Visual Aesthetics and Human Preference2013)AR.pdf · PS64CH04-Palmer ARI 8 November 2012 9:0 Visual Aesthetics and Human Preference Stephen E. Palmer, Karen B. Schloss, and Jonathan

PS64CH04-Palmer ARI 8 November 2012 9:0

Visual Aesthetics andHuman PreferenceStephen E. Palmer, Karen B. Schloss,and Jonathan SammartinoDepartment of Psychology, University of California, Berkeley, California 94720;email: [email protected], [email protected], [email protected]

Annu. Rev. Psychol. 2013. 64:77–107

First published online as a Review in Advance onSeptember 27, 2012

The Annual Review of Psychology is online atpsych.annualreviews.org

This article’s doi:10.1146/annurev-psych-120710-100504

Copyright c© 2013 by Annual Reviews.All rights reserved

Keywords

visual perception, color, harmony, shape, spatial composition, art

Abstract

Human aesthetic preference in the visual domain is reviewed fromdefinitional, methodological, empirical, and theoretical perspectives.Aesthetic science is distinguished from the perception of art and fromphilosophical treatments of aesthetics. The strengths and weaknesses ofimportant behavioral techniques are presented and discussed, includingtwo-alternative forced-choice, rank order, subjective rating, produc-tion/adjustment, indirect, and other tasks. Major findings are reviewedabout preferences for colors (single colors, color combinations, andcolor harmony), spatial structure (low-level spatial properties, shapeproperties, and spatial composition within a frame), and individual dif-ferences in both color and spatial structure. Major theoretical accountsof aesthetic response are outlined and evaluated, including explanationsin terms of mere exposure effects, arousal dynamics, categorical pro-totypes, ecological factors, perceptual and conceptual fluency, and theinteraction of multiple components. The results of the review supportthe conclusion that aesthetic response can be studied rigorously andmeaningfully within the framework of scientific psychology.

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Contents

1. INTRODUCTION . . . . . . . . . . . . . . . . 782. FOUNDATIONAL ISSUES . . . . . . . 78

2.1. Aesthetics Versus Art . . . . . . . . . . 782.2. Defining Aesthetics . . . . . . . . . . . . 792.3. Philosophical Foundations . . . . . 802.4. On the Possibility of a Science

of Aesthetics . . . . . . . . . . . . . . . . . . . . 813. METHODOLOGICAL ISSUES . . . 82

3.1. Two-Alternative ForcedChoice . . . . . . . . . . . . . . . . . . . . . . . . . 82

3.2. Rank Ordering . . . . . . . . . . . . . . . . 823.3. Rating. . . . . . . . . . . . . . . . . . . . . . . . . 833.4. Production and Adjustment

Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . 833.5. Indirect Measures . . . . . . . . . . . . . . 833.6. Other Measures . . . . . . . . . . . . . . . 84

4. COLOR PREFERENCE. . . . . . . . . . . 844.1. Preference for Single Colors . . . 844.2. Preference for Color

Combinations . . . . . . . . . . . . . . . . . . 895. PREFERENCE FOR SPATIAL

STRUCTURE . . . . . . . . . . . . . . . . . . . . 915.1. Aesthetics of Low-Level

Visual Properties . . . . . . . . . . . . . . . 915.2. Aesthetics of Object Shape . . . . . 915.3. Aesthetics of Spatial

Composition . . . . . . . . . . . . . . . . . . . 926. INDIVIDUAL DIFFERENCES. . . . 967. THEORIES OF AESTHETIC

RESPONSE. . . . . . . . . . . . . . . . . . . . . . . 977.1. Mere Exposure . . . . . . . . . . . . . . . . 977.2. Arousal Dynamics . . . . . . . . . . . . . 977.3. Prototype Theory . . . . . . . . . . . . . 987.4. Fluency Theory . . . . . . . . . . . . . . . 987.5. Multicomponent Theories of

Aesthetic Response to Art . . . . . . . 1008. CONCLUSIONS AND ISSUES

FOR FUTURE RESEARCH . . . . . . 101

1. INTRODUCTION

We make judgments and decisions every daybased on our internal aesthetic responses to as-pects of the world around us. We decide to wear

this sweater rather than that one because weprefer its color, we buy this poster rather thanthat one because we like its graphic composi-tion, or we simply choose to sit facing this direc-tion rather than that one in the park because wefind the view more pleasurable. Such mundanebut ubiquitous aesthetic considerations are sodeeply woven into the fabric of our mental livesthat we seldom reflect on what our preferencesare or why we have them, but these are ques-tions of scientific interest and import. Wearingthis or that sweater, buying this or that poster,or facing this or that direction in the park maynot seem to have much impact on our materiallives, and yet, if we consider the alternative—aworld in which we have no such preferences orcould make no such choices—what a drab, dull,wearisome world it would be!

Given the pervasive influence of aestheticresponses on our mental lives, it is surprisinghow little is known about them. The study ofaesthetic preference is actually one of the oldesttopics in psychology, having been pioneeredby Gustav Fechner (1876), one of the foundersof modern scientific psychology. Interest inaesthetics has waxed and waned greatly sincethen, but a revival is now in progress as partof a movement to create an interdisciplinaryaesthetic science (Shimamura & Palmer 2012).Our goal in this article is to review recentcontributions to this field. Because there are somany, we have concentrated on behavioral re-search about aesthetic preferences in the visualdomain. There is thus no coverage of many im-portant topics in empirical aesthetics, includingstudies of nonvisual modalities (e.g., musiccognition), facial attractiveness (Rhodes 2006),neuroaesthetics (Chatterjee 2011), or aestheticprocesses in the creator (Turner 2006), all ofwhich are active fields of current research.

2. FOUNDATIONAL ISSUES

2.1. Aesthetics Versus Art

Because art is so closely associated with visualaesthetics in most people’s minds, many read-ers are likely to assume that the present article

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reviews the psychological study of visual art. Itdoes not. To understand why, we begin by mak-ing a principled distinction between art and aes-thetics, which are often taken to be essentiallyequivalent.

Perhaps the most straightforward differenceis that significant aesthetic experiences can (anddo) occur anywhere in response to seeing anysort of object, scene, or event, whereas art islimited to the subset of human artifacts in-tended to be viewed as art, whether in a mu-seum, a gallery, or one’s own living room. Whoamong us has not experienced the ineffable de-light produced by the sight of a delicate dew-kissed rose, a majestic snow-capped mountain, alone seagull gliding silently over a sunset sea, orsome other scene of comparable natural beauty?These objects and scenes are not construed asart—unless and until someone paints or pho-tographs them—and yet who can deny thatseeing them produces aesthetic experiences?

A second difference is that art has tradi-tionally been identified with positive aestheticexperiences, whereas aesthetic response, ingeneral, spans the range from very positiveexperiences (consider again viewing the rose,mountain, and seagull described above) to verynegative ones (consider instead the sight ofa decaying carcass oozing with maggots or abloody, festering wound). Thus, although artcan and (sometimes) does produce significantpositive aesthetic experiences in beholders,there is much more to aesthetic experience thanjust people’s reactions to art. By the same token,however, there is much more to art than just theaesthetic responses it evokes in viewers. Art isa social, cultural, institutional, and commercialenterprise of enormous proportions, involvingmuseums, galleries, curators, auctions, critics,collectors, historians, books, royalties, repro-ductions, and a host of other aspects whoserelation to personal aesthetic response to worksof art is often tangential, at best. Although aes-thetics and art are indeed related, they are con-ceptually distinct and should be treated as such.The present article reviews the psychology ofvisual aesthetics, not the psychology of art.

2.2. Defining Aesthetics

Ideally, we would now provide a proper, closed-form definition of aesthetics, something like“The study of human minds and emotions in re-lation to the sense of beauty.” This seems help-ful because it identifies minds and emotions ascentral concepts, but its problem lies in presup-posing that the reader already knows what “thesense of beauty” is. For those who do not, thiskind of definition is either disturbingly vacuousor implicitly circular. For present purposes, wetake aesthetics to be the study of those mentalprocesses that underlie disinterested evaluativeexperiences that are anchored at the positiveend by feelings that would accompany verbalexpressions such as “Oh wow! That’s wonder-ful! I love it!” and at the negative end by “Ohyuck! That’s awful! I hate it!” Such a defini-tion is nakedly and unapologetically subjective,yet grounded scientifically in certain kinds ofbehaviors, much like defining pain as that di-mension of human experience that leads one tosay “Ouch!” and to hold or rub the injured bodypart. This definition will do nothing to explainthe nature of aesthetic experiences to someonewho has never had any, but for those who have,it allows them to be identified within our mentallives.

One might try to anchor such a defini-tion objectively in the physical environment byproposing prototypical exemplars that wouldtie aesthetic experiences to external objects thatregularly and reliably elicit them. In dealingwith more objective experiential dimensions,such as “red,” prototypes are indeed useful:“Red” is that visual experience common to theappearance of ripe strawberries, fresh blood,classic fire engines, etc., when viewed understandard daylight conditions. Unfortunately,this strategy is not available for aesthetics sim-ply because people so seldom agree. If one wereto claim that aesthetic response refers to thekind of positive experiences elicited by viewing,say, Van Gogh’s Starry Night, Michelangelo’sDavid, Frank Lloyd Wright’s Fallingwater, andAnsel Adams’s Moon Over Half Dome, at leasttwo important problems arise.

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One problem is that different people’saesthetic responses differ so greatly that theremay be some individuals who have stronglynegative aesthetic experiences to all of thesupposedly positive prototypes. This potentiallack of agreement about aesthetic responseeven to such prototypes effectively derails theprototype strategy. The other problem is thataesthetic response, at least in our view, does notrefer just to positive experiences or even just toextreme experiences. We assume that virtuallyeveryone has some aesthetic response tovirtually everything they see (e.g., Palmer et al.2012b, Reber 2012). In most cases it may lingeronly fleetingly, if at all, in the “fringe” of humanconsciousness but it can come into focal aware-ness under appropriate circumstances, such aswhen the aesthetic response is extreme (seeingsomething so wonderful or so awful that the aes-thetic response spontaneously bursts into con-sciousness), when one’s attention is directed toaesthetic response by context (in viewing paint-ings in a museum or gallery or even in shoppingfor home furnishings), or when one is givenexplicit instructions to do so (in a laboratoryexperiment). The fact that aesthetic responsesare not always conscious, however, does notmean that people don’t have them all the time.

Another possible strategy for defining aes-thetic response objectively is to “neurologize”it, as advocated in the emerging field of neu-roaesthetics (e.g., Ramachandran & Hirstein1999, Zeki 1999). The substantive claim is thataesthetics can be defined by some specifiedtype(s) of activity in some specified set(s) ofneurons in some specified area(s) of the brain.The hope is to identify the neural activity thatactually produces aesthetic experiences, what-ever that might be. Mary may love Van Gogh’sStarry Night and hate Da Vinci’s Mona Lisa,whereas Bill may have the opposite reactions,but if positive (and negative) aesthetic expe-riences arise from correlated amounts and/ortypes of neural activity in the same or func-tionally similar brain areas, perhaps aestheticresponse can be objectively identified with thatneural activity. The difficulty is that such neu-rological criteria cannot logically supplant the

behavioral manifestations of subjective, experi-ential criteria within individuals. The reason isthat, even though neural activity may be onto-logically prior to subjective experience (i.e., thespecified brain activity causes the aesthetic ex-perience), it is epistemologically secondary andderivative. The logic of this claim is that, to findthe kind of brain activity that causes people’saesthetic responses, one must first identify be-havioral manifestations of the target subjectiveaesthetic experiences and then determine whatbrain activity is correlated with those kinds ofbehavioral events. Without the behavioral mea-sures, the neural correlates project can never getoff the ground.

2.3. Philosophical Foundations

There is a long history of philosophical inquiryinto aesthetics that predates all of the psycho-logical studies we review below. As with manyphilosophical issues, its history begins in antiq-uity with treatments by Plato and Aristotle, andit continues, largely unresolved, to the presentday. We do not attempt to review this extensiveliterature in any detail, as it has been reviewedelsewhere (e.g., Dickie 1997). We do want tocomment, however, on a few central issues thatare specifically relevant to the content of thisreview.

Much of the philosophical literature on aes-thetics is explicitly concerned with art. Platoand Aristotle actually took quite different viewson this subject. Plato denounced art as mereimitation that imparts no true knowledge of re-ality and even damages the soul. Aristotle ex-tolled it precisely because people are indeedable to learn from (and even delight in) imita-tion. They agreed, however, on the importanceof unity, harmony, and integration—see Plato’sdiscussion of a good argument in the Phaedrusand Aristotle’s analysis of tragic plot structurein the Poetics—a theme that was elaborated bylater writers, including Kant and Dewey.

Kant’s (1892/1951) views on philosophicalaesthetics in his Critique of Judgment havebeen particularly influential. His approach wasdecidedly psychological because he identified


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beauty explicitly with the viewer’s mental expe-riences rather than the object’s physical prop-erties. Aesthetic judgments, he claimed, werecharacterized by three key features: their sub-jectivity, their disinterested nature, and theirclaim of universality. Aesthetic judgments aresubjective because they rest on personal experi-ences (e.g., liking/disliking), which have no ob-jective empirical content with respect to the ob-ject itself, but only with respect to the relationbetween the object and the viewer. Unlike manyother such liking/disliking judgments, however,aesthetic judgments are “disinterested” in thesense that they do not involve desire. Preferringa larger to a smaller piece of cake would notcount as an aesthetic judgment in Kant’s frame-work, because such a judgment is (presumably)about one’s desire to consume the larger one.

Kant argued further that aesthetic judg-ments “claim universal validity.” This assertioncan be confusing. Clearly, aesthetic judgmentsare not universally valid because not everyoneagrees about them. Nevertheless, Kant claimsthat when someone makes an aesthetic judg-ment, he or she believes that others ought toshare that judgment, as if beauty were an ob-jective property of things rather than of our ex-periences on viewing them. Kant thus arguedthat aesthetic judgments involve more complexcognitive investments than judgments of “mereagreeability” or “individual preference,” whichare merely matters of taste for which there ap-pears to be no basis for discussion. Here Kantrefers to the importance of the “harmoniousfree play of the imagination” in aesthetic judg-ments. Although it may not be entirely clearwhat this phrase means, it is evident that itrefers to some relatively complex mental pro-cesses that are likely absent in the appreciationof “mere preferences.”

We agree with Kant that aesthetic judg-ments are subjective and disinterested, but notthat they necessarily involve claims of universalvalidity and/or free play of the imagination.Although the latter may be a hallmark of manypeople’s views about works of art, they arehardly required in characterizing people’s pref-erences for colors, either singly or in combina-

tion (see section 4) or in their preferences forthe shapes of rectangles or the spatial composi-tion of simple pictures (see section 5). Rather,we see the aesthetic preferences reviewedbelow to be basic aspects of what become quitecomplex aesthetic responses that people have toartworks or scenes of natural splendor. Our be-lief is that eventually enough will be understoodabout how such simple preferences combineand interact with cognitive and emotionalfactors to explain more complex evaluativeaesthetic claims that are often couched in uni-versal terms and rely on Kantian free play of theimagination. We freely admit that whether thisbelief is true and how this understanding mightunfold in an aesthetic science are as yet unclear.

2.4. On the Possibility of a Scienceof Aesthetics

Although many writers and researchers findit obvious that aesthetic experiences can bestudied scientifically (e.g., Arnheim 1974,Berlyne 1971, Fechner 1876, Jacobson 2006,Shimamura & Palmer 2012), others do not(e.g., Dickie 1962). Indeed, some would surelyclaim that a science of aesthetics is not onlyimpossible, but also oxymoronic (because sci-ence is objective and lawful, whereas aestheticresponse is subjective and whimsical). Aestheticresponses are surely subjective (see above),but just as surely, that does not preclude theirbeing studied objectively through behavioralmethods (see below). For example, people’sexperiences of color are not only subjective, butalso can differ substantially over individuals ow-ing to color blindness and/or color weakness,and yet there is nevertheless a well-establishedand technically sophisticated science ofcolor vision (e.g., Kaiser & Boynton 1996,Koenderink 2010). There is no logical certaintythat a scientific approach to aesthetics willsucceed in identifying regularities in people’saesthetic responses to visual displays, but thisis clearly an empirical issue that can only besettled by trying. Below we summarize numer-ous results that provide significant insight intoaesthetic preferences using rigorous scientific


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methods, providing strong empirical supportfor the viability of a science of visual aesthetics.

It is important to note that the factual ba-sis of a science of aesthetics is not to settlewhether some image or object is “objectivelybeautiful”—we agree with Kant that this isimpossible—but rather to determine whether(or to what degree) some representative set ofindividuals judge or experience it as beautiful(or ugly). A science of aesthetics thus concernsaccurately describing people’s aesthetic judg-ments and discovering the causes and/or rea-sons for those judgments. We now turn to thequestion of how one might do so.

3. METHODOLOGICAL ISSUES

Typically, the goal of a descriptive science ofvisual aesthetics is to determine average rela-tive aesthetic preferences for some set of visualdisplays among a particular population, givena specific task to judge some aesthetic qual-ity or qualities. The population, the displays,the task, and the aesthetic qualities of interestmust all be specified prior to collecting data.The displays can be virtually any visible object,event, or image. The task is typically for theobserver to attend to his/her experience of thedesignated aesthetic quality of the displays (e.g.,their color or shape or overall attractiveness)and to indicate his/her evaluation. A varietyof behavioral responses are possible—ratings,rankings, descriptions of likes or dislikes—butwhatever they are, they must be measured be-haviorally. Additional physiological measure-ments can also be made, such as galvanic skinresponse, functional magnetic resonance imag-ing, or event-related electrical potentials. Ourreview focuses on behavioral measures of aes-thetic experiences, however.

Many of the methodological choices will bedictated by the specific hypothesis being tested:e.g., that American observers (the population)will tend to prefer color combinations of similarhue (the aesthetic quality) for a smaller squarewithin a larger square (the displays). The sam-ple of observers is important because people indifferent cultures, economic groups, and social

groups may have systematically different aes-thetic experiences to the same visual displays.The large interobserver variability in aestheticpreferences within such groups dictates usinglarger rather than smaller samples of randomlyselected individuals.

If the goal is direct measurement (i.e., scal-ing) of average preferences among a full setof N visual displays, there are three primarybehavioral tasks: two-alternative forced choice(2AFC), rank ordering, and preference ratings.Other methods can provide useful informationbut suffer from problems that limit their utility.

3.1. Two-Alternative Forced Choice

In most respects the optimal task is 2AFC, inwhich observers indicate which of two simul-taneously presented visual displays they “likebetter” (prefer aesthetically) for all possiblepairs. The average probability of choosingeach display over all others is then taken asa global measure of its relative preference.2AFC paradigms have the advantages of simpleresponses to indicate choice, trials containingonly two displays, essentially no memory load,and minimal response bias effects. Its primarydrawback is requiring N(N−1) = N 2−Ntrials to measure preferences for N stimuli(e.g., 90 trials for 10 stimuli, but 9,900 trialsfor 100 stimuli). A more efficient alternativethat has recently been developed is a MarkovChain Monte Carlo procedure that can beused when the display set is embedded withina dimensional space (Sanborn et al. 2010).

3.2. Rank Ordering

At the other end of the methodological spec-trum is a rank-ordering task in which all dis-plays of interest are presented simultaneously,and the observer is required to order them frommost to least preferred. The average rank or-der for each display is then taken as a measureof relative preference. Although individual rankorders are, by definition, merely ordinal, the av-erage rank over observers provides more quan-titative information. The primary advantage is


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that rank ordering requires only a single trial.Its disadvantages derive from the fact that thisone trial can be inordinately complex, requiringsimultaneous presentation of all N displays (dif-ficult or impossible on a computer screen if thedisplays are large and/or spatially complex) anda potentially long and complex series of judg-ments to arrive at the required single, coherentordering.

3.3. Rating

Ratings of aesthetic preference are an attrac-tive alternative paradigm, especially when thenumber of displays to be measured (N) is largeand/or the visual displays require significantspace. The ratings can be made on discrete re-sponse scales of a given resolution (e.g., a seven-point Likert scale) or continuous scales (e.g.,a line-mark rating), where the latter are oftenbetter if memory for specific prior ratings is aconcern. Observers are shown a single displayon each trial and asked to rate how much theylike it. Average ratings for displays are takenas relative preference measurements. The re-sponses are relatively simple, and only N trialsare required to measure preferences for N dis-plays. Because it can be difficult for observersto make consistent ratings across trials, espe-cially at the beginning, it is useful to displaythe entire set (or a representative sample) ofdisplays together with instructions to indicatethe most and least liked alternatives to anchorthe response scale before the experimental trialsbegin.

3.4. Production and Adjustment Tasks

Perhaps the most important alternative tasksfor assessing aesthetic response are produc-tion and adjustment tasks, in which observersare required to produce the most aestheticallypreferred possibility given various constraints.Such tasks are particularly useful when theexperimental requirements imply a combina-torial explosion of possible displays. For ex-ample, suppose that one wanted to study aes-thetic preference for all color pairs using a set of

100 possible colors. Because there are 100 ∗ 99distinct permutations of the two colors, 9,900trials would be required for a single observationper display, even using an efficient rating task.An adjustment task, such as fixing one color oneach trial and asking the observer to adjust thesecond color to produce the most aestheticallypleasing combination, would be a much moreefficient procedure, requiring only 100 trials.The dependent variable would be the probabil-ity of each adjusted color being chosen for eachfixed color. This efficiency comes at the con-siderable cost of producing only a very complexand incomplete partial ordering, with many tiesfor the many combinations that are seldom, ifever, produced.

3.5. Indirect Measures

Various physiological measurements (e.g.,galvanic skin response, functional magneticresonance imaging, event-related electricalpotentials) can theoretically be taken as covertaesthetic responses. For example, observerscan be instructed simply to look at each displaywithout requiring any behavioral response,with the average magnitude of the physiolog-ical measurement(s) of interest being used tocreate a scaling of the displays as if they werebehavioral ratings. Obviously, such methodswill be valid only to the extent that the phys-iological measurements have been validatedby comparing direct behavioral measures (e.g.,2AFC probabilities or aesthetic ratings) withthe given covert physiological measure(s).

One can also study indirect behavioral mea-sures, such as the probability of looking first atone of two presented displays or the total timespent fixating each of them. Like physiologicalmeasures, they must be validated against directbehavioral measures of preference, which arelogically primary. When this is not possible—such as with infants or animals—indirect mea-sures are the only alternative. When combinedwith direct measures, indirect measures can of-ten reveal nonconscious processes at work thatare difficult or impossible to study with directmeasures.


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3.6. Other Measures

All of the above methods rely to some degreeon direct, quantifiable, behavioral measures ofaesthetic response in which observers are re-quired to evaluate or create images in termsof their aesthetic reactions to them. There areother kinds of measurements that may proverelevant to understanding aesthetic responsewithin the framework of some theory or hy-pothesis. For example, one might believe thatthe aspect ratio of an image’s rectangular shapeor the power function of its spatial frequencyspectrum contribute to its aesthetic success (seebelow). Such physical measurements can becombined (e.g., by correlation) with concurrentbehavioral measurements, as described above.The behavioral measures are sometimes ne-glected, however, because an alternative indexis taken as a proxy for aesthetic success, suchas the fact that images hang in museums, areincluded in art books, or are included in someother collection of high repute.

There are several pitfalls to this strategy.One is the implicit assumption that images in-cluded in high-profile collections are aesthet-ically superior to other images for the studiedpopulation. This might be true, but it shouldnot be taken for granted. A second more se-rious caveat is that correlation does not implycausation. Even if it were true, say, that peo-ple aesthetically prefer pictures that are widerthan they are tall to those that are taller thanthey are wide, it is not necessarily true that thecause of this preference is their aspect ratios.People might actually prefer landscapes to por-traits, for example, where landscapes tend to bewider and portraits taller.

The third problem is perhaps the most se-rious of all: the frequent failure of art theoristsand critics to analyze appropriate contrast setsof images. If an author attributes the successof some acknowledged masterpiece to, say,the upward-pointing triangular compositionof the principal elements, one should not takethe claim as conclusive unless correspondingsamples of other paintings with such upward-triangle compositions are judged aesthetically

and contrasted with similar judgments ofpaintings with, say, “downward-triangle” or“quadrilateral” compositions. Few art theoristsor critics consider such contrast sets, however.

4. COLOR PREFERENCE

4.1. Preference for Single Colors

Early researchers claimed that adult prefer-ences for single colors were not systematicenough to warrant further investigation (e.g.,Allesch 1924, Chandler 1928, Cohn 1884).Eysenck (1941) rejected this conclusion, argu-ing that previous failures arose from unstan-dardized colors and inadequate statistical anal-yses. He found reliable effects in analyses ofboth his own data and von Allesch’s previouslyreported “chaotic” data. Modern studies usingstandardized colors and sophisticated statisticaltechniques have clearly established that, despitelarge individual differences, group color prefer-ences show systematic and reliable patterns asa function of the three primary dimensions ofcolor: hue (basic color), saturation (vividness,purity, or chroma), and lightness (brightness orvalue) (e.g., Granger 1955b, Guilford & Smith1959, Hurlbert & Ling 2007, McManus et al.1981, Ou et al. 2004, Palmer & Schloss 2010).

Hue. Hue preferences among American andBritish adults follow a relatively smooth curvi-linear function in which cool colors (green,cyan, blue) are generally preferred to warm col-ors (red, orange, yellow), with a maximum atblue and a minimum around yellow to yellow-green (see Figure 1). The majority of thevariance is due to differences along the vio-let to yellow-green axis of cone-contrast colorspace [i.e., S−(L+M), where S, M, and L re-fer to the output of the short-, medium-, andlong-wavelength cones] and/or the blue-yellowdimension of higher-level color-appearancespace, with only minor differences along thered to blue-green dimension of cone-contrast(L-M) and/or the red-green dimension of colorappearance (Hurlbert & Ling 2007, Ling &Hurlbert 2009, Palmer & Schloss 2010).


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Saturation. Western adults generally pre-fer colors of higher saturation to those oflower saturation for context-free patches ofcolor (McManus et al. 1981, Ou et al. 2004,Palmer & Schloss 2010). Some findings sug-gest that preferences decrease for colors of veryhigh saturation, which were reportedly “toovivid” (Granger 1955b). Preference for high-saturation colors varies as a function of gen-der, culture, and object-context, however (seebelow).

Lightness. Western adults tend to prefer col-ors of increasing lightness, at least to somepoint (Guilford & Smith 1959, McManus et al.1981), although this effect is not always evi-dent (Palmer & Schloss 2010) (see Figure 1).Lightness is confounded with saturation acrosshues, however, and because people generallyprefer highly saturated colors, different hueshave their peak preference at different lightnesslevels: e.g., yellow at high lightness levels, redand green at medium lightness levels, and blueand purple at low lightness levels (Guilford &Smith 1959).

Hue x lightness interactions. Of consider-able theoretical importance is the frequent find-ing that dark colors show a different hue prefer-ence function than light and medium-lightnesscolors of equal chroma, at least for warm colors(see Figure 1). In particular, dark shades of or-ange (browns) and yellow (olives) are stronglydisliked relative to lighter, equally saturatedoranges and yellows and relative to dark red(Guilford & Smith 1959, Palmer & Schloss2010). Such effects are theoretically importantbecause they are difficult to explain within clas-sic psychophysical models of color appearance.They are consistent with ecological explana-tions of color preference, however (see below).The overall pattern of preferences for singlecolors is thus complex, but there are clear andrepeatable regularities. For additional reviewsof previous work on single color preferences,see Ball (1965) and Whitfield & Wiltshire(1990).

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Figure 1Average color preference ratings from Palmer & Schloss (2010) for theBerkeley Color Project 32 chromatic colors. The 32 chromatic colors includeeight hues (R, red; O, orange; Y, yellow; H, chartreuse; G, green; C, cyan;B, blue; and P, purple) at each of four different saturation/lightness levels:saturated (circles; the highest chroma colors available on the display), light(triangles; the colors roughly midway between the saturated colors and white),muted (diamonds; the colors roughly midway between the saturated colors andneutral gray), and dark (squares; the colors roughly midway between thesaturated colors and black).

4.1.1. Theories of single color preference.The foregoing discussion specifies which colorspeople like, but not why they like the ones theydo or even why they have color preferences atall. Several types of explanations have been sug-gested, including physiological, psychophys-ical, emotional, and ecological hypotheses.Because all of these models have been fit tothe results shown in Figure 1 (see Palmer &Schloss 2010), we use them as a benchmark.

The most physiologically based theory sug-gests that people like colors to an extent thatdepends on a weighted average of cone con-trasts relative to the background color, as com-puted very early in visual processing: L-M andS-(L+M), where S, M, and L represent the out-puts of short-, medium-, and long-wavelengthcones, respectively (Hurlbert & Ling 2007).This model fits Hurlbert & Ling’s (2007)own data well, but even an augmented modelwith two additional psychophysical variables—luminance and saturation (Ling & Hurlbert


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2009)—accounted for only 37% of the variancein the data of Figure 1, presumably becausetheir color sample did not include the highlysaturated and easily named colors of Palmer &Schloss’s (2010) sample.

A similar, but purely psychophysical, hy-pothesis is that color preferences are based onconscious color appearances. Palmer & Schloss(2010) tested this possibility using a weightedaverage of observer-rated redness-greenness,blueness-yellowness, saturation, and lightnessof each color, roughly analogous to their co-ordinates in Natural Color System color space(Hard & Sivik 1981). This model accountedfor 60% of the variance in Figure 1. The factthat this is considerably better than the cone-contrast model suggests that later, consciousrepresentations of color predict color prefer-ences better than earlier, nonconscious ones.

A third explanation can be constructed interms of the emotional associations of col-ors. Perhaps people like colors to the extentthat they like the emotions that are evokedby or associated with those colors. Ou et al.(2004) measured color-emotions through sub-jective ratings and used those ratings to pre-dict color preferences. Their results showedthat three factor-analytic dimensions predictedcolor preferences: active-passive, light-heavy,and cool-warm, with active, light, and cool col-ors being preferred to passive, heavy, and warmones. Palmer & Schloss (2010) fit observers’subjective ratings of those three dimensions tothe data in Figure 1 and found that it accountedfor 55% of the variance. Ou et al. (2004) didnot discuss how color-emotions arise or whysome color-emotions predict preferences bet-ter than others. It is surprising, for example, thathappy-sad was not included because it is suchan important evaluative dimension. The diffi-culty is that the more desirable emotion (happy)is associated with less preferred hues (yellow),whereas the less desirable emotion (sad) is asso-ciated more desirable hues (blue) (Terwogt &Hoeksma 1995).

An ecological explanation, called the “eco-logical valence theory” (EVT), was formulatedand tested by Palmer & Schloss (2010). They

proposed that people like/dislike a specificcolor to the degree that they like/dislike all ofthe environmental objects that are associatedwith that color. The ecological rationale is thatit will be adaptive for organisms to approachobjects whose colors they like and avoid objectswhose colors they dislike to the extent that theircolor preferences are correlated with objectsthat are beneficial versus harmful to them (cf.Humphrey 1976). Palmer & Schloss (2010)reported strong support for the EVT throughempirical measurements of what they callweighted affective valence estimates (WAVEs)for the 32 chromatic colors in Figure 1. TheWAVE for each color measures the extent towhich people like the set of objects that areassociated with that color. For example, theylike blues and cyans, at least in part, becausethey like clear sky and clean water, and theydislike browns and olive-colors, at least in part,because they dislike feces and rotting food.Average valence (liking/disliking) ratings weremeasured for all objects named as associatesof each color, with each object valence ratingbeing weighted by the similarity between thegiven object’s color and the color with which itwas associated. The weighted average valenceover all associates for each color explained 80%of the variance in the data shown in Figure 1with no estimated parameters.

Several further predictions of the EVT havesince been confirmed. One is that if preferencesfor colored objects causally influence pref-erences for corresponding colors, then colorpreferences could be changed by exposure toaffectively biased samples of colored objects.Strauss and colleagues (2012) found that whenone group saw positive red images (e.g., straw-berries and cherries) and negative green images(e.g., mold and snot) and another group sawpositive green images (e.g., kiwi fruit and leafytrees) and negative red images (e.g., blood andlesions), the two groups showed the predictedcross-over interaction in preference changesfor the corresponding colors from ratingsmade before versus after seeing the affectivelybiased colored images. Seeing positive redimages selectively increased preference for red,


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for example, and seeing positive green thingsselectively increased preference for green.

The EVT also predicts changes in colorpreference for people who have highly positive(or negative) emotional investments in a socialinstitution that has strong color associations,such as athletic teams, gangs, religious orders,and universities. People should come to likethe colors associated with the institution corre-spondingly more or less, depending on whethertheir feelings about it are positive or negative.Indeed, students at two rival universities—theUniversity of California, Berkeley and StanfordUniversity—preferred their own university’scolors more than their rival’s colors, and themagnitude of these differences was correlatedwith their self-reports of school spirit for theirown university (Schloss et al. 2011). Theseresults support the EVT’s claim that colorpreference is influenced by preferences for theinstitution because it is highly unlikely thatpeople choose their university and cultivatetheir level of school spirit based on pre-existingcolor preferences.

4.1.2. Infant color preferences. Infant colorpreferences are studied by measuring infants’looking biases when presented with side-by-side color pairs. Longer (e.g., Bornstein 1975)and/or earlier fixations (Teller 1979) are takenas indices of relative preference. Infant colorpreference tends to be investigated from around3–4 months, by which time infants’ color visionis fully trichromatic (e.g., Knoblauch et al. 2001,Teller 1998).

When infant looking biases are measuredfor highly saturated colors, the hue preferencefunction tends to have the same general shapeas the corresponding adult hue preferencefunction (Figure 1, open circles), with amaximum around blue and a minimum aroundyellow or yellow-green (Bornstein 1975,Franklin et al. 2008, Teller et al. 2004, Zemachet al. 2007). Other patterns have sometimesbeen reported (Adams 1987), perhaps due todifferences in luminance and/or saturation.The similarity of the infant and adult huepreference functions for saturated colors has

led to the general view that color preferencemight be innate. More recent findings undercutthis possibility, however. Franklin et al. (2009)measured looking biases in 4- to 6-month-oldinfants using the same colors Hurlbert & Ling(2007) used for adults and found that, unlikethose of adults (see above), infant preferencesvaried primarily on a red to blue-green di-mension (L-M cone contrast), with reddercolors being more preferred, and were notstrongly related to the violet to yellow-greendimension [S−(L+M) cone contrast]. Morerecently, Taylor et al. (2012b) have discoveredthat 4- to 6-month-old infants exhibit a huex lightness interaction that is clearly differentfrom the corresponding interaction in adults.Indeed, babies show a bias toward looking atdark-yellow and light-red and a bias againstlooking at light-blue and dark-green, nearlythe opposite of corresponding looking biasesin adults (r = −0.46). These findings clearlydemonstrate that color preferences changedramatically during an individual’s lifetime.

4.1.3. Gender differences. Gender differ-ences have been reported among Westernadults in their relative preferences for satu-rated versus desaturated colors. In particular,men tend to prefer saturated colors more thanwomen do, and these differences are stronglycorrelated (r = +0.73) with observers’ judg-ments of how active/passive colors are, withmales generally preferring more active col-ors and females more passive ones (Palmer &Schloss 2011). Gender differences in saturationdevelop with age, being absent for young chil-dren (∼6–9 years), beginning to appear duringadolescence (12–13 years), and being clearly ap-parent by adulthood (17–18 years) (Child et al.1968). Gender differences have also been re-ported in hue preference at certain ages. Specif-ically, girls ages 3–12 tend to prefer pink andpurple, whereas boys of that age tend to preferred and blue (Chiu et al. 2006, Iijima et al. 2001,Picariello et al. 1990). Such gender differencesin color preference may develop from exposureto gender-specific toys that are stereotypicallycolored: pink and purple for girls’ toys and red,


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blue, and black for boys’ toys ( Jadva et al. 2010,LoBue & DeLoache 2011, Pennell 1994). Thisexplanation fits well with an ecological theory(Palmer & Schloss 2010), according to whichcolor preferences are determined by prefer-ences for correspondingly colored objects.

Further evidence about ecological effectsin gender differences has come from fittingWAVE data (see section 4.1.1) collected sep-arately for adult males and females in England.Taylor & Franklin (2012) found a pattern con-sistent with EVT predictions: male WAVEspredicted male preferences (r = 0.86) betterthan they predicted female preferences (r =0.58), whereas female WAVEs showed the op-posite trend (female-female r = 0.67; female-male r = 0.58).

4.1.4. Cross-cultural differences. Althoughsome writers have claimed that color pref-erences are universal across cultures (e.g.,Birren 1961, Eysenck 1941), modern empiricalresearch reveals that both similarities anddifferences exist. The strongest case for auniversal preference is for bluish colors (Adams& Osgood 1973, Hurlbert & Ling 2007, Ouet al. 2004, Saito 1996). There are exceptions,however, such as blue being ranked among theleast preferred colors in Kuwait (Choungourian1968). Blue was apparently also disliked in An-cient Rome, as evidenced by its relative disusein historical artifacts (Pastoureau 2001), whichmay have been due to ecological factors. Be-cause blue was greatly liked by Rome’s archen-emies, the Celts, the Romans may have dislikedblue by the same logic as Berkeley studentsdislike of Stanford’s colors and vice versa (seesection 4.1.1). A second contender for univer-sality is the robust dislike of dark-yellow (olive),which has been reported for Chinese, British(Ou et al. 2004), Japanese (Yokosawa et al.2012), and American (Palmer & Schloss 2010)observers. Note that even if such universal colorpreferences exist, they may reflect universalfeatures of human ecology rather than innatepreferences: e.g., clear sky and clean water areuniversally blue, whereas biological wastes androtting food are universally dark yellow.

According to the ecological valence theory,WAVEs for a given culture should predict thatculture’s color preferences better than anotherculture’s color preferences, and vice versa. Thisprediction is based on the idea that differentcultures frequently have different color-objectassociations and/or different valences forthe same objects, both of which can affectcolor preferences. Preliminary results supportthis prediction for American versus Japanesecultures (see Palmer & Schloss 2010). JapaneseWAVEs accounted for Japanese preferences(r = 0.66) better than for American prefer-ences (r = 0.55), whereas American WAVEsaccounted for American preferences (r = 0.89)better than for Japanese preferences (r = 0.74).

It must be noted, however, that a radicallydifferent pattern of preferences emerged in thenonindustrial Himba people of rural Namibia,who greatly preferred highly saturated col-ors, largely independent of hue (Taylor et al.2012a). This large saturation effect is most con-sistent with a psychophysical account of colorpreference because saturation is a potent vari-able in high-level models of color appearance. Itmay also be due to novelty, as the Himba’s chro-matic environment is considerably more natu-ral than that of industrialized societies, with farfewer instances of highly saturated colors.

Many researchers have appealed to colorsymbolism to explain cultural differences. Forexample, there appears to be a stronger pref-erence for white and whitish colors in Japan,Korea, and Taiwan than in other countries (e.g.,the United States, Germany, Australia, PapuaNew Guinea, and South Africa), perhaps be-cause white symbolizes cleanliness, purity, andthe sun, which are more highly valued in theseAsian cultures than in the other cultures studied(Saito 1996). Similarly, Chinese observers pre-ferred red more than British participants did,perhaps due to its role as a symbol of good luckin China (Hurlbert & Ling 2007). The idea thatcolor symbolism influences color preferencesis conceptually consistent with ecological ac-counts of color preference (Palmer & Schloss2010), according to which color preferencesare determined by people’s preferences for the


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“things” that are associated with those colors,provided that the “things” include abstractionssuch as purity and good luck as well as con-crete objects and social institutions. Solid em-pirical evidence for the role of color symbolismin color preference is largely lacking, however.

4.1.5. Object-based differences. A questionof considerable applied interest is whether pref-erences for context-free colored patches (seeabove) generalize to preferences for coloredobjects. The answer depends importantly onthe degree to which the objects in questionhave prototypical or characteristic colors. It ishighly improbable, for example, that even a res-olute blue-lover and yellow-hater would pre-fer blue bananas to yellow ones. The moresensible question is whether context-free colorpreferences generalize to artifacts that couldconceivably be any color.

Schloss et al. (2012) studied adult prefer-ences for visually presented colors of manysuch artifacts: interior room walls, room trim,couches, throw pillows, dress shirts/blouses,ties/scarves, and T-shirts. The overall patternof hue preferences was similar to that forcontext-free colored squares (Figure 1), withcool hues (especially blues) being liked bet-ter than warm ones. The primary exceptionwas that people tended not to like large things(such as walls and couches) to be red. In con-trast, the pattern of preferences for differentlightness levels were markedly different acrossobjects, often depending on practical consider-ations, such as liking lighter wall colors to makerooms appear brighter and/or larger. Interest-ingly, context-free squares were the only casein which highly saturated colors were preferredto less saturated ones (Saito 1983, Schloss et al.2012).

Object color preferences are most likelyto deviate from context-free color preferenceswhen there are color conventions for that typeof object (Taft 1997). Such conventionality ef-fects can be present even within a basic-level ob-ject category, such as luxury sedans being mostpreferred in achromatic colors (black, grays,or white), consistent with their conventional

formality as serious, sophisticated cars, whereasa VW “Bugs” were preferred in brighter, moresaturated colors, including yellow, consistentwith their conventional informality as fun,sporty cars (Schloss et al. 2012). These effectsmay be considered weaker cases of the proto-typical banana example described above, butones in which sociocultural conventions, ratherthan naturalness, drive color preferences.

4.2. Preference for ColorCombinations

Historically, theories about the aesthetics ofcolor combinations have been predicated onseveral untested assumptions that have led toa confusing literature. For example, the terms“preference” and “harmony” are often usedinterchangeably, and preference for a combi-nation as a whole is frequently confused withpreference for a figural color against a back-ground color (e.g., Chevreul 1839; Granger1955a,c; Itten 1973). Schloss & Palmer (2011)used a clearer empirical framework for as-sessing the aesthetics of color combinationsby distinguishing among three types of judg-ments for figure-ground color pairs: (a) pairpreference (how much the two colors are likedtogether), (b) pair harmony (how well thecolors go together, regardless of preference),and (c) figural preference (how much theforeground color is liked when viewed againsta colored background). They explain thedistinction between preference and harmonyin terms of a musical analogy: Nearly everyonewould agree that Mozart’s music is moreharmonious than Stravinsky’s, and yet somepeople prefer Stravinsky’s music to Mozart’swhereas others prefer Mozart’s to Stravinsky’s(see also Albers 1971, Ou et al. 2004, Whitfield& Wiltshire 1990). Although figural preferenceinvolves a judgment about a single color, itis relevant to color combinations because thesame color can look strikingly different ondifferent background colors due to the classicperceptual phenomenon of simultaneous colorcontrast (Chevreul 1839, da Vinci 1942/1956,Helmholtz 1866/1925, Walraven 1976).


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4.2.1. Theories of pair preference and/orharmony. Chevreul (1839) presented one ofthe most influential theories of color harmonyand preference, terms that he used inter-changeably. He proposed two distinct typesof harmony: harmony of analogous colors andharmony of contrast. Other theories includeItten’s (1973) claim that any combination ofcolors is harmonious if the colors produceneutral gray when mixed together as paints,Munsell’s (1921) and Ostwald’s (1932) the-ories that colors are harmonious when theyhave certain relations in color space (e.g.,constant in hue and saturation but varyingin lightness), and other theories proposed byGoethe (1810/1970), and Moon & Spencer(1944a,b). Note that none of these theorieswere formulated on the basis of aestheticmeasurements, although some have sincebeen tested empirically. (For a more thoroughreview, see Westland et al. 2007.)

4.2.2. Empirical research on pair preferenceand/or harmony. Studies of aesthetic pref-erences for color combinations have producedconflicting claims, often because researchershave not distinguished among the three typesof judgments described above. For example,Granger (1955a,b) reported that harmony andpreference (he also used the terms interchange-ably) increased with hue contrast, but severalsubsequent studies show that both harmonyand preference decrease with hue contrast (Ouet al. 2004, Ou & Luo 2006, Schloss & Palmer2011, Szabo et al. 2010). Granger’s findingthat preference increases with hue contrast isconsistent, however, with subsequent findingson figural color preferences, which increasewith hue contrast (Helson & Lansford 1970,Schloss & Palmer 2011).

Schloss & Palmer (2011) measured bothpair preference and pair harmony for the samefigure-ground color pairs (a small square withina larger square) with the same observers givendifferent instructions (see above). Both mea-sures increased with hue similarity (cf. Chuang& Ou 2001, Ou & Luo 2006, Ou et al. 2011,Szabo et al. 2010), consistent with Chevreul’s

harmony of analogous colors. The two mea-sures differed in relation to lightness, however,in that pair preference increased with light-ness contrast, whereas pair harmony did not.Harmony also tends to be greater for lighterpairs and for pairs that are more similar in satu-ration (Ou & Luo 2006, Schloss & Palmer 2011,Szabo et al. 2010). Nevertheless, the overallcorrelation between ratings of pair preferenceand pair harmony is highly positive (r = +0.79)in Schloss & Palmer (2011). This fact explainswhy these concepts have so often been equated:people tend, on average, to like harmoniouscolor combinations (see also Ou et al. 2004).Observers agreed more about their ratings ofpair harmony (average between-observer r =+0.51) than about their ratings of pair prefer-ence (average between-observer r = +0.36),primarily because pair harmony ratings werealmost completely independent of pair pref-erences for the two component colors. Strik-ingly, more than 80% of the variance in peo-ple’s pair preference ratings can be explainedby a linear combination of the pair’s har-mony rating, the preference ratings for the twocolors alone, and the lightness difference be-tween figure and ground colors (multiple-r =0.89).

Ratings of figural preference for colorsagainst colored backgrounds are measurablydifferent from both pair preference and pairharmony ratings (Schloss & Palmer 2011). Av-erage figural preferences are highly correlatedwith preference for the corresponding singlefigural color against a neutral background (r =0.87) and also with average preference for pairsof colors containing the figural color as figure(r = 0.74). After the influences of these twopreference factors were removed, however,preferences for hue contrast and lightnesscontrast were evident (cf. Helson & Lansford1970). It is likely that these effects are respon-sible for Chevreul’s claims about harmonies ofcontrast. Indeed, given the general preferencefor saturated colors (see Figure 1), it is notsurprising that observers would prefer figuralcolors against highly contrastive backgroundhues; simultaneous color contrast effects would


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tend to increase the perceived saturation of thefigural color.

5. PREFERENCE FOR SPATIALSTRUCTURE

Virtually every two-dimensional visualdisplay—from painted portraits to pho-tographed landscapes to abstract graphicdesigns—is composed of elements arranged inspace, usually within a rectangular frame. It istherefore important to consider how the spatialproperties and arrangements of those elementsinfluence people’s aesthetic responses to suchdisplays (e.g., Arnheim 1988).

5.1. Aesthetics of Low-LevelVisual Properties

A common theme in aesthetic judgments oflow-level visual properties is that images arepreferred when their structure mirrors thatof natural scenes. Such findings suggest thatpeople prefer images that have the statisti-cal structure to which the human visual sys-tem has adapted, whether evolutionarily orontogenetically.

5.1.1. Spatial frequency. By the time visualinformation reaches the cortex in area V1, spa-tial analysis is dominated by local spatial fre-quency filters that perform something like apiecewise Fourier analysis of the input (cf. DeValois & De Valois 1990). Spectral analyses ofpaintings and natural scenes reveal that theyhave similar amplitude spectra, being highestfor low spatial frequencies and decreasing ap-proximately linearly with the logarithm of spa-tial frequency in the classic spectrum of 1/Fnoise (Graham & Field 2007, Graham & Redies2010). Not only do more “natural”-lookingpaintings have the same 1/F power spectrum,but increases within two octaves of three cy-cles/degree are correlated with viewers’ feel-ings of visual discomfort (Fernandez & Wilkins2008). It is not clear, however, whether 1/Fpower spectra are either necessary or sufficient

for producing positive aesthetic responses toimages.

5.1.2. Line orientation. Viewers generallyprefer horizontal and vertical lines to obliqueones in Mondrian-like images (e.g., Latto et al.2000), and, generally speaking, more paint-ings contain horizontal and vertical lines thanoblique ones (Latto & Russell-Duff 2002).Given that the visual stimulation to which peo-ple are exposed also tends to have more hori-zontal and vertical lines than oblique lines, atleast at higher spatial frequencies (Switkes et al.1978), these results provide further evidencethat people prefer images that mirror the sta-tistical properties of low-level spatial structurein their seen environment.

5.2. Aesthetics of Object Shape

5.2.1. The golden ratio. One of the oldestand most frequently studied questions aboutthe aesthetics of shape concerns the so-calledgolden ratio or golden section. It is obtainedby dividing a line into two parts such that theproportion of the entire line to the longersegment is equal to the proportion of thelonger segment to the shorter segment, a ratioof approximately 1.6:1. If the golden ratio char-acterizes the length-to-width ratio, or aspectratio, of a rectangle, it defines a shape that hasbeen claimed from antiquity to be particularlypleasing aesthetically (e.g., Green 1995). Thegolden ratio appears frequently, both in nature(e.g., a person’s height relative to his/her armspan) and in aesthetically acclaimed humanartifacts (e.g., the base-to-height ratio of theGreat Pyramids and the facial dimensions of DaVinci’s Mona Lisa) (e.g., Atalay 2004, Konecni2003, Konecni & Cline 2002, Livio 2002).

Early empirical studies showed that so-called golden rectangles are most preferred,with preference diminishing in both direc-tions as the length-to-width proportions de-viate from 1.6:1 (Fechner 1871, 1876). Suchclaims remain controversial (e.g., Boselie 1984,Green 1995), with some successful replica-tions (e.g., Lalo 1908, Thorndike 1917) but


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many failures (e.g., Angier 1903, Haines &Davies 1904, Thorndike 1917). Interestingly,preferred aspect ratios of rectangles differ fordifferent semantic categories of objects. Peoplepreferred invitation cards for “serious” events(e.g., a classical piano recital) to have aspectratios close to the golden ratio (peaking at1.4:1), but the preference functions of thosefor more casual “fun” events (e.g., a child’sbirthday party) were virtually flat from 1:1 to1.5:1 (Raghubir & Greenleaf 2006). A reason-able summary of this field of research is thatalthough many people prefer shapes whose di-mensions are in the general neighborhood ofthe golden ratio, such preferences can and dovary relatively widely across both observers andcontexts (McManus 1980).

5.2.2. Complexity and symmetry. In aninfluential theory of preference for abstractpolygon shapes, Birkhoff (1933) proposed thataesthetic preference (M) should vary directlywith the number of elements (O) and inverselywith the complexity (C, expressed as thenumber of noncollinear sides) according to theequation M = O/C. Experimental tests of thisclaim have yielded disappointingly low corre-lations, however, with various other authorsgiving their own formulations (e.g., Boselie &Leeuwenberg 1985, Eysenck 1941, Eysenck& Castle 1971). One problem with this equa-tion is that it predicts monotonic increasesin preference with complexity. In contrast,intermediate complexity of about 10 sides isgenerally preferred by both adults (Martindaleet al. 1988, Munsinger & Kessen 1964b)and children (Munsinger & Kessen 1964a).These results are consistent with Berlyne’s(1971) arousal theory of aesthetics (see below).Interestingly, preference effects of complexityshow strong contrast effects with massiveamounts of familiarization: People familiarizedwith simple stimuli later tended to prefer morecomplex stimuli, whereas those familiarizedwith complex stimuli tended later to prefersimpler stimuli (Tonio & Leder 2009).

More recent research on shape prefer-ences has focused on symmetry structure while

holding the number of elements constant, fol-lowing prior results showing that more sym-metrical dot configurations were more easilyprocessed perceptually and better remembered(Garner & Clement 1963). In general, peopletend to prefer shapes that are more symmetri-cal, although there are large and relatively stableindividual differences in such effects ( Jacobson& Hofel 2002, Palmer & Griscom 2012), as isdiscussed below.

5.2.3. Contour curvature. A recently discov-ered phenomenon of shape preference is thatpeople tend to like objects with curved contoursmore than similar objects with sharp contours(Bar & Neta 2006). Further research has shownthis to be true for abstract shapes as well as rec-ognizable objects (Silvia & Barona 2009) andthat the preference for curved objects holds forobjects with neutral or positive valences, butnot for ones with negative valences (Leder et al.2011). These findings have been interpreted tomean that sharp contours are more threateningthan curved ones, given the tendency for objectswith sharp contours to be harmful.

5.2.4. Categorical Prototypes. People alsotend to prefer object shapes to the extent thatthey conform to categorical prototypes (Rosch1975). There is a large, well-known literatureon facial attractiveness, showing that peopletend to like symmetrical, average faces (for a re-view, see Rhodes 2006). We do not count this asan aesthetic preference because it is not indif-ferent in the Kantian sense, presumably beingdriven by sexual attraction. Nevertheless, theidea that people tend to like prototypical exem-plars has found support in other domains, suchas color (Martindale & Moore 1988), furniture(Whitfield & Slatter 1979), surrealist paintings(Farkas 2002), and exemplars of other semanticcategories (Martindale et al. 1988).

5.3. Aesthetics of Spatial Composition

Whatever the shape of the objects within animage, an important further aspect of aestheticconsideration is spatial composition: the way


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in which the objects are positioned relative toeach other and to the surrounding frame. Aprimary focus of studies of spatial compositionis the balance of the image in terms of how theelements are distributed around the frame’scenter. Arnheim (1988) and Alexander (2002)both view the understanding and analysis ofcenters as indispensible aspects of art and de-sign. They typically provide many illustrationsof their ideas, but no experimental evidenceto justify them. Below we review some of thescientific findings related to frame structure,balance, and centers, as well as more complexaspects of spatial composition.

5.3.1. The structure of a rectangular frame.Many of the factors related to spatial com-position depend critically on the relation be-tween the elements and their surrounding rect-angular frame.1 Arnheim (1974) speculated thata frame has a “structural skeleton” definedby dynamic forces propagated from its sides.Recent empirical measurements have sup-ported Arnheim’s intuitions remarkably well(Palmer 1991, Palmer & Griscom 2012, Palmer& Guidi 2011). When observers rated the“goodness of fit” for the placement of a singleprobe circle at 35 locations within a rectangu-lar frame, the center of the frame always hadthe highest average goodness rating, which de-creased monotonically with distance from thecenter. The next highest ratings were along thevertical and horizontal axes of symmetry, withsmaller increases along the angle bisectors ateach corner of the frame, but notably not alongthe diagonal that runs through the center. In-deed, the central axis of many shapes holds aspecial salience for other perceptual measures,such as contrast sensitivity (Kovacs & Julesz1994). Subsequent research revealed that peo-ple generally prefer images in which the dot“fits well” within the frame, with average pref-erence ratings being very highly correlated with

1By “frame” we mean to include cases in which there is noactual frame around the image, but simply the rectangularborders of the image.

average goodness-of-fit ratings (r = 0.95), al-though large individual differences were evi-dent (Palmer & Griscom 2012) (see section 6).

5.3.2. Balance and centers. Some of the earli-est empirical studies of balance investigated im-ages containing a fixed line of some particularlength, width, and color plus a test line of someparticular length, width, and color. Observerswere asked to adjust the position of the testline to make the entire display appear balanced(Pierce 1894, Puffer 1903). All else being equal,test lines were placed farther from the centerthan the fixed line if they were shorter and/orthinner than the fixed line, but they were placedcloser to the center if they were longer and/orthicker. Although these results are consistentwith physical concepts of balance, darker colorswere treated as if they were heavier (Bullough1907), thus defying explanation by a purely me-chanical analogy, since colors have no physicalweight.

Other studies have evaluated perceived bal-ance for works of art and compositions of ab-stract shapes. When asked to place a fulcrumbelow the balance point of an artwork image,observers generally indicated pictures were bal-anced near the center of the frame, with moreimages being balanced slightly left of center(McManus et al. 1985). For abstract displaysof one or two colored squares (red, green, orblue), position strongly influenced perceivedbalance, but there were also significant interac-tions with color, with red affecting balance mostand blue least. Locher et al. (2005) found similareffects of color when comparing perceived bal-ance in Piet Mondrian’s original abstract paint-ings versus variants with interchanged colors.Observers judged the originals to be balancednear the center and to be more balanced thanthe color variants. Based on judgments of “bal-ance center,” the perceived weight of a regionvaried as a function of its size and color, withred being perceived as heaviest, blue intermedi-ate, and yellow lightest. In another study, whenartwork was modified in ways that influencedbalance and composition (e.g., by deleting el-ements), observers perceived compositionally


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balanced images to be more visually “right”(Locher 2003). Finally, when participants ar-ranged paper cutout shapes in an empty frameto produce displays that were “both interest-ing and pleasant,” about half of the designs dis-played symmetry at some point in the creationprocess, and the physical weight was equally dis-tributed around the center in the majority offinal designs (Locher et al. 1998).

A quite different claim for the aesthetic im-portance of centers comes from Tyler’s (1998)finding that one of the two eyes in nonprofileportraits of human faces almost always lies ator very near the frame’s vertical midline. Thisstrong center bias was much more pronouncedfor the eye than for the face as a whole, themouth, or even the single eye in profile por-traits. McManus & Thomas (2007) challengedTyler’s claim, finding that people showed nopreference for portraits in which one eye wascentered compared with otherwise identical im-ages in which neither eye was centered. Contro-versy continues over reasons for the centered-eye phenomenon, with McManus & Thomas(2007) claiming that it arises from geometricconstraints on positioning a single head withina pictorial frame, and Tyler (2007) rebuttingtheir objections.

5.3.3. Compositional biases in picturesof meaningful objects. Palmer, Gardner/Sammartino, and colleagues have attemptedto understand aesthetic preferences for spatialcomposition of pictures containing just one ortwo meaningful objects within a rectangularframe. Using several different measures, Palmerand colleagues (2008) found strong, systematictendencies for symmetrical objects that facedforward to be preferred at the center of theframe (the center bias) and for objects thatfaced rightward or leftward to be located off-center to the left or right of center, respectively(the inward bias). An inward bias is also evi-dent in artists’ paintings and drawings of ani-mals (Bertamini et al. 2011).

Sammartino & Palmer (2012a) found sim-ilar biases in preferences for vertical positionof single object pictures. For example, when

the image of an object was symmetrical abouta horizontal axis (e.g., an eagle seen from di-rectly above or below), they found a center bias.When objects “faced” upward (e.g., a bowl) ordownward (e.g., a light fixture), they found aninward bias: People preferred the bowl belowthe frame’s center and the light fixture above it.Ecological biases were also evident, in that peo-ple preferred the position of the object withinthe frame to be consistent with its typical lo-cation relative to the observer: Eagles werepreferred higher in the frame, whereas sting-rays were preferred lower. Complex patterns ofpreferences arose from the combined influencesof these different biases.

Other ecological effects are also evident inpeople’s aesthetic responses to the depictedperspective and size of a focal object in animage. Previous research on canonical per-spective had demonstrated that people canrecognize everyday objects better from theperspectives from which they are more likely tobe seen (e.g., Palmer et al. 1981). Preferencesfor different perspective views follow a similarpattern (Palmer et al. 1981, Sammartino& Palmer 2012b). Analogously, there is acanonical size effect in aesthetic judgments.People tend to prefer small objects (such as abutterfly) to be smaller within the frame andfor larger objects (such as an elephant) to belarger within the frame (Konkle & Oliva 2011),even if each observer sees only different-sizedpictures of a single object (Linsen et al. 2011).

Further studies have examined semantic ef-fects in the composition of pictures containingtwo objects that were either closely related ineveryday use (e.g., a champagne bottle and acake or a liquid detergent bottle and a sponge)or quite distantly related (e.g., a champagnebottle and a sponge or a liquid detergent bottleand a cake) (Leyssen et al. 2012). The resultsconsistently showed that people prefer relatedobjects to be much closer together and unre-lated objects to be much farther apart within theframe, even if placing two related objects nearbyproduced an unbalanced composition. Indeed,when 2AFC comparisons were performed tolook explicitly for balance versus semantic


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relatedness effects, the semantic bias was clearlyevident, but there was no bias toward balance.Although this finding appears to contradict pre-vious research on balance (see above), prior re-search has almost exclusively used abstract ge-ometrical shapes that do not have meaningfulecological relations to one another.

McManus et al. (2011) recently exploredvarious factors that affect the way in whichdifferent complex photographic images werecropped within a fixed-size rectangular frame.Participants adjusted the position of the rect-angular frame within a larger image so thatthey achieved the most aesthetically pleasingimage that contained a specified location. Somelow-level, image-based features, such as color,had little effect on cropping choices, whereasothers, such as detail, had more substantial ef-fects, but higher-level, meaning-based featuresseemed to dominate.

The bulk of these results on spatial compo-sition of pictures of meaningful objects supporta general conclusion that prior knowledgeplays a crucial role. Even the inward biasdepends on observers knowing which side isan object’s front, and all the other forms ofecological bias rest on observers knowing theecological statistics of meaningful objects: theperspective from which they are seen (canonicalperspective), how big they are (canonical size),and where they tend to be positioned verticallyrelative to eye level (which might be termed“canonical elevation”). Although it has neverbeen demonstrated in aesthetic judgments, itseems clear that there would also be large aes-thetic differences due to an object’s orientationrelative to the frame (canonical orientation). Allof these effects are consistent with perceptualfluency theories of aesthetic preference (seebelow): People like pictures of objects to thedegree that they are easily perceived (Reberet al. 2004, Winkielman et al. 2006).

5.3.4. Higher-level spatial composition:Effects of meaning, titles, and context.Higher-level factors, such as meaning, emotion,and/or the interpretation of images in differentcontexts, are often thought to be important in

aesthetic preference, especially in philosophicaldiscussions, such as Kant’s (1892/1951) writ-ings about the importance of “free play of theimagination.” Only recently have such factorsbeen investigated experimentally.

Millis (2001) explored the effect of differentkinds of titles for illustrations and photographson four qualities of the aesthetic experience:interest, thoughts, emotions, and aestheticappreciation. Metaphorical titles, which gaveadditional information not immediately evi-dent from the image itself (e.g., titling an imageof a woman picking flowers One Day at a Time)increased aesthetic appreciation over purelydescriptive titles (e.g., Woman Picking Flowers)or no titles at all. Russell (2003) examined theeffects of providing viewers with the artist’snames and actual titles and/or brief descriptionsof abstract or semi-abstract images on boththe viewers’ judgments of “meaningfulness”and their aesthetic judgments. He expectedmeaningfulness to increase aesthetic appeal,but found this to be true only in a within-subjects design. Leder et al. (2006) extendedthis kind of investigation to both abstract andrepresentational artworks and examined theeffects of exposure duration. Titles did notinfluence liking or content understandingoverall, but they did interact with durations.Descriptive titles were more effective at brief1-second exposures, and elaborative titles weremore effective at longer 10-second exposures.

Extending their prior research on biasesin object placement within a frame (Palmeret al. 2008), Sammartino & Palmer (2012b)studied systematic effects of different titles onpreference for the horizontal placement andperspective views of objects. When the titlewas purely descriptive (e.g., Racehorse and ManWalking), they replicated their previous results,with observers preferring the object near thecenter of the frame and facing inward. Whenthe title elaborated the context in a way thatimplied unseen objects or portions of an event(e.g., Front Runner versus Dead Last for therunning horse or Journey’s End versus StartingOut for the man walking), they found that themost preferred composition was dramatically


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different: Compositions that were least pre-ferred with neutral titles were most preferredwith elaborative titles that fit the composition.They found analogous effects of elaborativetitles on different perspective views of objects.

6. INDIVIDUAL DIFFERENCES

It is widely acknowledged that people differenormously in their aesthetic preferences forall kinds of different modalities and domains(e.g., McManus 1980, McManus et al. 1981).This fact, more than any other, underlies thewell-known adages, “Beauty is in the mind ofthe beholder” and “There is no accountingfor taste.” In spatial composition, for example,McManus & Weatherby (1997) found that av-erage positional preferences were close to thegolden section value in horizontal placement,but individuals differences (IDs) were so largethat few, if any, participants showed prefer-ence functions that looked much like the groupaverages.

In color preferences, Ling & Hurlbert(2009) used their extended, four-parametercone contrast model (see section 4.1.1) to fitindividuals’ preference data. It accounted wellfor IDs (average multiple-r = 0.71), indicat-ing that observers differ in the polarity andimportance of these four dimensions for sin-gle colors. From an ecological perspective,Palmer et al. (2012a) found that, as predictedby the EVT, WAVE measurements for in-dividual observers were more highly corre-lated with their own color preferences (averager = +0.55) than with those of other observers(average r = +0.40).

In preferences for two-color combinations,Schloss & Palmer (2011) found that eventhough average preference ratings correlatedvery strongly with average harmony ratings(+0.79; see above), the same correlations forindividuals ranged widely from about zero to+0.75. Furthermore, these IDs varied with theamount of formal color training participantshad completed, following an inverted-U func-tion, with a maximum for intermediate amountsof training and lower correlations for both

untrained and highly trained individuals. Thispattern is consistent with Berlyne’s inverted-Uprediction (see below).

Eysenck (1940) studied people’s aestheticrankings of sets of spatial images of 18 differ-ent types (including portraits, landscapes, andphotographs of medieval clocks) as well as tocolors, odors, and polygons. After correlatingthe rankings and performing a factor analysis,he identified a single “general objective fac-tor of aesthetic appreciation” (which he called“t” for “good taste”) that varied with the ex-tent to which an individual’s rankings agreedwith the average rankings of the entire group. Itturned out to be relatively constant for individ-uals across domains. Although Eysenck’s inter-pretation of the t-factor as an objective measureof good taste seems misguided (see section 2.3),this measure might be an important variablein characterizing systematic IDs in aestheticpreferences.

Palmer & Griscom (2012) have recentlyproposed that “preference for harmony” maybe the primary aesthetic ID that underliesEysenck’s t-factor. Following Schloss &Palmer’s (2011) findings of IDs in preferencefor color pairs (see above), they studied theextent to which people’s judgments of aestheticpreference among stimuli in different domains(color pairs, configural shapes, spatial compo-sitions, and music) correlated with their ownjudgments for the same stimuli in their degreeof harmony, where harmony is a dimensioncharacterized by simplicity, regularity, andparts that fit well together, regardless of prefer-ence. They found that the correlations betweenaverage preference and average harmony rat-ings over all individuals were quite high inall domains (ranging from +0.97 for music to+0.47 for configural shape) but that the samecorrelations for individuals were extremelyvariable: Some people like harmonious stimuliand others dislike them, with IDs in preferencefor harmony in music ranging from −0.73 to+0.97. Most importantly, the correlations be-tween a difference-score measure of preferencefor harmony were systematically above chancefor all pairs of different domains, ranging from


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+0.60 for color pairs and music to +0.32 forspatial composition and music. Preference forharmony can explain Eysenck’s t-factor be-cause the preference judgments of individualswho prefer harmonious stimuli will necessarilybe more highly correlated with average prefer-ences to the degree that people generally preferharmonious stimuli in that domain, as it wasfor all four of Palmer and Griscom’s domains.Furthermore, they found that specific trainingin an aesthetic domain (art or music) systemat-ically lowered people’s preference for harmonyin that domain, consistent with Berlyne’s(1971) analysis of the effects of experience.

7. THEORIES OF AESTHETICRESPONSE

The foregoing review summarizes many ofthe most important findings about human aes-thetic preference in visual domains as indexedby behavioral responses. Although the prefer-ences discussed are fairly basic, the hope is thatthey constitute a foundation on which a bet-ter understanding of more complex aestheticresponses can be built, perhaps eventually in-cluding people’s powerful aesthetic reactions toworks of art. It is by no means obvious that thiscan be accomplished, and some writers have ex-plicitly claimed that it cannot (Markovic 2010).We now turn to ask why people like the thingsthey do. Several accounts have been suggested.

7.1. Mere Exposure

One possible explanation of why people likesome things more than others is the “mere ex-posure” effect: People tend to like objects andimages more as the frequency of seeing themincreases (Zajonc 1968). One might try to ex-plain aesthetic preference for, say, photographsof inward-facing objects over outward-facingobjects (Palmer et al. 2008) by appealing tothe fact that people simply see more pictures ofinward-facing objects. The problem is that onemust also explain the latter fact. It is tempting tosay that it is because photographers like inward-facing pictures more, but then one still needs to

explain this preference in some way other thaninvoking the mere exposure effect again—i.e.,that photographers have seen more pictures ofinward-facing objects. The root problem is thusthat explanations based purely on exposure fre-quency lead to infinite regress.

We do not deny that exposure frequency in-fluences people’s aesthetic judgments; certainlyit does. Cutting, for example, has provided acompelling historical analysis of its influence onaesthetic judgments of certain paintings that areincluded in the Impressionist canon (Cutting2003, 2006). The ultimate explanation of theirinclusion does not come from mere exposure,however, but rather from differences in theavailability of certain paintings at an importanthistorical juncture when the canon was defined:The pictures that were available to the publicwere included rather than those only availableto private owners. The important conclusionfor present purposes is that, although mereexposure can explain the perpetuation andeven the amplification of a bias across time, itcannot satisfactorily explain its ultimate cause.

7.2. Arousal Dynamics

Daniel Berlyne developed a complex and influ-ential theory of aesthetic response based on apsychobiological conception that pleasure is amatter of the viewer’s degree of arousal whileviewing an image (Berlyne 1957, 1960, 1971).Arousal, in turn, was proposed to depend ona complex psychobiological response to threetypes of variables—collative, psychophysical,and ecological—each of which producedresponses in a primary reward system and aprimary aversion system. The combined effectof these two systems was claimed to producean inverted-U function of the underlyingvariable, with aesthetic pleasure first increasingas a function of arousal and then decreasing asarousal became too great. Collative variablesare those related to the viewer’s expectations(e.g., novelty, complexity, uncertainty, conflict,surprisingness, unfamiliarity), psychophysicalvariables are those related to sensory dimen-sions of the stimulus (e.g., intensity, pitch,


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brightness), and ecological variables are thoserelated to meaningfulness and associations toenvironmental objects. According to Berlyne’stheory, as a relevant variable increases (e.g.,complexity), a primary reward system becomesincreasingly active and generates positive affect.As complexity continues to increase, how-ever, an aversion system becomes active andgenerates negative affect. The reward systemsaturates before the aversion system does, lead-ing to the classic inverted-U curve so closelyassociated with Berlyne’s theorizing. Berlyneclaimed that collative variables were the mostimportant, and the bulk of his empirical workwas directed at understanding them. We willnot attempt a full review of the theory or the ev-idence relevant to its evaluation. Suffice it to saythat although Berlyne amassed much evidencein support of his ideas (e.g., Berlyne 1971,1974), some of its key predictions have not beensupported empirically (Martindale et al. 1990).

7.3. Prototype Theory

Rosch’s (1975) transformative research on pro-totype effects in categorization suggested a dif-ferent avenue to explaining visual preferencesthat was developed primarily by Martindale andWhitfield. Simply put, people may prefer pro-totypical examples of categories to nonproto-typical ones. Preferences for prototypes havebeen demonstrated for colors (Martindale &Moore 1988), furniture (Whitfield & Slatter1979), faces (Light et al. 1981), exemplars of se-mantic categories (Martindale et al. 1988), andsurrealist paintings (Farkas 2002).

This work can be viewed as an elaborationof the effects of certain ecological variableswithin Berlyne’s framework, with the caveatthat many of the effects appear to show mono-tonic increases with prototypicality rather thanthe inverted-U-shaped function that Berlyne’stheorizing implies. The more recent ecolog-ical effects described above for preferenceeffects due to canonical position in the world(Sammartino & Palmer 2012a), canonicalperspective (Palmer et al. 1981, Sammartino& Palmer 2012b), and canonical size (Konkle

& Oliva 2011, Linsen et al. 2011) can all beinterpreted as supporting the importance ofprototypes, albeit with respect to viewpoint-based features of images of objects relative toviewers rather than to category membership.Nevertheless, even preferences for both pro-totypical examples and canonical features seemtoo limited to be taken as a general theory ofaesthetic response. They are more like ubiqui-tous factors that influence aesthetic preference.Moreover, prototype theory, by itself, does notclarify why prototypes should be preferred.

7.4. Fluency Theory

An interesting answer to the “why” questionis provided by fluency theory, which is perhapsthe single most general explanation of aestheticpreference (e.g., Reber 2012; Reber et al.1998, 2004). Fluency theory posits that peopleprefer visual displays to the extent that theyare processed more easily (or fluently). It doesa good job of predicting aesthetic effects dueto many low-level features (for a review, seeOppenheimer & Frank 2008), such as prefer-ences for larger (Silvera et al. 2002), more sym-metrical ( Jacobson & Hofel 2002), more highlycontrastive displays (Reber et al. 1998). It canalso explain categorical prototype effects (seeabove) because prototypes are well known tobe more easily and quickly processed than non-prototypical examples (e.g., Rosch 1975). Thekinds of spatial composition effects describedabove for untitled, single-object pictures—namely, the fact that people tend to prefer thefocal object to be at or near the center of theframe, to face inward both horizontally and ver-tically, and to be depicted at a height, perspec-tive, and size within the frame that reflect theircharacteristic height, perspective, and size rela-tive to a standard observer (Palmer et al. 2012b;Sammartino & Palmer 2012a,b)—are also con-sistent with perceptual fluency. The reason isthat these compositional choices tend to makethe object most recognizable as the kind ofobject it is (cf. Estes et al. 2008, Palmer et al.1981) and therefore lead to the easiest (mostfluent) processing for the purpose of object


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identification. These compositional choicesamount to selecting the most likely (or“default”) settings of the spatial variablesassociated with the representational schemafor that kind of object (cf. Palmer 1975, Palmeret al. 2012b). Finally, fluency theory evenprovides a plausible causal explanation of mereexposure effects: The more often a given imageis seen, the more easily and fluently it willbe processed, and, by hypothesis, increasedfluency leads to increased preference.

One problem with fluency theory is that itdoes not square well with Berlyne’s inverted-Uresults for preference as a function of complex-ity. More complex stimuli presumably alwaysrequire more processing than simpler ones, soit seems that fluency theory should predict amonotonic decrease in preference as a functionof complexity. The typical result, however, is aninitial increase in preference with greater com-plexity, until some optimum level is reached,after which a decrease with additional complex-ity is found (see Berlyne 1971 for a review). Itis also unclear how fluency would account forpreferences for single colors, especially in lightof the strong evidence of links to preferencesfor ecological objects (Palmer & Schloss 2010).

A deeper challenge to fluency theory asa complete account of aesthetic response isthat its basic premise—that the most easilyprocessed image will be the most aestheticallypleasing one—seems to contradict a centraltenet of art theory: namely, that the aesthetic(or perhaps the artistic) impact of an image isincreased by intentionally violating the viewer’sexpectations. Indeed, the most fluent image ofan object would seem to be the most boringimage of it—analogous, perhaps, to the formu-laic paintings created by Komar and Melamidas the most pleasing pictures for people indifferent cultures (Wypijewski 1997)—whereasaesthetically pleasing images are expected to bemore interesting. Indeed, much of the recenthistory of art, from Impressionism onward,can be understood as a continual process ofviolating the rules and conventions of priorart practice. It is presently unclear how oneshould view the relation between art history

and aesthetics, however. Perhaps art has simplybecome increasingly dominated by novelty andthe quest to conceptually stretch the boundariesof art to the detriment of the aesthetic responsesuch art evokes in many or most viewers.

An equally troubling, and possibly related,problem is that fluency theory seems to leaveout a crucial variable in aesthetic judgment:namely, the meaning or message of the image asintended by the artist and/or as inferred by theviewer (which assuredly may not be the same).It is perhaps such semantic considerations thatlie, in part, behind Kant’s suggestion that aes-thetic judgments necessarily involve “free playof the imagination” in the beholder, and it isdisturbing that such considerations seem to beeither irrelevant or possibly even contradictedby perceptual fluency accounts. More recently,fluency theory has been extended to conceptual(rather than perceptual) fluency in an attemptto account for effects of such higher-level se-mantic variables (Reber et al. 2004, Whittle-sea 1993). The common principle is that fluent(easy) processing is aesthetically valued in bothperceptual and conceptual domains.

Sammartino & Palmer (2012b) have pro-posed an alternative to fluency theories of aes-thetic response to pictures of objects and scenesbased on their concept of representational fit.The suggestion is that people prefer images tothe extent that their spatial composition opti-mally conveys an intended or inferred meaningof the image, thus including a semantic variablethat is missing from fluency theory. Interest-ingly, it also makes the same predictions asfluency when the message intended or inferredfor the image is simply the default one: to depictthe focal object(s) or event(s), as it typically is instock photography. Under such circumstances,the best horizontal and vertical position, size,and perspective of the object(s) will be thosethat make it optimally recognizable. Whenthe intended or inferred meaning is different,however, as it is with the elaborated titles in thestudies described above, the otherwise non-preferred compositional choices of position,perspective, and size that are most compatiblewith the message can produce the highest


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aesthetic response in viewers (Sammartino &Palmer 2012b). Fluency and representationalfit may also be viewed as compatible accounts,however, differing primarily in emphasis,with fluency theory focused on the ease ofprocessing and representational fit theoryfocused on the reasons that processing is easy.

7.5. Multicomponent Theories ofAesthetic Response to Art

Although the focus of this review has intention-ally not been on aesthetic responses to viewingart objects, it is useful to see how the kind ofwork reviewed above fits within theoreticalframeworks designed to account for the aes-thetic appreciation of art. Shimamura (2012),for example, has recently presented a verygeneral approach to understanding people’saesthetic responses to art. To the extent thatpart of people’s appreciation of art concernsaesthetic dimensions of their experiences, itis presumably relevant. Shimamura’s frame-work is summarized by its acronym, I-SKE,which stands for the intention (I) behind theartwork in relation to the viewer’s sensory (S),knowledge-based (K), and emotional (E) re-sponses. I-SKE is thus a very general frameworkthat seems to encompass much of what mattersin people’s aesthetic responses to art objects.

The research reviewed in this articlegenerally concerns the aesthetic effect ofsensory-perceptual information and its relationto knowledge-based components. Most ofthe biases identified in preferences for spatialcomposition, for example, are knowledgebased. Surprisingly, knowledge is relevant evenfor simple color preferences, because theyturn out to depend strongly on people’s storedknowledge of their affective responses to theobjects that are characteristically associatedwith those colors (Palmer & Schloss 2010).

Leder and colleagues (2004) have formu-lated a detailed information-processing modelthat attempts to analyze and synthesize thesemany diverse aspects of people’s appreciationof art, and especially of modern art. It consists

of a series of five processes, each of which isinfluenced by many factors. The five processesare proposed to occur in the following order:(a) perception, which responds to stimulusfactors, such as complexity, symmetry, color,contrast, and organization; (b) implicit clas-sification, which involves integrating thisperceptual information into related informa-tion stored in memory concerning familiarity,prototypes, and conventions; (c) explicit clas-sification of the artwork in terms of its styleand content; (d ) cognitive “mastering” of itsstyle and content by interpreting them withinone’s knowledge about related art and withrespect to the viewer’s self; and finally (e)evaluation of satisfaction in terms of both thework’s cognitive aspects (e.g., understandingof meaning and ambiguity) and the cumulativeinfluence of all the stages of processing on theviewer’s affective state. Of the stages, the firsttwo are deemed to be automatic, the last twoto be deliberate, with explicit categorizationbeing a mixture of both. In addition to theseprocesses, however, there are many contextualconsiderations that influence the outcome ofthe processing, including the situation in whichthe viewer finds the artwork (museum, gallery,psychology experiment, etc.), what previousexperiences the viewer has had with similarartworks, what social and linguistic informa-tion the viewer has surrounding the currentexperience, and what his/her affective state iscoming into the current viewing experience.

Comparatively less attention has beenpaid to emotional components of aestheticresponse. However, Silvia’s recently proposed“appraisal theory” of aesthetic response isbased primarily on analyzing the diverseemotional responses a viewer might have to artobjects (e.g., Silvia 2005a,b, 2006, 2012). Hedivides emotions into several kinds—positiveemotions (happiness, enjoyment, pleasure),2

2Silvia does not mention negative emotions (sadness, misery,pain) as a category, but perhaps he intends them to be impliedby virtue of being the inverses of the positive emotions.


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knowledge emotions (surprise, interest, con-fusion), hostile emotions (anger, disgust,contempt), and self-conscious emotions (pride,shame, guilt, embarrassment)—and argues thatall are relevant to understanding aesthetic re-sponses to art. Positive emotions are classicallyemphasized in most aesthetic research (seesection 2.1), but Silvia argues that the otherclasses of emotions are indeed relevant topeople’s reactions to art. This is especiallytrue for modern art, where many artists clearlyintend their works to elicit surprise, interest,anger, disgust, shame, and/or guilt rather thanpleasure.

8. CONCLUSIONS AND ISSUESFOR FUTURE RESEARCH

We have attempted to summarize, critique,and synthesize the emerging scientific litera-ture concerning human aesthetic response toand preferences for visual stimulation usingbehavioral methods. We began with the claimthat this body of research exemplifies a branchof aesthetic science from a psychologicalperspective. We submit that the results wehave described constitute clear advances inthe understanding of this domain. We hopeto have convinced the reader that meaningfulresearch in this field is not only possible, butalso interesting, important, and fun.

Nevertheless, many critical issues need tobe addressed in future research. One is how toconnect this body of work with the ongoingliterature on behavioral studies of the percep-tion of art. As we said at the outset, aestheticresponse and perception of art are conceptu-ally distinct, but overlapping, domains. Canaesthetic preferences for simple features suchas individual colors, color combinations, form,texture, and spatial composition somehow becombined to understand the far more complex

appreciation of fine art, or do these featuresform truly emergent wholes (Gestalts) thattranscend understanding in terms of theirsimpler components? Needless to say, this isa daunting task, but there are some bodies ofartwork that lend themselves well to beginningto answer the question, such as Piet Mondrian’s“grid” paintings in red, yellow, and blue andJosef Albers’s Homage to the Square series.

A second critical issue is how to extend thepresent body of research more fully into theburgeoning topics of emotion and cognitivemeaning. Much of the research describedabove concerns what most would call purelyformal characteristics of aesthetic appreciation.Indeed, this is why the art of Mondrian andAlbers is closer to the reviewed research thanthat of, say, Monet, Picasso, or Duchamps. For-mal aspects are clearly important, but they arejust one part of what one wants to know aboutaesthetic response. Although some inroads havebeen made recently in this direction both theo-retically and empirically, the field is wide openfor more systematic research that builds on theincreasingly sophisticated understanding ofboth topics that has arisen in psychology.

A third critical issue, not unrelated to thefirst two, is how to connect behavioral researchon aesthetic preferences with the emergingfield of neuroaesthetics. As we also said at theoutset, understanding the neural correlates ofaesthetic response depends fundamentally onthe development of behavioral methods andresults. As this behavioral foundation becomesfirmer, more precise and detailed studies of thebrain regions that produce aesthetic responsescan be undertaken with considerable prospectsof success. We expect that eventually there willbe a synergy between behavioral and neuralstudies of aesthetics that will help both enter-prises reach the common goal of understandingvisual aesthetics and human preference.

DISCLOSURE STATEMENT

The authors are not aware of any affiliations, memberships, funding, or financial holdings thatmight be perceived as affecting the objectivity of this review.


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ACKNOWLEDGMENTS

The preparation of this article was supported in part by the National Science Foundation undergrants #0745820 and #1059088 and a Google Gift to S.E.P. Any opinions, findings, conclusions,and/or recommendations expressed in this article are those of the authors and do not necessarilyreflect the views of the National Science Foundation or Google.

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PS64-FrontMatter ARI 15 November 2012 14:20

Annual Review ofPsychology

Volume 64, 2013 Contents

Prefatory

Shifting Gears: Seeking New Approaches for Mind/Brain MechanismsMichael S. Gazzaniga � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 1

Biological Bases of Behavior

The Endocannabinoid System and the BrainRaphael Mechoulam and Linda A. Parker � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �21

Vision

SynesthesiaJamie Ward � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �49

Scene Perception, Event Perception, Object Recognition

Visual Aesthetics and Human PreferenceStephen E. Palmer, Karen B. Schloss, and Jonathan Sammartino � � � � � � � � � � � � � � � � � � � � � � � � �77

Attention and Performance

Detecting Consciousness: A Unique Role for NeuroimagingAdrian M. Owen � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 109

Executive FunctionsAdele Diamond � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 135

Animal Learning and Behavior

The Neuroscience of Learning: Beyond the Hebbian SynapseC.R. Gallistel and Louis D. Matzel � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 169

Evolutionary Psychology

Evolutionary Psychology: New Perspectives on Cognitionand MotivationLeda Cosmides and John Tooby � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 201

Origins of Human Cooperation and MoralityMichael Tomasello and Amrisha Vaish � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 231

vi

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Language and Communication

Gesture’s Role in Speaking, Learning, and Creating LanguageSusan Goldin-Meadow and Martha Wagner Alibali � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 257

Nonverbal and Verbal Communication

The Antecedents and Consequences of Human Behavioral MimicryTanya L. Chartrand and Jessica L. Lakin � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 285

Intergroup Relations, Stigma, Stereotyping, Prejudice, Discrimination

Sexual PrejudiceGregory M. Herek and Kevin A. McLemore � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 309

Social Neuroscience

A Cultural Neuroscience Approach to the Biosocial Natureof the Human BrainShihui Han, Georg Northoff, Kai Vogeley, Bruce E. Wexler,

Shinobu Kitayama, and Michael E.W. Varnum � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 335

Organizational Climate/Culture

Organizational Climate and CultureBenjamin Schneider, Mark G. Ehrhart, and William H. Macey � � � � � � � � � � � � � � � � � � � � � � � � 361

Industrial Psychology/Human Resource Management

Employee RecruitmentJames A. Breaugh � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 389

Learning and Performance in Educational Settings

Self-Regulated Learning: Beliefs, Techniques, and IllusionsRobert A. Bjork, John Dunlosky, and Nate Kornell � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 417

Teaching of Subject Matter

Student Learning: What Has Instruction Got to Do With It?Hee Seung Lee and John R. Anderson � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 445

Health Psychology

Bringing the Laboratory and Clinic to the Community: MobileTechnologies for Health Promotion and Disease PreventionRobert M. Kaplan and Arthur A. Stone � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 471

Research Methodology

Multivariate Statistical Analyses for Neuroimaging DataAnthony R. McIntosh and Bratislav Misic � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 499

Contents vii

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Social Network Analysis: Foundations and Frontiers on AdvantageRonald S. Burt, Martin Kilduff, and Stefano Tasselli � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 527

Indexes

Cumulative Index of Contributing Authors, Volumes 54–64 � � � � � � � � � � � � � � � � � � � � � � � � � � � 549

Cumulative Index of Chapter Titles, Volumes 54–64 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 554

Errata

An online log of corrections to Annual Review of Psychology articles may be found athttp://psych.AnnualReviews.org/errata.shtml

viii Contents

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Documents

Visual Aesthetics and Human Preference2013)AR.pdf · PS64CH04-Palmer ARI 8 November 2012 9:0 Visual Aesthetics and Human Preference Stephen E. Palmer, Karen B. Schloss, and Jonathan