Brain activity, underlying mood and the environment: A systematic review

Journal of Environmental Psychology 65 (2019) 101321

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Journal of Environmental Psychology journal homepage: www.elsevier.com/locate/jep

Brain activity, underlying mood and the environment: A systematic review a,∗

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Michael Francis Norwood , Ali Lakhani , Annick Maujean , Heidi Zeeman , Olivia Creux , Elizabeth Kendalla a b

The Hopkins Centre, Menzies Health Institute Queensland, Griffith University, University Drive, Meadowbrook, Queensland, 4131, Australia Centre of Applied Health Economics, Menzies Health Institute Queensland, Griffith University, Parklands, 4029, Australia

A R T I C LE I N FO

A B S T R A C T

Handling Editor: Sander van der Linden

This review explores how different environments affect brain activity and associated mood response. MEDLINE, CINAHL, Web of Science, PsychInfo and EMBASE were searched for peer-reviewed literature published prior to February 2019. 26 sources were included and divided into either a laboratory (n = 17) or naturalistic (n = 9) design. Most (n = 16) compared natural environments against urban/non-natural environments. Natural environments were associated with low frequency brainwaves and lower brain activity in frontal areas, indicating comfortable and subjectively restorative feelings. Urban environments appear to induce brain responses associated with negative affect (demonstrated in an overactive amygdala region). Furthermore, urban environments were associated with activation of the posterior cingulate cortex associated with top-down processing/effortful attention. A sensory accumulation effect is suggested, where the realism of an experimental condition, and therefore validity of participant responses, is greater when more senses are engaged. Longitudinal research is needed to determine whether chronic exposure to environments can promote change in brain behaviour.

Keywords: Brain activity Mood-state Built environment Natural environment

1. Introduction In any given environment, a person is exposed to a variety of influential stimuli which impact individual quality of life, psycho-emotional health and overall health and wellbeing (Brown et al., 2008; Dadvand et al., 2016; Trasande et al., 2008; Triguero-Mas et al., 2015). For example, research has established that odours in built environments effect mood (Lehrner, Eckersberger, Walla, Pötsch, & Deecke, 2000), noise impacts sleep quality (Carter, 1996; Halonen et al., 2012) and depressive symptoms (Orban et al., 2015), temperature effects decision making (Gaoua, Grantham, Racinais, & El Massioui, 2012) and haptic experience effects motivation for outdoor activity (Brown, 2017). In particular, access and exposure to natural environments are associated with improved health outcomes across a variety of domains. For example, exposure to the natural environment is associated with better general health (De Vries, Verheij, Groenewegen, & Spreeuwenberg, 2003), better sustained attention (Lee, Williams, Sargent, Williams, & Johnson, 2015) and people experience less stress when in a room with real nature compared to synthetic nature (Beukeboom, Langeveld, & Tanja-Dijkstra, 2012). Evidently, various characteristics of the built and natural environment have considerable impact on quality of life outcomes. However, the physiological impact of diverse environments is

not as well understood (Bailey, Allen, Herndon, & Demastus, 2018; Chiang, Li, & Jane, 2017). Frumkin et al. (2017) detailed an agenda for future natural environment research and emphasised the importance of investigating the physiological mechanisms underpinning self-reported and observed psycho-emotional health outcomes. Measuring brain activity is an objective method of assessing the physiological impact of engaging with the environment (Keshavarz, Campos, & Berti, 2015; Schäfer et al., 2015). Brain imaging is useful for measuring the effects of unconscious or masked stimuli (Teplan, 2002; Whalen et al., 1998). For example, the brain has well recognised patterns of activity for various emotional responses (LeDoux, 2009) and states of alertness or rest (Aston-Jones, 2005; Greicius, Krasnow, Reiss, & Menon, 2003). Different neuroimaging techniques produce different outputs. Electroencephalography (EEG) is a non-invasive measure of surface cortical electrical activity generated by brain structures (Teplan, 2002). EEG responses to emotional stimuli are well studied (Carretié, Iglesias, & Garcı́;a, 1997; Cuthbert, Schupp, Bradley, Birbaumer, & Lang, 2000; Pollatos, Kirsch, & Schandry, 2005). For example, EEG patterns have identified left frontal activation associated with increased positive mood (Waldstein et al., 2000) and frontal alpha activation with relaxed wakefulness (Cacioppo, Tassinary, & Berntson, 2007). Functional

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Corresponding author. 11 Hefferan Street, Fairfield, Brisbane, Queensland, 4103, Australia. E-mail addresses: michael.norwood@griffithuni.edu.au (M.F. Norwood), a.lakhani@griffith.edu.au (A. Lakhani), a.maujean@griffith.edu.au (A. Maujean), h.zeeman@griffith.edu.au (H. Zeeman), olivia.creux@griffithuni.edu.au (O. Creux), e.kendall@griffith.edu.au (E. Kendall). https://doi.org/10.1016/j.jenvp.2019.101321 Received 8 April 2019; Received in revised form 26 June 2019; Accepted 5 July 2019 Available online 06 July 2019 0272-4944/ © 2019 Elsevier Ltd. All rights reserved.

Journal of Environmental Psychology 65 (2019) 101321

M.F. Norwood, et al.

magnetic resonance imaging (fMRI) is an alternative neuroimaging method that maps haemodynamic changes in the brain with increased blood flow being interpreted as increased brain activity (Logothetis, Pauls, Augath, Trinath, & Oeltermann, 2001). fMRI findings have established the role of the amygdala and hippocampus in processing fear (Zelikowsky, Hersman, Chawla, Barnes, & Fanselow, 2014) and negative affect (Doré et al., 2018), and the role of the subcallosal cingulate in processing sadness (Phan, Wager, Taylor, & Liberzon, 2002). Nearinfrared spectroscopy (NIRS) measures brain activity through the detection of cerebral blood flow. NIRS allows for non-invasive brain activity measurement, is easy to use, and is often a cheaper alternative to fMRI and EEG. Further, NIRS is well suited to studies of emotion (Balconi & Molteni, 2016). For example, in the emotional processing of visual and auditory stimuli, where NIRS showed activation of temporal areas (Maria et al., 2018); activation in prefrontal areas when participants recall emotional events (Ohtani, Matsuo, Kasai, Kato, & Kato, 2005); improved mood following mindful breathing being associated with frontal activation (Matsubara et al., 2018). In summary, research has established the presence of different patterns of brain activity in response to different emotional responses. An initial study measuring the effect of environment on brain activity and associated moods was conducted by Ulrich (1981), who found increased alpha waves and less subjective emotional stress among participants who viewed slides of nature. However, the body of research since then has not been reviewed. In cognitive psychology, brain measures are criticised for being unable to add any new information to the literature since the seventies (Sawyer, 2011) and it is unclear if this is also the case for environmental psychology. Given the need for further research to understand the effects of engaging with diverse environments on the brain (Coburn, Vartanian, & Chatterjee, 2017; Frumkin et al., 2017), this paper presents a systematic review of research investigating the impact of engaging with built and natural environments on brain activity and associated mood. Specifically, this review sought to:

Table 1 Search information. Database

Date

Search

Findings

MEDLINE Web Of Science CINAHL PsychInfo EMBASE 2017–Feb 2019

19-09-17 19-09-17 19-09-17 21-09-17 21-09-17 25-02-19

Title Title Title Title Title Titles

484 1,696 192 962 635 731

Note. The searches from 2017–Feb 2019 included all databases.

landscape*). Details of the fields searched and sources identified from each database have been included in Table 1 below. 2.2. Eligibility criteria Studies were included in this review if they were peer-reviewed and written in the English language. No date restrictions were set. Studies must have investigated the impact of exposure to environment on mood or emotion using a measurement of brain activity (structure or function). Furthermore, studies must have clearly described the methodology and environments to which participants were exposed. 2.3. Screening and study selection Papers were screened in five steps, (a) duplicate screening using the reference management system Endnote© (Reuters, 2013), (b) exclusions based on title, (c) duplicate screening based on visual search, (d) exclusions based on abstract, and (e) exclusions based on full text. Fig. 1 below includes a flow-chart detailing the screening process. 2.4. Data extraction and synthesis Data was extracted and information was collated on the following: (i) citation, (ii) sample, (iii) setting (laboratory or naturalistic) (iv) neuroimaging technique (e.g. NIRS) (v) mood or emotion measure (e.g. self-report scale), (v) conditions (e.g. urban, natural, urban green, virtual etc.), (vi) outcomes (e.g. EEG signal, NIRS output, subjective scores), (vii) identified brain regions, and (viii) author conclusions.

1.) Identify how environments affect brain activity and associated psychological responses. 2.) Synthesise current research and identify research gaps. 2. Method This systematic review was guided by the PRISMA approach (Moher, Liberati, Tetzlaff, & Altman, 2009) for conducting systematic reviews and details around the review aims and methods are also registered within the PROSPERO register for systematic review protocols (Lakhani et al., 2018b).

2.5. Quality appraisal The quantitative version of the McMasters rating tool (Law et al., 1998) was used to assess studies methodological quality. The McMasters Critical Review Form for Quantitative Studies is a methodological quality assessment tool frequently used within systematic reviews synthesising quantitative studies (see Lakhani, Norwood, Watling, Zeeman, & Kendall, 2018a; Lakhani, Townsend, & Bishara, 2017; Li et al., 2016; Norwood et al., 2019; Swanberg et al., 2018). The review form includes eight overarching criteria. These eight criteria are underpinned by 14 domains which can be answered as yes, or no response options. Furthermore, some domains have ‘not applicable’ or ‘not addressed’ response options. Consequently, studies within this review were rated against these 14 domains. For each study, a rating of ‘yes’ was designated with a 1, while ‘no’ or ‘not addressed’, designated with a 0. Two researchers independently rated the studies and any quality appraisal inconsistencies between reviewers were discussed by the first, second, and fourth author until a consensus was reached.

2.1. Search strategy The following databases were searched for peer-reviewed literature published at any date (with dates in brackets): MEDLINE (19 September 2017), CINAHL (19 September 2017), Web of Science (19 September 2017), PsychInfo (21 September 2017) and EMBASE (21 September 2017). A second search was made in the same databases for papers published from 2017 to February 25th, 2019. The following search string was utilised throughout all databases: (Electroencephalography OR EEG OR “quantitative EEG” OR qEEG OR “event related potential” OR ERP OR “brain oscillations” OR “brain activity” OR “cerebral activity” OR “brain response*” OR “Magnetic resonance imaging” OR MRI OR “functional magnetic resonance imaging” OR fMRI OR “time resolved spectroscopy” OR “Near-infrared spectroscopy” OR NIRS OR “Positron Emission Tomography” OR PET OR Magnetoencephalography OR MEG) AND (built OR urban OR “physical environment” OR “spatial environment” OR “virtual environment” OR environment OR communit* OR natural OR nature OR green* OR “blue space” OR park OR parks OR wilderness OR bush OR countryside OR forest OR garden* OR

3. Results Twenty-six studies were included in the review (see Table 2); the screening process is displayed in Fig. 1. Of the 26, 17 took place in a laboratory setting and 9 were naturalistic. 2

Journal of Environmental Psychology 65 (2019) 101321

M.F. Norwood, et al.

Fig. 1. Screening and selection process.

and a control condition. Three studies used a virtual environment design. Two used EEG, both exploring the notion of ‘presence’ in a virtual environment, and one NIRS exploring the effect of 3D versus 2D displays. Together, studies show the importance of presenting virtual environments with as many senses stimulated as possible to increase effects on mood and feeling of presence. In the following sections (3.3.1–3.3.5) neurological and subjective results are listed as the included studies reported them. Synthesis and critical discussion of included studies’ findings in relation to each other and previous literature is provided in sections 3.4 and 4.

3.1. Characteristics of studies Fourteen studies employed EEG as the measure of brain activity, seven employed NIRS or time-resolved spectroscopy (NIRS/TRS) and five employed fMRI (See Table 2 for full characteristics of studies table). 3.2. Quality assessment Inter-rater reliability between the two researchers was moderate to substantial (Cohen-Kappa (unweighted) = 0.56). The methodological quality assessment of each article is provided in Table 3. The range of quality appraisal scores for included papers was 9–13 (out of 14) and the average was 11.19.

3.3.1. Laboratory: urban vs natural environment images Eight studies compared urban and natural environments using images as stimuli (Chang & Chen, 2005; Kim et al., 2010; Kim & Jeong, 2014; Lee, 2017; Martínez-Soto et al., 2013; Roe et al., 2013; Tang et al., 2017; Ulrich, 1981). All eight studies reported the natural environment conditions as producing positive or subjectively favoured states. Three studies employed EEG and two of these found that natural scenes were associated with low frequency waves in various regions of the brain (Chang & Chen, 2005; Ulrich, 1981). The third used an EEG cap that does not provide frequency bands as outputs (Roe et al., 2013). EEG data output was interpreted by the manufacturer's software as indicating levels of: short-term excitement, long-term excitement, meditation, engagement and frustration. The researchers further sorted these into two larger categories of arousal (short and long-term

3.3. Summary of findings Fifteen studies compared urban and natural environments either in the laboratory using images (n = 8) or in naturalistic settings (n = 7). Seven of the studies used EEG and eight used fMRI/NIRS and all found neurological responses and associated emotional responses to natural images or features of the actual environment. Eight studies compared the effects of specific environmental characteristics either in the laboratory (n = 6) or in naturalistic settings (n = 2). Five of the studies used EEG and three used fMRI/NIRS and all found neurological responses and associated emotional responses differed between the specific characteristic of the environment in question 3

N = 10 (7 male and 3 female)

Outdoor virtual environment (VE) and the comparison of four different characteristics: vision, hearing, haptics and olfaction 12 indoor environments of various temperatures, odours and sounds

Direct/indirect lighting and brightness and colour temperature

Azevedo, Jorge, and Campos (2015) (Portugal)

Choi, Kim, and Chun (2015) South Korea

Shin et al. (2015) South Korea

4

Vecchiato et al. (2015) Italy

EEG - 19 or 24

N = 12 (7 male and 5 female)

N=5

Three immersive virtual reality environments: empty room, modern furniture, cutting edge design

‘Traditional Neighbourhood’ (TND) design vs more recent urbanised design

6 conditions. Location in forest (wild) or tended nature (interior,

Hollander and Foster (2016) USA

Chiang et al. (2017) China

EEG- NeuroSky product but electrode number not stated

EEG - 40 electrodes

EEG - Emotiv EPOC Affective suite with 14 electrodes EEG - Emotiv EPOC Affective suite but electrode quantity unclear EEG - 8 electrodes

N = 28 (16 male and 12 female)

N = 12 (6 male and 6 female)

N = 12 (8 male and 4 female)

Three urban settings including one large green space

Aspinall et al. (2013) UK

Stroop, mood (Profile of Mood States-short form) and preference

Heart rate. Environment ratings of familiarity, novelty, comfort, pleasantness, arousal and presence

Visual analogue scales of ‘cool, refresh, comfortable’. Emotional valence and arousal (Self-Assessment Manikin)

Stress (Stress examination sheet)

Presence

Electrocardiogram and subjective satisfaction

EEG - 2 electrodes on occipital regions

N = 249 (n for each condition = 66, 47, 65, 23, 30, 18) EEG N = 64

Five green locations varying in colour, size and scent and one no vegetation location.

Valence, arousal, scene attractiveness, willingness to visit scene

EEG - Emotiv EPOC Affectiv suite with 12 electrodes

N = 20 (8 male and 12 female)

Qin, Zhou, Sun, Leng, and Lian (2013) China

Roe, Aspinall, Mavros, and Coyne (2013) UK

EEG – electrode quantity unclear

N = 38 (10 male and 28 female)

Chang and Chen (2005) Taiwan

Mood and feelings (Zuckerman Inventory of Personal Reactions), heart rate Blood volume pulse, electromyography, state-anxiety (State-Anxiety Inventory)

EEG - 4 electrodes on frontal regions

N = 18 (9 male and 9 female)

Slides of either nature with water, nature mostly vegetation and urban scenes Six conditions of slides of office settings with/without windows, indoor plants and views of nature or urban scenery Urban and landscape pictures

Ulrich (1981) Sweden

Relevant additional measures

Brain activity measure

Participants

Experimental condition/aim of study

Reference and location

Table 2 Characteristics of studies.

Interior setting alpha in frontal lobe higher than edge setting

In green setting EEG indicated meditation state was higher and engagement and frustration were lower. Haptic sensory experiences were most important for the feeling of presence and increased excitement as measured by the EEG. Alpha waves in frontal lobes in less stressful conditions (temperature of 25 degrees Celsius, no road noises or unpleasant smells). High beta in temporal lobe in hot, urban noisy and unpleasant smell conditions. Theta band power in the right frontotemporal and left temporo-parietal regions associated with direct/indirect lighting over purely direct lighting. Theta power in the right frontal region showed a positive correlation with lighting seen as “cool”. High presence showed increased theta power across frontal and left temporal lobes. High familiarity and comfort in frontal midline. Pleasantness resulted in alpha band desynchronization of left parietal and frontal sites. Pleasant and comfortable VEs showed a desynchronization of the mu rhythm in the left hemisphere. EEG headset predetermined outputs for ‘meditation’ and ‘attention’ were higher in TND

No significant differences found between environmental satisfaction and EEG Theta power associated with area satisfaction and delta power associated with height satisfaction

Nature and plants had significantly higher alpha levels in right prefrontal lobe. Any window view resulted in high alpha in left prefrontal lobe. Urban scenes correlated with arousal. Landscape scenes correlated with interest. Landscape scenes associated with greater meditation and lower arousal scores.

Higher alpha in both nature scenes compared with urban in central parietal lobe

Findings in terms of brain activity

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Only measured EEG through one location limiting scope of study

Pilot study

Theta and ‘coolness’ associated in Shin et al. (2015) and coolness was associated with pleasantness.

A possible link to Theta power and environments seen as pleasant which is a finding reported by Vecchiato et al. (2015).

Self-report data on preference would have been useful to help define and further support the EEG reports on levels of excitement

Found contrasting results in EEG engagement levels from the Aspinall, Mavros, Coyne, and Roe (2013) study and explain this with study context and stimuli used. Controlled for factors such as temperature, wind and humidity Only used two electrodes on occipital lobe Report EEG as varying greatly between participants.

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Journal of Environmental Psychology 65 (2019) 101321

N = 13

N = 28 males

N = 38 (20 male and 18 female) n = 19 in each condition

Outdoor nature walk vs Indoor walk

Urban and rural photographs

Comfortable and uncomfortable residential environments

Photographs of high (HRP) or low (LRP) restorative potential

Nature vs urban walk

Urban, Mountain, Forest and Water

Bailey et al. (2018) USA

Kim et al. (2010) South Korea

Kim and Jeong (2014) South Korea

Martínez-Soto, Gonzales-Santos, Pasaye, and Barrios (2013) Mexico

Bratman, Hamilton, Hahn, Daily, and Gross (2015) USA

Tang et al. (2017) Taiwan

5 N = 31 (14 male and 17 female)

N = 30 (18 male and 12 female)

N = 10 (5 male and 5 female)

Walk through green space and urban space

Tilley et al. (2017) UK

N = 95 (n for each condition = 20, 14, 20, 13, 14, 14) N = 43 (3 male and 5 female). 8 selected for interview after

N = 180 n = 30 in each condition(82 male and 98 female)

edge and exterior) and vegetation density (high, medium and low).

Six conditions of various urban and urban nature settings.

Participants

Experimental condition/aim of study

Neale et al. (2017) UK

Reference and location

Table 2 (continued)

Perceived restorativeness scale

Rumination (Reflection Rumination Questionnaire)

fMRI

fMRI

Stress

Cognition (Stroop test and Digit span backward test stated as measures of concentration)

Video elicitation interview one week following walk

Relevant additional measures

fMRI

fMRI

fMRI

EEG - Emoitv EPOC Affective suitewith 5 electrodes

EEG- Emotiv EPOC Affective suite with 14 electrodes EEG - Emotiv EPOC Affective suite with 14 electrodes

EEG – Neurosky product with1 electrode stated

Brain activity measure

Cuneus more active in urban than mountain and water. Further, when compared to

Lower blood flow in subgenual prefrontal cortex following nature walk but not following urban walk.

When moving from Urban to Green, EEG “engagement” decreased, and “excitement” increased; EEG “frustration” increased but self-report contradicted this. When moving from Green to Urban, “frustration” levels were maintained. “Engagement” was more stable in green compared with urban. Both walks reduced high frequencies across frontal lobes (focus) and posterior cortex (anxiety) and promoted global alpha (relaxed state) and theta waves in frontal alpha waves in posterior (non-directive meditative or mindfulness). Theta waves in frontal and alpha waves in posterior greater for outdoor walk. Activation in rural condition: anterior cingulate gyrus, globus pallidus, putamen and head of the caudate nucleus. Activation in urban condition: hippocampus, parahippocamus, amygdala and primary visual cortex Comfortable environment showed activation in calcarine gyrus but uncomfortable activated ventrolateral prefrontal cortex, anterior cingulate cortex, medial prefrontal cortex, hippocampus, parahippocampal gyrus, amygdala and insula HRP image activation included the middle frontal gyrus, middle and inferior temporal gyrus, insula, inferior parietal lobe and cuneus. LRP activation included the superior frontal gyrus, precuneus, parahippocampal gyrus and posterior cingulate cortex. No significant differences in stress scores.

“engagement” and “frustration” higher in green and “excitement” higher in urban busy

Findings in terms of brain activity

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Behavioural data unable to support their hypothesis. Similar findings in brain activation to Tang et al. (2017), apart from activation in cuneus in urban setting attributed to affect and activation valence. Tang et al. (2017) find activation in cuneus for urban setting and attribute this to greater visual processing. Subgenual was the a priori target of analysis for its association with rumination and sadness. Outcomes were corroborated with a paper report rumination scale and other physiological measured taken to further support the authors conclusions. Activation in cuneus in urban setting attributed to visual processing.

No behavioural data gathered or included in the analysis but preference of photos was for natural.

Behavioural measures used cannot support EEG findings of increase in relaxation or meditation during walk.

Some differences in EEG outputs compared with Neale et al. (2017).

High alpha in frontal lobe interpreted as alert relaxation and supported by behavioural measures. Unable to study interaction effects of vegetation density and location. Some different EEG outputs from Tilley, Neale, Patuano, and Cinderby (2017).

Comments/limitations

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Journal of Environmental Psychology 65 (2019) 101321

6

Park, Song, Choi, Son, and Miyazaki (2016) Japan

Song, Igarashi, Ikei, and Miyazaki (2017) Japan Park et al. (2007) Japan

N = 15 females

N = 12 males N = 24 males

Fresh roses

Forest (Shinrin-yoku) vs city

Foliage

N = 18 (9male and 9 female)

NIRS

N=8

Viewing forest scene and urban scene (urban scene from an isolated roof top) Traditional garden (Korea)

Juong et al. (2015) South Korea

Lee (2017) South Korea

NIRS

N = 18 females

Plants (Dracaena deremensis)

Igarashi, Song, Ikei, and Miyazaki (2015) Japan

NIRS

TRS

NIRS

NIRS

NIRS

N = 19 males

3D vs 2D images of water lily

Brain activity measure

Igarashi et al. (2014) Japan

Participants

Experimental condition/aim of study

Reference and location

Table 2 (continued)

Mood (Profile of Mood States), Comfort, naturalness, arousal (modified semantic differential method)

Comfort; relaxation; (modified semantic differential method), mood (Profile of Mood States) Cortisol

Comfort; relaxation (semantic differential method), Mood (Profile of Mood States) Mood (Profile of Mood States), anxiety (State-Trait Anxiety Inventory)

Comfort; relaxation (modified semantic differential method)

Autonomic nerve activity (heart rate variability)

Relevant additional measures

Left prefrontal area showed less activity after walk in forest compared to city Right prefrontal cortex significantly less activation in foliage compared to plain condition

Garden images reduced O2Hb concentration in right and left prefrontal cortices; city images increased O2Hb concentrations. O2Hb concentration was significantly lower in response to garden images compared to city images. Significant reduction of blood flow in right prefrontal cortex but not left (rose condition)

Lower and more stable blood flow to frontal region of brain in natural viewing condition

No active control used. Authors did not report the association between selfreport ratings and neural activity.

Sex differences found which encourage further investigation. Reach same conclusion as Igarashi et al. (2015) but report opposite finding

In their 2014 study report right prefrontal cortex as less active Reach the same conclusion as Lee (2017) but report the opposite findings

Martinez-Soto (2013) find activation in cuneus for natural setting and attribute this to affect and activation valence. Apart from this findings are similar between studies.

water, urban activated right cingulate gyrus and left precuneus (dorsal posterior cingulate cortex). No differences between forest and urban. Right prefrontal cortex less active for 3D condition which was also seen as more natural and had lower sympathetic activity. Real plant increased O2Hb concentration in right and left prefrontal cortices. Picture plant did not.

Comments/limitations

Findings in terms of brain activity

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Table 3 Quantitative studies review form.

Purpose clearly stated Relevant literature reviewed Sample described in detail Sample size justified Reliable outcome measure Valid outcome measure Intervention described in detail Contamination avoided Co-intervention avoided Statistical significance reported Appropriate analysis method Clinical importance reported? Drop-outs reported Conclusion appropriate Total/14

a

b

c

d

e

f

g

h

i

j

k

l

m

n

o

p

q

r

s

t

u

v

w

x

y

z

1 1 1 0 1 1 1 1 1 1 1 0 0 1 11

1 1 1 0 1 1 1 1 1 1 1 1 1 1 13

1 1 1 0 1 1 1 1 1 1 1 1 0 1 12

1 1 1 0 1 1 1 1 1 1 1 1 1 1 13

1 1 1 0 1 1 1 0 1 1 1 1 0 1 11

1 1 1 0 1 1 1 0 0 1 0 1 0 1 9

0 1 0 0 1 1 1 1 1 0 1 1 0 1 9

1 1 1 0 1 1 1 1 1 1 1 1 0 1 12

1 1 1 0 1 1 1 1 1 0 1 1 1 1 12

1 1 1 0 1 1 1 1 1 1 1 1 1 1 13

1 1 1 0 1 1 1 1 1 1 1 1 0 1 12

1 1 0 0 1 1 1 0 1 0 1 0 1 1 9

1 1 1 0 1 1 1 1 1 1 1 0 1 1 12

1 1 1 0 1 0 1 1 1 1 1 0 1 1 11

1 1 1 0 1 1 1 1 1 1 1 0 0 1 11

1 1 1 0 1 1 1 1 1 1 1 1 0 1 12

1 1 1 0 1 1 1 1 1 1 1 1 0 1 12

1 1 1 1 1 1 1 1 1 1 1 1 0 1 13

1 1 0 0 1 1 0 1 1 1 1 0 1 1 10

1 1 1 0 1 1 1 1 1 1 1 0 0 1 11

1 1 1 0 1 1 1 1 1 1 1 1 0 1 12

1 1 1 0 1 1 1 1 1 1 1 1 1 1 13

1 1 1 0 1 1 1 1 1 1 1 1 0 1 12

1 1 1 0 1 1 1 1 1 1 1 1 0 1 12

1 1 1 0 1 1 1 1 1 1 1 1 1 1 13

1 1 1 0 1 1 1 1 1 1 1 1 0 1 12

For each criterion: 1 = criterion met; 0 = criterion not met or unclear if met. a. Roe et al. (2013); b. Ulrich (1981); c. Chang and Chen (2005); d. Park et al. (2007); e. Chiang et al. (2017); f. Qin et al. (2013); g. Hollander and Foster (2016); h. Vecchiato et al. (2015); i. Tilley et al. (2017); j. Tang et al. (2017); k. Lee (2017); l. Aspinall et al. (2013); m. Kim et al. (2010); n. Azevedo et al. (2015); o. Igarashi et al. (2014); p. Martínez-Soto et al. (2013); q. Shin et al. (2015); r. Choi et al. (2015); s. Juong et al. (2015); t. Kim and Jeong (2014); u. Song et al. (2017); v. Bailey et al. (2018); w. Park et al. (2016); x. Neale et al. (2017); y. Bratman et al. (2015); z. Igarashi et al. (2015).

brain response and mood, with one using roses (Song et al., 2017) and the other green foliage (Park et al., 2016). Participants entered the room and looked at a box on a table. When the box was removed, it revealed an empty space or the natural item. Both studies found less activation in the right prefrontal cortex during exposure and associated this with physiological and psychological relaxation and improved mood, however the lack of an active control means it is difficult to confirm effects were due to the natural element, or just the presence of anything at all. Four studies explored specific characteristics of environments and the effect on the brain. An inner forest location promoted alpha activity more than forest edge (on the border of forest and open land) and outer forest locations (viewing forest from a position outside the forest on open land) and were reported as being more subjectively relaxing (Chiang et al., 2017). Igarashi et al. (2015) aimed to compare the effects of viewing a real natural item (foliage plants) versus a projected image of the same natural item. Real nature promoted a stronger physiological response in the prefrontal cortices than artificial nature, with no differences in subjective responses (Igarashi et al., 2015). Increased theta activity was found in right fronto-temporal and left temporo-parietal when participants were placed in more subjectively pleasant lighting environments (Shin et al., 2015). Finally, the combination of natural sounds, no odours and 25C temperature was rated as the least stressful environment for individuals and this was associated with low frequency EEG waves (Choi et al., 2015). Individual characteristics of an environment effect mood and the brain response has shown some success as a part of a measurement of this mood.

excitement and low meditation) and interest (engagement and low frustration). The subjective findings concluded that natural scenes provided significantly higher levels of attractiveness, willingness to visit, and level of pleasure. EEG results established that urban images were correlated with the arousal group while landscape scenes correlated with the interest group and high meditation (Roe et al., 2013). Five studies used fMRI/NIRS and all found that various urban environments were associated with activity in frontal and prefrontal lobes (Kim et al., 2010; Lee, 2017), parts of the limbic system (Kim et al., 2010; Kim & Jeong, 2014) and the cuneus and cingulate gyrus (Martínez-Soto et al., 2013; Tang et al., 2017). Urban scenes were associated with a fear response (Kim et al., 2010; Ulrich, 1981), stress (Chang & Chen, 2005; Choi et al., 2015), effortful cognitive processing (Martínez-Soto et al., 2013; Tang et al., 2017), and sadness (Ulrich, 1981); whereas natural scenes were associated with happiness (Kim et al., 2010), wellbeing (Chang & Chen, 2005), cognitive restoration (Martínez-Soto et al., 2013; Tang et al., 2017) and attention (Ulrich, 1981). 3.3.2. Naturalistic: urban vs natural environment Seven studies compared a natural, green environment with a nonnatural (urban or indoor) environment (Aspinall et al., 2013; Bailey et al., 2018; Bratman et al., 2015; Joung et al., 2015; Neale et al., 2017; Park et al., 2007; Tilley et al., 2017). All seven studies reported that natural environments were positively experienced when compared to non-natural environments. Four studies employed EEG and found that natural settings promoted increased meditation, relaxation (Bailey et al., 2018) and engagement (Neale et al., 2017), and non-natural environments promoted higher levels of arousal (Aspinall et al., 2013; Neale et al., 2017), frustration and engagement (Aspinall et al., 2013; Tilley et al., 2017). Three studies used fMRI/NIRS and found that natural settings were associated with decreased activity in the subgenual prefrontal cortex (Bratman et al., 2015) and prefrontal cortices (Joung et al., 2015; Park et al., 2007). These researchers concluded that natural settings were cognitively restorative (Aspinall et al., 2013; Bailey et al., 2018; Neale et al., 2017), relaxing (Park et al., 2007) and reduced rumination (Bratman et al., 2015).

3.3.4. Naturalistic: specific characteristics of an environment Two studies explored the effects of specific environmental characteristics in a naturalistic setting (Hollander & Foster, 2016; Qin et al., 2013). Qin et al. (2013) explored subjective preferences of specific characteristics - such as colour, area and height - of a natural environment. Then, using EEG, they compared brain waves of participants in environments reported as preferred versus non-preferred. They found that environments with participant reported preferred height and area characteristics were associated with lower frequency waves (Qin et al., 2013). Another study found that traditional neighbourhood design (TND), which was characterised as having mixed-use buildings with clear patterns and structure to the roads and architecture, promoted EEG outputs associated with meditation and attentiveness compared to a non-TND, which was described as more recently built and having little architectural character and taller, single use buildings (Hollander & Foster, 2016).

3.3.3. Laboratory: specific characteristics of an environment Six studies explored the effects of specific environmental characteristics in a laboratory setting (Chiang et al., 2017; Choi et al., 2015; Igarashi et al., 2015; Park et al., 2016; Shin et al., 2015; Song et al., 2017). Two studies explored the effect of an individual natural item on 7

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by delta, theta and alpha activity that indicated favourable mood states (Qin et al., 2013; Shin et al., 2015). This finding is similar to those in portable EEG studies finding states of ‘meditation’ higher in natural settings (Aspinall et al., 2013; Roe et al., 2013) and preferred neighbourhood designs (Hollander & Foster, 2016). Subjectively, these conditions were reported as preferred by participants in terms of being more attractive, relaxing, facilitating a favourable mood state/arousal or as decreasing anxiety or stress. Clearly, the synthesis of included literature suggests that lower frequency EEG bands may be the signature of more comfortable or restorative environments and associated positive subjective moods. This finding is supported, and partly explained, by two existing studies. Firstly, Hagerhall et al. (2015) who used EEG to investigate the brain response and mood of participants viewing (n = 35) either images of statistical fractal patterns (similar to the natural geometrical patterns of clouds, rivers and mountains found in nature) or exact fractal patterns (mathematically exact geometric patterns). Participants were exposed to three sets of three fractal image patterns, where images gradually moved from exact fractals to statistical fractal patterns. The findings were such that alpha levels increased and were significantly different between viewing exact fractals to statistical fractals. In other words, viewing fractals aligned with natural images were associated with higher levels of alpha power in the left hemisphere of the brain. Secondly, Coburn et al. (2019) presented participants with images of visual patterns in architecture. They found a buildings naturalness rating was higher when it had low-level visual features, such as contrast and scaling, (including intricate and layered fractal scales similar to statistical fractal patterns) and that a higher naturalness rating was preferred. This aligns with the included studies finding higher alpha in natural settings, made up of statistical fractal patterns, and lower alpha in urban settings, or exact fractal patterns (Bailey et al., 2018; Chang & Chen, 2005; Chiang et al., 2017; Choi et al., 2015). This suggests that reported mood in relation to any environment may be partly down to the brain response to the patterns in the setting. In general, EEG findings matched those obtained through fMRI and NIRS data. Higher levels of alpha EEG are said to correlate with lower activation in fMRI studies (Goldman, Stern, Engel Jr, & Cohen, 2002; Laufs et al., 2003; Moosmann et al., 2003). Exposure to natural environments was associated with positive self-reported feelings of relaxation, restoration, and comfort or preferences, higher alpha on EEG and low activation on fMRI and NIRS (Igarashi et al., 2014; Joung et al., 2015; Lee, 2017; Park et al., 2007, 2016; Song et al., 2017). This pattern of findings supports the conclusion that the positive effect of natural environments on mood is likely to occur via a brain-mediated process. Although alpha rhythms are consistently associated with comfortable and restorative environments, there are some contrasting findings. For example, Choi et al. (2015) reported alpha in the frontal lobes as representing relaxation and high beta in the temporal lobe in uncomfortable conditions which were too hot, noisy and contained unpleasant odours which they interpreted as reflecting stress. In contrast, Chang and Chen (2005) interpreted frontal beta as relaxation. However, both studies support their own conclusion with behavioural or psychological data. This informs us of the importance of supplementing EEG with other data to assist in interpretation. As one included author states “not all neuroscience literature agrees on EEG indicators, with various measures and labels being reported in previous research” (Bailey et al., 2018). For example, as reported here, alpha activity is frequently linked with beneficial effects of nature, however, alpha activity is also associated with creative ideation (Fink & Benedek, 2014) and boredom or mental fatigue (Elpidorou, 2018; Fan, Zhou, Liu, & Xie, 2015). Given the ambiguity around the mood states associated with brain activity, subjective measures complimenting objective brain imaging techniques are vital towards establishing the impact of engaging with built and natural environments on individual mood states and in assisting researchers from not over-interpreting brain imaging data.

3.3.5. Laboratory: virtual environment (VE) designs Three studies used a VE design. Two studies required participants to rate the feeling of ‘presence’ (i.e., the feeling of being actually present within the virtual environment). One study reported that a greater sense of presence, familiarity, and comfort was associated with theta activity in frontal lobes and increased frontal and midline theta activity represented focused attention and positive emotions (Vecchiato et al., 2015). The second study reported the effects of each sense (e.g. smell, sound, touch) on presence felt in VE and found touch was the most important sensory stimulus (Azevedo et al., 2015) and that this was associated with an increase in EEG measures of excitement. The third study compared the relaxing effects of viewing 3D versus 2D images of the natural environment. 3D images promoted greater levels of decreased right prefrontal activity compared to 2D images, presumably because 3D images were more subjectively relaxing (Igarashi et al., 2014). 3.4. Synthesis This review has found evidence from multiple studies that higher alpha is recorded by EEG in frontal lobes in response to natural and other positively perceived settings (Bailey et al., 2018; Chang & Chen, 2005; Chiang et al., 2017; Choi et al., 2015). Similarly, fMRI/NIRS findings demonstrated less activation in frontal lobes in response to natural scenes (Igarashi et al., 2014; Joung et al., 2015; Lee, 2017; Park et al., 2007, 2016; Song et al., 2017), indicating lower stress and increased comfort and relaxation. In contrast, studies generally showed that urban environments induced greater activation in the posterior cingulate cortex reflecting purposeful attention (i.e. top-down) and a higher demand for information processing (Martínez-Soto et al., 2013; Tang et al., 2017). Urban scenes placed greater demand on visual processing (Kim et al., 2010; Tang et al., 2017). Studies also showed that the limbic system (e.g. hippocampus, amygdala, insula) was more active in uncomfortable urban, and other uncomfortable environments, reflecting more negative emotional stimulation (Kim et al., 2010; Kim & Jeong, 2014). In contrast, natural environments stimulated activation in the basal ganglia which was absent when viewing an urban scene (Kim et al., 2010) and reflected happiness. The findings also demonstrated low frequency theta power may reflect subjectively reported ‘pleasant’ characteristics of an environment, such as in ambient lighting and VE environment (Shin et al., 2015; Vecchiato et al., 2015). Theta power was also associated with satisfaction about the area of a setting and delta power was associated with satisfaction about height of a setting (Qin et al., 2013). In support of this, high frequency beta waves in the temporal lobe may reflect stress in an uncomfortable indoor environment (e.g. uncomfortable heat, odour, noise) (Choi et al., 2015). In virtual environments, touch is the most important sensation that enhanced the feeling of presence (Azevedo et al., 2015), feeling of presence is associated with increased theta power across frontal and left temporal lobes (Vecchiato et al., 2015). 4. Discussion 4.1. Preferred or positively experienced environments The findings have generally confirmed that natural environments and pleasant sensory experiences trigger low frequency rhythms in the frontal lobes associated with lower stress and higher relaxation. Higher alpha rhythms in the frontal lobes are found primarily in nature-based conditions (Bailey et al., 2018; Chang & Chen, 2005; Chiang et al., 2017; Choi et al., 2015) but also in settings with comfortable levels of heat, noise and scent (Choi et al., 2015) representing lower stress and relaxed wakefulness (Thakor & Sherman, 2013, pp. 259–303). When participants indicated preference for a particular design feature in the natural or non-natural environment, this preference was accompanied 8

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Restoration Theory (ART). In an incompatible person-environment interaction, cognitive resources are depleted; an environment with restorative qualities allows recovery from this state (Kaplan, 1983). Several studies find natural environments as restorative and alpha EEG, lower blood-oxygen-level dependent (BOLD) signals and lower blood flow in the pre-frontal cortex may represent a physiological signature of this process (Aspinall et al., 2013; Chiang et al., 2017; Martínez-Soto et al., 2013; Neale et al., 2017; Tang et al., 2017). Furthermore, activation in the cingulate gyrus was found in urban environments which reflects directed, effortful attention which is depleting of cognitive resources (Martínez-Soto et al., 2013; Tang et al., 2017).

4.2. Undesirable environments Non-natural urban environments often induce a negative emotional response compared to natural environments. Findings from this review suggest that urban scenes activated the amygdala and hippocampus when compared to natural scenes. Studies interpreted this as the presence of negative mood or emotion in urban environments and corroborated this with subjective data (Kim et al., 2010; Kim & Jeong, 2014). Past research supports this interpretation by reporting an over reactive amygdala as being associated with mood disorders (Drevets & Raichle, 1992), amygdala-hippocampal connectivity as related to increased negative emotion (Doré et al., 2018) and that the hippocampus and amygdala play a role in fear and responses to threat (Moscarello & Maren, 2018). In contextual fear, the dorsal hippocampus has been shown to be responsible for spatial properties of the context and the amygdala for the emotional valence of the context (Zelikowsky et al., 2014). Similarly, it is possible the activation of the amygdala and hippocampus in an urban environment may represent the two regions working on one process but with separate purposes. An alternative, not altogether contradictory, analysis is also possible. Activation of the amygdala is not always a sign of negative mood but of emotional valence (Zelikowsky et al., 2014). Kim et al. (2010) found subjects rated the natural scenes as “peaceful” and the urban scenes as “suffocated”. It is possible to interpret the presence of amygdala activity in the urban scene as representing greater emotional valence as opposed to simply negative emotion. The lack of amygdala activity in natural scenes may reflect the relatively lowly charged feeling of “peaceful”. It is important studies provide precise subjective data and account for valence rather than assuming amygdala activity equates to negative mood. Down regulation of the posterior hippocampus has been shown to be predictive of reduced negative affect (Doré et al., 2018); providing a possible neural mechanism to facilitate the decrease of negative affect in uncomfortable urban environments. Practices such as meditation (e.g. mindfulness) have been shown to alter amygdala and hippocampus activity and positively affect associated mood and emotional responses (Hatchard et al., 2017; Kral et al., 2018; Leung et al., 2018; Saleem & Samudrala, 2017). This suggests that in typically uncomfortable environments, practices such as mindfulness may be able to promote fewer amygdala and hippocampal responses associated with negative emotional states, and possibly the negative emotional states themselves.

4.4. Virtual environments A relatively new method to exploring person-environment interaction is the use of VE. The VE data supports laboratory and naturalistic data in terms of lower EEG wave bands in comfortable environments. However, specifically, they find theta power associated with comfortable VE's high on feelings of ‘presence’ (Vecchiato et al., 2015). Another study found ‘excitement’ was associated with the feeling of presence but a lack of access to EEG manufacturers algorithms means this finding is hard to contextualise (Azevedo et al., 2015). The main contribution to the findings from this paper made by VE studies is regarding the cumulative effect of different sensory experiences on feelings of presence. This should be considered by any research design which aims to investigate the environments effect on people in a laboratory setting. 4.5. Sensory accumulation effect & ecological validity The variety of designs employed - and outcomes found - in included studies is informative of the need for environments which consider all the senses in their design. A synthesis of the studies suggests a sensory accumulation effect of the environment on the brain where in research design, the effect and realism is greater when stimuli for each sense is added. For example, Ulrich (1981) used colour slides as stimuli, but even in this early research the author recognised there was more to nature than just what we see. Currently, portable EEG and NIRS allows for research which can employ all the senses, a possibility which is important considering the findings that real plants are associated with greater blood flow than fake plants (Igarashi et al., 2015), 3D images facilitate physiological relaxation more than 2D (Igarashi et al., 2014), area and height characteristics of a setting have different EEG outputs (Qin et al., 2013), touch is important for realism of VE (Azevedo et al., 2015), indoor lighting variants may effect theta power (Shin et al., 2015) and temperature, noise and smell effect EEG in the same settings (Choi et al., 2015). Therefore, the sensory accumulation effect may account for people's preference for real over fake nature (Igarashi et al., 2015) and the previous finding that real plants in a hospital waiting room resulted in lower perceived stress in patients when compared to the effect of fake plants, even though patients' attention was not specifically drawn to whether or not the plant was real or artificial (Beukeboom et al., 2012). An extension to this is how much effect exposure time has on brain activity. There is large variation between exposure time from a few seconds (Igarashi et al., 2015; Song et al., 2017) to 90 minutes (Bratman et al., 2015) and although even limited exposure appears to be required for brain activity to change in the short-term, longer exposure may change the strength or nature of a person's reaction. A recent study found 2 hours of nature contact a week was associated with self-reporting of good health or higher well-being (White et al., 2019). The authors suggest longitudinal studies are required to develop green exposure guidelines. Clearly an understanding of a ‘dose-response’ relationship between greenspace and its effects on the brain, mood or cognition has yet to be fully established and is worthy of further study. The sensory accumulation effect is particularly important given the

4.3. Support for theory Within some included studies we find neurological evidence for the person-environment compatibility model, which states that human functioning in busy sensory environments, such as some urban locations, comes at a cost of more effortful processing (Kaplan, 1983). For example, Kim et al. (2010) suggest urban scenes result in greater activation of the primary visual cortex and that this is because of greater processing needs. Additionally, two studies report urban environments as associated with activity in the posterior cingulate cortex reflecting purposeful, top-down attention and more effortful information processing (Martínez-Soto et al., 2013; Tang et al., 2017). This may also be true in non-urban environments with enclosed rooms more likely to activate the cingulate gyrus over preferred open rooms (Vartanian et al., 2015). Similarly, the person-environment compatibility model states that effortful processing results in attention depletion which can promote stress, distractibility and reactance (Kaplan, 1983). The notion of attentional demand being higher in urban environments is becoming common (Grassini et al., 2019). It is recommended built environments are designed to minimise sensory noise to reduce redundant additional neural processing. This will delay the onset of attention depletion and associated negative effects. Within this synthesis are several studies supporting Attention9

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increase of VE studies and the finding that different qualities effect the presence experienced (Azevedo et al., 2015) and that presence may have an EEG signature (Vecchiato et al., 2015). VE studies in particular need to be aware of how incorporating as many sense stimuli as possible may increase presence, and which stimuli are more important. When designing research conditions in VE, the inclusion of touch as well as the more common senses of hearing and sight might be more important (Azevedo et al., 2015). These findings from VE studies may also inform laboratory brain imaging studies where touch is also a less common sense included in design. Again, exposure time is of interest to VE; the inclusion of additional senses also increases realism and presence. However, an interaction between exposure and presence may exist. Specifically, longer exposure may also increase a sense of presence. In addition, this sensory accumulation effect shows the importance of ecologically valid study designs. Designs which focus on one sense may not provide a full picture of the effect of the chosen environment on the person or the brain e.g. urban vs nature pictures without sound, smell and touch (Tilley et al., 2017). The intervention design necessary to control for all variables will hinder participants’ use of all senses. For example, this may involve preventing movement to control for activation of motor areas (Vecchiato et al., 2015). However, this review suggests research has maintained a positive trajectory in design quality in this regard. Future research must aim, when possible, to continue to improve on past design to provide a realistic and full picture of an environments effect on the brain.

outcomes in nature? Are natural environments able to produce stronger emotional responses and if so, will this change its effects? In contrast, the strength of the response may play a primary role in the negative outcome's studies report from over exposure to uncomfortable urban environments. A stable finding across time seems to be alpha activity and associated psychological measures showing relaxation and lower stress, but less explored is how different environments may change the brain and brain activity over time. If exposure to nature is ‘restorative’, how do different doses of nature protect against resource depleting environments? Research can also explore whether chronic exposure to different, e.g. stressful or restorative, environments will effect change in brain structure and activity. To our knowledge, only one paper has looked at lifelong exposure to the natural environment and associated brain structure. Davand et al. (2018) found more exposure to greenspace was associated with increased grey matter volume in prefrontal cortices and the left premotor cortex, and increased white matter volume in the right prefrontal cortex, left premotor cortex and both cerebellar hemispheres. Further, they linked this to less inattentiveness and better working memory. This study suggests extended exposure to greenspace may effect brain development and associated cognitive functions. This type of research will provide policy makers, architects and urban planners with knowledge of the full extent different environments have on individuals and if it can have long-term effects on the brain, mood and behaviour. There is also scope to measure brain activity associated with less conscious uncomfortable and comfortable characteristics of an environment. Does the brain respond to these environments before a person is explicitly aware of being stressed or uncomfortable? Based on brain activity reported in various temperatures and settings the suggestion is that brain activity will appear before conscious, explicit knowledge. This provides a method to explore the effect of the environment on human response objectively, using the current knowledge on neurological response and associated mood states.

4.6. Future directions Martínez-Soto et al. (2013) found that the natural environment triggered greater activation in frontal, temporal, and parietal regions associated with interest, brain stimulation, and emotion contemplation (e.g. left middle frontal gyrus, insula, cuneus). Although not in direct contrast with studies reporting lower activation in the frontal region, this finding suggests it may be necessary to pinpoint specific locations, or networks of locations, to better understand the impact of different environments. Unfortunately, this requires a balance between immobile and expensive high-resolution imaging equipment (such as MRI/fMRI) being used to determine specific activation locations and connectivity; and mobile, lower quality devices such as EEG and NIRS being used to explore specific environments. A single study found that the subgenual prefrontal cortex, which is associated with rumination, was less active following a walk in nature over an indoor walk; the conclusion that nature walks reduce rumination was supported by self-report data (Bratman et al., 2015). Considering the implications rumination has on depressive symptoms (Joormann & Gotlib, 2008), this finding warrants further study. Longitudinal study using subjective and objective (brain imaging) rumination and depression scale data is recommended. Exposure to nature reduces depressive symptoms (e.g. Chawla, 2015; Cohen-Cline, Turkheimer, & Duncan, 2015) and further study of this type may explore if less rumination following passive nature exposure is a mechanism of this process. Additionally, like the previous suggestion (see end of section 4.2.), research could explore if meditative practices can reduce rumination and depressive symptoms when people are in uncomfortable urban environments, perhaps also as a reflection of less subgenual prefrontal cortex activity. Two studies found increased amygdala activity in urban conditions but not natural conditions (Kim et al., 2010; Kim & Jeong, 2014). This is equated to negative emotion, but the amygdala is shown to be over reactive to strong emotional responses, not just negative ones. Future work may explore if the lack of amygdala activity in natural environments is due to a weaker emotional response. This stimulates many further questions. Perhaps, a small dosage of positive emotion is all that is required to encourage positive outcome's from the natural environment? Perhaps an emotional response is not required at all for positive

5. Conclusion In cognitive psychology, it has been argued that brain imaging studies have produced no additional findings since classic experimental studies in the 1970s (Sawyer, 2011). Similarly in environmental psychology, Ulrich (1981) found that natural environments promoted EEG alpha rhythms and relaxed physiological states and this finding has been repeated many times since (Bailey et al., 2018; Chang & Chen, 2005; Chiang et al., 2017; Choi et al., 2015) and supported by fMRI and NIRS studies (Igarashi et al., 2014; Joung et al., 2015; Lee, 2017; Park et al., 2007, 2016; Song et al., 2017). However, unlike this claim in cognitive psychology, brain activity measures have broadened our understanding of specific characteristics of an environment. For example, it has helped explain the extent specific attributes of an environment associate with physiological relaxation. This includes effects of temperature, noise, vegetation density, real vs fake plants and lighting. Individuals are not always aware of these subtle changes in the environment, but the brain reacts; brain imaging has been an insightful way to measure these sub-conscious changes in individual's physiological relaxation and help further our understanding of the effect of the environment on a person. Declaration of interest The authors report no conflicts of interest. Funding Australian Research Council (ARC) (award number: LP150100320). The ARC have no input as to the design and analysis of the study. 10

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