Lokaverkefni til BS-prófsí sálfræði

Up high, down low, too slowEffects of nature on directed attention differ between high and low spatial

frequencies

Brynjólfur Gauti Jónsson

Málfríður Guðný Kolbeinsdóttir

Júní 2018

Up high, down low, too slowEffects of nature on directed attention differ between high and low spatial frequencies

Brynjólfur Gauti Jónsson

Málfríður Guðný Kolbeinsdóttir

Lokaverkefni til BS-gráðu í sálfræði

Leiðbeinandi: Heiða María Sigurðardóttir

Sálfræðideild

Heilbrigðisvísindasvið Háskóla Íslands

Júní 2018

Ritgerð þessi er lokaverkefni til BS gráðu í sálfræði og er óheimilt að afrita ritgerðina á

nokkurn hátt nema með leyfi rétthafa.

©Brynjólfur Gauti Jónsson og Málfríður Guðný Kolbeinsdóttir, 2018

Prentun:

Reykjavík, Ísland 2018

Research has shown that viewing images of natural environments can promote attentional

restoration significantly better than urban environments, but results have been mixed. Other

studies have also found that visual complexity can have a mediating effect on one’s prefer-

ence for environments. The objective of this study was to investigate whether the restorative

effects of natural environments on directed attention are dependent on visual information con-

tained in high and low spatial frequency components. 60 university students and young adults

participated in this study. Participants performed three rounds of the Sustained Attention to

Response Test (SART) between which they were shown images according to their experi-

mental condition. The images portrayed natural or urban environments and were filtered to

contain either only high spatial frequencies or only low spatial frequencies. When looking

only at the high spatial frequency components of images, watching images of natural envi-

ronments led to worse performance on the SART than urban ones, a trend that was reversed

for lower spatial frequencies. Our results indicate that the restorative effect of natural envi-

ronments on directed attention might be confounded by greater visual complexities of natural

environments conveyed via high spatial frequencies.

3

Acknowledgements

This thesis was written under the guidance of Heiða M. Sigurðardóttir. We want to thank her

for her invaluable assistance and guidance. We would also like to thank Sabine Süsstrunk

for giving us permission to use École Polytechnique Fédérale de Lausanne’s RGB-NIR

Scene Dataset.

4

Contents

Abstract 3

Introduction 6

Method 13

Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Stimulus Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Sustained Attention to Response Task (SART) . . . . . . . . . . . . . . . 15

Image Categorization Task . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Results 19

Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Attentional restoration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Categorization of images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Discussion 22

References 25

Appendix 32

Removed Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Spatial Frequency Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Low Pass Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

High Pass Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

FFT Shift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Inverse FFT Shift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

5

As well as being an important part of daily life, constant attention is required in many lines of

work (e.g. airport radar technicians, security guards) and a small slip can have dire conse-

quences. Of particular importance to someone working under such pressure is their capacity

for directed attention: Inhibiting unimportant stimuli in order to focus on a particular task

(Theeuwes, Atchley, & Kramer, 2000), a mechanism that is susceptible to fatigue (Kaplan,

1995; Kaplan & Berman, 2010). Finding factors that help one recover from such fatigue can

be an important goal.

Previous research has shown that natural environments and interactions within

them are one such factor (Berman, Jonides, & Kaplan, 2008; Faber Taylor & Kuo, 2009;

Lee, Williams, Sargent, Williams, & Johnson, 2015; Lin, Tsai, Sullivan, Chang, & Chang,

2014), and the mere viewing of images which depict these environments can have restorative

benefits when compared to urban environments (Berto, 2005; Gamble, Howard Jr, & Howard,

2014). One theory set forth to explain this phenomenon is Attention Restoration Theory

(ART) (Kaplan, 1995; Kaplan & Berman, 2010).

The framework of ART is based on a distinction between voluntary and involun-

tary attention originally made by William James in 1892 (Kaplan, 1995). Voluntary atten-

tion is reminiscent of directed attention, a key component of top-down executive function-

ing, while involuntary attention shares similarities with more automatic bottom-up attention

(Theeuwes et al., 2000). To avoid confusion with James’s definitions of attention, ART uses

the terms directed attention and fascination, of which there are two types, soft and hard fasci-

nation (Kaplan & Berman, 2010). ART claims that directed attention and fascination are two

separate cognitive functions that work in tandem. Directed attention is utilized in day-to-day

tasks that require conscious effort, such as doing homework assignments, and is susceptible

to fatigue as one has to pay attention to a particular stimulus while actively inhibiting other

competing stimuli (James, 1983). Soft fascination is a gentle bottom-up attention that does

not interfere with other thoughts such as a calm meadow or a pleasant painting, whereas hard

fascination encompasses perceptions that preclude any other thoughts such as athletic or vio-

lent events (Kaplan & Berman, 2010). According to ART, activating soft fascination allows

fatigued supplies of directed attention to be renewed (Kaplan, 1995). A mainstay of ART is

that natural environments often incite this sense of fascination, giving directed attention time

to rest (Kaplan, 1995; Kaplan & Berman, 2010). But what is it about nature that gives it this

restorative property, even when it is only experienced through images?

6

According to ART, so-called restorative environments provide an opportunity for

soft fascination, or reflection, thus motivating involuntary attention and helping people re-

cover from directed attentional fatigue (Kaplan, 1995). Restorative environments are defined

as those environments that possess four distinct qualities. First, they must provide an escape

from fatiguing daily activities (sense of being away). Second, there must be elements that

effortlessly catch attention (soft fascination). Third, individuals must feel immersed in the

environment (sense of extent). Fourth and finally, the environment must be compatible with

a person’s current goals (compatibility). Natural environments often have all of these com-

ponents, and exposure to them could allow a person to recover from directed attention fatigue

(Kaplan, 1995; Kaplan & Berman, 2010).

Berto (2005) used the sustained attention to response task (SART; Robertson,

Manly, Andrade, Baddeley, & Yiend, 1997) to measure participants’ ability to sustain di-

rected attention before and after viewing three different sets of images. Conditions involved

supposed restorative environments (trees, lakes etc.), non-restorative environments (build-

ings, cars etc.), or geometric figures. Only participants who viewed restorative environments

showed significant improvements on the SART test (Berto, 2005).

A study by Berman (2008) included two experiments. The former had participants

perform the digit span backward task (DSB), an attentionally demanding task that depends

on directed-attention abilities (Wechsler, Coalson, & Raiford, 1997) before and after a walk

in an environment that was either urban or natural. Only participant who walked in the natu-

ral environment showed improvements on the DSB task. In another experiment, participants

merely watched images of either natural or urban environments on a computer screen between

tasks, consisting of the DSB as well as the attentional network test (ANT), a tool used to test

three networks of attention (alerting, orienting and executive control) (Fan, McCandliss,

Sommer, Raz, & Posner, 2002). Results were comparable, and interestingly, only the execu-

tive portions of the ANT task were improved, which is consistent with the theoretical effects

of nature on directed attention (Berman et al., 2008). A study by Gamble (2014), showed

the same improvements in the executive portions of the ANT, this time also in older adults

(Gamble et al., 2014).

In Faber (2009), the attentional performance of children diagnosed with ADHD

significantly improved after walking in a park compared to downtown and neighborhood

7

walks (Faber Taylor & Kuo, 2009). Restorative effects have also been seen when natural envi-

ronments are partly integrated into urban environments and can impact attentional restoration

even without participants being aware of it. This is seen in studies by Lin (2014), where partic-

ipants watching images of streetscapes only improved on the DSB task when the streetscapes

contained trees, even if participants were not aware of them (brief flashes of trees), and in

Lee (2015) where university students taking a 40 second break on a green-roof (a building

with a garden on its roof) made significantly fewer errors on the SART test than students

who took a break on a bare concrete roof (Lee et al., 2015; Lin et al., 2014).

Thus it would seem that there is evidence of a mechanism that restores attention

and interacts preferably with natural environments, but independent systematic reviews by

Ohly et al. (2016) and Pullin et al. (2010) found mixed results. In their review, Pullin et

al. (2010) analyzed effect sizes from 5 different studies and found that effect sizes were non-

significant after adjusting for pre-test differences (Bowler, Buyung-Ali, Knight, & Pullin,

2010). Echoing these results, Ohly et al. (2016) pooled together results from 24 articles

finding significant effect sizes for some measures of attention but many others showed non-

significant effects (Ohly et al., 2016). In light of these results we might ask whether some

visual factors other than the semantic type of environment (nature vs. urban) are having

confounding effects on attentional restoration. To try to answer this question we look to

visual aesthetics and the study of visual complexity.

The study of visual aesthetics goes all the way back to the ancient Greeks and

their use of the golden ratio in architecture (Livio, 2002). Gustav Fechner (1886), a pioneer

of experimental aesthetics, suggested a statistical study of the subject, and among other things

had a special fondness for the golden ratio (Phillips, Norman, & Beers, 2010). In his book

Aesthetic Measure (1933) George Birkhoff suggested that the pleasure derived fromwatching

an object could be calculated via the function M = OC , where M is its aesthetic score, O is

a measure of the object’s order or harmony and C is its complexity or number of chaotic

elements. According to this measure, humans would prefer objects with greater harmony and

less complexity, which is supported by research showing that being exposed to more complex

or disorderly environments can have negative effects (Chae & Zhu, 2013; Heintzelman, Trent,

& King, 2013; Kotabe, 2014).

Recent research has found that natural environments are consistently rated as being

8

more disorderly than urban environments (Kotabe, Kardan, & Berman, 2017). If natural

environments are more disorderly than urban, we might expect preference for them to be

lower. Contrary to this, Kaplan et al. (1972) showed that while the visual complexity of

an environment is on its own a poor predictor of preference, visual complexity is predictive

of preference when this relationship is separately assessed within each environmental type

(natural/urban) (Kaplan & Wendt, 1972).

A more modern approach to visual complexity is fractal geometry and the frac-

tal dimension, which is a measure of the number of elements located in a space (Machado

et al., 2015). Van Den Berg (2016) utilized a method coined by Benoit Mandelbrot in 1981

in which images of natural and urban environments were cut into smaller pieces and mag-

nified to their original size. If a magnified image retained many different visible elements

its fractal complexity was considered to be higher (Mandelbrot, 2007). The purpose of this

study was to quantify any mediating effect of fractal complexity on preference for natural

environments over urban. Participants viewed the original images as well as two different

magnifications (400% and 1600%) and then answered statements intended to measure the

images’ complexity and restorative qualities. For unmagnified images there was no difference

in rated complexity between environmental types, but for magnified images natural environ-

ments were rated as significantly more complex. Natural images were consistently rated as

beingmore restorative, and this difference was significantly greater for magnified images. Re-

searcher also found that the effects of environmental type on perceived restorativeness greatly

decreased after correcting for the images’ complexity score, indicating a mediating effect of

complexity on the relationship between environment and restorativeness (Van den Berg, Joye,

& Koole, 2016).

In a recently published study, Kotabe et al. (2017) performed several experiments

to test what they call the nature-disorder paradox. To explain this paradox, they tested

three hypotheses, but found evidence supporting only one, the nature-trumps-disorder hy-

pothesis. According to this hypothesis, preference for nature simply overpowers our aversion

towards disorder. In their experiments, participants scored natural and urban environment

images on three scales: preference, naturalness, and disorder. In each experiment, natural-

ness was significantly and highly positively correlated with disorder as well as with pref-

erence. The correlation between disorder and preference was either low or insignificant, but

after adjusting preference scores by regressing them onto naturalness they found that disorder

9

was highly negatively correlated with this residual preference (Kotabe et al., 2017). Linear

models of preference regressed upon naturalness, disorder and their interaction showed the

same positive effects of nature and negative effects of disorder as well as negligible or non-

significant interaction effects. In support of their hypothesis the effect size of naturalness on

preference was significantly larger than that of disorder, though this difference was reduced

when adjusting for low-level visual features of the images implying an asymmetric role of

visual features in participants’ naturalness and disorder scoring. The researchers confirmed

these findings for a nature-trumps-disorder effect in a second experiment that was an exact

conceptual replication. When participants were unable to form semantic interpretations of

the environments, due to a random shuffling of the images’ pixels or rapid and horizontally

inverted presentations (50ms), this trumping effect of nature of disorder disappeared. Inter-

estingly, when participants were shown merely nouns conveying a range of natural and urban

semantics (e.g. office, park, lake), the results from the first two experiments resurfaced and

showed remarkably similar correlations and linear models. What’s more, the effect sizes of

naturalness and disorder for the noun experiment were almost identical to the effect sizes

from the first experments calculated after adjusting for low-lever visual features, indicating

that scene semantics explained a large portion of this nature-trumps-disorder effect (Kotabe

et al., 2017).

Among other important visual features of scenes affecting our aesthetic prefer-

ences are lighting (Masuda & Nascimento, 2013) and colour (Palmer & Schloss, 2010). In

their study Kardan et al. (2015) sought to unearth the connections between these visual fea-

tures and the perceived naturalness of environments as well as our preference for them. To

do this, the researchers had participants rate scene images by perceived naturalness as well as

preference and tracked their reaction times (how long it took for them to give a rating). Re-

searchers then regressed these naturalness scores onto images’ visual features, leaving them

with two kinds of naturalness scores, which they called low-level naturalness (the variance

captured by their model) and high-level or semantic naturalness (the non-modelled residuals

of the model). They then used these scores to predict participant’s preference of these environ-

ments. In support of this two-fold naming, the researchers found that low-level naturalness

better predicted preference when the reaction times were shortest (bottom-up judgement), but

semantic naturalness performed best when reaction times were longer (top-down judgement)

(Kardan et al., 2015). These two different effects are in agreement with their later research

10

(Kotabe et al., 2017). It is thus well documented that people thus have an aesthetical pref-

erence for natural environments over urban ones, and some underlying low-level as well as

high-level factors have been researched, but how does viewing pictures of nature help restore

our directed attention?

While ART is an influential theory, another competing theory, the psycho-

evolutionary theory (PET) by Ulrich (1983), also aims to explain differences in preference

and restorative properties of natural and urban environments (Ulrich, 1983). Like in Kaplan’s

ART, restorative effects are attributed to bottom-up processes that are activated by certain

types of environments. What separates these theories is that whereas Kaplan (1995) attributes

the restorative elements of nature to a replenishment of directed attention, Ulrich (1983)

views them as a product of early affective responses to the environment, what he calls an

initial affective response (like/dislike), that can then be modified by an individual’s cognitive

appraisal of the environment (Kaplan, 1995; Ulrich, 1983). Nonetheless, Valtchanov and

Ellard (2015) proposed that these theories could be two sides of the same coin, as the

mechanisms behind both are based on bottom-up processes that set in motion a restorative

process due to exposure to certain types of environments (Valtchanov & Ellard, 2015).

Based on functional neuroimaging research by Yue, Vessel and Biederman (2006), they

suggested a link between visual pathways and this potential reward system activated by what

Kaplan (1995) calls soft fascination but Ulrich (1983) named an initial affective response

(Biederman & Vessel, 2006; Valtchanov & Ellard, 2015; Yue, Vessel, & Biederman, 2006).

Yue, Vessel & Biederman (2006) found that viewing images of environments that

were rated as aesthetically pleasing led to greater neural activation in the ventral striatum, a

critical component in the brain’s reward system (Yager, Garcia, Wunsch, & Ferguson, 2015),

and parahippocampal cortex (PHC), a brain area involved in scene recognition (Epstein &

Ward, 2009; Park, Brady, Greene, & Oliva, 2011), than when viewing non-pleasing images,

and theorized that this was caused by cortical µ-opioid receptors in the ventral visual path-

ways (Biederman & Vessel, 2006; Yue et al., 2006). These receptors might mediate reward

processing and serve as a gateway to drug addiction in humans (Contet, Kieffer, & Befort,

2004; Nummenmaa et al., 2018), are sparse in the early stages of the visual pathway (V1 and

V2), but increase in density as one moves towards the PHC, an area with a high density of

µ-opioid receptors (Yue et al., 2006).

11

One area in the posterior part of the PHC, the parahippocampal place area (PPA),

has been shown to react more strongly to images of spatial scenes than of singular objects (Ep-

stein &Kanwisher, 1998). In particular the PPA responds strongly to the spatial boundary of a

scene, but does not differentiate between the objects one would expect to find inside the scene

(Park et al., 2011). It is widely acknowledged that our visual system extracts different infor-

mation from different spatial frequencies, low spatial frequencies (LSF), conveyed by faster

magnocellular pathways, carrying coarse information about a scene (e.g. spatial layout and

structure), while information from high spatial frequencies (HSF), conveyed more slowly by

parvocellular pathways, provide more fine-grained information (e.g. edges and outlines of

objects located within a scene) (Bar, 2004; Hegdé, 2008; Schyns & Oliva, 1994). In a series

of studies, Schyns & Oliva (see Oliva, 2005 for a review) found evidence for coarse-to-fine

sequencing, meaning that LSF information is processed faster and utilized in a top-downman-

ner to help process HSF information [Schyns & Oliva (1994); Oliva & Schyns (1997); Oliva

(2005)).

While neuroimaging studies have found that the PPA is differentially sensitive to

the spacial frequencies of a scene (see Kauffmann, Ramanoël, & Peyrin, 2014 for a review),

there are mixed results as to whether LSF or HSF cause greater activation (Peyrin, Baciu,

Segebarth, & Marendaz, 2004; Rajimehr, Devaney, Bilenko, Young, & Tootell, 2011; Zeid-

man, Mullally, Schwarzkopf, & Maguire, 2012). Meanwhile, Musel et al. (2014) showed

that the PPA shows greater activation to coarse-to-fine stimuli presentations, meaning that

stimuli were presented in rapid sequence ranging from primarily LSF to HSF (@ Schyns &

Oliva, 1994), than the other way around (Musel et al., 2014). Thus, it would seem that the

PPA responds to both LSF and HSF components of scenes but might process them differently.

In sum, research has seen positive effects of exposure to natural environments on

attentional restoration (Berman et al., 2008; Berto, 2005; Faber Taylor & Kuo, 2009; Gamble

et al., 2014; Lee et al., 2015; Lin et al., 2014) but not after pooling effect sizes (Ohly et

al., 2016) or adjusting them for pre-test differences (Bowler et al., 2010). In spite of these

mixed results there seems to be a trend where overall natural environments have a positive

effect but this might be confounded by other factors. Some research into these factors has

been done in the nature-trumps-disorder effect (Kotabe et al., 2017), the mediating effects

of fractal complexity on preference (Van den Berg et al., 2016), and distinctions between

low-level and high-level naturalness (Kardan et al., 2015). Building on this research and

12

the fact that HSF and LSF components are integral to visual perception as well as conveying

distinct information, the authors of this paper designed and performed an experiment to test for

differences in the effects of natural and urban environments on attentional restoration, based

on the spatial frequencies representing them. Higher frequencies show finer details of a scene

(i.e. edges and lines) without carrying with them its broad context. Based on the nature-

disorder-paradox and the fact that natural scenes are consistently rated as more disorderly

than urban ones, we might expect the positive effects of nature on attentional restoration to be

negatively confounded by this edge-based representation. If this effect of disorder is related

to a scene’s fractal complexity, the coarse information carried in LSF might show a weaker

negative confounding than in HSF.

Method

Participants

60 (M=32) participants with a mean age of 28 (σ=6) took part in the experiment. Subjects

were selected with a convenience sample and assigned double blindly into a 2x2 mixed facto-

rial experiment via quasi-random assignment where researchers had no knowledge or choice

over which participants were assigned to which blocks. More precisely, a list of random

block assignments was generated using the blockrand package for R according to which par-

ticipants were automatically assigned to one of four experimental conditions (Snow, 2013).

All participants saw both natural and urban environments but the order in which they were

presented was randomized to balance any ordering effect. This leaves two levels of spatial

filter, high pass or low pass, and two ordering levels, nature images first or urban images first.

Tests revealed no significant differences between participants in different ordering conditions

so they were pooled into two groups of 30 participants each: High pass filter (mean age=29,

sd.age = 7, n.males = 20), or low pass filter (mean age=27, sd.age =5, n.males = 12).

Participants in this study were volunteers and received no reward for participation.

13

Stimulus Materials

Images were downloaded from École Polytechnique Fédérale de Lausanne’s RGB-NIR Scene

Dataset (Brown & Süsstrunk, 2011). The original image set contains 477 images in 9 cate-

gories: country, field, forest, indoor, mountain, old building, street, urban, water. Out of these

477 images, 100 images depicting urban environments (indoor, old building, street, urban)

and another 100 depicting natural environments (country, field, forest, mountain, water)

were chosen as stimuli. Care was taken so that natural images did not include people and had

no discernible manmade structures, and urban images had no or limited natural features.

The dataset was preprocessed using the EBImage package for R (Pau, Fuchs, Skl-

yar, Boutros, & Huber, 2010). First the images were resized to 1280x800 pixel sizes and their

luminance histograms equalized followed by conversion to grayscale using CIE 1931 lumi-

nance weights (Fairman, Brill, & Hemmendinger, 1997). Research has shown that color is a

vital feature for scene categorization, especially for lower spatial frequencies (Oliva, 2005),

but images were grayscaled for this study since the colour for HSF images can be completely

different from LSF images which could create undue biases between groups. The grayscaled

images were then converted to the frequency domain using a fast Fourier transform and fil-

tered by calculating their Hadamard product with equally sized 2D radial kernels having peaks

at the middle of their respective arrays, and containing numbers between 0 and 1. The radial

kernels can be seen in figure 1 as well as example pre-processed stimuli from the RGB-NIR

Scene Dataset (Brown& Süsstrunk, 2011). The high pass filter was calculated with a standard

deviation of s = 0.02, while the low-pass filter was calculated by creating a matrix of ones

and subtracting from it a kernel with standard deviation s = 0.1. R scripts used for image

filtering can be found in the appendix, and contains snippets of code posted to StackOverflow

by rayryeng (rayryeng, 2016). The final stimulus set consisted of 400 images (100 LSF ur-

ban images, 100 LSF nature images, 100 HSF urban images and 100 HSF nature images)

in 1280x800 pixel size that were stretched out to fill a 1920x1080 computer screen.

14

High Pass Low Pass None

Ker

nels

Urb

anN

atur

al

Filter type

Env

iron

men

t

0

1/2

1

Kernel V

alues

Figure 1: Example stimuli and radial kernels. Images are from École Polytechnique Fédéralede Lausanne’s RGB-NIR Scene Dataset (Brown and Süsstrunk, 2011).

Measures

Sustained Attention to Response Task (SART)

SART (Sustained Attention to Response Task), as its name implies, measures a participant’s

ability to sustain directed attention, and is predictive of everyday lapses of attention in partic-

ipants with brain injury as well as control groups (Robertson et al., 1997). SART is a continu-

ous performance paradigm where participants must press a key whenever a non-target stimu-

lus appears (go trials) but withhold presses when a target appears (no-go trials). Even though

the test is short and easy to understand it offers little chance for practice effects, although par-

ticipants might form a response pattern where they oscillate between lower response times

with higher error rates and higher response times with lower error rates (Peebles & Bothell,

2004). In order to do well on the test, participants must stay vigilant to keep themselves from

developing an automatic response and pressing when the no-go target appears. This coupled

with the temporal rhythm of the test and random placement of no-go targets makes the test

very cognitively fatiguing.

The application of SART used in this experiment was based on a python version by

Cary Stothart, but adapted for the current study’s needs (Stothart, 2017). The stimuli used for

15

the SART were digits ranging from 1 to 9, all shown equally often in five different font sizes,

with the digit 3 being the target. This means that go trials (8 out of 9 trials) far outweighed

no-go trials (1 out of 9 trials). Each SART block consisted of of 270 trials, thereof 27 no-go

trials and 216 go trials, where nine digits in five different font sizes were shown six times each.

The font sizes used were 1.20, 1.80, 2.35, 2.50, 3.00 corresponding to height in centimeters

on the screen. For each block a list of all 270 trials was prepared and then randomly shuffled.

Each digit was presented for 250 ms followed by a 900 ms mask consisting of an X enclosed

in a ring with radius 1.50 cm. Participants thus had a window of 1150ms to respond positively

by pressing the space-bar after which the response was recorded as a negative and the next

trial began.

D-prime D-prime, or d’, calculates participants’ sensitivity to stimuli, a higher d’ indicating

greater sensitivity. Put more formally it quantifies the distance between the means of the

noise distribution and the signal distribution in units of standard deviations. It is calculated

via the formula: d′ = Φ−1(H) − Φ−1(F), where Φ is a function that projects z-scores to

their respective probabilities H is the hit rate and F is the false-alarm rate of a participant.

For the purpose of calculations this study assumes thatH and F are normally distributed with

equal standard deviations (Stanislaw & Todorov, 1999). Concerning this study, a hit (true

positive) is defined as a digit other than 3 being shown and the participant pressing the space-

bar. Similarly a false alarm (false positive) occurs when the participant presses the space-bar

while the digit 3 is shown.

C criterion The C criterion, or c, measures response bias in participants, a higher c indicat-

ing a bias towards responding positively (pressing the space-bar more often). It is defined

as the distance between the criterion and the intersection point of the signal and noise distribu-

tions, often called the neutral point. It is calculated as: c = −Φ−1(H)+Φ−1(F)2 , or the negative

average of the quantiles corresponding to the false alarm rates’ and hit rates’ z-scores (Stanis-

law & Todorov, 1999).

Reaction Time In this study reaction time is measured in milliseconds and is simply the

time between a stimulus appearing on the screen and the participant pressing the specified

keyboard button. Evaluating reaction times alongwith signal detectionmeasures has been rec-

16

ommended for the SART since it has demonstrated a sizeable speed-accuracy trade-off [Hel-

ton, Weil, Middlemiss, & Sawers (2010); Manly, Robertson, Galloway, & Hawkins (1999);

Robertson et al. (1997); Shalgi, O’Connell, Deouell, & Robertson (2007)).

Image Categorization Task

Since the filtering of the images could cause a bias in participants’ ability to semantically

categorize the environments shown, all participants performed an image categorization task.

In this task each participant was shown 100 images sampled without replacement from the

full set of 400 images and had to make a two alternative forced choice as to whether an natural

or man-made environment was depicted. This means that participants saw both high pass and

low pass filtered images regardless of their experimental condition. Each image was shown

for one second after which participants had an unlimited amount of time to answer, pressing

the n button for nature and m button for man-made.

Procedure

The study was approved by the National Bioethics Committee and the Icelandic Data Protec-

tion Authority. Participants met the researchers at the University of Iceland in a laboratory

provided by the Department of Psychology. After hearing a short description of the study and

beingmade aware of any possible complications participants whowished to partake gave their

written consent.

The experiment took place in a dark, closed room and was programmed in Python

using the PsychoPy software package (Peirce, 2007) and executed on a 27’ ASUS ROG

PG279 monitor, with refresh rate 60 Hz and resolution 1920x1080. A researcher first read

a prewritten text to introduce the experiment after which participants were seated approxi-

mately 60 cm from the computer screen and started the experiment. Further instructions were

shown on the computer screen. Participants were asked to give equal weight to responding

quickly and responding correctly.

Participants were randomly assigned a slot with precomputed quasi-random block

assignment. Half of the participants were shown high spatial frequency (HSF) images be-

tween SART tests and the other half were shown low spatial frequency (LSF) images. All

17

participants were shown both natural and urban scenes but the order in which they were shown

was randomized to balance out any ordering effect. Participants went through a SART prac-

tice block consisting of 18 trials (16 go and 2 no-go). A press on the spacebar key was

required for go trials. Upon completion of the practice block, the researcher answered any

remaining questions before leaving the participant alone in the closed room. Participants had

unlimited time to read preliminary instructions but afterwards had only preprogrammed fixed

times to watch images and read short messages.

Each participant completed three blocks of the SART. Following the first and sec-

ond SART blocks, a series of randomly shuffled images were shown for 3 s each. Half of

the participants saw 100 urban images after the first SART block and 100 natural images

after the second block. The remaining participants viewed natural images first and then ur-

ban images. Images shown to a participant had all been filtered to contain either only HSF

or LSF information, depending on the participants’ subgroup assignment. After completing

three blocks of the SART and going through both image conditions, participants could stretch

their arms before starting the final part of the study. A random sample of 100 images from

the full 400 images (both environments, both spatial frequency conditions) was chosen and

participants were told to categorize them into natural or urban environments. Each image

was shown for 1 s and participants made a two-alternative forced-choice between natural and

urban environments. Figure 1 shows the experimental layout for each of the four groups.

SART 1

High Pass

NatureSART 2

High Pass

UrbanSART 3

SceneCategorization

SART 1

High Pass

UrbanSART 2

High Pass

NatureSART 3

SceneCategorization

SART 1Low Pass

Nature

SART 2Low Pass

Urban

SART 3Scene

Categorization

SART 1Low Pass

Urban

SART 2Low Pass

Nature

SART 3Scene

Categorization

Group 4

Group 3

Group 2

Group 1

Figure 2: Experimental layout. All participants performed three rounds of SART andwatchedtwo types of images before performing the categorization task.

18

Results

Descriptive Statistics

Statistical analyses were performed in the RStudio IDE, version 1.1.447 (RStudio Team,

2016), for R, version 3.5.0 (R Core Team, 2018). First, speed-accuracy trade-off was esti-

mated by correlating total errors and mean response times for each participant over all non-

practice SART blocks.A sizeable negative correlation between errors and reaction times was

seen, r = −.81, 95% CI [−.88, −.71], t(58) = −10.66, p < .001. This indicates a strong

speed-accuracy trade-off which agrees with previous research (Robertson et al., 1997).

Following this, participants’ post-test scores were adjusted for pre-test differences,

pre-test scores being scores from the first SART block performed before any images were

shown, and post-test scores being scores from the second and third SART blocks. Adjust-

ments were performed by subtracting each participant’s pre-test score from each of their post-

test scores, transforming the three dependent variables, d’, c and RT (reaction time), into the

new variables,∆d’,∆c and∆RT. Table 1 shows descriptive statistics for these variables. Pos-

itive means for ∆d’ in all groups indicates a general improvement on the SART test, though

high standard deviations indicate a number of scores below zero as well. For∆d’, semblance

of a pattern can be seen where nature had a more positive effect for the LSF group but ur-

ban environments had a more positive effect for the HSF group. Interestingly, the means are

almost identical although mirrored across spatial frequency conditions.

Table 1: Means for dependent variables with standard deviations in parentheses

Δd’ Δc ΔRT

Filter Nature Urban Nature Urban Nature Urban

Low Pass 0.32 (0.85) 0.2 (0.95) 0.05 (0.31) 0.02 (0.31) -0.02 (0.06) -0.02 (0.06)High Pass 0.21 (0.76) 0.38 (0.73) -0.03 (0.22) 0.06 (0.32) 0 (0.06) 0.01 (0.05)Note:Δ stands for participants’ scores subtracting pre-condition scores

Attentional restoration

A 2 (spatial frequency; between subjects) x 2 (environment; within subjects) repeated mea-

sures ANOVA was performed to test for differences in attentional restoration. Independent

19

variables were the filter type (high pass/low pass) and environment type (natural/urban) of

images as well as their interaction. Dependent variables were ∆d’, ∆c and ∆RT. After the

ANOVA tests, effects were quantified by contrasting the estimated marginal means (EMM)

for environments within each filter and testing the null hypothesis that this contrast was equal

to zero (H0 : M = ∆nature − ∆urban = 0 and H1 : M = ∆nature − ∆urban ̸= 0)

The effects of environmental type and spatial frequency on ∆d’ showed no signif-

icant main effects of filter type F(1, 58) = 0.03, p = 0.867, or environment F(1, 58) = 0.15,

p = 0.701. Interaction effects were significant, F(1, 58) = 4.08, p = 0.048. Pairwise con-

trasts for the LSF group showed no significant difference in effects (M = 0.118, SE = 0.102,

p = 0.253). Interestingly, for the HSF group the effect was greater as well as reversed but

non-significant (M = -0.174, SE = 0.102, p = 0.094), indicating the possibility that urban

environments had a greater positive effect on signal sensitivity.

The effects of environmental type and spatial frequency on ∆c showed non-

significant main effects of filter type, F(1, 58) = 0.08, p = 0.782, and environment F(1,

58) = 1.01, p = 0.319, but nearly significant interaction effects, F(1,58) = 3.50, p = 0.066.

Pairwise contrasts showed for the LSF group no significant effect difference (M = 0.026,

SE = 0.042, p = 0.543). Again, the difference in effects was greater and reversed for the

HSF group, but now also significant (M = -0.085, SE = 0.042, p = 0.046). This implies

that participants were significantly more biased towards responding positively (pressing the

space-bar) after watching images of natural environments than urban.

For ∆RT, there were non-significant main effects of filter type, F(1, 58) = 2.19,

p = .144, and environment, F(1, 58) = 0.73, p = .395, as well as non-significant interaction

effects F(1, 58) = 0.40, p = .530.Since all effects were far from significant no contrasting

tests were performed.

In sum, tests showed no significant main effects of spatial frequency or environ-

ment but peculiarly their interactions showed significant results for c and nearly significant

ones for d’. It seems that the effect of natural environments compared to urban environments

on participant sensitivity is reversed between spatial frequency groups, being positive for LSF

but negative for HSF. The interaction effects of environment and spatial frequency on Δd’ and

Δc are visualized on figure 2.

20

∆c ∆d'

urban nature urban nature

0.1

0.2

0.3

0.4

0.5

−0.05

0.00

0.05

0.10M

argi

nal M

eans

Spatial Filter

Low Pass High Pass

Figure 3: Predicted values for dependent variables by mixed ANOVA models. Interestinglythe effects of environment are reversed between spatial frequency conditions.

Categorization of images

To exclude the possibility of bias caused by some images’ environments being harder to recog-

nize than others, all participants categorized 100 images each into one of four groups,HSF ur-

ban, HSF nature, LSF urban or LSF nature. Of all 6000 categorization trials, 5892 (98.2%)

were categorized correctly. As seen on table 3 there is no easily discernible pattern of error

between the image groups.

Table 2: Percent correctly categorized images

Image Filter

Environment High Pass Low Pass

Nature 98.1% 97.5%Urban 98.7% 98.6%

Significance tests were performed to quantify any systematic differences. Since

each participant categorized all four image groups the tests performed were 2 (environment;

within subjects) x 2 (spatial frequency; within subjects) repeated measures ANOVA. The

dependent variable was percent correct responses and the independent variables were the type

of filter applied (high pass/low pass) and environment shown (nature/urban). There were no

significant main effects for spatial frequency, F(1, 59) = 0.79, p = 0.377, but the main effect

of environment was nearly significant, F(1, 59) = 3.69, p = 0.059. Interaction effects were

non-significant, F(1, 59) = 0.15, p = 0.699. To investigate this effect of environment, images

21

were sorted by decreasing error rates and the images with the highest error rates compared to

each other.

After sorting the images by error rates the hardest image was found to have an error

rate of 47% while the second 20%. It turned out that the anomalous image was a high pass

filtered image of a garden with fences and buildings in the background. Another significance

test was performed after removing this image. The main effects were now non-significant for

environment, F(1, 59) = 1.67, p = .201, and filter, F(1, 59) = 2.66, p = .108, as well as a

non-significant interaction effect F(1,59) = 1.21, p = .275, implying that the difference in

sorting-difficulty were caused by this image.

In sum, after removing a stray natural image showing several manmade objects,

results showed that participants were adept at recognizing which environments were depicted,

regardless of the type of filter applied. The removed image can be seen in figure 4 in the

appendix.

Discussion

Results from this experiment showed that, when looking at only the high spatial frequency

(HSF) component of images, watching images of natural environments lead to worse perfor-

mance on the SART than after watching images of urban environments, an effect that was

not seen for the low spatial frequency (LSF) group. This was evidenced by the fact that

viewing images of natural environments had a more negative effect on c than urban ones,

i.e. caused participants to be more biased towards pressing the spacebar, as seen by signifi-

cant pairwise differences in estimated marginal means (EMM). Effects on d’, a measure of

sensitivity to stimuli, showed the same trend as for c, although ANOVA tests were significant

but not pairwise EMM comparisons. All together, these results show that in the HSF group,

participants who viewed images of natural environments where biased towards pressing the

space-bar more often, but were also worse at discriminating between stimuli. Since the SART

test places high demand on sustained directed attention so that participants can keep from de-

veloping an automatic pattern of pressing the space-bar, these results would suggest that HSF

natural images had a smaller restorative effect on directed attention than HSF urban images.

The reversal in the effects of environment type between filter groups was also

22

interesting. In the HSF group urban environments had a more positive effect than nature but

in the LSF group the effects of natural environments, although not significant, were more

positive. This might conform with the research by Kotabe et al. (2017) into how one can

have a positive preference for natural over urban environments, while disliking disorderly

environments, even though natural environments are consistently rated as more disorderly

(Kotabe et al., 2017). To explain this phenomenon, they came up with the nature-trumps-

disorder hypothesis, which states that the positive effects of nature on preference simply

overpower the negative effects of disorder (Kotabe et al., 2017). In this study, it seems that

the negative effects of disorder overpowered the positive effects of nature in the HSF group,

but not in the LSF group.

One possible explanation of this difference could be the images’ fractal complexity,

or the number of distinct objects found within them. Van Den Berg et al. (2016) found that

images’ fractal complexity had a highly mediating effect on their restorative effects, as rated

by participants (Van den Berg et al., 2016). Images’ LSF components contain few distinct

objects since they convey no outlines or edges, but the HSF components on the other hand

carry most of the information about object outlines.

Research has found evidence to support the restorative effects of natural environ-

ments on directed attention, as is theorized by Attentional Restoration Theory (ART) [Berman

et al. (2008); Lin et al. (2014); Faber Taylor & Kuo (2009); Lee et al. (2015)), even when

only experienced through images (Berman et al., 2008; Berto, 2005; Gamble et al., 2014), but

systematic reviews have found results to be mixed (Bowler et al., 2010; Ohly et al., 2016). It

is not entirely clear what aspects of natural environments are behind this effect. It might be

attributed to a positive affective response to the environment (Ulrich, 1983), a stimulation of

involuntary attention by soft fascination (Kaplan, 1995; Kaplan & Berman, 2010), or it might

have something to do with the visual features of the environment causing us to receive reward

from µ-opioid receptors in our ventral visual pathways (Biederman &Vessel, 2006; Yue et al.,

2006). Whatever the underlying factors may be, more research is required to identify them.

To our knowledge, this is the first study researching how natural and urban envi-

ronments affect directed attention when broken into distinct spatial frequency components.

The fact that effects differ so much by spatial frequencies promotes further research into this

subject, and knowledge of these effects could be of aid to architects and urban planners. If

23

our results are true, optimal restorative effects might by achieved when the HSF components

of urban environments are be mixed with the LSF components of natural environments.

These are only speculations as of yet and should not be directly applied without

further research. Participants in this study are not representative of the population as theywere

chosen by a convenience sample consisting mostly of university students and young adults.

Moreover, applications to real world scenarios are limited since this study utilized grayscale

images. Colour has been shown to be a vital feature in scene recognition, especially when

conveyed through LSF components of images (Oliva, 2005). Attribution of our results to the

nature-disorder-paradox are as of yet theoretical, as the images were not rated for observed

disorder.

In conclusion, this study has shown that the restorative effects of nature on directed

attention are not set in stone, and might rely heavily on the information conveyed by different

spatial frequencies. Further research is required to investigate whether this phenomenon is

linked to the nature-disorder-paradox or some other effect.

24

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Appendix

Removed Image

Figure 4: The photo with the highest error rate. The presence of benches, fences and abuilding may have caused participants to categorize it as an urban image.

32

Spatial Frequency FilteringLow Pass Filtering

low_pass_filt <- function(im, s = 0.1) {source("fftshift.R")source("ifftshift.R")require(EBImage)m <- dim(im)[1]n <- dim(im)[2]imff <- fft(im)imff <- fftshift(imff)x <- 1:m - ((m + 1)/2)y <- 1:n - ((n + 1)/2)z <- outer(x, y, FUN = function(X, Y) (sqrt(X * X + Y * Y)))z <- z/max(z)z <- exp(-z^2/(2 * s^2))imff <- imff * zimff <- ifftshift(imff)imffinv <- fft(imff, inverse = TRUE)imffinv <- Re(imffinv)imffinv <- imffinv * sum(im)/sum(imffinv)as.Image(imffinv)

}

33

High Pass Filtering

hi_pass_filt <- function(im, s = 0.02) {source('fftshift.R')source('ifftshift.R')require(EBImage)m <- dim(im)[1]n <- dim(im)[2]imff <- fft(im)imff <- fftshift(imff)x <- 1:m - ((m + 1)/2)y <- 1:n - ((n + 1)/2)z <- outer(x, y, FUN = function(X, Y) (sqrt(X*X + Y*Y)))z <- z/max(z)z <- 1 - exp(-z^2/(2*s^2))imff <- imff * zimff <- ifftshift(imff)imffinv <- fft(imff, inverse = TRUE)imffinv <- Re(imffinv)imffinv <- imffinv * sum(im) / sum(imffinv)as.Image(imffinv)

}

34

FFT Shift

fftshift <- function(img_ff, dim = -1) {rows <- dim(img_ff)[1]cols <- dim(img_ff)[2]swap_up_down <- function(img_ff) {

rows_half <- ceiling(rows/2)return(rbind(img_ff[((rows_half+1):rows), (1:cols)],

img_ff[(1:rows_half), (1:cols)]))}swap_left_right <- function(img_ff) {

cols_half <- ceiling(cols/2)return(cbind(img_ff[1:rows, ((cols_half+1):cols)],

img_ff[1:rows, 1:cols_half]))}if (dim == -1) {

img_ff <- swap_up_down(img_ff)return(swap_left_right(img_ff))

}else if (dim == 1) {

return(swap_up_down(img_ff))}else if (dim == 2) {

return(swap_left_right(img_ff))}else {

stop("Invalid dimension parameter")}

}

35

Inverse FFT Shift

ifftshift <- function(img_ff, dim = -1) {rows <- dim(img_ff)[1]cols <- dim(img_ff)[2]swap_up_down <- function(img_ff) {

rows_half <- floor(rows/2)return(rbind(img_ff[((rows_half+1):rows),

(1:cols)], img_ff[(1:rows_half), (1:cols)]))}swap_left_right <- function(img_ff) {

cols_half <- floor(cols/2)return(cbind(img_ff[1:rows, ((cols_half+1):cols)],

img_ff[1:rows, 1:cols_half]))}if (dim == -1) {

img_ff <- swap_left_right(img_ff)return(swap_up_down(img_ff))

}else if (dim == 1) {

return(swap_up_down(img_ff))}else if (dim == 2) {

return(swap_left_right(img_ff))}else {

stop("Invalid dimension parameter")}

}

36