With people spending up to 90% of their time in indoor spaces, windows and the visual connection that they afford to the outside, can play an important role in ensuring physical and psychological well-being. This is particularly relevant in urban settings, a substantial part of our lives, whilst still being significantly understudied. What we know from other environments may not translate to cities, and there may be important differences between the expressed preferences of individuals and their behaviour. Therefore, this study sought to define suitable methods and metrics to measure view preference in urban environments. Participants were asked to observe urban views whilst three types of data were collected: subjective preference ratings; eye-tracking measures and verbal reasoning. We found that when views were preferred, the gaze of the observers was more exploratory, with a higher occurrence of fixations and number of saccades. In addition, participants tend to prefer the presence of people, well-maintained buildings and orderly presented colours. A new link was revealed between the degree of visual exploration and the preference rating of a visual scene. This characteristic pattern of oculomotor behaviour may guide the criteria for framing selected views and accordingly inform window design in buildings.
Get full access to this article
View all access options for this article.
References
1.
KlepeisNENelsonWCOttWRRobinsonJPTsangAMSwitzerPBeharJHernSCEngelmannWH. The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants. Journal of Exposure Analysis and Environmental Epidemiology2001; 11: 231–252.
2.
Ne’EmanEHopkinsonRG. Critical minimum acceptable window size: a study of window design and provision of a view. Lighting Research and Technology1970; 2: 17–27.
3.
LudlowAM. The functions of windows in buildings. Lighting Research and Technology1976; 8: 57–68.
4.
Newsham GR, Veitch JA, Zhang MQ, Galasiu AD. Comparing better building design and operation to other corporate strategies for improving organizational productivity: a review and synthesis. Intelligent Buildings International. First published online on 09 May 2019. DOI: 10.1080/17508975.2019.1588700.
5.
UlrichRS. View through a window may influence recovery from surgery. Science1984; 224: 420–421.
6.
VerderberS. Dimensions of person-window transactions in the hospital environment. Environment and Behavior1986; 18450–466.
7.
KoWHSchiavonSZhangHGrahamLTBragerGMaussILinY-W. The impact of a view from a window on thermal comfort, emotion, and cognitive performance. Building and Environment2020; 175: 106779–106779.
8.
StoneNJ. Windows and environmental cues on performance and mood. Environment and Behavior1998; 30: 306–321.
9.
TennessenCMCimprichB. Views to nature: effects on attention. Journal of Environmental Psychology1995; 15: 77–85.
10.
AriesMBCVeitchJANewshamGR. Windows, view, and office characteristics predict physical and psychological discomfort. Journal of Environmental Psychology2010; 30: 533–541.
11.
TuaycharoenNTregenzaPR. View and discomfort glare from windows. Lighting Research and Technology2007; 39: 185–200.
12.
Hellinga HI. Daylight and view – the influence of windows on the visual quality of indoor spaces. PhD Thesis. Technische Universiteit Delft, Delft, Netherlands, 2013.
13.
TuaycharoenNTregenzaPR. Discomfort glare from interesting images. Lighting Research and Technology2005; 37: 329–341.
14.
HopkinsonRG. Glare from daylighting in buildings. Applied Ergonomics1972; 3: 206–215.
15.
ShinJYYunGYKimJT. View types and luminance effects on discomfort glare assessment from windows. Energy and Buildings2012; 46: 139–145.
16.
LeatherPPyrgasMBealeDLawrenceC. Windows in the workplace: sunlight, view, and occupational stress. Environment and Behavior1998; 30: 739–762.
17.
LeeJXuJ. Why do businesses grow faster in urban areas than in rural areas?Regional Science and Urban Economics2020; 81: 103521–103521.
18.
EEA. Ensuring quality of life in Europe’s cities and towns. [Internet] Copenhagen, Denmark: European Environment Agency, 2009.
19.
UlrichRSSimonsRFLositoBDFioritoEMilesMAZelsonM. Stress recovery during exposure to natural and urban environments. Journal of Environmental Psychology1991; 11: 201–230.
20.
ValtchanovDEllardCG. Cognitive and affective responses to natural scenes: effects of low level visual properties on preference, cognitive load and eye-movements. Journal of Environmental Psychology2015; 43: 184–195.
21.
HerzogTR. A cognitive analysis of preference for urban spaces. Journal of Environmental Psychology1992; 12: 237–248.
22.
NasarJLTerzanoK. The desirability of views of city skylines after dark. Journal of Environmental Psychology2010; 30: 215–225.
23.
StaatsHJahnckeHHerzogTRHartigT. Urban options for psychological restoration: common strategies in everyday situations. PLoS ONE2016; 11: e0146213.–e0146213..
24.
KaplanSKaplanRWendtJS. Rated preference and complexity for natural and urban visual material. Perception and Psychophysics1972; 12: 354–356.
25.
FraněkMŠefaraDPetružálekJCabalJMyškaK. Differences in eye movements while viewing images with various levels of restorativeness. Journal of Environmental Psychology2018; 57: 10–16.
26.
Van den BergAEJoyeYKooleSL. Why viewing nature is more fascinating and restorative than viewing buildings: a closer look at perceived complexity. Urban Forestry and Urban Greening2016; 20: 397–401.
CEN. European standard EN 17037. Daylight of buildings. CEN-CENELEC Management Centre: Rue de la Science 23, B-1040 Brussels: European Committee For Standardization, 2018.
MatusiakBSKlöcknerCA. How we evaluate the view out through the window. Architectural Science Review2016; 59: 203–211.
32.
HolmesTZankerJM. Using an oculomotor signature as an indicator of aesthetic preference. i-Perception2012; 3: 426–439.
33.
BerlyneDE. Aesthetics and psychobiology, New York: Appleton-Century-Crofts, 1971.
34.
Al-AklNMKaraanENAl-ZeinMSAssaadS. The landscape of urban cemeteries in Beirut: perceptions and preferences. Urban Forestry and Urban Greening2018; 33: 66–74.
35.
NorwoodMFLakhaniAMaujeanAZeemanHCreuxOKendallE. Brain activity, underlying mood and the environment: a systematic review. Journal of Environmental Psychology2019; 65: 101321.–101321..
36.
OrquinJLMueller LooseS. Attention and choice: a review on eye movements in decision making. Acta Psychologica2013; 144: 190–206.
37.
Browning MHEM, Saeidi-Rizi F, McAnirlin O, Yoon H, Pei Y. The role of methodological choices in the effects of experimental exposure to simulated natural landscapes on human health and cognitive performance: a systematic review. Environment and Behavior. First Published Online on 23 February 2020. DOI: 10.1177/0013916520906481.
38.
Kahneman D, Frederick S. Representativeness revisited: attribute substitution in intuitive judgment. In: Gilovich T, Griffin D, Kahneman D, editors. Heuristics and biases: the psychology of intuitive judgment. New York: Cambridge University Press, 2002.
39.
CampbellDTFiskeDW. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin1959; 56: 81–105.
40.
AxinnWGPearceLD. Mixed method data collection strategies, Cambridge, UK: Cambridge University Press, 2006.
41.
Findlay JM. Active vision: the psychology of looking and seeing. Oxford: Oxford University Press, 2003.
42.
HendersonJM. Human gaze control during real-world scene perception. Trends in Cognitive Sciences2003; 7: 498–504.
43.
BuswellGT. How people look at pictures: a study of the psychology and perception in art, Oxford, England: Oxford University Press, 1935.
44.
YarbusAL. Eye movement and vision, Boston, MA: Springer, 1967.
45.
HolmqvistK. Eye tracking : a comprehensive guide to methods and measures, Oxford: Oxford University Press, 2011.
46.
EcksteinMKGuerra-CarrilloBMiller SingleyATBungeSA. Beyond eye gaze: what else can eyetracking reveal about cognition and cognitive development?Developmental Cognitive Neuroscience2017; 25: 69–91.
47.
KasparKKönigP. Overt attention and context factors: the impact of repeated presentations, image type, and individual motivation. PLoS ONE2011; 6: e21719.–e21719..
48.
GidlöfKAnikinALingonbladMWallinA. Looking is buying. How visual attention and choice are affected by consumer preferences and properties of the supermarket shelf. Appetite2017; 116: 29–38.
49.
EUR-LEX. Regulation (EU) 2016/679 of the European Parliament and of the Council. General Data Protection Regulation. [Online], http://data.europa.eu/eli/reg/2016/679/2016-05-04 (accessed 5 December 2018).
50.
ChamilothoriKWienoldJAndersenM. Adequacy of immersive virtual reality for the perception of daylit spaces: comparison of real and virtual environments. LEUKOS2018; 15: 203–226.
51.
RodriquezRPattiniA. Tolerance of discomfort glare from a large area source for work on a visual display. Lighting Research and Technology2014; 46: 157–170.
52.
Field A, Hole G. How to design and report experiments. London: SAGE, 2003.
53.
CohenJ. Statistical power analysis for the behavioral sciences, 2nd ed.. Hillsdale, NJ: Lawrence Erlbaum Associates, 1988.
54.
MappAPOnoHBarbeitoR. What does the dominant eye dominate? A brief and somewhat contentious review. Perception and Psychophysics2003; 65: 310–317.
55.
DurandACGouldGM. A method of determining ocular dominance. Journal of the American Medical Association1910; 55: 369–370.
56.
KaplanRKaplanS. The experience of nature: a psychological perspective, New York: Cambridge University Press, 1989.
57.
Pitt DGZ, Ervin H, editor. The Q-Sort method: use in landscape assessment research and landscape planning. Proceedings of our national landscape: a conference on applied techniques for analysis and management of the visual resource. Incline Village, Nevadam. 23 April to 25 April, 1979. Scientific Article No. A2592 Contribution No. 5631 of the Maryland Agricultural Experiment Station, Department of Horticulture.
58.
OlmosAKingdomFAA. A biologically inspired algorithm for the recovery of shading and reflectance images. Perception2004; 33: 1463–1473.
59.
PeirceJW. PsychoPy—psychophysics software in Python. Journal of Neuroscience Methods2007; 162: 8–13.
60.
PeirceJW. Generating stimuli for neuroscience using PsychoPy. Frontiers in Neuroinformatics2009; 2: 10–10.
61.
HyönäJRadachRDeubelH. The mind’s eye: cognitive and applied aspects of eye movement research, Amsterdam: Elsevier Science BV, 2003.
62.
HerzogTRGaleTA. Preference for urban buildings as a function of age and nature context. Environment and Behavior1996; 28: 44–72.
63.
HerzogTRShierRL. Complexity, age, and building preference. Environment and Behavior2000; 32: 557–575.
64.
HerzogTR. A cognitive analysis of preference for field-and-forest environments. Landscape Research1984; 9: 10–16.
65.
HerzogTR. A cognitive analysis of preference for waterscapes. Journal of Environmental Psychology1985; 5: 225–241.
66.
HerzogTR. A cognitive analysis of preference for natural environments: mountains, canyons, and deserts. Landscape Journal1987; 6: 140–152.
67.
SungY-TWuJ-S. The visual analogue scale for rating, ranking and paired-comparison (VAS-RRP): a new technique for psychological measurement. Behavior Research Methods2018; 50: 1694–1715.
68.
NolandRBWeinerMDGaoDCookMPNelessenA. Eye-tracking technology, visual preference surveys, and urban design: preliminary evidence of an effective methodology. Journal of Urbanism: International Research on Placemaking and Urban Sustainability2017; 10: 98–110.
69.
Mirza L, Byrd H. Measuring view preferences in cities: a window onto urban landscapes. Cities and Health. First published online on 01 Jun 2020. DOI: 10.1080/23748834.2020.1765449.
70.
The MathWorks I. MATLAB and Statistics Toolbox Release 2018 Natick, Massachusetts, 2018.
71.
NyströmMHolmqvistK. An adaptive algorithm for fixation, saccade, and glissade detection in eyetracking data. Behavior Research Methods2010; 42: 188–204.
72.
Rosenthal, R. Applied Social Research Methods: Meta-analytic procedures for social research. Thousand Oaks, CA: SAGE Publications, Inc, 1991.
73.
FieldAP. Discovering statistics using SPSS, 3rd ed.. London: SAGE, 2009.
Stanley J. Reliability. In: Thorndike RL, Angoff WH, editors. Instructional science. 2nd ed. Washington: American Council of Education, 1971, pp. 356–442.
76.
BrownW. Some experimental results in the correlation of mental abilities. British Journal of Psychology1910; 3: 296–322.
77.
SpearmanC. Correlation calculated from faulty data. British Journal of Psychology1910; 3: 271–295.
78.
FotiosSUttleyJYangB. Using eye-tracking to identify pedestrians’ critical visual tasks, (part 2) fixation on pedestrians. Lighting Research and Technology2014; 47: 149–160.
79.
HerzogTRKaplanSKaplanR. The prediction of preference for familiar urban places. Environment and Behavior1976; 8: 627–645.
80.
TwedtERaineyRMProffittDR. Beyond nature: the roles of visual appeal and individual differences in perceived restorative potential. Journal of Environmental Psychology2019; 65: 101322.–101322..
81.
RaynerKRotelloCMStewartAJKeirJDuffySA. Integrating text and pictorial information: eye movements when looking at print advertisements. Journal of Experimental Psychology: Applied2001; 7: 219–226.
PietersRWarlopL. Visual attention during brand choice: the impact of time pressure and task motivation. International Journal of Research in Marketing1999; 16: 1–16.
84.
TatlerBWHayhoeMMLandMFBallardDH. Eye guidance in natural vision: reinterpreting salience. Journal of vision2011; 11: 1–23.
85.
CoburnAKardanOKotabeHSteinbergJHoutMCRobbinsAMacDonaldJHayn-LeichsenringGBermanMG. Psychological responses to natural patterns in architecture. Journal of Environmental Psychology2019; 62: 133–145.
86.
CottetMVaudorLTronchèreHRoux-MicholletDAugendreMBraultV. Using gaze behavior to gain insights into the impacts of naturalness on city dwellers’ perceptions and valuation of a landscape. Journal of Environmental Psychology2018; 60: 9–20.
87.
BertoRMassaccesiSPasiniM. Do eye movements measured across high and low fascination photographs differ? Addressing Kaplan’s fascination hypothesis. Journal of Environmental Psychology2008; 28: 185–191.
88.
TatlerBW. The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor biases and image feature distributions. Journal of Vision2007; 7: 1–17.
89.
BindemannM. Scene and screen center bias early eye movements in scene viewing. Vision Research2010; 50: 2577–2587.
90.
LeATDPayneJClarkeCKellyMAPrudenziatiFArmsbyEPenacchioOWilkinsAJ. Discomfort from urban scenes: metabolic consequences. Landscape and Urban Planning2017; 160: 61–68.
91.
Kardan O, Demiralp E, Hout MC, Hunter MR, Karimi H, Hanayik T, Yourganov G, Jonides J, Berman MG. Is the preference of natural versus man-made scenes driven by bottom–up processing of the visual features of nature? Frontiers in Psychology [Internet]. Frontiers Media SA, 2015 April 23; 6.
92.
BermanMGHoutMCKardanOHunterMRYourganovGHendersonJMHanayikTKarimiHJonidesJ. The perception of naturalness correlates with low-level visual features of environmental scenes. PLoS ONE2014; 9: e114572.–e114572..
93.
IttiLKochC. A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research2000; 40: 1489–1506.
94.
ParkhurstDLawKNieburE. Modeling the role of salience in the allocation of overt visual attention. Vision Research2002; 42: 107–123.
95.
FoulshamTUnderwoodG. What can saliency models predict about eye movements? Spatial and sequential aspects of fixations during encoding and recognition. Journal of Vision2008; 8: 1–17.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
