ORIGINAL RESEARCH
published: 10 September 2021
doi: 10.3389/fpsyg.2021.685815
Validating Visual Stimuli of Nature
Images and Identifying the
Representative Characteristics
Edited by:
Ricardo Garcia Mira,
University of A Coruña, Spain
Reviewed by:
Assaf Harel,
Wright State University, United States
Pourabi Chaudhury,
Institute of Psychiatry – COE,
IPGME&R, India
Elizabeth Louise Freeman,
Sheffield Hallam University,
United Kingdom
*Correspondence:
Terri Menser
tmenser@houstonmethodist.org
† ORCID:
Terri Menser
orcid.org/0000-0003-3095-2590
Juha Baek
orcid.org/0000-0002-4977-9970
Jacob Siahaan
orcid.org/0000-0003-2945-6258
Jacob M. Kolman
orcid.org/0000-0003-3205-1462
Domenica Delgado
orcid.org/0000-0003-3672-8186
Bita Kash
orcid.org/0000-0002-9491-6815
Specialty section:
This article was submitted to
Environmental Psychology,
a section of the journal
Frontiers in Psychology
Received: 25 March 2021
Accepted: 20 August 2021
Published: 10 September 2021
Citation:
Menser T, Baek J, Siahaan J,
Kolman JM, Delgado D and Kash B
(2021) Validating Visual Stimuli
of Nature Images and Identifying
the Representative Characteristics.
Front. Psychol. 12:685815.
doi: 10.3389/fpsyg.2021.685815
Terri Menser1,2*†, Juha Baek1†, Jacob Siahaan1†, Jacob M. Kolman1†,
Domenica Delgado3† and Bita Kash1,4†
1 Center for Outcomes Research, Houston Methodist, Houston, TX, United States, 2 Department of Population Health
Sciences, Weill Cornell Medical College, New York, NY, United States, 3 Center for Innovation, MD Anderson Cancer Center,
Houston, TX, United States, 4 Department of Health Policy and Management, Texas A&M University, College Station, TX,
United States
This study fills a void in the literature by both validating images of nature for use in
future research experiments and examining which characteristics of these visual stimuli
are found to be most representative of nature. We utilized a convenience sample of
university students to assess 129 different nature images on which best represented
nature. Participants (n = 40) viewed one image per question (n = 129) and were asked
to rate images using a 5-point Likert scale, with the anchors “best represents nature” (5)
and “least represents nature” (1). Average ratings across participants were calculated
for each image. Canopies, mountains, bodies of water, and unnatural elements were
identified as semantic categories of interest, as well as atmospheric perspectives and
close-range views. We conducted the ordinary least squares (OLS) regression and the
ordered logistic regression analyses to identify semantic categories highly representative
of nature, controlling for the presence/absence of other semantic categories. The
results showed that canopies, bodies of water, and mountains were found to be
highly representative of nature, whereas unnatural elements and close-range views were
inversely related. Understanding semantic categories most representative of nature is
useful in developing nature-centered interventions in behavioral performance research
and other neuroimaging modalities. All images are housed in an online repository and we
welcome the use of the final 10 highly representative nature images by other researchers,
which will hopefully prompt and expedite future examinations of nature across multiple
research formats.
Keywords: nature therapy, ecotherapy, functional magnetic resonance imaging, image validation, validation study
INTRODUCTION
Natural settings have been shown to improve various health outcomes (Berman et al., 2014; Berto,
2014; Franco et al., 2017) and can be important to one’s overall well-being (Capaldi et al., 2015; Dean
et al., 2018). Studies have examined nature’s effect on recovery in a variety of patient populations
including cancer (Blaschke, 2017), dementia (Uwajeh et al., 2019), and surgery (Ulrich, 1984; Ulrich
et al., 1993; Diette et al., 2003). The health outcomes from those studies found associations between
exposure to nature and the ability to cope with illness (Blaschke, 2017), decreased stress and anxiety
(Bratman et al., 2019), improved cognitive functioning (Uwajeh et al., 2019), and decreased pain
(Ulrich, 1984; Ulrich et al., 1993; Diette et al., 2003). Furthermore, exposure to commonly accessible
natural settings such as forests (Hansen et al., 2017) and blue spaces (i.e., areas in close proximity
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Validating Visual Stimuli of Nature Images
to bodies of water) (Gascon et al., 2017) have been shown to have
a positive association with health. Forest therapy has been shown
to improve well-being (Hansen et al., 2017), mental health in
psychiatric patients (Bielinis et al., 2019), sleep quality in cancer
patients (Kim et al., 2019), and physiological improvements in
patients coping with chronic widespread pain (Han et al., 2016).
Common physiological dependent variables that assess
nature’s effects are shifts in blood pressure, heart rate, and
cortisol levels in order to evaluate decreases in stress and
possible reduction of cardiovascular disease risk (Haluza et al.,
2014; Twohig-Bennett and Jones, 2018; Mygind et al., 2019).
Psychological factors that are often studied in research focused
on nature’s effects include self-assessments of mental health
components (e.g., changes in concentration, stress levels, etc.)
(Bowler et al., 2010; Haluza et al., 2014). Both physiological and
psychological responses can give insight to the quantification of
effective nature exposure doses (Barton and Pretty, 2010; Cox
et al., 2017; Browning et al., 2020). However, the management
of experimental conditions in natural settings are a common
obstacle when assessing nature’s effects in outdoor settings of
the true environment (Tennessen and Cimprich, 1995; Bowler
et al., 2010). As a result, researchers have utilized artificial, indoor
settings (Jo et al., 2019), and visual stimuli such as images
(Gamble et al., 2014; Van den Berg et al., 2016), videos (Pilotti
et al., 2014; McAllister et al., 2017; Bourrier et al., 2018), and
virtual reality (Valtchanov et al., 2010; Calogiuri et al., 2018;
Browning et al., 2020) for controlled nature experiments.
An initial challenge to conducting such studies is definitional.
“Nature” is a metaphysically troubling concept, complicated by
debates on socially constructed meaning and broad conceptions
which deconstruct the distinction between the “natural”
and “human” (Frumkin et al., 2017). The current Medical
Subject Headings (MeSH) term “Nature” is maximally broad,
“restrict[ed] to Nature as an abstract or philosophical concept”
as “the system of all phenomena in space and time; the totality
of physical reality” (U.S. National Library of Medicine, 2020),
and is not indexed with terms related to the environment or
ecology. Research agendas on nature and human health may
eschew such philosophical quandaries in favor of practical,
example-based definitions, including elements such as “plants
and non-human animals . . . together with abiotic elements
such as sunset or mountain views” and settings ranging from
managed parks to so-called “wilderness” (Frumkin et al., 2017,
p. 1). Indeed, example-based selections of stimuli do “reflect
practical experimental needs” over theory (Zhang et al., 2019,
p. 1180). However, given the potentially wide range of disparate
intuitions about what is natural, and in particular the complex
challenges of producing rigorous and reproducible functional
magnetic resonance imaging (fMRI) studies (Poldrack et al.,
2008), the construct validity of pictorial stimuli purportedly
representing nature should be tested. For instance, Berman et al.
(2014) used survey methods in which participants rated nature
scenes for similarity, provided unprompted single-term labeling
(including nature/natural, manmade, etc.) to images, and rated
images numerically for degree of naturalness, with an analysis
to correlate the high-naturalness scenes with common low-level
visual features (saturation, hue, brightness, entropy, gradient,
and straight vs. curved edge density) that may be indicative of
such scenes in general.
The use of fMRI technology with validated nature images
offers a means of examining nature’s effect on the brain in
a controlled setting with replicable results. Few studies have
taken advantage of fMRI methodology to better understand
neurological responses to nature (Kim et al., 2010a,b; Bratman
et al., 2015); fMRI studies have largely focused on patterns
in scene recognition and discrimination between subtypes of
semantic categories of scenes. Walther et al. (2009) identified
regions of the brain associated with distinguishing types of
natural environment (beaches, buildings, forests, highways,
industry, and mountains). Park et al. (2011) and Kravitz et al.
(2011) likewise found a family of visual areas involved in scene
recognition and spatial factors associated with scene recognition,
in part using images depicting natural scenery.
There are limited studies that offer validated nature images
for experimental use. The SYNS dataset (SYNS dataset, 2015;
Adams et al., 2016) uses a variety of nature-related subcategories
informed by land use categories in the United Kingdom, but the
underlying study’s focus on low-level feature analysis (surface
attitude), specifically for image-3D perceptual relations, did
not seem to involve or require an independent validation step
of global semantic categorization. There are also a variety of
databases containing validated image stimuli, but not necessarily
indexed by or validated for “naturalness” semantic categories,
including: food behaviors (King et al., 2018), morals (Crone
et al., 2018), fears (Michałowski et al., 2017), and disgust
(Haberkamp et al., 2017). The purpose of this study is to validate
images of nature, as a semantic category, and secondarily to
identify semantic subcategories found to be most representative.
The availability of these images both expedites and encourages
future examinations of neuroactivity in response to nature
exposure, and can facilitate a range of other research designs
focused on nature.
MATERIALS AND METHODS
Study Participants and Image Selection
Images of nature were selected by two researchers (TM and
DD) from four open source websites using six search terms
(refer to Supplementary Appendix 1 for complete details) in
addition to searching the International Affective Picture System
(Lang et al., 1997) for both nature and urban images; the latter
contributed only to the control set of urban visual stimuli. The
resulting 129 nature images were presented as an online survey,
open for 30 days from January 21 to February 20, 2019, to a
total of 40 undergraduate students, medical students, and public
health graduate students from the Texas A&M University system.
Table 1 shows the composition of students from our recruitment
pool within the Colleges of Engineering, Medicine, and Public
Health from fall of 2018 (Texas A&M University, 2020). We
provided compensation to the participants for their time in the
form of a $20 gift card. The study protocol was approved by
the Houston Methodist Research Institute’s Institutional Review
Board (Pro00020819).
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TABLE 1 | Demographics of the Texas A&M University Student Population for
Colleges of Engineering, Medicine, and Public Health (2018).
Gender
Ethnicity
Background Male Female White Hispanic Asian Black Other
Population (%) 16,597 5,829 10,393
(74) (26) (46.34)
4,911
(21.90)
3,046 656 3,420
(13.58) (2.93) (15.25)
Procedures
Participants viewed the survey which consisted of the selected
images and rated each one based on a 5-point Likert scale,
with “1” corresponding to “least representing nature” and “5”
to “best representing nature.” A researcher (JS) reviewed each
image to make a list of the major features in the image. Then,
two researchers (TM and JS) condensed these images into
categories and features that aligned with previous restorative
health literature, noting the established divide between blue and
green space. While this further subcategorization was subjective,
the coders had no pre-established concept of what elements
would be predictive of high ratings of naturalness which is
what prompted the post hoc analysis. JS initially coded the
images, guided by TM; to confirm the categorizations, another
researcher (JB) reassessed the 129 images any differences in
coding were decided by TM.
As a result, the research team identified semantic
subcategories – bodies of water, canopies (vegetation over
eight feet tall), mountains, unnatural elements (i.e., objects and
man-made structures, such as boats and walkways, respectively) –
and image framing properties – atmospheric perspectives and
close-range views – that were coded for each image to conduct
post hoc analyses to discern which features were predictive of
receiving a high rating for nature representation as shown in
Figure 1. An atmospheric perspective was defined as scenery
where objects are perceived as distant when the scattering of
lights blurs the outline of objects which could make distant
mountains appear blue and more nearby mountains appear clear
(Kalloniatis and Luu, 1995). Close-range views were considered
a view focused on a singular object or small area (e.g., flowers,
plants, etc.). We formed three levels of representations based on
naturalness Likert rating: high (top 25%), moderate (25–75%),
and low (75% and below). The top 10 highly representative
nature images used for our fMRI study were selected based on
the naturalness ratings displaying the semantic categories and
image properties identified as representative of nature.
Data Analysis
Descriptive statistics were used to calculate mean, minimum,
and maximum values of naturalness scores by semantic
category. A two-sample t-test was performed to compare average
naturalness scores between images that include a semantic
category and those that did not include that semantic category.
We conducted the multivariate ordinary least squares (OLS)
regression and multivariate ordered logistic regression (OLR)
analyses to identify semantic categories highly representative of
nature, controlling for the presence/absence of other categories.
In the OLS model, the dependent variable was a continuous
variable of naturalness score and the independent variables were
all semantic categories coded in a binary manner (presence
of a semantic category: 1 or absence: 0). The results were
represented as coefficients and 95% confidence intervals (CIs).
In addition, the OLR model had a dependent variable with
three categories (high, moderate, and low) of naturalness scores
and all independent variables included in the OLS model.
The odds ratios (ORs) and 95% CIs were presented in this
model. Statistical analyses were conducted using Stata version
15 (StataCorp LLC, College Station, TX, United States). All
statistical tests were two-sided, and a P-value < 0.05 was
considered statistically significant.
RESULTS
The average value of ratings for 129 nature images was 4.14
(standard deviation = 0.49, minimum value = 2.73, maximum
value = 4.88). Table 2 shows the descriptive statistics of the
number, average score of naturalness ratings, and levels of
naturalness (high, moderate, and low) by semantic subcategory
or framing property. We found that canopies (N = 76) and
bodies of water (N = 66) were the most common throughout
all images while atmospherics perspectives (N = 25) and close-
range views (N = 25) were the least common. Unnatural features
(mean = 3.76) and close-range views (mean = 3.66) scored the
lowest naturalness ratings, whereas mountains (mean = 4.49),
atmospheric perspectives (mean = 4.41), bodies of water
(mean = 4.37), and canopies (mean = 4.27) had higher scores than
the average naturalness ratings (4.14).
The two-sample t-tests showed that all semantic categories
and framing properties had a significant difference in naturalness
ratings when compared to images without them (Table 3).
Particularly, nature images that included canopies, mountains,
bodies of water, and atmospheric perspectives had significantly
higher average scores than those that did not include them,
respectively (P < 0.001). On the other hand, average scores of
naturalness ratings were significantly lower in nature images with
unnatural elements and close-range views than those without
these elements (P < 0.001).
Table 4 shows the results of multivariate OLS regression
and multivariate OLR analyses. We found that canopies,
mountains, and bodies of water were positively associated with
naturalness ratings in both models, consistently, indicating
that these semantic categories are highly representative of
nature. In addition, unnatural elements and close-range views
were found to be negatively associated with ratings of nature
representation. However, atmospheric perspectives were not
significant in these models.
Finally, the principal investigator (TM) selected the final 10
images among the highest scoring 31 images scored 4.6 or higher
from the total of 129 images rated to be used in a related project.
All of these images and their respective ratings are available
for future research and are housed in an online repository
(Supplementary Appendix 2; see data availability statement).
Table 5 describes the average naturalness ratings, and semantic
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FIGURE 1 | Semantic and framing features. (A) Atmospheric perspectives, (B) bodies of water, (C) canopies, (D) close-range views, (E) mountains, and (F)
unnatural elements.
TABLE 2 | Average scores of naturalness ratings and levels of naturalness by
semantic subcategory or image property.
TABLE 3 | Comparison of average naturalness scores between presence and
absence by semantic subcategory or image property.
N Average score
Level of naturalness (N, %)
of naturalness
High
Moderate
Low
(top 25%) (25–75%) (≤75%)
Semantic subcategories
Canopies
76
4.27
Mountains
43
4.49
Bodies of water 66
4.37
Unnatural
elements
28
3.76
Image properties
Atmospheric 25
4.41
perspectives
Close-range
25
3.66
views
Total
129
4.14
28 (36.8)
25 (58.2)
26 (39.4)
0 (0.0)
35 (46.1
17 (39.5)
36 (54.5)
15 (53.6)
13 (17.1)
1 (2.3)
4 (6.1)
13 (46.4)
13 (52.0) 10 (40.0)
2 (8.0)
0 (0.0)
7 (28.0) 18 (72.0)
34 (26.4) 62 (48.1) 33 (25.6)
categories included in the set of 10 highly representative nature
images. The average natural ratings of the image set ranged
between 4.62 and 4.88 with 6 out of 10 images including all three
semantic categories: canopies, mountains, and bodies of water.
DISCUSSION
This study yielded a set of validated nature images to be
used in nature-based research including fMRI methodological
Presence
Absence
P-value
N mean (min, max) N mean (min, max)
Semantic subcategories
Canopies
76 4.27 (2.73, 4.88) 53 3.95 (2.82, 4.87) <0.001
Mountains
43 4.49 (3.73, 4.88) 86 3.96 (2.73, 4.87) <0.001
Bodies of water 66 4.37 (2.89, 4.88) 63 3.90 (2.73, 4.69) <0.001
Unnatural
elements
28 3.76 (2.73, 4.43) 101 4.24 (3.34, 4.88) <0.001
Image properties
Atmospheric
perspectives
25 4.41 (2.89, 4.76) 104 4.08 (2.73, 4.88) <0.001
Close-range
views
25 3.66 (2.89, 4.44) 104 4.25 (2.73, 4.88) <0.001
approaches. These images are available to researchers1 to
encourage the study of nature’s effects and to minimize the initial
outlay of resources and decrease the time required to conduct
nature studies. The semantic subcategories that were found to
be most predictive of high ratings of nature representation
included canopies, bodies of water, and mountains which aligns
with the limited studies available in the current literature on
nature in these areas.
High naturalness scores for open spaces and atmospheric
perspectives, and low naturalness for close-range views, align
1https://centerforhealthandnature.org/research/fmri-image-validation/
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TABLE 4 | Results of multivariate ordinary least squares (OLS) regression and multivariate ordered logistic regression (OLR) models.
Semantic subcategory/image property
Multivariate OLS model
Multivariate OLR model
Coef. (95% CI)
Canopies
Mountains
Bodies of water
Unnatural elements
Atmospheric perspectives
Close-range views
0.25 (0.12, 0.38)
0.32 (0.18, 0.47)
0.29 (0.18, 0.41)
−0.61 (-0.74,-0.48)
−0.03 (-0.20, 0.14)
−0.26 (-0.44,-0.08)
N = 129; Coef, coefficient; OR, odd ratio; 95% CI, 95% confidence interval.
P-value
< 0.001
< 0.001
< 0.001
< 0.001
0.726
0.005
OR (95% CI)
4.38 (1.43, 13.4)
13.99 (3.8, 51.49)
6.97 (2.55, 19.09)
0.02 (0.01, 0.07)
0.83 (0.19, 3.51)
0.09 (0.02, 0.52)
P-value
0.010
< 0.001
< 0.001
< 0.001
0.798
0.007
with studies that have considered the openness vs. closedness of
spatial boundaries as important mediators to consider, both in
survey-based scene classification (Zhang et al., 2019), and in fMRI
studies of brain activity (Kravitz et al., 2011; Park et al., 2011).
Zhang et al. (2019) in particular found a high correlation between
natural-open and between manmade-closed; relative distance
(i.e., nearness/farness of the exemplar terrain in how the shot is
framed) is also a factor to consider (Kravitz et al., 2011). Other
lower-level image properties should also be considered in image
selection; for instance, correlations have been found between
images rated as highly natural and edge density, fewer straight
and more curved edges, and “less hue diversity” (Berman et al.,
2014, p. 17). Note that unlike much of this literature, we remained
agnostic regarding whether or not these nature images constitute
“scenes” as such, but there are valuable related discussions of
the role of natural scene recognition qua scene in the literature,
and related low-, mid-, and high-level visual features which
contribute to the perception of scenes (Groen et al., 2017).
The high representation of canopies coheres with the
restorative effects that have been discussed through specifically
forest therapy as a type of nature exposure. Forest therapy,
defined as having interaction in the forest (e.g., walks, exercises,
etc., in the forest environment) has also been shown to result
in positive changes in well-being (Shin et al., 2010) and has
resulted in a feeling of restoration when compared to an urban
environment (Takayama et al., 2019). There are also multiple
theories that canopies (i.e., vegetation over eight feet tall) are
TABLE 5 | Top 10 highly representative nature images, average naturalness
ratings, and semantic categories.
Rank
1
2
3
4
5
6
7
8
9
10
Image
number
108
7
37
87
88
112
34
56
55
17
Average
Canopies Mountains Bodies of
naturalness
water
4.88
4.87
4.87
4.76
4.75
4.72
4.70
4.69
4.63
4.62
a feature that can support a viewer’s ability to learn about an
environment (Hunter and Askarinejad, 2015). Canopies can
provide a sense of organizational symmetry with the pairing of
trees that could encourage a viewer to explore the environment
even further (Hunter and Askarinejad, 2015).
The association of bodies of water with high levels of
nature representation is likely related to its restorativeness; a
prior study found a higher likelihood of participants rating
both natural and man-made scenery to have higher perceived
restorativeness when water elements were present (White et al.,
2010). Additionally, there has been fairly extensive studies to
understand the benefits of blue space (i.e., areas near bodies of
water) – a systematic review of 35 quantitative studies on blue
spaces found consistent positive association with well-being and
mental health (Gascon et al., 2017).
The inclusion of mountains in the higher-rated images can
be explained by the awe-evoking effects that have been reported
in the literature. Although there are no known therapeutic
applications of the mountain sceneries, studies have examined
participants’ differential responses to mountain scenery and
neutral nature settings, finding that mountain scenery conveys
a higher degree of vastness (Joye and Bolderdijk, 2015). The
lack of studies focused on the effects of exposure specifically to
time in the mountains is likely due to the inaccessibility of this
environment, but our results show that this feature of nature is
highly predictive of ratings of nature representation.
We encourage the use of these validated images to promote
the use of fMRI technology to discern neurological responses
to better understand the effects of nature. For nature studies,
controlling for conditions to assess and accurately quantify
the results can be difficult to do in the natural environment
(Velarde et al., 2007); fMRI studies offer an opportunity
to alleviate the difficulties in the quantification of subjective
results. Lack of available validated images from fMRI studies
encumbers reproducible science (Eklund et al., 2016; Munafò
et al., 2017; Poldrack et al., 2017) which limits scientific
progress in understanding the benefits of nature. Additionally,
the availability of validated images can help to identify factors
that cause false-positive fMRI results (Bennett et al., 2009;
Eklund et al., 2012). The use of fMRIs and images of natural
and urban sceneries have identified neurological responses that
suggested that human beings have an inherent preference for
living in natural environments (Kim et al., 2010a,b; Bratman
et al., 2015). In addition, researchers found that viewing water
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Validating Visual Stimuli of Nature Images
sceneries after urban sceneries enabled activation systems (Tang
et al., 2017). Understanding the restorative effects of natural
environments, and more abstractly of visual features associated
with natural environments (Berman et al., 2014), is useful in
developing nature-centered interventions, and for utilizing fMRI
technology for examinations of neuroactivity. Future work in
using these images should also account for regions normally
activated by natural scene recognition as identified in prior
literature (Walther et al., 2009; Kravitz et al., 2011; Park et al.,
2011), which can both aid in interpretation of results and
also corroborate or challenge the survey-based ratings of these
scenes as exemplifying the semantic category of “natural” and
associated subcategories.
A limitation of this study is how our questionnaire only
assessed nature representation for the nature stimuli and did not
use the same scale for the urban images that were made available
as a control; instead we assessed how representative the control
images were of an urban environment which has previously been
used as the visual stimuli for the control group in nature studies
(Kim et al., 2010a,b). A second limitation is how our study may
not be representative for a general population as recruitment
was university-based (partially mitigated by the diversity of the
particular university used). Choosing this recruitment method
was a tradeoff which allowed for transparent reporting of
overall population demographics of the recruitment pool even
when individual demographics could not be obtained; this is in
contrast to methods utilizing Amazon Mechanical Turk (Xiao
et al., 2016; Zhang et al., 2019), which can affordably recruit a
larger participant pool for image rating tasks, but with variable
population demographics and with the added need to filter out
frequent cases of “bad actors” either through intermittent trial
surveys or by a response-outlier cutoff. Third, our study sought to
validate images for specific experimental purposes in restorative
health studies; others (Zhang et al., 2019) have noted that the
use of a limited number of example subtypes of natural scenes
can limit more theoretical explorations of global properties, and
this limitation should be kept in mind when applying the image
set presented here. Fourth, we have not filtered, normalized,
or evaluated the role of low-level visual features in our image
set, as others (Berman et al., 2014; cf. Groen et al., 2017) have
done, which would be of interest particularly in the design of
artificial scenes or built environments to mimic the qualities
associated with images rated highly for naturalness. We left open
that our survey responses may align with or deviate from these
expected correlations.
Further studies should utilize stimuli that have been validated
for nature representation in order to assess nature’s restorative
effects and its mechanisms. More studies should also identify
neurological responses in addition to shifts in emotional states
such as awe, stress relief, and well-being in patient populations, as
distinct from neutral scene-recognition neurological responses.
Understanding changes in neuroactivity and emotions could aid
in the implementation of therapeutic interventions that can tailor
to the specific needs of patients.
DATA AVAILABILITY STATEMENT
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and
accession number(s) can be found below: https://osf.io/spf8y/
?view_only=ea16da9ea27c486eb9f8997fd67ee897.
ETHICS STATEMENT
The studies involving human participants were reviewed
and approved by the Houston Methodist Research Institute’s
Institutional Review Board. Written informed consent for
participation was not required for this study in accordance with
the national legislation and the institutional requirements.
AUTHOR CONTRIBUTIONS
TM and BK were responsible for the conception of this the study.
TM and DD contributed to the study design and survey content.
JK formed the participant survey and tracked participation. JB
and JS conducted the statistical analyses for this study under TM’s
guidance and supervision. TM, JB, and JS drafted the manuscript.
JK, DD, and BK contributed to manuscript drafts and edits. TM,
JB, and JK were responsible for manuscript revision. All authors
read and approved the submitted version.
FUNDING
We thank the Center for Health & Nature and the Houston
Methodist Physician Provider and Resiliency Program for
supporting this work.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpsyg.
2021.685815/full#supplementary-material
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