrand 0.14
ASPLrand 2.33
CC–clustering coefficient; ASPL–average shortest path length; Q–modularity
measure; CCrand–Clustering coefficient of random graph; ASPLrand–average
shortest path length of random graph.
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FIGURE 1 | 2D visualization of the 47 cue words (nodes) semantic networks. Edges denote symmetrical, binary relations between nodes. Colors represent category
membership.
The network analysis computes a single value for each network
measure for the different networks. To statistically examine the
differences in the network measures across the four cohorts, we
apply the bootstrapped partial networks analysis (Bertail, 1997;
Kenett et al., 2014) to generate a distribution of values for each
of the network measures from the empirical data. This method
randomly chose half of the nodes comprising the entire network
(23 out of 47 nodes). Next, partial networks were constructed
for both groups for this subset of nodes. Network measures
were computed for each partial network and this procedure was
reiterated 1,000 times. This resulted in a sample distribution of
1,000 samples for all measures (CC, ASPL, and Q) for all four
networks, which we test via a one-way ANOVA on the group
effect of each of the network measures (Figure 2B).
CC
An Age Generation (GenZ, Mill, GenX, and Boomers) one-way
ANOVA revealed a significant main effect of Age Generation
on CC, F(3, 3996) = 39.43, p<0.001, η2= 0.03. Post-hoc
TABLE 3 | Network measures for the different networks.
GenZ Mill GenX Boomers
CC 0.72 0.71 0.72 0.71
ASPL 2.86 3.01 2.86 2.95
Q 0.48 0.51 0.51 0.52
CCrand 0.08 0.08 0.09 0.09
ASPLrand 2.30 2.29 2.28 2.29
CC–clustering coefficient; ASPL–average shortest path length; Q–modularity
measure; CCrand–Clustering coefficient of random graph; ASPLrand–average
shortest path length of random graph.
paired-samples t-test analyses reveal that this effect is driven
by difference in the change in CC across the Age Generation
networks: A significant decrease in CC for the GenZ group
compared to the Mill group, t(1998) = 3.22, p<0.001, d= 0.13;
A significant decrease in CC for the Mill group compared to the
GenX group, t(1998) = 6.38, p<0.001, d= 0.29; Finally, no
significant differences are found between the CC of the GenX
group compared to the Boomers group, p>0.20, d= 0.06.
ASPL
An Age Generation (GenZ, Mill, GenX, and Boomers) one-way
ANOVA revealed a significant main effect of Age Generation
on ASPL, F(3, 3996) = 15.40, p<0.001, η2= 0.01. Post-hoc
paired-samples t-test analyses reveal that this effect is driven
by difference in the change in ASPL across the Age Generation
networks: A significant increase in ASPL for the GenZ compared
to the Mill group, t(1998) = 2.52, p<0.01, d= 0.12; A
significant increase in ASPL for the Mill group compared to the
GenX group, t(1998) = 3.37, p<0.001, d= 0.16; Finally, no
significant differences are found between the ASPL of the GenX
group compared to the Boomers group, p>0.85, d= 0.01.
Q
An Age Generation (GenZ, Mill, GenX, Boomers) one-way
ANOVA revealed a significant main effect of Age Generation
on Q, F(3, 3996) = 13.37, p<0.001, η2= 0.01. Post-hoc
paired-samples t-test analyses reveal that this effect is driven by
difference in the change in Q across the Age Generation networks:
A significant increase in Q for the GenZ group compared to the
Mill group, t(1998) = 3.83, p<0.01, d= 0.15; No significant
difference in Q for the Mill group compared to the GenX group,
p>0.66, d= 0.03; Finally, a marginal significant difference in Q
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FIGURE 2 | Network analysis for Age Generation. (A) 2D visualization of the Age Generation 47 cue words (nodes) semantic networks. Edges denote symmetrical,
binary relations between nodes. Colors represent category membership. Node numbers correspond to their word labels that appear in Appendix Table A1.
(B) Bootstrapping analysis of CC, ASPL, and Q for the Age Generation groups. X-axes–Group, Y= axes–network measure (CC, ASPL, and Q) values.
is found for the GenX group compared to the Boomers group,
t(1998) = 1.93, p<0.05, d= 0.09.
Interim Observations
Our findings are similar to previous observations (Dubossarsky
et al., 2017;Wulff et al., 2018), demonstrating that older
generation cohort groups have more organized, segregated
semantic networks (lower CC, higher ASPL and Q). Furthermore,
the semantic neighborhoods of Beauty and Wellness were more
segregated from each other in the older generation cohort.
Sex-Based Networks
Finally, for our exploratory analysis, we estimated the Female
and Male semantic networks, collapsing across the different
age generation cohorts. We then computed and compared
their network measures (Table 4), and similarly visualized
the networks using the force-directed layout of the Cytoscape
software (Shannon et al., 2003) to plot the graphs (Figure 3A).
To statistically examine possible differences in the
network measures across the networks, we similarly apply
the bootstrapping analysis approach. We then conduct t-test
analyses of the sex effect on each of the network measures (CC,
ASPL, and Q; Figure 3B). An independent t-test analysis on
TABLE 4 | Network measures for the different networks.
Female Male
CC 0.72 0.71
ASPL 2.95 2.90
Q 0.46 0.51
CCrand 0.14 0.13
ASPLrand 2.32 2.75
CC–clustering coefficient; ASPL–average shortest path length; Q–modularity
measure; CCrand–Clustering coefficient of random graph; ASPLrand–average
shortest path length of random graph.
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FIGURE 3 | Network analysis for Sex. (A) 2D visualization of the Sex 47 cue words (nodes) semantic networks. Edges denote symmetrical, binary relations between
nodes. Colors represent category membership. Node numbers correspond to their word labels that appear in Appendix Table A1.(B) Bootstrapping analysis of
CC, ASPL, and Q for the Sex groups. X-axes–Group, Y= axes–network measure (CC, ASPL, and Q) values.
CC revealed that the Female network had a significantly smaller
CC (M= 0.710, SD = 0.02) than the Male network (M= 0.712,
SD = 0.02), t(1998) = 3.55, p<0.001, d= 0.10. An independent
t-test analysis on ASPL revealed that the Female network had a
significantly larger ASPL (M= 2.179, SD = 0.12) than the Male
network (M= 2.158, SD = 0.13), t(1998) = 3.73, p<0.001,
d= 0.17. An independent t-test analysis on Q revealed that
the Female network had a significantly larger Q (M= 0.407,
SD = 0.032) than the Male network (M= 0.403, SD = 0.032),
t(1998) = 2.31, p= 0.021, d= 0.13.
Interim Observations
Overall, these findings reveal that the Male semantic network
is less structured than that of the Female semantic network
(higher CC, lower ASPL and Q). The communities around Beauty
and Wellness are more segregated for women than for men. As
representative of qualitative differences observed, women relate
Education to Beauty, while men relate Talent to Wellness.
Semantic Neighborhood Analysis
Next, we qualitatively identified and compared the direct
neighbors (directly connected nodes) of the cue words Beauty
and Wellness across the seven networks that we examined
(General, Female, Male, GenZ, Mill, GenX, and Boomers;
Figure 4) to identify similarities and differences in their
neighbors. Specifically, for Beauty, the terms Elegance,Feminine,
Gorgeous,Lovely,Sexy, and Stylish were direct neighbors
across all networks. For Wellness, the terms Aerobics,Fitness,
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FIGURE 4 | Semantic neighbors of Beauty and Wellness across the seven estimated semantic networks.
Health,Holistic,Lifestyle,Medical,Nutrition, and Thrive were
direct neighbors across all networks. Furthermore, a few terms
originally assigned to Beauty or Wellness ended up being direct
neighbors of the other concept: The term Education (originally
assigned as a Wellness category) was a direct neighbor of
Beauty (in the Female and Baby Boomers networks); whereas
the terms Delicious,Exotic, and Talent (originally assigned as
Beauty categories) were direct neighbors in some of the Wellness
networks (Supplementary Tables 1, 2). Finally, we examined
the categories that comprise the direct neighbors of Beauty and
Wellness (Supplementary Tables 1, 2). We find that the direct
neighbors of Beauty came from the Beauty +Wellness category
(8/16 overall neighbors) and the Beauty category (7/16 overall
neighbors). In contrast, the direct neighbors of Wellness are
comprised from the entire Wellness category (15/22) with only
three direct neighbors from the Beauty +Wellness category
(3/22). This finding indicates that the Beauty +Wellness category
as derived from the word2vec analysis is more closely related
to the concept of Beauty, and that terms in the Wellness
category derived similarly are more directly related to the concept
of Wellness.
Semantic Community Analysis
Finally, we conducted data-driven community detection analysis
to compute the consensus partition for each of the seven
networks. We computed the Rand similarity index to measure the
similarity of each of the data-driven partitions and the corpus-
based partition (Figure 5). The higher the Rand similarity index
is, the more similar the data-driven partition is to the corpus-
based partition. This analysis revealed that while the General
network was highly similar to the corpus-based partition (Rand
index = 0.68), the sex-based networks were the most similar
to the corpus-based partition (Rand index = 0.73 for both
networks). Furthermore, this analysis reveals that as the age of
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FIGURE 5 | Rand similarity index between corpus-based partition (cue word
categories) and each of the seven data-driven consensus partitions for each
of the networks.
the cohort increased, the overall similarity between the consensus
partition to the corpus-based partition decreased (from a Rand
index = 0.72 for GenZ to a Rand index = 0.67 for Baby Boomers).
This finding is in line with our findings on the varying network
properties over the different generations: In older generation
cohorts, networks are more segregated.
Next, we examined how well the corpus-based categories
map on to the data-driven identified communities in each of
the networks we analyze. For each network, we measured how
many category members of a specific category map on to the
different communities identified in that specific network. Then
we computed the standard deviation of the distribution of the
different category words across these communities (Table 5).
A low SD score indicates that a category is equally distributed
across the communities while a high SD score indicates that a
category largely maps onto one or a few number of categories
(Hayn-Leichsenring et al., 2020). This analysis reveals that in all
networks the cue words Beauty and Wellness always map onto
different communities (leading to a SD of 0). Furthermore, in all
networks, words related to the Wellness category are the most
dispersed across communities (having the lowest SD). Finally,
across all networks, the Beauty +Wellness category has the
highest SD, indicating that cue words in this category largely map
to a small number of clusters (2–3), with a majority of the cue
words in one community. This finding may be related to our
findings that most cue words in this category relate to the concept
of Beauty.
DISCUSSION
People are pre-occupied by notions of beauty and wellness
(Grammer et al., 2003;Koskinen et al., 2017). As a concrete
representation of this pre-occupation, the world of commerce
links beauty to wellness in the sales of their products and services.
The Global Wellness Institute reports that the beauty is the largest
TABLE 5 | Standard deviations (SD) of the distribution of the different category
words into the identified communities in the different semantic networks.
General Female Male GenZ Mill GenX Boomers
Beauty 2.92 3.40 2.83 2.75 2.92 2.92 2.74
Wellness 1.87 2.22 1.87 2.12 1.87 1.87 2.45
Beauty +Wellness 7.78 6.93 9.19 7.78 4.95 3.54 4.27
Concepts 0 0 0 0 0 0 0
Lower SD represents a more equal distribution.
commercial sector at a billion dollars in an overall 4.2 trillion
dollar wellness market (Global Wellness Institute, 2017). In this
study, we asked: How do people think about beauty? About
wellness? Are these concepts linked and do they vary by aged
and sex?
To address these questions, we applied computational network
science methods to analyze free-association responses generated
to terms related to beauty and wellness, collected from a large
sample of participants that varied in sex and age (Kenett et al.,
2011;Siew et al., 2019). This approach allowed us to ask
these questions across different resolutions: A general network,
comprised from the entire data, served as a benchmark; age
generation cohort-based networks, to compare differences in
people of different ages; and sex-based networks, compared
differences between men and women.
Our general network revealed two distinct semantic
neighborhoods, surrounding each of the terms Beauty and
Wellness separately. Thus, these two concepts are conceptually
separable, with related terms that link them. Our work is in
line with previous lexical based analysis of aesthetic concepts
(Kuehnast et al., 2014;Hosoya et al., 2017;Menninghaus et al.,
2019). For example, Menninghaus et al. (2019) found that
elegance, grace, and sexy were terms that largely overlapped with
the concept beauty, but also differed from each other with regard
to their unique meanings. Our approach extends their study by
examining a broader range of terms related to beauty (see Sabb
et al., 2008;Sabb et al., 2009 for a similar arguement).
We found that Beauty was closely related to Elegance,
Feminine,Gorgeous,Lovely,Sexy, and Stylish. These findings
suggest that some associations to Beauty are consistent across sex
and age and incorporate physical and shared cultural notions. For
Wellness, the terms Aerobics,Fitness,Health,Holistic,Lifestyle,
Medical,Nutrition, and Thrive were also linked directly in all
networks. These associations suggest that wellness is associated
with active practices that promote health and thriving; the latter
implying a more abstract sense of fulfilling our potential.
Our cohort network analysis revealed that older generations
had more organized and segregated semantic networks (lower
CC; higher ASPL and Q). These findings are similar to previous
studies on the structure of the mental lexicon across cohorts
(Zortea et al., 2014;Dubossarsky et al., 2017;Wulff et al., 2018,
2019;Cosgrove et al., 2021). Furthermore, we found two effects
of the age cohort on the semantic neighborhood of Beauty and
Wellness: First, for older cohorts, the semantic neighborhoods
of Beauty and Wellness were more segregated from each other.
Second, the semantic network of the Baby Boomers was the
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most segregated, precipitating into many sub-communities,
potentially indicating narrower and more nuanced dissection of
these concepts. Two possible hypotheses can account for these
cohort effects: The first is based on more lived experience–older
adults accumulate more knowledge, which leads to heightened
segregation in their semantic memory network. The second is
based on socio-cultural dynamics–older adults are increasingly
aware of stereotypical models of beauty and wellness related
to young, healthy bodies, awareness that develops their notion
of these concepts, leading to heightened segregation in their
semantic memory network. Our findings cannot distinguish
between these two competing hypotheses, which requires
additional, follow up research to be examined directly.
Comparing the sex-based semantic network, we found
differences in how females and males think of these concepts.
The Female semantic network was more organized with the
concepts Beauty and Wellness being more distinct, while the Male
semantic network had two semantic neighborhoods, for Beauty
and Wellness. Both the Female and Male semantic networks had
semantic neighborhoods (“communities” of terms) around the
concepts of Beauty and Wellness. The Female semantic network,
however, had a third intermediate and smaller community
between the neighborhoods of Beauty and Wellness, serving
as a “bridge” community in between the Beauty and Wellness
semantic communities (Figure 3). This “bridge” community
included terms that focus on an appearance and individuality
(Exotic,Luscious,Delicious,Uniqueness, and Individuality).
Next, we examined which terms comprise the semantic
neighborhoods of Beauty and Wellness across the various
networks (General, Sex-based, Generation-based). This analysis
allows us to identify the terms relevant to these hard-to-define
concepts and examine how they vary in relation to age and sex
(Supplementary Tables 1, 2). Overall, we found similarities and
differences for these semantic neighborhoods across the various
networks. In comparing the male and female networks, the
male Beauty semantic neighborhood had more terms from the
Beauty +Wellness category that related to physical appearances
(e.g., Alluring,Attraction). As for the Wellness, the male semantic
neighborhood was also larger, and included more general terms
(e.g., Education,Hygiene).
The semantic neighborhood of Beauty for the Millennials and
Generation X groups had unique terms related to appearance
(e.g., Attraction,Curvy), compared to the two other groups.
Furthermore, the Baby Boomers cohort was the only group that
had Education as part of the semantic neighborhood of Beauty.
As for age generation differences in the semantic neighborhood
of Wellness, the semantic neighborhoods of Wellness across
the age generation cohorts were mostly similar, but also had
unique terms for the Generation Z (Delicious), Millennials
(Philanthropy), Generation X (Education,Spiritual), and Baby
Boomers (Individuality,Vibrancy). One could speculate that
millennials are attuned to social and communal wellbeing and
older people are more concerned with their personal wellbeing.
We demonstrate that methods from network science can
be applied to investigate abstract concepts such as Beauty and
Wellness. What people mean by these terms can be highly variable
and yet they seem to have a shared understanding. Our method
of analysis offers a window into the conceptual space occupied
by these terms common to how people think and also to how
they differ across demographic variables. Critically, our method
provides a quantitative, empirical driven approach to analytical
philosophical investigations of conceptual analysis (Wittgenstein,
1953;Rosch, 1975;Lakoff and Johnson, 1980;Ramsey, 1992). An
underlying assumption in conceptual analysis—the analysis of
the meaning of concepts conducted by philosophical inquiry—
is that there is a cognitive representation of knowledge, as
proposed by Collins and Loftus (1975). One way that conceptual
analysis is conducted is by philosophers proposing and rejecting
definitions for a concept, based on intuition and logicism
(Ramsey, 1992). Our approach, in contrast, combines empirical
data collection with computational representation of semantic
memory as networks and uses the semantic neighborhood of a
concept as a way to define it. This offers a quantitative approach
that can facilitate such conceptual analysis. In addition, our
approach resonates with the distributional hypothesis, that states
that a concept is “characterized by the company that it keeps”
(Harris, 1970).
Side stepping philosophical and conceptual challenges of
defining such terms (Corbin and Pangrazi, 2001;Scruton,
2011;Redies, 2014;Menninghaus et al., 2019), we approach
this challenge from an empirical, data-driven endeavor. Our
approach focuses on the semantic neighborhoods of Beauty and
Wellness and allows us to examine how these neighborhoods
relate to each other. Our approach informs us about the stability
and variability in conceptualizing beauty and wellness across
age and sex. Importantly, it affirms modern characterization of
both of these concepts to reflect physical, mental, and cultural
attributes. Indeed, our results indicate that beauty is about being
elegant and sexy at the same time; wellness is about being healthy
and spiritual at the same time. Our findings indicate that a core
meaning of these two concepts, operationalized by their semantic
neighborhood, is stable across age and sex and age.
The cultural salience of these concepts is reflected in the
fact that beauty and wellness industries are booming. People
avidly pursue and consume beauty and health related products
(Koskinen et al., 2017). Visiting fitness centers and beauty salons,
taking vitamins, reading self-help books and wellness blogs, going
on activity holidays or quieting down at silent retreats are all
examples of wellness practices today, that are practiced across all
ages (Crawford, 2006;Koskinen et al., 2017).
Our findings convey insights about beauty and wellness that
can be used in an applied manner. Given the current massive
beauty and wellness industries (Global Wellness Institute, 2017),
there is great commercial interest in how these concepts relate
for different sex and age potential consumers (Chen et al., 2015;
Ostapenko, 2015;Valentine, 2016;Khan et al., 2017;Lee et al.,
2019). Our findings regarding terms that stay stable or change
as direct neighbors of the concepts beauty and wellness may
be used to identify the associated relevance of terms to specific
populations. A scrutiny of Figures 13and Supplementary
Tables 1, 2 offers a rich source of how conceptualization of
these terms vary by demographics. For example, in the Female
semantic network, the neighborhoods of Beauty and Wellness are
connected via an intermediate subcommunity of terms that relate
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to physical attributes and pleasure from food. Women relate
Education to Beauty, while men relate Talent to Wellness.
Our findings may also impact philosophical aesthetical
conceptualization of the notions of beauty and wellness
(Zangwill, 2005, 2018;Scruton, 2011, 2018;Sartwell, 2017). For
example, perhaps the main philosophical debate on beauty is
whether beauty is subjective, e.g., “in the eye of the beholder,
or whether it is an objective feature of beautiful things (Sartwell,
2017). Our findings of terms that are semantic neighbors of the
term Beauty and are stable across all network comparisons we
conducted may contribute to this philosophical debate. These
stable semantic neighbors may indicate an objective aspect of
beauty, that is shared across people that vary in age and sex.
Thus, the application of network science methodologies on
empirical human data may inform philosophical discussions,
discussions that go all the way back to ancient Greece
(Sartwell, 2017).
Our study has several limitations. One limitation is that we
cannot distinguish whether our age generation effect is due
to a cohort effect or reflects changes across the lifespan. This
limitation applies to other similar “aging” studies (Dubossarsky
et al., 2017;Wulff et al., 2018;Cosgrove et al., 2021). Longitudinal
research is needed to examine effects of aging on the evolution
of the concepts of beauty and wellness (Costanza et al.,
2012, 2017;Costanza and Finkelstein, 2015;Rudolph et al.,
2020a). While cross-sectional age generation cohort analysis is
common in aging studies (Costanza et al., 2012), longitudinal
and time varying methods to study individual variation in
age, compared to aggregated groups, should be applied in
such follow up studies to our current research (Costanza
et al., 2017). Such an individual-based research is crucial given
that age-generation classification varies across cultures and
individuals’ self-identity.
A second limitation is that our semantic networks were
small (47 nodes) and comprised solely from terms that were
identified as strongly related to either the terms Beauty,Wellness,
or Beauty +Wellness. Perhaps the stability we find in the
semantic neighborhoods of Beauty and Wellness arise from an
“under-sampling” of the mental lexicon. In addition, while we
find significant differences in the network measures across our
different analyses, the effect sizes are extremely small. Such small
effect sizes may be due to the small size of these networks.
Additional study with a larger number of terms and categories
is required to replicate and generalize our findings.
A third limitation is that our analysis is conducted at the
group level, aggregating over participants for the different
conditions we examined. Thus, a similar analysis as conducted
in our study at the individual level would be helpful. While
currently still a challenge, a few methods have been proposed
to estimate individual-based semantic networks (Morais et al.,
2013;Zemla et al., 2016;Benedek et al., 2017;He et al.,
2020;Wulff et al., 2021). Thus, follow up research is needed
to estimate individual-based semantic networks related to the
concepts of beauty and wellness, to replicate and extend our
current findings.
A fourth limitation is related to how we identified and selected
the different terms we use for our semantic network analysis,
via word2vec analysis of the Google news corpus. Research has
indicated that performance of semantic space models such as
word2vec strongly depends on the choice and scope of the text
corpus used, which can become the determining factor in how
well the model captures human performance (Recchia and Jones,
2009). In regard to our study, the specific newspaper sources and
articles that are analyzed from the Google news corpus to extract
the word vectors related to beauty and wellness may be biased.
Such bias may be related to narrow scope of meanings related
to these terms or biased by specific age of the authors. Thus,
additional research is needed to replicate and extend our findings
by using semantic spaces based on other textual corpora which
will allow verifying and generalizing our findings.
A fifth limitation relates to our collected sample. In our
study, we only focused on native English speakers living in the
United States. Thus, there are certainly important linguistic and
cultural differences related to the perception and comprehension
of Beauty and Wellness across different languages and cultures.
For example, Jackson et al. (2019) conducted a large scale
computational linguistic analysis of emotion words across 174
languages. The authors found both language and cultural
differences but also potentially universal structure in emotion
linguistic spaces (Jackson et al., 2019). The work of Jackson
et al. (2019) provides a promising future direction for our study,
potentially conducting a similar large scale linguistic analysis to
identify Beauty and Wellness universal terms and then replicate
our empirical semantic network analysis approach.
Finally, our sex-based semantic network analysis was
exploratory. While our analysis revealed interesting, preliminary
results on differences in how men and women represent the
concepts of beauty and wellness, we did not design the study
to match the number of men and women across the four age
generation cohorts. Thus, additional research is needed to
examine such sex-based differences, which are hypothesis driven
and matched across the sample of men and women.
A key aspect of our semantic neighborhood and semantic
community analyses is based on our corpus-based categories and
terms, derived from word2vec (Chowdhury, 2003;Mikolov et al.,
2013). While we find that Wellness related terms comprise a
cohesive semantic neighborhood surrounding the term Wellness,
the Beauty +Wellness terms mostly related to Beauty. In
addition, some terms that were identified via word2vec as
related to the Beauty/Wellness categories were in the semantic
neighborhood of the Wellness/Beauty categories. These findings
also further illustrate the challenge in using corpus-based
approaches (e.g., word2vec) to capture mental thought (De
Deyne et al., 2016b;Kenett et al., 2017;Hayn-Leichsenring
et al., 2020;Kumar et al., 2020). Thus, our findings relate to an
open debate in computational semantics and suggest limitations
of corpus-based methods such as word2vec to predict human
behavior (Griffiths et al., 2007;Hutchison et al., 2008;Mandera
et al., 2015, 2017;Vankrunkelsven et al., 2018;Kenett, 2019;
Kumar, 2021).
In conclusion, we applied computational network science
methods to study the semantic structure of beauty and wellness,
their relation to each other and how they differ across age and sex.
Our approach offers an empirical way to clarify the contribution
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fpsyg-12-696507 August 5, 2021 Time: 11:3 # 14
Kenett et al. Beauty and Wellness Networks
of knowledge in aesthetic experiences in so far as language
rather than perception is being used to probe how people
think about beauty and wellness (Chatterjee and Vartanian,
2014, 2016). We found that the semantic neighborhoods of
beauty and wellness are largely stable across different age and
sex. However, we also find unique differences across these
comparisons and find generation cohort effects, showing how
nuanced associations of these concepts vary by age. Mapping
the space of concepts that characterize beauty, wellness, and
their connection advances our understanding of these socially
salient concepts.
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in
the Supplementary Material, further inquiries can be directed to
the corresponding author.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by University of Pennsylvania Institutional Review
Board. The patients/participants provided their written informed
consent to participate in this study.
AUTHOR CONTRIBUTIONS
All authors conceived and planned the study and wrote the
manuscript together. AC provided the theory for the study. YK
and LU provided the computational methods to run and analyze
the study. YK collected and analyzed the data.
FUNDING
This project was supported, in part, by the Global Wellness
Institute to AC.
ACKNOWLEDGMENTS
We thank Kelly Porter for her help in data collection in
this project. We also thank Rohan Ghuge for his help in
stimuli construction.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpsyg.
2021.696507/full#supplementary-material
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Conflict of Interest: The authors declare that the research was conducted in the
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APPENDIX
APPENDIX TABLE A1 | Cue-words used in the continuous free association task.
Category Stimuli
Beauty Attractive (12); Charm (31); Dazzling (35); Delicious (16); Exotic (33); Glitter (36); Lovely (11); Passion (9); Perfection (37); Romance (14); Sexy
(15); Sleek (34); Stunning (10); Stylish (32); Talent (13).
Wellness Aerobics (42); Education (45); Fitness (19); Health (17); Holistic (41); Hygiene (39); Leisure (38); Lifestyle (44); Medical (18); Mindfulness (20);
Nutrition (40); Philanthropy (22); Recreation (23); Spiritual (21); Thrive (43).
Beauty +Wellness Aesthetic (3); Alluring (26); Curvy (8); Elegance (24); Enchanting (28); Fabulous (1); Feminine (6); Freshness (25); Gorgeous (2); Individuality
(27); Intimacy (5); Luscious (7); Simplicity (4); Uniqueness (29); Vibrancy (30).
Concepts Wellness (46); Beauty (47)
Numbers (in parentheses) correspond to node labels in Figures 2,3.
Frontiers in Psychology | www.frontiersin.org 18 August 2021 | Volume 12 | Article 696507
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