extracting design aesthetic heuristics from scientific literature

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Extracting Design Aesthetic Heuristics from Scientific Literature

Ana Cadavid, Stefany Ruiz-Córdoba, Jorge Maya

EAFIT University, Colombia *Corresponding author e-mail: [email protected]

Abstract: Product aesthetics is affected by around 50 variables. These variables interact to shape the user’s aesthetic feeling. During the aesthetic design (AED), the designer only use their intuition and is not aware of most of these variables, taking into account some Gestalt laws and absolute standards of beauty (Golden Proportion). Due to the complex nature of AED, a heuristic approach seems the most appropriate to support this task. This paper describes a method for Aesthetic heuristics extraction from Scientific Literature comprising a protocol for literature selection, extraction and classification of the heuristics. The extraction method is in turn based on modern heuristics. We applied the protocol to extract the aesthetic heuristics of the aesthetic variables “Peak-shift” and “unexpected”. The heuristics were translated to an understandable language for the designers and subsequently used in re-designing a pepper mill.

Keywords: Aesthetic, Design, Heuristics.

1. Introduction to aesthetic experience: its relationship with the user experience

Aesthetics plays a fundamental role in product design (Bürdek 2005); (Hekkert 2015); (Ulrich,

Eppinger 2006). This is the discipline that studies the aesthetic experience (Levinson 2003).

Following Crilly et al., (2004). We use the terms Product-Aesthetics, first, relating to “what

the product presents to the senses”, and secondly, by Aesthetic-Experience, we mean “the

pleasure attained from sensory perception of the product” (Hekkert and Leder 2008). This

definition separates aesthetic experience from others types of experience (Hekkert 2006).

Since the 80’s, it is known that if consumers have to choose between two products with

similar price and function, they will choose the most attractive one (Kottler and Rath 1984);

ANA CADAVID, STEFANY RUIZ-CÓRDOBA, JORGE MAYA

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Nussbaum 1988). Bloch (1995) gives three reasons why a product with an attractive and

appealing Product-Aesthetics may support a successful product: First, in saturated markets

appropriated product-aesthetics is noticed by consumers, leading to render competitors

obsolete or considered as mere copies. Second, a product with an appropriated product-

aesthetics makes a good first impression concerning also other product attributes. Product-

aesthetics would then enrich our lives by providing sensory pleasure, i.e., a good aesthetic-

experience (Bloch 1995). Third, a product with good product-aesthetics is not easily

discarded, and we could become attached to it; this favours to buy again products with the

same brand (Mugge, et al 2011).

That is why a poor understanding of the needs and aspirations of the user, resulting in

inadequate aesthetics is one of the main factors for the failure of products on the market

(Cooper 2001). Consequently, it is necessary a structured and systematic management of

aesthetics in the design process to get a product of higher aesthetic-assessment i.e., a more

attractive and beautiful product for the user (da Silva, et al 2013), making that the product

fulfil important aspirations and values of the person (Dell'Era and Verganti 2007); (Crilly, et

al 2004, 2009).

We define the Aesthetic Design Process, ADP, as all the design activities aiming to get

certain aesthetic-assessment, resulting from the interaction with the product. This

assessment must fulfil the user, designer and company´s aesthetic requirements. Clearly,

ADP could be affected by the design of the functionality and usability of the product;

however, in this paper we are interested only in the aesthetics bases on the product´s

appearance. It is by manipulating the geometric form, shape, materials, sounds and all the

other physical variables affecting the human senses that the designer make up a product-

aesthetics to obtain a user´s intended aesthetic-experience. The aesthetic-experience

depends on four groups of variables explained below.

1.1 Variables influencing the aesthetic experience

Different studies have contributed to the construction of a general scientific and systematic

framework about the aesthetic-experience composed of about 50 variables comprising four

groups: absolute standards of beauty, perceptual, cognitive and socio-cultural (Hekkert

2008, 2015); (Crozier 1994); (Berlyne 1966); (Crilly, et al 2004); (Bloch 1995) (Bloch, et al

2010); (Veryzer 1993, 1999); (Kӧhler 1992); (Padovan 1999), (Schofield 2011); (Wittkower

1960); (Arnheim 1974, 1988).

Hekkert and Leder (2008) argue that designers must stick to these principles or rules unless

there is a reason to not do so. If designers do not use them, is because they want to highlight

the product as different or unique, must produce a special message or want to generate a

particular response to the user (Hekkert and Leder 2008). However, although there are

reasons to do not use the rules, Hekkert and Leder (2008) argue that "the careful application

of the rules is a way to ensure product acceptance and appreciation", p 282. This is


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important in projects of high financial risk such as automotive design, where loses are high.

Table 1 presents an overview of the names and bibliography of some variables belonging to

the four groups.

Absolute standards of beauty

This group of about 10 variables comprises absolute standards of beauty imposed

historically from art, architecture and philosophy. For example, the application of golden

ratio (1:1.61) proportion would make a beautiful product. These standards are actually

variables because some of them are not well defined or applied consistently. For instance,

the golden ratio proportion is commonly applied with values near, but not exactly, to 1:1,

61.

See figure 1.

Figure 1 Although cigarette boxes are commonly cited as presenting golden-ratio proportion, this

sample of famous brands actually present proportions of 1:1,56, 1:1,53 and 1:1,59.

Perceptual variables

This group of around 15 variables share the same human biological functional basis,

belonging to perception are form. They are studied from the psychology of perception,

psychophysics and psychology of aesthetics. Some variables are from Gestalt psychology,

describing how humans organize visual elements in groups or wholes to create a perceptual

order and understanding what we see, looking for patterns to see relations (Kӧhler 1992).

There are other aesthetic perceptual variables not considered in Gestalt psychology, but

studied from other psychology branches. For instance peak shift refers to the inclination of

some animals to respond positively to exaggerated stimuli beyond the threshold at which

they are normally used to respond to. Figure 2, left, shows how an exaggerated colour

stimulus, a highly saturated and luminous colour, might explain why we would assess

positively the aesthetics of the decorative ceramic pot rabbit: we are not used to ceramic

pot rabbits with such colours.


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Figure 2 Left: Example of the peak shift variable; middle: the Juiciy Salif lemon squeezer as an example of mere exposure, right: a Converse ® sneaker.

Cognitive variables

These variables, around 14, are determined by the way the user processes information

obtained from an aesthetic artefact (Wilson and Keil 2009). For these variables, knowledge,

experience and recurring information structures are important (Hekkert and Leder 2008).

One example is the Mere exposure phenomenon, Zajonc (1968): the frequency of exposure

to a stimulus increases its aesthetic appreciation (Hekkert and Leder 2008). Figure 2, middle,

shows the Juicy Salif lemon squeezer by Philip Stark. Although its forms are very uncommon

to a juicer, presumably, it became quickly a 1990’s design icon and commercial success for

Alessi, fostered by its recurrent appearances in specialized and large public media (Snelders

and Lloyd 2003).

Cultural/social/ individual variables

This category includes four variables depending on cultural and social aspects affecting the

individual. To illustrate, let’s see the congruency with personality variable. Kim, Sung (2013)

and Ling (2010) argue that such congruency explains the user’s aesthetic preference for a

product which physical characteristics or attributes match or express their own personality

traits. For example, in Figure 2, right, we have the traditional Converse sneakers. This brand

is intended for uncomplicated people, lovers of style and customization. Most of the brand is

acquired by people who love Rock, Metal, Punk, Ska and skaters and BMX amateur (Murison

2008). According to this variable they would find their sneakers attractive and beautiful

because its simple, classical, against fashion appearance.


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Table 1 Some examples of the four groups of variables explaining AesthExp.

1.2 TWO strategies for the Aesthetic Design Process, ADP and some issues related

Two strategies are discernible for ADP: manual and computer based. The manual strategy

ADP has been based on manual 2D drawing and creating 3D physical models and prototypes

(French 1996); (Wallschlaeger, et al 1992). It has been present all throughout the history of

design, requiring just a designer and tools for drawing or constructing mock-ups or models.

Hekkert and Leder (2008) argue that the intuition that has traditionally guided the ADP:

“Designers do not need to know the principles to apply them; they intuitively design accordingly, since the principles are as much part of their creative nature as they are of the observer’s aesthetic perception. As Ramachandran and Hirstein (1999) claim for artists, these rules or principles are ‘a set of heuristics that artists either consciously or unconsciously deploy to optimally titillate the visual areas of the brain”

The computer based strategy requires different amounts of human intervention and a deep

knowledge of Product-Aesthetics, aesthetic-experience and aesthetic-assessment (Bentley

Corne, 2002); (Galanter 2012). Besides, the elusive and fuzzy character of the aesthetic-

experience (Shimamura 2012) and the complexity of aesthetic-assessment (Galanter 2012);

(Kozbelt, Kaufman 2014), (Tinio, Smith 2014) makes that formalizing this knowledge to

translate into operative software requires relatively high complex mathematical techniques,

such as artificial neural-networks, genetic algorithms, fuzzy logic and the like. Most

computer aesthetics endeavours remain in research labs and experimental artistic fields,

Absolute standards of beauty

Perceptual Variables

Cognitive variables Cultural-social-individual variables

Good proportion (Padovan 1999, Scholfield 1958, Wittkower 1960).

Balance (Arnheim 1984, Dondis, 1973, McManus et al 2011).

Harmony (Hutcheson, Levinson 2003).

Hue (Gao 2007, Granger 1955, Becker 2009)

Gestalt Laws: similarity, symmetry, proximity etc. (Desolneux, Kӧhler 1992, Arnheim, 1954; McWhinnie 1986, Beaumont 1985)

Peak-shift (Martindale, 1999, Ramachandran, Hirstein 1999)

Contrast (Kӧhler 1992; Arnheim 1954,)

Complexity (Nadal, McWhinnie, 1969, Creusen et al 2010,)

Originality (Snelders, Hekkert 1999, Hekkert, 1995, Tyagi, 2013)

Identifiability (Cila 2013, Abbott et al 2009)

Mere exposure (Bornstein, 1989, Moreland, Zajonc 1982, Hekkert 1995, Whitfield 1983)

Cultural standards and values (Nisbett et al 2001, Hofstede 1980, Balcetis, Lassiter, 2010)

Congruency with personality (Kim, Sung 2013, Eysenck 1972, Lin 2010, Chamorro et al 2009)

Connectedness (Drost 2013)


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with little application in the real practice of product design. Therefore, we are not further

interested in this strategy.

In the figure 3 we focused on the manual strategies, we show the classification of these

heuristics: tacit when is used by the designer’s intuition or explicit when they are articulated

and used in a deliberated manner.

Figure 3 Classification of aesthetic heuristics according to their tacit or explicit nature

1.3 The use of heuristics in design

Pahl and Beitz (2008) consider that most design methods are of heuristic nature. Following

the bounded rationality framework, time, knowledge and other resources are scant, so the

heuristics are seen as useful cognitive processes necessary to make decisions and solve

problems that cannot be handled only by logic and probability (Gigerenzer 1999). Thus, it is

suggested that to understand the real world decisions differently (i.e. the design process as a

decision-making process), heuristics are a psychologically adequate notion (Gigerenzer

1999). The use of heuristics can provide designers rules to support the design process to

arrive to a solution that is reasonably close to the best possible answer or optimal solution

(Nisbett and Ross 1982), (Yilmaz 2010) while disposing of limited time and knowledge to

work on it (Gigerenzer 1999). Therefore, heuristics provide designers with cognitive

strategies, which allow them to “jump” to a new solution spaces, seeing the same elements

already known but offering new ideas and solutions (Yilmaz 2010). In brief, the


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methodological use of heuristics respond to the real conditions of the ADP in many

companies where low costs, high quality and low time to market is required for the design

process.

Tacit Heuristics used by intuition

In the ADP the designer usually has great freedom to use tacit heuristics to create aesthetic

products. These heuristics are draw from three sources. First, they are rooted in

evolutionary and biological mechanisms (Voland and Grammer 2003). Biological evolution

has endowed us with cognitive-affective-perceptive mechanisms to automatically prefer

some properties in things that we recognize as having higher aesthetic value (Voland and

Grammer 2003). Secondly, these heuristics are formed through years of academic education

and professional experience (Woelfel 2013). In design schools’ workshops, the learning by

doing method is extensively applied to learn to design the aesthetic variables of a product

(see Hannah (2002)).

Therefore, the student has to get a result with certain product-aesthetics (for instance, to be

balanced and well proportionated) and adjust it if inadequate. This, supported by the

teacher cycle, is iterated many times. We believe that this teaching method does not favour

this aesthetic-heuristics to emerge and to be articulated remaining tacit for designers. The

third source, is during professional practice, where many of the aesthetic rules can emerge,

serendipitously, and be applied. However, professional practice shows that usually this non-

articulated knowledge is not capitalized. These heuristics are prone to be forgotten. In

conclusion, even if widely used, we consider these heuristics as of unreliable nature due to

their uncertain availability process.

These explicit aesthetic-heuristics have three sources. First, they can be obtained by the

designers themselves from their own practice in two ways: by deliberate introspection-

reflection or, sometimes, they are found in a serendipitous way. Differently to the intuitive

used heuristics, these aesthetic-heuristics are articulated, and somehow capitalized: they

constitute partly the aesthetic know-how of designers or companies. A problem with these

heuristics is that they are hardly available because companies keep them secret to remain

competitive. Second, we can find aesthetic-heuristic tools-rules that have been posited by

design scholars in a deliberate manner from their own practice and from aesthetics-

architecture-arts-design literature. These are some examples of literature which comprise

around 15 variables to support the ADP: (Albers 2006); (Ashford 1969); (Bevlin 1970, 1994);

(Block, Atterberry 1989); (Bowers 1999); (Bürdek 2005); (Fisher 1984, 2007); (Hambidge

1920); (Hannah 2002); (Leborg 2006); (Lewalski 1988); (Lidwell, et al 2010); (Mayall 1968);

(Quarante 1994); (Sherin 2011); (Wallschlaeger, et al 1992); (Wong 1993). Most of these

heuristics belong to Gestalt variables and to absolute standards of beauty. Even if the

amount of textbooks on the subject is impressive, they fail to provide guidance on when and

how apply those heuristics (Michalewicz and Fogel 2004). The problem with these heuristics


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is that the literature is large but the heuristics are actually scant because they only represent

a fraction of the 50 variables influencing the aesthetic-experience. Therefore there are over

30 variables influencing the aesthetic-experience that designers are not aware of. In other

words, if the designer is not aware of the existence and effect of all aesthetic-variables on

the ADP, there will be a poor control over them or they even would not be included in the

product design (Otto, Wood 2000). The third category of heuristics is those extracted

deliberately by researchers on the subject. These heuristics allows designers to have more

aesthetics’ design possibilities, than they have by their intuition.

To the date no study has reported an attempt to make a repertoire of heuristics of all

Aesthetic-Variables and make them available to the designers in a way that they can

understand and be able to apply to ADP. Therefore, the specific problem treated in this

publication is how to propose a method to extract aesthetic heuristics from scientific

literature.

In the remaining of this paper, in section 1.5 we will review the state of the art of methods

to extract design heuristics; in section-2, we propose a systematic method to extract them

from the scientific literature and in section-3, apply it and show some examples of its results.

1.5. State of the art: Design Heuristic extraction methods

Three ways to extract design-heuristics were found: the study of designers design process,

the analysis of award-winning products and systematic literature review. The study of

designers made for (Ramirez 2014), is based on finding and extracting strategies that expert

designers used in their design process. 30 expert designers participated through interviews,

observations of their social and personal world and also, observations of their experience at

different levels (Ramirez 2014): designers often used their intuition and tacit knowledge

acquired through their years of experience. According to Ramirez (2014), the observation

and verbalization methods are not sufficient to extract the design strategies of the

designers, because designers use some implicit strategies that they cannot express through

interviews.

Yilmaz (2010) extracted design-heuristics from the Analysis of 400 design award-winning

products. She identified some key features; as functionality, form, user-interaction and

physical states. Subsequently, she performed a content analysis of the needs, design criteria,

and the design solution of the products. These were grouped by similar features and finally

compared between them to extract the heuristics. The characteristics found in these studies

were grouped and compared, to find common patterns that were finally expressed as 77

design-heuristics (Yilmaz 2014), with only 3 of them being aesthetic heuristics.

The third study found to extract heuristics is based on the extraction of visual design-

heuristics (or principles) using a literature review method (Kimball 2013). 47 Publications

between 1904 and 2011 were chosen on visual-design, web-design, graphic-design and


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typography topics. 198 design principles were obtained (balance, contrast, repetition,

rhythm, etc.). 38 principles, the most frequent, were chosen as the most representative.

After revision of these three strategies to extract design heuristics, we decided that the

extraction of heuristics from scientific literature (Kimball 2013) is the most adequate method

to our heuristics extraction. By using a systematic scientific literature review on aesthetic on

a sound and reliable basis, enriching in this way the traditional heuristics found in art,

architecture and design textbooks. We might approach to the aesthetics variables from

different cognitive and affective disciplines, besides neuro-aesthetics enriching in this way

the content of the heuristics not just by studies that come from art, architecture or design,

which are the base of the existing methods for the APD.

2. Method for extracting Aesthetic Design Heuristics from Scientific Literature

First at all, we wanted to check if it was possible to extract aesthetic design heuristics only by

reading the texts and extract them when we identified one heuristic, and doing this

exhaustively to guarantee that most of the heuristics were extracted. This extraction had an

exploratory intention because the conditions for this process were unknown. After that, the

question would be to analyse if Kimball’s (2013) method can be used as such or if it needed

to be adapted to our particular research conditions.

The method should comply with time constraints and not requiring specialized knowledge.

Consequently, computational methods to recognize heuristics inside texts were dismissed.

Only a manual approach was feasible.

The method proposed for performing the aesthetic-heuristics extraction is made up of two

methodological elements complementing each other: first, the systematic literature review

method, currently used in the field of medicine (Spotted, et al 2009) and Systems

Engineering (Torres 2014) which is applied to find and select the literature most pertinent

among huge knowledge bases; and second, the implementation of meta-heuristics to

support the identification, choosing and extraction of the heuristics. Meta-heuristics are

universally valid and applicable heuristics that allow finding agile solutions (Gigerenzer 1999)

because they possess a fast (quick to apply) and frugal (simple and plain) character. They

had to be flexible enough to be applied to texts with different technical jargon. Four meta-

heuristics are proposed to be used in the methodology.

1. Recognition Heuristic: If there are two objects and only one of those is

recognized, the familiar object has a higher value and therefore must be taken

as the most accurate decision.

2. Minimalist Heuristic (minimum intuition): Useful when you cannot use the

recognition heuristic, i.e. when the two objects are recognized. Therefore,


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take the decision in which exists a minimal intuition to be the most accurate

decision.

3. Take the last: When you only have an intuition about which way to go or a

landmark, but it is unknown if option is more valid than another. You must

then start with the option that was discarded before stopping the last search.

4. Take the best: "ignore the rest": Choose the most valid option that has not yet

been tried.

2.1 Protocol of the Method to extract aesthetic design heuristics

The method consists of three main phases. See Figure 3. The Aesthetic Variable peak shift

will be used as example to demonstrate the process.

Figure 4 Main phases of the method

Phase 1:

Reading comprehensive frameworks on aesthetics:

Since the works of Berlyne, much more scientific studies on aesthetics have become

available. However, to the best of our knowledge, the first systematic framework of

aesthetics in product design was published by Hekkert, Leder (2008) and updated and

enlarged by Hekkert (2014). These two chapters contain a comprehensive view of all design

aesthetics variables affecting the AesthExp. We used them to have a general Knowledge

before starting the search. Reading Leder (2011) and Hekkert (2006) complemented this

process.

Systematic method of literature review

To assure the validity and reliability of the proposed method, a systematic approach of

literature review was chosen to identify, assess and interpret relevant information about a

particular research problem. A literature review should have a review protocol that specifies


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the research question, a search strategy to help to detect the relevant literature and

inclusion and exclusion criteria (Kitchenham 2004).

The protocol steps are:

1. Define a literature search question: Identify the problem of search through the

formulation of a question with clear and precise terms. This clarifies the main

components of search and aid to direct it. For instance: How to apply peak shift in

product Design?

2. Define Research Databases: Three databases were selected, Scopus, Web of Science and

Google Scholar

3. Keywords definition: Defined according to the comprehensive framework on aesthetics

and prior knowledge of each design Aesthetic-Variable; used to compose Boolean

equations to seek the potentially relevant literature through selected search functions in

the databases. Keywords should include the name of the design Aesthetic-Variable, the

word aesthetics and its synonyms and other words that are relevant to each variable.

For example:

We took some words of the text like keywords: “Martindale (1990) and Ramachandran and

Hirstein (1999) consider the peak shift principle a prominent principle underlying our

AesthExp”. “Reviews the peak-shift literature in relation to operant and classical

conditioning procedures, errorless discrimination training, and physiological studies. Results

indicate that peak shift is a reliable behavioural phenomenon affected by stimulus

dimension, positive-negative stimulus separation, training procedure, and testing

procedure” (Purtle, 1973). “Because of peak shift, female birds that prefer to mate with

males with bright rather than dull plumage will show even greater preference for males with

supernormal or above-average brightness.” (Martindale 1990; p. 47).

We identified these keywords, for instance:

Variable: Peak shift, Design, aesthetics, Product, Beauty, perceptions.

Concepts: Stimulus, phenomenon, Evolution.

Disciplines: Psychology, evolutionary psychology, art, Neuroaesthetics, Ethology.

Main authors: (Martindale 1986), (Hekkert and Ledder 2008), (Ramachandran, et al 1999),

(Purtle 1973).

4. Inclusion Criteria Search: Should be established to identify scientific literature texts that

are directly related to the question and research objective. A text is selected if it satisfies all

the inclusion criteria, as specified below.

Text in English

Belong to at least one of the subject areas: psychology, design, art,

neuroaesthetics, social sciences, engineering and behavioural sciences.


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Must be related to the previously keywords defined.

For peak shift, the scientific literature must be in English and be related to at least one of

these fields: Psychology, arts and humanities, Social sciences, Engineering and others related

with the topic.

5. Selection Process: For the aesthetic-heuristics extraction, the minimalist heuristic is used

(Gigerenzer and Todd 1999). It consists in identifying and selecting the articles whose titles

contain the name of the aesthetic-variable and synonyms or words of the search equation.

Once the filter is applied, articles written by authors that the researchers can recognize are

selected, using the minimal recognition heuristic (Gigerenzer and Todd 1999). If the use of

the recognition and minimum intuition heuristic was not valid, we proceeded to filter by

authors who have more articles in relation to the search topic.

Finally, the selected articles in the previous point are summarized and studied. We only took

into account those studies that exposed aesthetic design variables. The process is illustrated

below:

Table 2 Boolean equations and search results

This method for the literature review is the result of an iterative search which provided the

required steps for a systematic and organized of literature review.

6. Saturation Point: reached when the research through the application of search equations

shows results redundant with those found previously; i.e., no new results. The searching

should then stop.

Equation Result

Peak shift + Aesthetic Ramachandran, Hirstein (1999). The science of art: A

neurological theory of aesthetic experience. Journal of

consciousness Studies, 6(6-7), 15-51.

Martindale, C. (1999). Peak shift, Prototypicality and

aesthetic preference. Journal of Consciousness Studies, 6(6-

7), 52-54.

Peak shift + Design No results

Peak shift + Perceptual This equation gave 16 papers as result. We proceed to apply

the inclusion criteria. Related areas: Neuroaesthetics,

Psychology, Art and humanities. Keywords: Peak shift.

Results after applying the inclusion criteria:

Verzijden et al., (2007). Song discrimination learning in zebra

finches induces highly divergent responses to novel songs.

Proceedings of the Royal Society of London B: Biological

Sciences, 274(1607), 295-301.

Bornstein (1974). Perceptual generalization: a note on the

peak shift.

Weiss, Weissman (1992). Generalization peak shift for

autoshaped and operant key pecks. Journal of the

experimental analysis of behavior, 57(2), 127.


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7. Application of the heuristics supported by an expert: in cases where the information

obtained from the proposed method is not enough, i.e. the method does not yield the

expected results or specific questions arise, we proceeded to consult an expert on

aesthetics, to provide the most relevant sources and clarify the doubts.

3. Results

Equation 1 search (“peak shift” and “aesthetic”) in SCOPUS yields two articles: Martindale

(1999), Ramachandran, Hirstein, W. (1999). The extraction of aesthetic-heuristics from the

paper “The Science of Art: A Neurological Theory of Aesthetic Experience (Ramachandran

and Hirstein 1999)” is shown below:

“If a rat is rewarded for discriminating a rectangle from a square, it will respond even more vigorously to a rectangle that is longer and skinnier than the prototype”

“The artist can generate caricatures by exploiting the peak shift effect along dimensions other than form space, e.g., in ‘colour space’ or ‘motion space’”

“Furthermore, just as one can speak of a peak shift principle along very abstract dimensions, contrast can also emerge in dimensions other than luminance or colour”.

Since the extracted aesthetics heuristics are in psychological jargon, they were difficult to

recognize and understand to not trained people. Therefore they had to be translated into a

more understandable language for product designers in order to be effectively used in the

design process.

For the translation process, first we understood and analyse the main message of the

original aesthetic design heuristic. Then we took some words or general ideas of the original

heuristic and develop a “translated heuristic” as rules or guidelines using product designers’

jargon. For example:

Redesign Process using Peak shift variable:

The first two authors made some examples of a redesign of a pepper mill to illustrate how

these aesthetic-heuristics can affect the aesthetic design process and examine their

usefulness during the concept creation. They designed with the aid of some of Peak shift

heuristics with the purpose to call the attention of the user; exaggerating strongly stimulus


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like colours (hot colours, fluorescent colours), or shape (big/large shape) or texture (rough,

Spiky, slimy, Porous).

The results are presented in the Figures 5 and 6 as example:

Figure 5 Redesign of the product using a parametric 3D modeller and Rendering Software


Redesign Process using unexpected variable:


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After obtained a set of heuristics from the aesthetic design variables that were extracted by

using the extraction method proposed in this paper, one of them was used by an

undergraduate product design engineering student to prove the usefulness of the heuristic.

For this case the student used a heuristic from the aesthetic design variable: unexpected for

redesign a pepper mill. Unexpected variable refers to a product that generates surprise or

amazement experience and elicits aesthetic experience (Ramírez 2014). To design surprising

products could be done by generating changes in materials, colours, shapes, or move away

from the familiar characteristics or even using familiar aspects in different context (Ramírez

2014).

The result are presented in the figure 7 as example

Heuristic: Move away the familiar elements (characteristics) of the product, in order to elicit surprise

Design 1 Design 2 Design 3

This product aims to create surprise through fear: Its shape is typically, delicate and geometric (familiar elements); however on the top there is a blood flowing which creates the scary sensation to call the attention and curiosity of the users.

The product generates surprise through its components: The designer takes elements of a different product category (tea infuser); in this case the cap simulates a tea bag, which is a completely isolated but is interesting and create an amazement experience.

Surprise through visual effects: The shape of this design is difficult to understand at first sight, but the product begins to be interesting when the user can understand the shape, the use and the function. In this case the pepper mill simulates a Moebius strip.


As the example demonstrates, each heuristic gives the designer the possibility to create

quickly and effortlessly a different product applying the peak shift aesthetic-variable. The


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student expressed that the heuristic process was easy and understandable. The creative

process for generating new ideas was also easier and faster because the peak-shift and

unexpected aesthetic-heuristics provided well-defined directions to explore the product-

aesthetics. These directions are not arbitrary; on the contrary, they are meaningful because

it is known that they influence the aesthetic-experience. We believe that these heuristics

allow getting very different ideas, and could facilitate solving design problem without

blocking the creativity.

The product aesthetic results are diverse and are focused on visual form. However, it is

important to take care of some characteristics on the redesign process, for example,

architecture and functional parts and principles, because they can alter other aesthetic

variables present in the original product. For that reason, in each redesign ideas we did not

change those characteristic of the product.

4. Conclusions

According to the literature review for the extraction of design aesthetic heuristics, the

exhaustive extraction process was not the best way to perform an aesthetic-heuristic

extraction, because, it did not guide in a systematic way the researcher. So we cannot

ensure that the information found under that method was the most appropriate considering

the time restrictions. Also, the researchers lacked of criteria to know when to stop the

search. All in all, the process was very slow and giving the impression of going nowhere.

Consequently, the combination of a systematic literature review and the fast and frugal

heuristics were the most appropriate way to extract the aesthetic heuristics: it allows

considering a large number of variables and different discipline jargon in a short period of

time, as was our case.

After using the first phase of the method, it was found that there are two steps that are

essential to have a successful search result. First, the prior knowledge of the topic that is

going to be searched is the conceptual base of the researcher that will allow him to use the

fast and frugal heuristics more effectively. Secondly, the clarity of the formulation of the

literature search question, which ensures a successfully search.

After the application of the search equation in Scopus, WoS and Google Scholar, we found

that the latter, allowed us accessing to different scientific journal, conferences and existing

books providing the most representative and relevant result for each Aesthetic-Variable in

this research.

The example suggests how design creativity can be increased by using aesthetic design

heuristic, because as said, the designer´s intuition resources are limited, so heuristics can

support designers to design different ideas without much cognitive effort. We believe that,


17

provided we have heuristics extracted from the whole repertoire of aesthetic variables, the

richness of different product aesthetic could be dramatically improved.

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extracting design aesthetic heuristics from scientific literature

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