Evolutionary aesthetics click here
Habitat theory, Gordon H. Orians click here
Prospect and refuge theory, Jay Appleton click here
Affective theory, Roger Ulrich click here
Information processing theory, Stephen & Rachel Kaplan click here
Other theoretical approaches click here
Conclusions click here
References click here
Although much research has been conducted of landscapes to better understand how they are perceived by people, indeed the field has been described as “rampantly empirical” (Porteous, 1982), there has been a general lack of theory to guide it. As Appleton aptly put it: Just as the Brisbane wicket after rain used to be said to reduce all batsmen to an equal plane of incompetence, so this absence of aesthetic theory brings the professional down to the same plane as the man in the street (1975b). While the years since Appleton’s comment have seen the emergence of a range of theories, described by some as a scattering of diverse theoretical origins (Sell et al, 1984), nevertheless they agreed with Appleton that the lack of a unifying theoretical structure does not allow a rational basis for ‘diagnosis, prescription and prognosis’.
While the lack of theory has been widely recognized, the reason for the void is less apparent. It may partly be that the philosophy of aesthetics and the literature on landscape design and art history have much about aesthetics but notoriously little of a practical orientation which could apply to landscape quality assessment (Dearden & Sadler, 1989). Also, because of rapid changes to landscapes, some argue that practitioners were not going to fiddle with theory while the landscape burned.
In this section, four theories are examined that have been developed to explain landscape aesthetics. Since all of these derive partly or wholly from an evolutionary perspective, a brief review of Darwin’s theory will precede the four derivative theories.
Charles Darwin’s theory is set out in his The Origin of Species (1859) and proposed that species develop and evolve through the process of natural selection, the process whereby the variations inherent in all species are favored or penalized by the environment, enabling those favored to be passed on to subsequent generations while those less suited are not perpetuated. “Survival of the fittest,” which is often incorrectly taken to mean the strongest winning, actually refers to the fitness of the organism to the prevailing environment, those best suited (fittest), go on and reproduce while those less suited gradually diminish in population.
Applied to aesthetics, and landscape aesthetics in particular, Darwin’s theory suggests that the landscapes preferred by humans enhances their survivability and reproducibility; thus landscape preferences are not a superficial whim but rather reflect an innate behavior which is critical in aiding, albeit ensuring, human survival. The commonality of park-like environments in our parks and backyards and the pleasure derived from viewing pastoral-type landscapes of large scattered trees amidst grass reflects, according to the evolutionary aesthetics argument, the prevailing landscape of the East African plains on which humans evolved.
Half the human brain processes data received by the senses, in particular, sight and sound. With the brain consuming a quarter of the body’s metabolic resources, it is obvious that the senses provide information vital to human survival.
Human traits that are common to all people, such as marriage, art and learning a language are regarded as evolutionary adaptations having universal application. Human traits evolved largely during the Pleistocene (from 2.6 million years to 11,700 years ago), the hunter-gatherer phase in our history, prior to the Neolithic (agricultural) revolution of 12,000 years ago. The credibility of evolutionary aesthetics may ideally be tested with existing hunter-gather communities but to date this has not been done.
Since the Pleistocene, humans have branched into numerous cultures, each of which overlay societal norms, traditions and cultural influences on individual behavior. While all humans learn a language, culture dictates whether it is English, French or Tamil. Thus the initial evolutionary drive such as in aesthetics may be conditioned by cultural variations. The prominence of the sublime, the picturesque and the Romantic in dictating aesthetic preferences in 18th – 19th century English culture is an example.
The simplicity of evolutionary theory and its power in explaining landscape beauty is appealing but does the evidence support it? Before answering this, we need to examine the various theories which have been developed in respect of landscape aesthetics. Although called by various names, they are variations on the theme of evolutionary aesthetics. The four theories that have been proposed to explain landscape aesthetics are:
- Habitat theory, Gordon H. Orians
- Prospect and refuge theory, Jay Appleton
- Affective theory, Roger Ulrich
- Information processing theory, Stephen and Rachel Kaplan
HABITAT THEORY, GORDON H. ORIANS
Habitat theory postulates that because the habitats in which humans are believed to have evolved were dominated by grasslands and scattered trees with water in close proximity, this became a preferred visual landscape for humans. The East African savanna has been regarded as the cradle of humanity (Leakey, 1963, 1976).
Gordon H. Orians, an evolutionary biologist at the University of Washington College of Arts & Sciences, described the evolutionary underpinnings of habitat theory:
natural selection should have favored individuals who were motivated to explore and settle in environments likely to afford the necessities of life but to avoid environments with poorer resources or posing higher risks (Orians & Heerwagen, 1992).
Savanna landscapes provide opportunities of openness and seclusion and may provide a plausible explanation for the importance of the pastoral landscape from the Arcadia of antiquity through the paintings of Claude and Poussin and the landscape gardens of Capability Brown to the municipal parks of today. The preference for park-like landscapes is the only landscape form that appears to have endured across the millennia. It is reasonable to ask:
Are many of the parks and backyards people have so assiduously created wherever they have lived in part an expression of an innate predisposition for the savanna? (Balling & Falk, 1982).
According to Orians (1986):
savannas of tropical Africa have high resource-providing potential for a large, terrestrial, omnivorous primate … In savannas … trees are scattered and much of the productivity is found within two meters of the ground where it is directly accessible to people and grazing and browsing animals. Biomass and production of meat is much higher in savannas than in forests.
Based on this, he suggests that:
“savanna-type environments with scattered trees and copses in a matrix of grassland should be highly preferred environments for people and should evoke strong positive emotions.”
To test the theory, Orians photographed African savanna trees, in particular, the Acacia tortulis (Umbrella Thorn Acacia Tree), and selected trees varying in height/width ratio, height of branches and extent of canopy layers to test four hypotheses:
- trees with lower trunks should be more attractive than trees with high trunks – easier to climb;
- trees with moderate canopy density should be more attractive than trees with low or high canopy density – right balance between being hidden and not being able to see out;
- trees with a high degree of canopy layering should be more attractive than trees with low or moderate degrees of layering – providing maximum opportunities for viewing out;
- the broader the tree canopy relative to its height, the more attractive the tree should be – greater carrying capacity of humans on branches. (Heerwagen & Orians, 1993).
Overall, he postulated that:
“tree shapes characteristic of environments providing the highest quality resources for evolving humans should be more pleasing than shapes characterizing poor habitats.”
Respondents rated attractiveness of black-and-white photographs of the trees on a six-point scale. Being black and white the photos emphasize the formalist characteristics of the tree. The study found that trunk height, canopy layering and canopy width/tree height ratio significantly influenced attractiveness scores, but the canopy width/canopy height did not have a significant effect.
The most attractive trees (Table 1) had highly or moderately layered canopies, lower trunks, and higher canopy width/tree height ratio. Factors such as broken branches, deformed trunks, and highly asymmetrical canopies, all indicators of resource depletion, depressed attractiveness scores.
Table 1 Comparison of the most attractive & least attractive treesHeerwagen & Orians, 1993
Interpreting their results, the authors noted that a low trunk is easier to climb than a high one; a broad umbrella-like canopy affords greater refuge from sun or rain than a narrow, high canopy (Heerwagen & Orians, 1993). The results were considered to support the functional – evolutionary perspective.
Orians & Heerwagen also compared the forms of African savanna trees with maple and oak trees found in Japanese parks and gardens. Comparing three morphological differences – height vs canopy width, trunk height vs total height, and canopy depth vs canopy width – they found close similarities:
Garden conifers are highly modified by pruning them to grow broader than tall; trunks are trained to branch close to the ground; foliage is trimmed to produce a distinct layering similar to that of a number of savanna species (Heerwagen & Orians, 1993).
While suggesting that achieving a growth form similar to that of savanna trees was a criterion subconsciously employed by Japanese gardeners, Orians recognised that many other factors also had an influence (Orians, 1986).
In another study, Heerwagen & Orians (1993) sought evidence for savanna-like scenes from the Red Books of Humphrey Repton, the 18th century English landscape architect. The Red Books illustrated the “before” and “after” appearance of properties, showing the effect of Repton’s planned landscaping. Examination of eighteen designs found that Repton frequently moved trees out into open space, thereby creating an uneven wood edge, a feature characteristic of savanna environments. In his book, The Art of Landscape Gardening, Repton noted that too many trees “make a place appear gloomy and damp.”
According to Sommer & Summit (1995), research on tree preferences in Argentina, Australia and United States found that:
“respondents preferred canopies to be moderately dense and trunks that bifurcated near the ground. Trees with high trunks and skimpy or very dense canopies were considered to be least attractive by all these groups, findings considered to be consistent with the savannah hypothesis.”
They used computer drawn images of tree shapes to test preferences with variations in height and width and found preferences for large canopies, low trunk height and thin trunk thickness, the first two properties being consistent with savanna hypothesis and trunk thickness being irrelevant.
Both Balling & Falk (1982) and Lyons (1983) assessed the preferences for a range of environments illustrating savanna, deciduous forest, coniferous forest, tropical rain forest and desert. Savanna was found to be the most preferred of the five biomes. They found that preference for savanna was highest among the age 8 – 11 year olds after which it slipped behind deciduous and rain forest and, in Lyons’ study, behind rain forest (Figure 1). Balling & Falk found that overall preference for natural environments changed as a function of age. While the scores differ between the studies, the pattern is similar: high scores among the young that fall progressively with age, stabilizing in adulthood.
Both studies found the preference for savanna was strongest when a lush green savanna was used in preference to a drier African-like savanna. The difference was so striking that Lyons dropped the lush green savanna. The use of the greener savanna in the Balling & Falk study probably accounts for its higher ratings.
While Balling & Falk believed the results provide limited support for the hypothesis that people have some innate preference for savanna-like environments, Lyons disputed this saying it could be related to its familiarity for children who play in savanna-like parks and backyards.
Woodcock (1982) examined preferences for three biomes: rain forest, savanna and mixed hardwoods and found the hardwood to be the most preferred:
3.04 dense hardwood with underbrush
3.73 open hardwood with open ground
This is possibly because the participants were more familiar with the North American hardwood biome (Kaplan & Kaplan, 1989).
Fenton (1985) analyzed the underlying dimensions of meaning or content that individuals use in discriminating natural settings. He found that the majority of participants preferred scenes characterized by: open grasslands, verdant, water, natural, and with pathways. He viewed these findings as supporting Kaplan’s theory, but they also lend support to Orians’ habitat theory.
In a contrary finding, Schroeder (1991), studying preferences for scenes in an arboretum in Chicago, found natural deciduous wood scenes, large trees, and water attracted the highest ratings but scenes of trees and lawn – the classic pastoral landscape, were less preferred.
Among the evidence cited to support habitat theory is the observation that no archaeological evidence has been found to indicate early human occupation of dense forest, rainforests or deserts (Isaac, 1980). Use of fire by indigenous people, including the Australian Aborigines and the North American Indians, encouraged the development of savanna-like vegetation. While the purpose of this was to create favorable conditions for game, it raises the question, whether it was unconsciously directed to create a preferred savanna-like landscape. In both cases, the cessation of fires after European settlement resulted in the gradual loss of the savanna appearance.
Orians (1980) cites the perceptions of early explorers in North America who seemed to prefer savanna-like landscapes, although this may be to provide grazing land and reduce hiding opportunities for the Indians. Bourassa notes that similar preferences were apparent among explorers and settlers in Australia and New Zealand (1991). In his book, Future Eaters (1994), Tim Flannery included a chapter titled “Like Plantations in a Gentleman’s Park,” in which he wrote of the settlers’ efforts to transform the Australian landscape into an English Capability Brown-type landscape of extensive lawns and scattered large trees.
Many early paintings of the Australian landscape also displayed park-like environments. Favored scenes among painters were pastoral landscapes, environments which also made for good grazing land and which did not require clearing to be productive. By contrast, Bernard Smith (1971) refers to von Guerard’s paintings of virgin forest that amply convey the depressing effect so frequently mentioned by travelers and settlers.
Returning to their interest in the savanna after a gap of nearly 30 years, Falk & Balling (2010) showed rainforest residents in Nigeria, photographs of five biomes, rain forest, deciduous forest, coniferous forest, savanna and desert. The respondents overwhelmingly selected savanna scenes as representing the most desirable place to live. The authors regarded this as support for their hypothesis that humans have an innate preference for savanna settings which they see as modified by experience and enculturation.
Hartmann & Apaolaza-Ibáñez (2010) tested the savanna hypothesis with reference to the use of natural settings in advertisements. Their sample was 750 villagers in northern Spain. They found the ratings for savanna advertisements were not significantly different than those for other landscapes such as oak, beech or pine forests (Figure 2). European-type landscapes that were familiar to participants were the more preferred suggesting that familiarity plays a similar influence on preferences as innate preferences.
This finding was reinforced by several other studies. Adevi & Grahn (2012) examined whether people settle amidst landscapes that they grew up in, and whether they feel more at home in such landscapes. Surveying over 1300 Swedes, they found a clear congruence between the landscapes people grew up in and where they currently live (Figure 3). They also found that people who grew up in coastal areas avoided forest areas and vice versa.
The study found that a majority of people felt more at home in the landscape they grew up in. Of those born in coastal areas, 73% felt at home there, whereas in forested areas, the figure was 63%, rolling hills and lakes, 54%, and agricultural areas, 52%. While the savannah hypothesis suggests that all people would feel at home living on the agricultural plains which was the closest to the savanna, Adevi & Grahn found that people settle in and feel most at home in the landscape they have grown up with.
Han (2007) examined student’s responses to the six major terrestrial biomes (i.e. desert, tundra, grassland, coniferous forest, deciduous forest, and tropical forest). Surprisingly, he found the strongest preference for tundra and coniferous forest, and that desert and grassland scenes were the least favored. However, the scenes selected of tundra were of snow-capped mountains, not the vast flat snow plains of Arctic latitudes. Also some scenes of the forests included water, which always rates high. Nevertheless, he was surprised by the unfavorable response for the savanna landscapes and for the high response to tundra scenes, that “neither an evolutionary perspective nor a cultural perspective can provide (a) reasonable explanation…” Han acknowledged that had the tundra been represented by typical Arctic scenes the response may have been less favorable. However, this does not detract from the low rating of savanna scenes.
While there are findings and anecdotal evidence supportive of the habitat hypothesis, these are not definitive. Savanna landscapes have rated relatively poorly against more familiar landscapes, indicating that familiarity may be as important a predictor of landscape preference.
PROSPECT AND REFUGE THEORY,
Jay Appleton defined habitat theory as:
the theory that aesthetic satisfaction experienced in the contemplation of the landscape stems from the spontaneous perception of landscape features which, in their shapes, colors, spatial arrangements and other visible attributes, act as sign-stimuli indicative of environmental conditions favourable for survival, whether they are really favourable or not (Appleton, 1975a).
Appleton’s prospect-refuge theory which derives from both habitat theory and information processing theory, has become one of the most widely quoted landscape theories. Hudson described it as a seminal contribution (1992). Appleton, formerly Professor of Geography at the University of Hull, England, described the theory in The Experience of Landscape (1975). The book’s name derives from the view of the philosopher, John Dewey (The title of Dewey’s book, Art as Experience (1934), may have inspired Appleton’s title), that beauty lay neither in beautiful objects nor in the eyes of the beholder but rather in the relationship between the individual and the environment – what Dewey calls ‘experience’. Such experience covers both the habitat theory and information processing theory that aesthetic satisfaction from landscapes derives from their favorability for survival.
In King Solomon’s Ring, Konrad Lorenz (1952) wrote of seeing without being seen, which relates to habitat theory. Appleton built on this, arguing that a landscape need only provide the appearance of satisfying survival needs. Certain sign-stimuli provided by the landscape comprise the core of Appleton’s prospect-refuge theory. He termed the sign-stimuli that provide opportunities to see as a prospect while those which provide an opportunity to hide he termed refuge. Appleton summarized his theory thus:
Habitat theory postulates that aesthetic pleasure in landscape derives from the observer experiencing an environment favorable to the satisfaction of his biological needs. Prospect-refuge theory postulates that, because the ability to see without being seen is an intermediate step in the satisfaction of many of those needs, the capacity of an environment to ensure the achievement of this becomes a more immediate source of aesthetic satisfaction.
Over a decade later, Appleton described how he developed his theory:
I was looking for a simple model that could relate the idea of preference to a typology of landscapes through the medium of the biological and, more particularly, the behavioral sciences (1988).
Appleton developed the imagery and symbolism of the theory. Prospects can be direct or indirect and include panoramas and vistas while refuges such as hides and shelters can be classified by function, by origin (natural or artificial), by substance (in the earth as caves or in vegetation), by accessibility and by efficiency. One senses that some of these are classification for classification’s sake, but Appleton is nothing if not exhaustive in the development of his theme.
He examined and classified hazards, surfaces and related components, discussed landscapes which are dominated by prospect, refuge or hazard, the place of man in nature, and then reviewed prospect and refuge in parks and gardens, in architecture and urban design, painting, film, literature, and the application of prospect-refuge theory to the landscape gardens of Capability Brown, Repton and le Nôtre’s Versailles. He commented on fashion and taste and finally described the application of the theory to case studies of landscapes in several countries.
The theory potentially offers an explanation to the perennial question of why people climb mountains. The answer is not “because it’s there” but rather because the mountain represents the best prospect available and, hence, being on top enhances survival. The fact that this may lead people to climb very high mountains and to even be killed in the attempt does not negate this hypothesis, it merely suggests that optimality applies in the selection of mountains to provide prospects and that high mountains may actually be sub-optimal for this purpose.
Many studies have tested Appleton’s prospect and refuge theory.
Using a very limited sample of four participants (including the authors), Clamp & Powell (1982) sought to test Appleton’s theory by rating 40 panoramas of landscapes for landscape quality, prospect, refuge, hazard, and the balance of prospect and refuge. The authors calculated that, although the quality ratings correlated well (four is insufficient for correlations), there were no significant correlations between preference and prospect-refuge balance. Some correlation was obtained between preference and prospect. They found a significant negative correlation between prospect and refuge – the finding is not surprising as something that provides a good prospect is unlikely to be a good refuge. Overall though, the study failed either to support conclusively or to negate the central claim of (the) theory and despite every effort (by the judges they) remained unconvinced that they were tapping some underlying perceptual force. The small sample limits the credibility of the study.
In a Spanish study, Abelló et al (1986) found preferences for forest landscapes, a preference for fertility and plant vigor, some pattern or rhythm, and a structural legibility in winter defoliation. The survival-promoting preferences tend to support Appleton’s thesis:
they correspond either to signs indicating environmental virtues (fertility and plant vigor healthy biomass) or hazards (environmental hostility present in defoliated wintry vegetation)…
Orians (1986) suggested that scenes with a high proportion of prospects compared with refuges would be favored as familiarity of the observer increases and the risks they present decrease accordingly. He observed that closed forests are deficient in prospect while desert and grassland scenes are deficient in refuge. By contrast, savannas provide a good combination of prospect and refuge. Orians & Heerwagen (1992) suggested that Appleton’s theory meant that an environment judged pleasant will be one with a balance between prospect and refuge opportunities, with screening elements to provide privacy and variability in desired levels of intimacy in a space.
Heerwagen & Orians (1993) tested the evidence for prospect and refuge in landscape paintings, by examining gender differences in preferences and by examining the before and after pictures by the English landscaper, Humphrey Repton, and by the painter, John Constable.
Sunsets in Landscape Paintings
Based on an assumption that paintings of sunsets represent refuge symbolism, it would be expected that artists would include references to places in which people could spend the night. Out of 46 paintings of sunsets and sunrises (including many by Frederick Church), 35 were sunsets and 11 were sunrises indicating they believed that the information provided by a sunset is much more valuable and requires more urgent attention than … a sunrise. The sunset paintings scored very highly in refuge symbolism: 66% scored highly in refuge compared with 9% for sunrises. Sunset paintings had more built refuges whereas sunrise paintings had very few. Paintings that included a built refuge also included additional refuge symbols: 46% had a light in the window, 12% had smoke from the chimney, while 7% had both a light and smoke.
Heerwagen & Orians’ hypothesis was that females find refuges more attractive: a greater affinity for enclosure and protected places than do males due to pregnancy and childcare, as well as protection from the elements which drain energy. To avoid being trapped or being taken by surprise, an open refuge would be advantageous. Content analysis of 108 landscape paintings, painted by both male and female artists (52 F, 56 M) was used. Prospect symbolism included open landscapes, opportunities for views (hills, mountains, rock outcrops), and a view of the horizon at least half the width of the painting. Refuge symbolism included houses and vegetative cover, especially in the foreground. In summary:
- Women’s paintings: nearly half were high in refuge symbolism compared with 25% for men’s paintings. 75% had no horizon or peephole, these being symbolic of prospects.
- Men’s paintings: nearly half were high with prospects compared with 25% for women’s paintings. Nearly 75% had moderate-high prospect symbolism compared with less than half for women’s paintings. The horizon was more than half the width in 58% of paintings compared with 14% of women’s paintings.
Before and After Scenes
Heerwagen & Orians examined the before and after designs of Humphrey Repton and John Constable, Repton for his landscaping of properties and Constable of his sketches for later paintings. In 18 scenes, Repton enhanced the refuge and prospect character of the properties by adding copses of trees at the water’s edge which increased refuge and by removing trees to open views to the horizon which increased prospect.
Examination of nine of Constable’s sketches and paintings indicated that he frequently altered the vegetation to open views to the horizon or to make refuge features such as houses more conspicuous. In six of the pairs, he enhanced the refuge conditions by adding buildings and changing vegetation. The findings by Heerwagen & Orians support the prospect and refuge symbolism as an unconscious organizing attribute.
Researching forest and field environments, Herzog (1984) used factor analysis to identify three dimensions: unconcealed vantage point, concealed vantage point, and large trees. Both the unconcealed and concealed vantage points were moderately well liked with similar ratings of 3.27 and 3.39 on a 5-point scale, suggesting little difference in the preferences for each type. He found significantly stronger preferences for large old trees (3.79). When these trees were viewed in combination with pathways, ratings of 4.0 were obtained. Herzog speculated that this may be due to the large old trees providing an “especially pleasing effect as pathway border elements” – an artistic explanation, but it might also suggest that the combination of tree and path provided ideal refuge and prospect combinations. Herzog was aware of Appleton’s theory, but confined the implications of the study to Kaplan’s information processing theory.
In a study of waterscapes, Herzog (1985) referred to Appleton’s prospect as an affordance in Gibson’s (1979) terms, but did not analyze his findings in these terms. He found preferences were, in order (5-point scale), mountain waterscapes (3.99), large water bodies (3.28), rivers, lakes and ponds (3.11), and swampy areas (2.13). He found swampy areas to be distinguished by low spaciousness (2.45) while large water bodies were distinguished by spaciousness (4.11) and coherence (3.66). Spaciousness could be equated with prospect, as both denote similar openness of view. The mountain waterscapes were high in spaciousness and would also be expected to be high in prospect, while swampy areas were low in spaciousness and would also be expected to be low in prospect.
Herzog & Smith (1988) examined canyons and urban alleyways to examine Appleton’s concept of hazard and how this related to Kaplan’s predictor variables of mystery. Overall, they found that both danger and mystery predict preference, the former negatively and the latter positively. In a sub-sequent study, Herzog & Miller (1998) found that mystery was a positive predictor of both danger and preference and that the curvature of paths produces positive mystery.
Hull & McCarthy (1988) used scenes of the Australian bush to assess the impact on preferences of wildlife in scenes. Three dimensions were identified: water, enclosure and concealed view, the latter corresponding, they acknowledged, with Appleton’s theory. In a concealed view, foreground vegetation concealed the view but not enough to block views to the middleground or background. They found that the presence of wildlife amounted to slightly less than 10% of the total range of scenic beauty with a stronger influence at low scenic quality.
Nasar et al (1983) examined the preferences expressed from two locations in a city park. At each location, the observer viewed the scene from a protected position and an unprotected position (Figure 4). They assessed the scene on a nine bi-polar adjective scale (e.g. repelling-inviting, relaxed-tense). They found that the open views were regarded as safer than closed views, which accords with Appleton’s theory. However, they also found that females preferred the enclosed observation point to the open one, while the opposite applied to males. The notion of males preferring viewing points with less refuge is contrary to Appleton’s theory.
Strumse (1996) found higher preferences for green, grassy fields among women than men (males 2.99, females 3.22; 5-point scale) which could reflect a preference for the “open and well defined settings, which most probably induce feelings of security.” Such landscapes offer good prospects in Appleton’s terms.
Woodcock (1982) assessed preferences for savanna, rain forest and hardwoods on the basis of primary and secondary prospect, and primary and secondary refuge. He found prospect to be positively related to preference while refuge appeared to be negatively related, an unexpected result which led him to propose additional predictors, including agoraphobia and claustrophobia.
Fischer & Shrout (2006) tested children aged between 8 and 15 for their liking of landscape paintings and their perception of prospect, refuge and hazard in the paintings. They found that the children’s preferences increased with the degree of prospect in the paintings, but refuge was unrelated to preference. Boys found paintings with more hazards to be of greater appeal though the degree of hazard was rated low. Age had no effect on perceptions of prospect, refuge or hazard. Paintings which rated high in both prospect and refuge were well liked, whereas those rated low were not well liked. Being correlational in nature, the authors do not claim the results confirm an evolutionary explanation, but they are unaware of any other theory that would explain the results.
Overall, the evidence is not wholly compelling for Appleton’s theory and indicates that some refinement may in order. While prospects generally correlate with preference, this may derive from the appeal of mountains and water. Refuges are generally regarded negatively. A strong dichotomy by gender is apparent, males preferring open prospects, females preferring safe vantage points. While Appleton regards the balance between prospect and refuge as important, few studies have attempted to tackle what this balance might be.
Kaplan’s concepts of coherence, complexity, legibility and mystery appear to have some overlap and parallels with Appleton’s prospect and refuge, for example, prospect and legibility, refuge and mystery, and these could be explored further.
Appleton’s theory has been described as a sociobiological account of aesthetic value (Carlson, 1992) while Bunkse (1977) described it as hide and seek aesthetics and questioned the theory’s ability to deal with ambiguities such as whether darkness is a prospect or refuge. He considered that the theory seems to answer many unanswered questions including the human preference for natural habitats rather than artificial ones, and in treating the vast differences in French and Japanese gardening styles as attempts to fulfill innate, biologically determined preferences. Bourassa (1991) regarded it as a rather extreme assertion of a biological basis for aesthetics.
Appleton considered that cultural differences can be explained by their biological underpinnings, a view not universally shared. Jeans (1977) stated: The survival of primitivist urges in man, like territoriality, is so overlaid by cultural accretions and modifications that it seems uselessly over-simplistic to seek to apply them to human behavior.
Several reviewers have observed that Appleton’s theory, which suggests that each scene has to be broken down into its prospect and refuge symbolism, is reductionist in the extreme (Bunkse, 1977). Jeans (1977) described it as ridiculously reductionist while Tuan described it as a tour-de-force of reductionism (1976). Ulrich considered Appleton’s theory, in which elements are seen to have actual or symbolic survival significance, to be a rather extreme, ethologically based adaptive position (Ulrich, 1983).
In 1990, Appleton published The Symbolism of Habitat: An Interpretation of Landscape in the Arts which extended the theme of The Experience of Landscape to the arts. Today, Appleton’s concepts are used consciously by landscape designers (Frey, 1986). They are cited in site planning text-books and are used in the analysis of literary landscapes and architecture (Hudson, 1992, 1993).
While there is a considerable level of endorsement for Appleton’s theory, it lacks strong supporting evidence. The findings of studies suggest the need for further elaboration and consideration of the theory.
AFFECTIVE THEORY, ROGER ULRICH
Affective theory considers that natural settings and landscapes can produce in their viewers, emotional states of well-being that can be detected through psychological and neuro-physiological measures. The main proponent of the theory is Roger Ulrich, Emeritus Professor, College of Architecture at Texas A & M University and visiting Professor of Architecture at Chalmers University of Technology in Sweden. Stephen and Rachel Kaplan’s Attention Restoration Theory (ART) provides an alternative approach to restoration to Ulrich’s theory.
Affect is used by Ulrich synonymously with emotion and include feelings such as pleasantness, calm, exhilaration, caution, fear and anxiety but excludes drives such as thirst and hunger (Ulrich, 1983). Although it is measured on a like-dislike dichotomy, it has also been shown to be highly correlated with scales such as beautiful – ugly or scenic quality scales (Ulrich, 1986).
The affective model of preference is based on the premise that emotional (i.e. affective) responses to landscapes occur before cognitive information processing. With the development of cognitive psychology in the 1960s, affects were regarded as products of cognition (i.e. they are post-cognitive). In a widely quoted paper, Feeling and thinking, preferences need no inferences, Robert Zajonc (1980) argued against the prevailing doctrine that affect is post-cognitive and provided experimental evidence that discriminations (like-dislike) can be made in the complete absence of recognition memory. He contended that affect is more powerful than cognition. He concluded that affect and cognition are:
“under the control of separate and partially independent systems that can influence each other in a variety of ways, and that both constitute independent sources of effects in information processing.”
Ulrich also cited evidence in support of affect being precognitive (Ulrich, 1986, Ulrich et al, 1991). Ruddell, et al (1989) considered that the affective state heavily influence the subsequent cognitive appraisal of a setting as contributing to or detracting from personal well-being.
Based on this premise, Ulrich constructed a model of affective reactions preceding cognition but both influencing the post-cognitive affective state and actions that then arise (Ulrich, 1983). He termed the framework a psycho-evolutionary theory, where the positive emotions and physiological effects have survival benefits.
In contrast to Kaplan’s cognitive perspective, Ulrich proposed that:
“immediate, unconsciously triggered and initiated emotional responses – not ‘controlled’ cognitive responses – play a central role in the initial level of responding to nature, and have major influences on attention, subsequent conscious processing, physiological responding and behavior” (Ulrich et al, 1991).
He also suggested that an “evolutionary perspective implies that adaptive response to unthreatening natural settings should include quick-onset positive affects and sustained intake and perceptual sensitivity.”
Basic to Ulrich’s framework is that of adaptive response, adaptive meaning the wide array of actions and functioning which can foster well-being. Adaptive behavior may, for example, comprise staying and viewing an attractive scene or setting out to explore it. Ulrich (1979) tested participant’s feelings before and after viewing slides of urban and natural scenes. The results (Figure 5) indicates that urban scenes generally resulted in more negative feelings (e.g. one grew sadder, less elated, less friendly), whereas the opposite occurred after viewing the nature slides. He found that individuals shown scenes of cities with trees and other vegetation showed significantly reduced feelings of fear and increased positive feelings of affectation and delight, compared with individuals shown scenes of treeless city scenes.
Ulrich, 1979 Figure 5 Affect Scores Before and After Slides – Urban group & Nature group
Negative feelings were lessened and positive feelings became more positive from viewing nature scenes. Ulrich showed that the variation attributable to slide content was highly significant and concluded that the importance of visual landscapes is not confined to aesthetics, but that they also give rise to emotional states, urban scenes having a negative effect and the nature scenes positive.
In a second study, Ulrich (1981) used psycho-physiological measures to assess the effect of viewing slides of nature with water, nature with vegetation, and urban environments with neither water nor vegetation. Alpha waves reflect brain electrical activity. High alpha amplitudes indicate low levels of arousal and of wakeful relaxation while anxiety is related to high arousal and low alpha amplitudes. Rapid heart rates reflect strong emotions such as anxiety or fear. He measured alpha waves and heart rates and asked subjects to rate their feelings using semantic ZIPERS scale before and after viewing the slides. The ZIPERS scale assesses feelings on five factors: fear arousal, positive affect, anger/ aggression, attentiveness, and sadness. A 5-point scale is used for each. Ulrich found:
- Attentiveness declined but less so for water scenes;
- Sadness increased markedly from viewing urban scenes but only slightly for vegetation and was constant for water;
- Fear arousal emotion increased slightly with urban scenes, decreased slightly with vegetation and declined more sharply with water.
The physiological measures showed that alpha amplitudes were consistently higher when viewing vegetation than urban scenes with water scenes lying between these. The significantly higher results for vegetation were cited as one of the most important findings of the study and support the conclusion that the subjects felt more wakefully relaxed while viewing the vegetation as opposed to urban scenes. Heart rates were generally higher while viewing either water or vegetation compared with urban scenes – water 71.3 beats/minute, vegetation 71.1, urban 70.2. Ulrich concluded people benefit most from visual contact with nature, as opposed to urban environments lacking nature, when they are in states of high arousal and anxiety.
In an early classic study, Ulrich (1984) reported on investigations of the recovery of patients in a hospital, comparing patients whose rooms viewed a blank wall with those who could see trees (Figure 6). The patients underwent cholecystectomy (gall bladder) operations. The study found that those who viewed the trees had shorter stays in hospital: 7.96 days vs 8.70 days, took fewer analgesics and received fewer negative evaluative comments in nurse’s notes: 3.96 per patient for those facing a wall compared with 1.13 for those facing trees.
The analgesic doses did not vary significantly between the two groups for the first day or the last days but for days 2 – 5 the difference was statistically significant. The results imply that hospital design and siting decisions should take into account the quality of patient window views.
Ulrich et al (1991) extended physiological measures to include skin conductance, pulse transit time, muscle tension and heart period. Participants were first tested, they then viewed a ten-minute stressful video on workplace accidents, and then viewed a second ten-minute video showing everyday outdoor settings – two natural (vegetation and water) and four urban. Pair-wise tests showed that, following viewing natural scenes, positive affect scores increased significantly compared with urban scenes. Results from the four physiological measures showed that the nature scenes reduced stress, indicating their greater recovery influence. The study also found that nature scenes resulted in more rapid recovery from stress, suggesting that even momentary viewings of trees through a window provides benefit.
Ulrich’s Theory of supportive design (Figure 7) proposes that patient stress will be reduced if the hospital environment fosters perceptions of control, positive distraction and social support . Andrade et al (2015) tested this by exposing participants to a hypothetical hospital environment with features that would provide social support (e.g. chairs, Internet), positive distraction (TV, plants, family photos), perceived control (adjustable lighting, openable windows) and combinations of these. Ulrich’s theory was confirmed – participants anticipated much less stress where features providing distraction and social support were present.
An early study using eye pupillary dilation as an autonomic measure of aesthetic reaction was undertaken by Wenger & Videbeck (1969). Applying the technique to both campers and non-campers, they found that, although the test provided a reliable pattern of differences between the two groups, the results were opposite of their expectations! On the basis of this finding, the authors concluded that another autonomic measure might be preferable and that the information processing hypothesis may better explain the observed pupillary movement.
Parsons (1991) noted that, although there is no direct empirical evidence supporting Ulrich’s theory, the sensory model of emotions by LeDoux (1986) & Henry (1980) model of endocrine responses in stressful situations “constitute prima facie evidence for the existence of subcortical ‘hardware’ and processing which is supportive.” He considered that the “immediate affective responses to environments may influence environmental preferences … and trigger physiological processes that can influence the immune system, and thereby, physical well-being.”
Overall, Ulrich’s research findings provide strong support for his theory that immediate, unconsciously triggered and initiated emotional responses – not ‘controlled’ cognitive responses – play a central role in the initial level of responding to nature (Ulrich et al, 1991). Subsequently, other researchers extended Ulrich’s findings into the restoration benefits of viewing nature (see theme: Health benefits of landscape).
INFORMATION PROCESSING THEORY
STEPHEN AND RACHEL KAPLAN
Stephen Kaplan Rachel Kaplan
University of Michigan
During the 1960s and 1970s environmental psychologists focused attention on the perception of the environment (see theme: Visual perception). Of particular relevance to landscape is the work of Stephen and Rachel Kaplan of the University of Michigan who applied the information processing approach to landscape aesthetics to explain the interactions between humans and the landscape.
The Kaplans hypothesize that the perceptual process involves extracting information from one’s environment (Kaplan et al, 1989b). They suggested that humans seek to make sense of the environment and to be involved in it. They identified four predictor variables, two of which (coherence and legibility) help one understand the environment and the other two (complexity and mystery) encourage its exploration (Table 2).
Table 2 Predictor Variables
Kaplan, Kaplan & Brown, 1989b; Kaplan, 1979.
Coherence is the ease of cognitively organizing or comprehending a scene – “good gestalt.” It involves making sense of the scene. It includes factors which make the scene more comprehensible to organize it into a manageable number of major objects and/or areas. Research indicates that people hold onto information about scenes in chunks and that up to five can be retained in the working memory. A scene with about five major units will be coherent. Repetition of elements and smooth textures help to identify an area. Changes in texture or brightness should correspond with an important activity in the scene – where it does not, the scene lacks coherence.
Complexity is the involvement component – a scene’s capacity to keep an individual busy, i.e. occupied without being bored or overstimulated. Often referred to as diversity, variety or richness it used to be regarded as the single most important factor. The Kaplans describes it as how much is “going on” in the scene – a single field of corn stretching to the horizon will not have the same level of complexity as many fields of many crops on undulating land with hedgerows and cottages. The more complex scene will tend to be preferred to the simple. My own research has consistently found diversity to among the strongest influences on scenic quality.
Legibility is the ability to predict and to maintain orientation as one moves more deeply into a scene. It entails “safety in the context of space” (Kaplan, 1979) and is similar, though much broader, to Appleton’s concept of refuge. Legibility, like mystery, involves an opportunity to promise to function, to know one’s way and the way back. It thus deals with the structuring of space, with its differentiation, with its readability. Legible scenes are easy to oversee, to form a mental map. Legibility is enhanced by distinctive elements such as landmarks, smooth textures, and the ease of compartmentalizing the scene into parts. While coherence focuses on the conditions for perceiving the scene, legibility is concerned with movement within it.
Mystery is the promise that more information could be gained by moving deeper into setting, e.g. a trail disappearing, a bend in a road, a brightly lit clearing partially obscured from view by foliage. New information is not present but is inferred from what is in the scene, there is thus a sense of continuity between what is seen and what is anticipated. A scene high in mystery is one in which one could learn more if one were to proceed further into the scene. The Kaplans used the term “mystery” reluctantly because they could not find a more suitable term. A better term might be “anticipation.”
In an early study, Kaplan et al (1972) focused on the single factor of complexity and found a 0.37 correlation with preference. A second study (R. Kaplan, 1975) found a correlation of 0.62 between complexity and two new variables, mystery and coherence. However, the correlation between complexity and preference, when assessed independently, was -0.47, in contrast with the original +0.37. She put this down to content, the later study being of urban scenes rather than of nature. Using regression analysis, the R2 for the three informational factors was a promising 0.49, indicating that together they accounted for around half the variance. Mystery was particularly significant (r = 0.56), coherence slightly weaker (0.33), and complexity a negative factor (-0.39).
Coherence and complexity are considered to involve minimal analysis, whereas legibility and mystery require more time and thought. (This might suggest some of the variables are pre-cognitive and others are post-cognitive, but the Kaplans assume they are all post-cognitive) Scenes of high preference tend to be those with legibility and mystery; coherence and complexity help create the scene, but high levels of these do not necessarily result in high preference.
Through the 1980s, further studies by the Kaplans, Herzog, Anderson and others reinforced and gave coherence to the definition of the informational variables. Following their review of over a decade’s research, the Kaplan’s concluded:
- In each of the studies the combination of these informational predictors yielded significant results.
- Complexity was a significant positive predictor in only a single study (and a negative predictor in urban scenes).
- Legibility’s role is hard to judge. In four of the five studies where it was included, legibility did not play a significant role. In Anderson’s study it was found to be a negative predictor.
- Coherence proved to be a significant predictor in the majority of the studies where it was included; in one case it was the only significant predictor in the regression analysis.
- Mystery (or anticipation) is the most consistent of the informational factors.
Many studies have tested the Kaplan’s theory.
Abello, Bernaldez & Galiano (1986) concluded from their analysis of forested landscape preferences that plant fertility/vigor factor was a key factor in preference followed by the strong expression of pattern/ rhythm/recurrent texture of landscape elements. Factor analysis indicated correlations of -0.84 and -0.89 of these respectively with the factors they identified. The authors acknowledged that the results lend support to an evolutionary or socio-ecological basis of landscape aesthetics, including Kaplan’s cognitive characteristics related to predictability (pattern recurrent textures) and meaning (legibility of structures, capacity of seeing through barriers).
Anderson’s (1978) study of forest management assessed informational factors with professional, resident and student groups. Table 3 summarizes these factors as predictors of preference for these groups. All the factors were consistent across all groups except for mystery, which played a negligible role for the preferences of professionals. Coherence and mystery were the best predictors of preference for residents and students.
Table 3 Informational Processing Factors as Predictors of Preference for Groups
Brown & Itami (1982) proposed a model that related scenic resource values to landscape preference components as defined by the Kaplan model. The Brown & Itami framework comprises two inter-related systems – the natural (landform) & cultural (land use). These describe the physical components. Landform reflects “immutable” components and the cultural system is reflected by land use and land cover pattern.
Brown & Itami model:
Brown et al (1986) tested this model by comparing the preferences obtained for scenes with those predicted by the Brown & Itami model and yielded a correlation of 0.61. A further analysis was undertaken by grouping scenes using factor analysis; four groupings were obtained (Table 4). Comparison of the predicted average values and preference ratings indicated identical rankings for the two procedures (5-point scale).
Table 4 Relationship between predicted values & preference ratings
Brown et al, 1986
According to the authors, the results provide support and encouragement for further work. The higher preference values occurred for smooth-textured grassy areas, suggesting that coherence is more important than indicated by the model. Similarly, low preference values occurred in relatively barren scenes, suggesting the importance of complexity.
Gimblett et al (1985) asked respondents to rate photographs based on the Kaplan’s dimension of mystery using a 5-point scale. Analysis found a high degree of agreement regarding mystery in the landscape and analysis of the photographs identified five attributes that were associated with mystery (Table 5).
Table 5 Physical Attributes of Mystery
Gimblett et al, (1985)
The five physical attributes were defined as follows:
- Screening: degree to which views of the larger landscape are visually obstructed or obscured
- Distance of view: measured from viewer to nearest forest stand; as distance increases, mystery decreases
- Spatial definition: degree to which the landscape elements surround the observer
- Physical accessibility: apparent means of moving through or into the landscape as a result of finely textured surfaces in the foreground; provides way of exploring landscape to gain more information
- Radiant forests are special cases in wooded areas where the immediate foreground is in shade and an area further in the scene is brightly lit. These were consistently ranked high for mystery.
Gobster & Chenoweth (1989) analyzed the physical, artistic and psychological variables of landscapes and found that all three aspects could explain preferences. The ten psychological descriptors included mystery, harmony, legibility, awe and pleasantness. They also found that the three variables were interrelated within a definable structure. A conceptual inter-relatedness was also found between descriptor variables with the artistic and psychological dimensions defining separate constructs relating to the compositional and affective-informational meanings. Multi-dimensional scaling indicated that the psychological descriptors yielded the highest multiple correlation of R = 0.84, significantly higher than that for the physical descriptors or artistic descriptors.
Aesthetic theories based solely on formal-artistic, bioevolutionary and other singular sets of properties (i.e. physical-ecological, psychological-affective) etc. may not do justice to the richness of human aesthetic response to landscapes. To build an aesthetic theory of landscapes, investigators need to broaden their understanding of the multidimensional nature of aesthetic preferences.
Gregory & Davis (1993) found that positive factors (trees, tree trunks and water depth) were linked to the legibility and coherence of a riverscape, while the negative factors (water color, bank channelization, channel sinuosity and debris in the river) were linked to the complexity and mystery of the scene. These are my interpretations; the authors did not assess the riverscapes in informational terms. Water color, bank stability and water depth together accounted for nearly 90% of the variation in the riverscape preferences.
Thomas Herzog, Professor of Psychology at Grand Valley State University in Michigan, has undertaken a series of studies to explain and assess the validity of the Kaplan’s information processing model.
In Herzog’s (1984) study of field and forest environments, moderate correlations (0.45 to 0.55) were obtained for the three predictors of the unconcealed vantage point dimension: identifiability (i.e. familiarity), coherence and spaciousness. These help one organize and make sense of a setting in Kaplan’s terms. Herzog comments that their prominence as predictors suggests that when one is out in the open, there is a premium on being able to figure out where one is and where one could get to quickly. In the large trees category, high ratings were obtained for the making-sense (i.e. identifiability, coherence, texture) and involvement (i.e. mystery) properties, which supports the Kaplan’s contention that scenes high in both of these properties will be most preferred.
Herzog (1985) used the same predictor variables to rate waterscapes (Figure 8) and found:
- Spaciousness was best shown in large water bodies; these also showed highest texture and coherence but lowest complexity and mystery – these water bodies lack interest and are easy to make sense of;
- By contrast the other water bodies are more interesting, being high in mystery and complexity yet being reasonably coherent; they thus reward immediate involvement yet hold out promise of more;
- The distinguishing features of (1) low textures of mountain waterscapes which suggest that they are difficult to navigate; (2) low spaciousness of swampy areas; (3) identifiability of rivers, lakes & ponds; and (4) large bodies of water have the most distinguishing features.
Waterscapes which were high in spaciousness, coherence and mystery but low in texture (e.g. uneven land) were preferred. Inter-correlations with preference were: spaciousness 0.42, coherence 0.33, mystery 0.09, texture -0.15. Those that were at least moderately high in making sense (understanding), and involvement (exploration) were preferred. The content of the water is also important; rushing water is preferred over stagnant creeks. Herzog found the information approach useful in accounting for waterscape preferences.
Herzog (1987) examined mountainous scenes using the same six predictor variables and preference as the criterion variable (Figure 9). He found:
- Deserts are low in spaciousness but are only moderate in other ratings;
- Snowy mountains are high in spaciousness but are of low complexity while smaller mountains are also high in spaciousness and identifiability;
- Narrow canyons have the most extreme profile being low in spaciousness, texture and identifiability but very high in mystery. Spacious canyons (e.g. Grand Canyon) are high in spaciousness, coherence and complexity.
Inter-correlations with preference were: identifiability 0.61, spaciousness 0.32, texture 0.22, mystery 0.13, thus identifiability corresponds fairly closely with preference. While the mountain categories are reasonably high on spaciousness, the two canyons differ markedly on this variable. The difference in identifiability between the mountain scenes is likely to be due to the familiarity of small ranges to the participants. The lower rating of texture for small mountains reflects their less smooth, more rugged appearance of the snowy mountains, in which snow and clouds tend to obscure their true ruggedness. As texture reflects the affordance of locomotion, the results suggest that this is not validly measured by texture.
Again, Herzog found the informational approach useful in accounting for natural landscape preferences and supported the approach of examining both content and cognitive processes in the evaluation of these preferences. The pattern of significant variables changes substantially when content categories are included. A positive predictor of preference is identifiability (i.e. familiarity) that gives “eloquent testimony to the strong cognitive need to make sense of the environment in such settings.”
During the period, 2002 to 2010, Herzog conducted a further series of studies with his graduate students into aspects of Kaplan’s predictor variables. They used color photographs of field and forest settings and samples of psychology students.
Herzog & Kutzli (2002) found danger and fear to be strongly related and danger and preference to be slightly negatively related. Visibility and ease of movement relate positively to preference but negatively with danger. Feelings of entrapment affect the positive relation between concealment and danger but without that, mystery has a positive relation to preference.
Regarding legibility, the least researched of Kaplan’s predictor variables, Herzog & Leverich (2003) examined it and the other variables in field/forest settings and included the degree of preference for the scene. The definitions used were: coherence – how well does the scene ‘hang together’?; complexity – how much is going on in the scene?; mystery – how much does the setting promise more to be seen if you could walk deeper into it?; and legibility – how easy would it be to find your way around in the setting? The study surprised the authors by finding a strong correlation between coherence and legibility, a relationship not found in previous studies. The study found that openness in the forest setting was a major component of legibility which is as one would expect – an open forest enables one to see where one is going compared with a closed forest.
Herzog & Kropscott (2004) followed up the Herzog & Leverich (2003) study with a larger sample and a focus on legibility and preference and the links between preference, danger and mystery. Using forest settings without paths, they found legibility and coherence to be independent positive predictors of preference. As would be expected, landmarks and visual access were positive predictors of legibility. Danger and mystery were both negatively correlated with preference.
Herzog & Kirk (2005) examined the influence of pathway curvature and border visibility on preference and danger. They found the former, pathway curvature had no influence on preference but border visibility and visual access were positively related to preference but reduced danger. Danger and preference were again negatively related. Mystery related positively with danger and negatively with visual access. They concluded that danger is more important than preference, and visibility is a significant predictor of both danger and preference.
Herzog & Byrce (2007) reanalyzed data from previous studies to examine further the negative relations between mystery and preference in forest settings. By classifying the settings into high and low visual access, they found in the high visual access setting that mystery related positively with preference but was not related to visual access. Conversely, in the low visual access setting, preference was not related to mystery but related positively with visual access. The concluded that mystery does relate positively with preference.
The basic predictor variables as established by the Kaplans were developed in other studies. Strumse (1994) applied them, together with perception-based variables (e.g. openness, smoothness, ease of locomotion) in western Norway, and found the informational variables were the most effective predictors of preference (r2 of 0.66). Ulrich (1977) developed focality (i.e. a focal point), as an extension of coherence, ground textures as a factor in complexity, and depth, or a sense of space, as an element in exploration and legibility. Whitmore et al (1995) applied the basic predictor variables to a canyon landscape, describing water, vegetation and landforms in informational terms.
The Kaplan’s theory has been subjected to a range of studies, and they all provide varying levels of support for its elements. There would appear to be a considerable degree of interpretation required of the application of these four predictor variables in the landscapes studied. The nebulousness of the concepts involved suggests the need for them to be expressed in operational terms.
Stephen Kaplan acknowledges that his approach is an evolutionary view based on habitat theory, with human preferences deriving from the adaptive value offered by particular settings (Kaplan, 1987). Preferences were regarded by Kaplan as:
“an intuitive guide to behavior, an inclination to make choices that would lead the individual away from inappropriate environments and towards desirable ones”
“The central assumption of an evolutionary perspective on preference is that preference plays an adaptive role; that is, it is an aid to the survival of the individual (1982).”
Every aspect of preference should provide some discoverable benefit or payoff. Deriving environmental preference occurs very rapidly and unconsciously. It is:
“the outcome of what must be an incredibly rapid set of cognitive processes which integrate such considerations as safety, access and the opportunity to learning into a single affective judgement.”
Kaplan considered that the character of predictor variables, and the nature of preference responses, support an evolutionary interpretation. In support, he cited the preferences for savanna (Balling & Falk, 1982), the similarity of landscaped parks to savanna (Orians, 1986) and the prospect-refuge theory of Appleton (1975). An evolutionary analysis, Kaplan asserted, achieves a number of objectives, it:
- Indicates the importance of preference;
- Provides an expectation of underlying commonality in preferences across individuals;
- Suggests that preference research has a substantial theoretical interest;
- Identifies variables likely to be effective in predicting preference.
An evolutionary viewpoint led Kaplan to conclude that:
Aesthetic reactions reflect neither a casual nor a trivial aspect of the human makeup. Aesthetics is not the reflection of a whim that people exercise when they are not otherwise occupied. Rather, such reactions appear to constitute a guide to human behavior that has far-reaching consequences (Kaplan, S, 1987).
Kaplan went on to state that organizing workspace, arranging one’s home, avoiding certain directions and approaching others may reflect factors such as coherence, legibility, mystery and complexity. He concluded that there is clearly more to aesthetics than optimal complexity and that the acquisition of new information and its comprehension (are) central themes underlying the preference process.
Zube summarized the Kaplan’s approach thus (1984):
The Kaplans propose that long term survival of the human species was dependent upon development of cognitive information processing skills which in turn led to preferences for landscapes that made sense to the observer. In other words, landscapes were preferred that could be comprehended, where information could be obtained relatively easily and in a non-threatening manner that provided opportunity for involvement, and that conveyed the prospect of additional information. According to this framework, landscapes that are preferred are coherent, legible, complex, and mysterious.
Balling & Falk (1982) summarized Stephen Kaplan’s contribution:
Taking an evolutionary perspective, S. Kaplan has asserted that the long-term survival of the extremely knowledge-dependent human species required that people should actually like to obtain information about landscapes, and that they should be able to process certain kinds of environmental information very efficiently.
OTHER THEORETICAL APPROACHES
In a paper titled: Capturing Landscape Visual Character Using Indicators: Touching Base with Landscape Aesthetic Theory, Ode et al, (2008) use what they regard as theoretical constructs by various authors to define visual indicators for landscape character definition. From the literature, they identify nine visual concepts that characterize the visual landscape: complexity, coherence, disturbance, stewardship, imageability, visual scale, naturalness, historicity, and ephemera. These are listed in Table 6 together with their theoretical source.
Table 6 Concepts describing landscape character based on theories
Ode et al, 2008
In respect to the theories presented, Ode et al describes them as theories developed for explaining and predicting preference which provide a basis for explaining what is important for our experience of landscape (which can) aid in identifying what characteristics of the visual landscape are important to describe. It is perhaps stretching to regard some of the list as theories, some are little more than concepts used for illustration. The authors expand each of the concepts to identify contributing indicators.
It may be that social scientists have either believed they have identified satisfactory theories, or that they have abandoned the search for theoretical underpinnings. This is certainly the view of Terkenli (2001):
“Although an all-encompassing theory may no longer be sought after in contemporary social sciences as in the past, the fact that no integrated, comprehensive theoretical and analytical frameworks have been thus far formulated that adequately address landscape study, assessment and planning has been in many regards, debilitating. Moreover, the plethora of processes of action and interaction among the various components and functions of a landscape dictate that almost all existing theoretical frameworks of analysis, as well as methodological tools, have some application in landscape study, planning, use or policy implementation.”
It remains to be seen whether further theories will be developed to explain landscape aesthetics.
While not specifically referring to landscape preferences, the article, “Four Fallacies of Pop Evolutionary Psychology” in Scientific American (Buller, 2009) suggests that the assumptions of evolutionary psychology are deeply flawed because much of its assertions are speculative, not based on evidence. While intellectually appealing, the theory of evolution applied to psychology is not a foregone conclusion.
Clearly, a robust theory of landscape which provides an all-encompassing framework with which to understand and to predict landscape preferences does not currently exist. At present, there is a range of theories that offer explanations of aspects of landscape preferences but which fall short of a definitive explanation.
Of the theories available, the Kaplan’s information processing theory appears the most supportable, based on the range of studies that have assessed its validity and explored the dimensions of the factors involved.
Orian’s habitat theory of savannah landscapes has support, but it is not definitive and there are hints that familiarity with savannah type landscapes, such as neighborhood parks may be as important a predictor. Appleton’s prospect-refuge theory has intuitive appeal but the studies undertaken fail to provide conclusive support, if anything tending to indicate its shortcomings and areas in which the evidence is contrary to the theory. Some of his elements have parallels with the dimensions of the Kaplan’s information processing (e.g. prospect and legibility, refuge and mystery) although it is acknowledged that each area is coming from very different intellectual positions.
Ulrich’s affective theory has good support from studies but, like habitat theory, its usefulness in understanding and predicting landscape preferences is limited. It focuses rather, on the positive effect that landscape can play on emotional states of well-being.
While the Kaplan’s theory offers the most comprehensive explanation of landscape preferences, it is not a theory that is readily applicable in a field situation to evaluate landscape. By contrast, the appeal of Appleton’s and Orian’s theories is that they offer explanations that can be readily applied in the field.
If the mark of solid theory is in its use in applications, then none of the theories currently available provide a useable framework for the evaluation of landscape in a field situation. While they can offer tantalizing glimpses of understanding, they fall well short of comprehensively enabling the evaluation of landscapes.
The conclusion of Gobster & Chenoweth (1989) is therefore appropriate, that existing theories based on artistic, bioevolutionary or other properties fail to capture the “richness of human aesthetic response to landscape.” They suggest the need for researchers to “broaden their understanding of the multidimensional nature of aesthetic preferences.”
More recently, there has been a polarization between advocates of innate preferences from an evolutionary approach, and ecological aesthetics based on an ecological understanding of landscape. Gobster et al, (2007) argue that they are not competing approaches but rather reflect widely different paradigms.
Abelló, R.P., F.G. Bernaldez & E.F Galiano, 1986. Consensus and contrast components in landscape preference. Env. & Behav., 18:2, 155 – 178.
Adevi, A.A. & P. Grahn, 2012. Preferences for landscapes; a matter of cultural determinants or innate reflexes that point to our evolutionary background? Landscape Research, 37:1, 27 – 49.
Anderson, E., 1978. Visual Resource Assessment: Local Perceptions of Familiar Natural Environments. PhD (Natural Resources) dissertation, University of Michigan.
Andrade, C.C. & A.S. Devlin, 2015. Stress reduction in the hospital room: Applying Ulrich’s theory of supportive design. Jnl. Env. Psych., 41, 125 – 134.
Appleton, J., 1975a, The Experience of Landscape. John Wiley, London.
Appleton, J., 1975b. Landscape evaluation: the theoretical vacuum. Trans. Inst. Brit. Geog., 66, 120 – 123.
Appleton, J., 1988. Prospects and refuges revisited, 27 – 44. In: Nasar, J.L. (Ed)., Environmental Aesthetics: Theory, Research and Applications. Cambridge University Press.
Appleton, J., 1990. The Symbolism of Habitat: An Interpretation of Landscape in the Arts. University of Washington Press, Seattle.
Balling J.D. & J.H. Falk, 1982, Development of visual preference for natural environments, Env. & Behav., 14:1, 5 – 28.
Bell, S., 1999. Landscape: Pattern, Perception, Process. E. & F.N. Spon, London.
Brown, T.J. & R.M. Itami, 1982. Landscape principles study: procedures for landscape assessment and management – Australia. Landscape Jnl, 1:2, 113 – 121.
Brown, T., T. Keane & S. Kaplan, 1986. Aesthetics and management: bridging the gap. Landscape & Urban Plg., 13, 1 – 10.
Bourassa, S.C., 1991. The Aesthetics of Landscape. Belhaven Press, London.
Buller, D.J., 2009. Four fallacies of pop evolutionary psychology. Scientific American, 300.1, 74 – 81.
Bunkse, E.V., 1977. Review of “The Experience of Landscape.” Annals Assoc. of Amer. Geog., 67:1, 149 – 151.
Carlson, A., 1992. Review of “The Symbolism of Habitat.” Jnl. Aesthetics & Art Criticism, 50:1, 79 – 80.
Clamp, P & M. Powell, 1982. Prospect-refuge theory under test. Landscape Research, 7:3, 7 – 8.
Darwin, C. 1859. The Origin of Species. John Murray, London.
Dearden, P. & B. Sadler, 1989, Themes and approaches in landscape evaluation research. In: Dearden, P. & B. Sadler (Eds), 1989. Landscape Evaluation: Approaches and Applications. University of Victoria, BC.
Fairclough, G., G. Lambrick & A. McNab (Eds), 1999. Yesterday’s World, Tomorrow’s Landscape. The English Heritage Historic Landscape Project 1992–94. London: English Heritage.
Falk, J.H. & J.D. Balling, 2010 Evolutionary Influence on Human Landscape Preference. Env. & Behav. 42, 479 – 493.
Fenton, D.M., 1985. Dimensions of meaning in the perception of natural settings and their relationship to aesthetic response. Aust. Jnl. Psych., 37, 325 – 339.
Fischer, M.A. & P.E. Shrout, 2006. Children’s liking of landscape paintings as a function of their perceptions of prospect, refuge, and hazard. Env. & Behav. 38, 373 – 393.
Flannery, T., 1994. Future Eaters: An Ecological History of the Australasian Lands and People. Reed Books.
Frey, S., 1986. A series of gardens, Bainbridge Island, Washington. Landscape Architecture, 76, 56 – 63, 128.
Gibson, J.J., 1979, The Ecological Approach to Visual Perception. Houghton Mifflin, Boston.
Gimblett, E.R., R.M. Itami & J.E. Fitsgibbon, 1985. Mystery in an information processing model of landscape preference. Landscape Jnl., 4:2, 87 – 95.
Gobster, P.H. & R.E. Chenoweth, 1989. The dimensions of aesthetic preference: a quantitative analysis. Jnl. Env. Mgt, 29, 47 – 72.
Gobster, P.H., J.I. Nassauer, T.C. Daniel & G. Fry. 2007. The shared landscape: what does aesthetics have to do with ecology? Landscape Ecol., 22, 959–972.
Gregory, K.J. & R.J. Davis, 1993. The perception of riverscape aesthetics: an example from two Hampshire rivers. Jnl. Env. Mgt., 39, 171 – 185.
Han, K.T., 2007. Responses to six major terrestrial biomes in terms of scenic beauty, preference and restorativeness. Env. & Behav., 39:4, 529-556.
Hartmann, P. & V. Apaolaza-Ibáñez, 2010. Beyond savannah: An evolutionary and environmental psychology approach to behavioral effects of nature in green advertising. Jnl. Env. Psych. 30, 119 – 128.
Heerwagen, J.H. & G.H. Orians, 1993. Humans, habitats, and aesthetics, In: Kellert, S.R. & E.O. Wilson (Eds), The Biophilia Hypothesis, Island Press.
Henry, J.P., 1980. Present concept of stress theory. In: Usdin, E. et al, (Eds), Proceedings of the Second International Symposium on Catecholamines and Stress, Sept 1-16, 1979, Czechoslovakia, Elsevier.
Herzog, T.R., 1984. A cognitive analysis of preference for field-and-forest environments. Landscape Research, 9, 10 – 16.
Herzog, T.R., 1985. A cognitive analysis of preference for waterscapes. Jnl. Env. Psych., 5, 225 – 241.
Herzog, T.R., 1987, A cognitive analysis of preference for natural environments: mountains, canyons, and deserts. Landscape Journal, 6:2, 140 – 152.
Herzog, T.R. & G.A. Smith, 1988. Danger, mystery, and environmental preference. Env. & Behav., 20:3, 320 – 344.
Herzog T.R. & E.J. Miller, 1998. The Role of Mystery in Perceived Danger and Environmental Preference. Env. & Behav., 30:4, 429 – 449.
Herzog T.R. & G.E. Kutzli, 2002. Preference and Perceived Danger in Field/Forest Settings. Env. & Behav., 34:6, 819 – 835.
Herzog T.R. & O.L. Leverich, 2003. Searching for Legibility. Env. & Behav., 35:4, 459 – 477.
Herzog T.R. & L.S. Kropscott, 2004. Legibility, mystery, and visual access as predictors of preference and perceived danger in forest settings without pathways. Env. & Behav., 36:5, 659 – 677.
Herzog T.R. & K.M. Kirk, 2005. Pathway Curvature and Border Visibility as Predictors of Preference and Danger in Forest Settings. Env. & Behav., 37:5, 620 – 639.
Herzog T.R. & A.G. Byrce, 2007. Mystery and Preference in Within-Forest Settings. Env. & Behav., 39:6, 779 – 796.
Hudson, B.J., 1992. Hunting or a sheltered life: prospects and refuges reviewed. Landscape & Urban Plg., 22. 53 – 57.
Hudson, B.J., 1993, The view from the verandah: prospect, refuge and leisure. Aust. Geog. Studies, 31:1, 70 – 78.
Hull, R.B. & M.M. McCarthy, 1988. Change in the landscape. Landscape & Urban Plg., 15, 265 – 278.
Isaac, G.A., 1980. Casting the net wide: a review of archaeological evidence for early hominid land-use and ecological relations. Current Argument on Early Man. Pergamon Press Oxford.
Jeans, D.N., 1977. Review of The Experience of Landscape. Aust. Geog., 13, 345 – 6.
Kaplan, R., S. Kaplan & J.S. Wendt, 1972. Rated preference and complexity for natural and urban material. Perception and Psychophysics, 12:4, 354 – 356.
Kaplan, R., 1975. Some methods and strategies in the prediction of preference. In: Zube, E.H., R.O. Brush & J.G. Fabos (Eds), 1975. Landscape Assessment: Values, Perceptions and Resources. Dowden, Hutchinson & Ross.
Kaplan, S., 1979, Perceptions and landscape: Conceptions and misconceptions. In: Elsner, G.H. & R.C. Smardon (Eds). 1979. Proceedings of Our National Landscape Conference on Applied Techniques for Analysis and Management of the Visual Resource, 23-25 April, 1979, Incline Village, Nevada. Pacific SW Forest & Range Experiment Station.
Kaplan, S., 1982. Where cognition and affect meet: a theoretical analysis of preference. 13th Conference of the Environment. Design Research Foundation, Washington DC.
Kaplan, S. 1987. Aesthetics, affect and cognition: environment preference from an evolutionary perspective. Env. & Behav., 19:1, 3-32.
Kaplan, R. & S. Kaplan, 1989. The Experience of Nature – A Psychological Perspective. Cambridge University Press.
Kaplan, R., S. Kaplan & T. Brown., 1989. Environment preference: a comparison of four domains of predictors. Env. & Behav., 21:5, 509 – 530.
Kellert, S.R. & , E.O. Wilson, 1995. The Biophilia Hypothesis. Island Press.
Leakey, L.S.B., 1963. Very early East African Hominidae, and their ecological setting. In: F.C. Howell & F. Bourliere (Eds), African Ecology and Human Evolution, Aldine, Chicago.
Leakey. R.E.F., 1976. Hominids in Africa. American Scientist, 64:2, 174 – 8.
LeDoux, J.E., 1986. Sensory systems and emotions: a model of affective processing. Integr. Psychiatry, 4, 237 – 248.
Litton, R. B., 1972. Aesthetic dimensions of the landscape. In: J. V. Krutilla (Ed.) Natural Environments: Studies in Theoretical and Applied Analysis, Johns Hopkins University Press, Baltimore, MD.
Lorenz, K., 1952. King Solomon’s Ring. London: Methuen.
Lowenthal, D., 1979. Age and Artifact: Dilemmas of Appreciation. The Interpretation of Ordinary Landscapes. Oxford University Press, New York.
Lowenthal, D., 1985. The Past is a Foreign Country. Cambridge University Press. Cambridge.
Lynch, K., 1960. Image of the City. MIT Press, Cambridge, Mass.
Lyons, E., 1983. Demographic correlates of landscape preference. Env. & Behav., 15:4, 487 – 511.
Nasar, J.L., 1983. Adult viewers’ preferences in residential scenes, a study of the relationship of environmental attributes to preferences. Env. & Behav., 15:5, 589 – 614.
Nassauer, J.I., 1995. Culture and changing landscape structure. Landscape Ecology, 10:4, 229 – 237.
Ode, A., M.S. Tveit & G. Fry, 2008. Capturing landscape visual character using indicators: touching base with landscape aesthetic theory. Landscape Research, 33:1, 89 – 117.
Orians, G.H., 1980. Habitat selection: general theory and applications to human behavior. In: J.S. Lockard, The Evolution of Human Social Behavior. Elsevier, New York.
Orians, G., 1986, An ecological and evolutionary approach to landscape aesthetics. In: Penning-Rowsell, E.C. & D. Lowenthal, Landscape Meanings and Values, Allen & Unwin, London.
Orians, G. & J. H. Heerwagen, 1992. Evolved responses to landscapes. In: J.H. Barlow, L. Cosmides & J. Tooby, (Eds), The Adapted Mind, Evolutionary Psychology and the Generation of Culture. Oxford University Press.
Parsons, R., 1991. The potential influences of environmental perception on human health. Jnl. Env. Psych., 11, 1 – 23.
Porteous, J.D., 1982. Approaches to environmental aesthetics. Jnl. Env. Psych, 2, 53 – 66.
Ruddell, E.J., J.H. Gramann, V.A. Rudis & J.M. Westphal, 1989. The psychological utility of visual penetration in near-view forest scenic-beauty models. Env. & Behav., 21:4, 393 – 412.
Schroeder, H.W., 1991. Preference and meaning of arboretum landscapes: combining quantitative and qualitative data. Jnl. Env. Psych., 11, 231 – 248
Sell, J.L., J.G. Taylor & E.H. Zube, 1984. Toward a theoretical framework for landscape perception. In: Saarinen, T.F., D. Seamon, & J.L. Sell (Eds), Environment Perception and Behavior: An Inventory and Prospect. Uni. Chicago Dept. Geography Research Paper 209.
Smith, B., 1971. Australian Painting, 1788 – 1970. Oxford University Press, Melbourne.
Sommer, R. & J. Summit, 1995, An exploratory study of preferred tree form. Env. & Behav., 27:4, 540- 557.
Strumse, E., 1994, Environmental attributes and the prediction of visual preferences for agrarian landscapes in western Norway. Jnl. Env. Psych., 14, 293 – 303.
Strumse, E., 1996. Demographic differences in the visual preferences for agrarian landscapes in Western Norway. Jnl. Env. Psych., 16, 17 – 31.
Terkenli, T.S., 2001. Towards a theory of landscape: The Aegean landscape as a cultural image. Landscape & Urban Plg., 57, 197 – 208.
Tuan, Y.F., 1974. Topophilia. Englewood Cliffs, NJ: Prentice-Hall.
Tuan, Y.F., 1976. Review of “The Experience of Landscape.” Professional Geographer, 28:1, 104 – 5.
Ulrich, R.S., 1977. Visual landscape preference: a model and application. Man-Environment Systems, 7:5, 279 – 293.
Ulrich, R.S., 1979. Visual landscapes and psychological well-being. Landscape Research, 4, 17 – 23.
Ulrich, R.S., 1981. Natural versus urban scenes, some psychophysiological effects. Env. & Behav., 13:5, 523 – 556.
Ulrich, R.S., 1983, Aesthetic and affective response to natural environment. In: I. Altman & J.F. Wohlwill, Behavior and the Natural Environment, Plenum Press, New York.
Ulrich, R.S., 1984. View through a window may influence recovery from surgery. Science, 22, 420 – 421, 27 April.
Ulrich, R.S., 1986. Human responses to vegetation and landscapes. Landscape & Urban Plg., 13, 29- 44.
Ulrich, R.S., R.F. Simons, B.D. Losito, E. Fiorito, M.A. Miles & M. Zelson, 1991. Stress recovery during exposure to natural and urban environments. Jnl. Env. Psych., 11, 201 – 230.
Wenger, W.D. & R. Videbeck, 1969. Eye pupillary measurement of aesthetic responses to forest scenes. Jnl. Leisure Research, 1:2, 149 – 161.
Whitmore, W., E. Cook & F. Steiner, 1995. Public Involvement in Visual Assessment: The Verde River Corridor Study. Landscape Jnl., 14:1, 26 – 45.
Woodcock, D.M., 1982. A Functionalist Approach to Environmental Preference. PhD dissertation, University of Michigan.
Zajonc, R.B., 1980. Feeling and thinking: preferences need no inferences. Am. Psychologist, 35:2, 151-175.
Zube, E.H., 1984. Themes in landscape assessment theory. Landscape Journal, 3, 104 – 110.