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Yokohama, Japan, May 19–21, 2011

Physical and sensory factors of textures thatappeal to human touch
Hikaru Nagano, Shogo Okamoto, and Yoji Yamada
Nagoya University, Nagoya, Japan

relationships between the factors of the textures and theirappeal to human touch.

Abstract. Some textures appeal to human touch, the reasons
for which have not been investigated. We investigate therelationship between the physical and sensory factors of 24
clay textures. Experimental results reveal that 70%–80% of
the textures’ appeal to human touch can be explained on thebasis of these factors.

II. D EFINITIONS OF A PPEAL TO H UMAN T OUCH
In this study, we assume that the textures’ visual appearance contribute to their appeal to human touch. Therefore,
in the experiment, participants evaluate textures without
touching them. In order to prevent the variations in cultural
background from affecting the intensity of the affinity, thesample textures should not be associated with something
specific. To this end, in this study, we use simple clay
plates with textured surfaces.
The participants rank all the sample textures in the orderof the intensity of their affinity to the textures, and wequantitatively assess the intensity of the affinity from therank. Finally, “touch” does not imply pushing, or holding
an object; it simply implies stroking the surface of textures.

Keywords: affinity to texture, sensory evaluation, texturaldimension.

I. I NTRODUCTIONERTAIN textures and materials that are used on adaily basis appeal to human touch. Well-polishedmetal surfaces and finely woven clothes are examples ofsuch materials. Discovering the reason for this affinity
could enable us to design products that attract customers.
In this study, we investigated the properties of materials
that appeal to human touch.
To the best of our knowledge, human affinity to suchmaterials has not been investigated. We assumed that ouraffinity is related to the physical and sensory characteristicsof the textures. In previous studies, tactile sensations had
been explained by the sensory factors of textures [1], [2],[3], [4]. Other studies have investigated the relationshipbetween the physical and sensory characteristics of textures[5], [6]. From the viewpoint of product design, Winakor etal. studied the effect of the textiles’ physical characteristics
on their sensory evaluation [7]. In addition, the physicaland sensory characteristics of some specific materials were
investigated. For example, Chen et al., Kim et al., and Matsuoka et al. and Picard et al. investigated the characteristics
of wrapping paper [8], sleeping wear [9] and car sheets[10], [11], respectively. Kawabata et al. proposed a method
to estimate sensory factors from the physical factors ofclothing fabrics [12]. However, the relationship between
the physical and sensory factors of the textures and theirappeal to human touch has not been reported thus far.
The objective of this study is to investigate to what
extent and how the physical and sensory factors of textures
correlate their appeal to human touch. When we see a
texture, our affinity to them is induced via its appearances.
Therefore, the physical factors of the textures may directly
induce human affinity. On the other hand, the affinity
to textures may be determined through human sensory
processing. We experimentally verify the hypothesis thatthe affinity is attributable to the physical and sensory
factors of the textures. This affinity may depend on
personal predispositions of participants, but we average
individual responses to investigate a potential trend in theC

III. S TIMULATION : PHYSICAL FACTORS OF TEXTURE
Rather than finely investigating the effect of single
physical factor, we are going to see the possible influenceof some physical factors. We made 24 different texturesa) Surface patterns
Grid
Top view

r rSide view

StripeTop view

r rSide viewr = 0.5/1.0/2.0 mmb) Samples displayed to participants

50 mm

Sample texture
White plastic plate

This work was partly supported by MEXT KAKENHI 22800030.
For e-mail address, see http://www.mech.nagoya-u.ac.jp/asi/en/

978-1-4244-9639-6/11/$26.00 ©2011 IEEE

Side view

50 mm

Fig. 1.

324

Clay sample texture

Plastic platecontrolled by four physical factors: surface colors, surface
gloss patterns, surface shape-types, and surface ridge andgroove widths. We adopted light clay (Hearty Soft White,
PADICO, Tokyo, Japan) as the texture material. The clay
was mold into 55.0 mm × 55.0 mm × 5.0 mm flat plates
using aluminum frame pairs. The surface color was either
blue or orange; the clay was mixed with paint colors before
molding. The color variation of blue and orange is a complementary relationship. These colors should cause a larger
deviation in sensory evaluations. The blue (PhthalocyanineBlue, Liquitex, Ohio, USA) and orange paints (Scarlet Red,
Liquitex, Ohio, USA) were (4.0BP, 1.5, 7), and (8.0R,
5.0, 13), respectively, in the Munsell color presentation.
A mixing ratio for the clay and paints was 100 g clay to
1.25 ml paint. For the glossiness of the textures, glossy
and glossless textures were prepared. To make the textures
glossy, the plates were varnished (SEALER Super Gloss,
PADICO, Tokyo, Japan) after being colored, mold, anddried. The JIS specular gloss of the textures were 2.4% ,94.2% , 1.7% , and 85.1% for the glossless and glossy blueand glossless and glossy orange, respectively. As shown in
Fig. 1, two shape types were used: grid and striped. The
groove and ridge widths were 0.5, 1.0, or 2.0 mm. In total,
24 types of textures were prepared (2 colors × 2 gloss
patterns × 2 shape types × 3 ridge and groove widths).
The physical factors of the textures were quantified byphysical factor scores. The physical factor scores werenormalized values with a mean of 0 and variance of 1 foreach of the four physical factors: surface color (1: blue,-1: orange), gloss (1: glossless, -1: gloss), shape type (1:stripe, -1: grid), ridge and groove width (-1.07: 0.5 mm,-0.27: 1.0 mm, 1.34: 2.0 mm).

terms whose scores did not deviate, such as “thick-thin”. In
addition, we merged adjective terms with similar meanings
such as “shiny-matte” and “glossy-dull”. In the end, 16
adjective pairs were left, as shown in Table I.
The participants were 12 voluntary students in their 20s
from the authors’ laboratory. As shown in Fig. 1b, a large
white plate with a 50 mm × 50 mm square window wasplaced on the sample texture so that the participants would
see only the textured surfaces and not the sides of thesamples. The participants were instructed to attempt tokeep their head positions fixed in order to retain the relative
position between the head and texture samples.
The order in which the textures and adjective terms used
to sort them were presented to each participant random. We
conducted experiments in a laboratory environment where
the temperature was approximately 24 ∘ C, and the textures
were placed at approximately 460 lx.
B. Factor analysis: data analysis of Task 1
We assigned values of 1–5 on the five-point adjective
scales obtained in Task 1. In order to reduce the effects ofindividual differences in sensory evaluation, the evaluation
values were normalized to within a single participant. To
decrease the number of variables used for the later analysis,
we applied a factor analysis on the evaluation values andextracted common factors as a synthesis of variables thatwere strongly correlated. The evaluation values 𝒙𝑖 for𝑝 adjective term pairs were broken down into common
factors 𝒇𝑖 and unique factors 𝒆𝑖 :𝒙𝑖 = 𝑨 𝒇 𝑖 + 𝒆 𝑖
𝑝×𝑚 𝑚×1
𝑝×1
𝑝×1(𝑖 = 1–𝑛),(1)where the factor loadings 𝑨 explain the strength of therelationships between common factors and adjective terms.
In this model, the correlation matrix 𝑹 of 𝑥 can be
represented as

IV. E XPERIMENTIn Task 1, the participants evaluated textures one after
the other using the semantic differential (SD) method.
Using the scores given by the participants, we quantified
the sensory properties of each texture. We then applied
factor analysis to these properties to obtain the independent
sensory factors.
After Task 1, the participants ranked all 24 texturesin order of the intensity of their affinity to the textures(Task 2). Based on the ranks of the textures, we calculated
the degrees of affinity to each texture. Finally, we investigated the relationships between the degrees of affinity andthe physical and sensory factors using multiple regression
analysis.

𝑹 = 𝑨 𝑨T + 𝑫

𝑝×𝑝

𝑝×𝑚 𝑚×𝑝(2)

𝑝×𝑝where 𝑫 =diag(𝑑1 2 , ⋅ ⋅ ⋅ ,d2𝑝 ) are unique factors. Matrix
𝑹∗ , where diagonal elements of the correlation matrix 𝑹
are replaced by the estimated communality ℎ𝑗 2 , is
⎡ 2⎤
ℎ1
𝑟12 . . . 𝑟1𝑝⎢ 𝑟21 ℎ2 2 . . . 𝑟2𝑝 ⎥⎢⎥𝑹∗ = ⎢ .
(3)..
.. ⎥ (= 𝑹 − 𝑫).
..
⎣ ..
.
.
. ⎦
𝑟𝑝1

𝑟𝑝2

ℎ𝑝 2

...

𝑹∗ is approximated by
ˆ𝑨
ˆT𝑹∗ = 𝑨

A. Task 1: sensory evaluation of textures
In order to quantify the sensory properties of the textures, we conducted sensory evaluations by using the
SD method. The participants evaluated the textures using
five-point scales in terms of a pair of adjective terms,
such as “rough-smooth”. The adjective terms used in theexperiments were chosen in reference to studies on visualand haptic perception. In the experiments, we showedboth English and Japanese terms on evaluation sheets. We
conducted preliminary experiments to remove and merge
the candidate terms in terms of their appropriateness forthe textures in this study. For example, we removed the(4)

TABLE IA DJECTIVE TERMS
harshglossyvague
comfortable
dry
rough
slippery
sharp

325

-

not harsh
glosslessclear
uncomfortablewetsmoothstickyblunt

unevenelegant
dark
soft
vivid
warm
simplepredictable-flatinelegant
light
hard
colorlesscoldcomplexunpredictableˆ is the approximation of 𝑨. 𝑨
ˆ is given by 𝑹∗ ’s
where 𝑨ˆ𝑚 largest eigenvalues 𝜆1 , ⋅ ⋅ ⋅ ,𝑚 and their corresponding
ˆ iseigenvectors 𝒄ˆ1 , ⋅ ⋅ ⋅ ,𝑚 . 𝑨
]
[√
√
ˆ
ˆ
ˆ
(5)
𝑨=
𝜆1 𝒄ˆ1 , ⋅ ⋅ ⋅ , 𝜆𝑚 𝒄ˆ𝑚 .

factor 4 as the “complex factor,” and factor 5 as the“uneven factor.” Factor 1 was assumed to be affected by
the fine roughness of the surface, which is associated withdryness, glossiness, and slipperiness. In contrast, factor 5
is affected by the macro/coarse roughness of the surface,which is related with the groove and ridge widths of thesurface. According to Table II, the cumulative contributing
rate of the five factors is approximately 0.87; therefore,
the dimension space of the texture sensations was wellestablished.

We extracted the potential factors that explain textures
by applying factor analysis to 15 adjective pairs excluding“predictable-unpredictable.” We applied varimax rotation
ˆ to facilitate interpretation of theto the factor loadings 𝑨
relationships between factors and adjective terms.

B. Degrees of affinity to textures
The degrees of affinity that resulted from Task 2 areshown in Fig. 2. The textures are arranged in descendingorder of degree. In terms of the surface gloss, the degree ofglossless textures was larger than that of glossy textures.
Textures with the top ten degrees are glossless textures.
Surface gloss was assumed to greatly affect the degrees
compared with the other physical factors. In terms of theeffects of the shape type, degrees of striped textures arelarger than those of grid textures. As to the surface color,the degrees of blue textures are slightly larger than thoseof orange textures; for the ridge and groove width, degrees
slightly increase in proportion to the width.

C. Task 2: texture ranking
After Task 1, all sample textures were simultaneously
presented to each participant. The participants ranked the
textures in terms of the intensity of their affinity to thetextures. He was allowed to give the same rank to a few
different textures if he felt it difficult to rank all of the
textures without any duplicate ranks. By being presented
with all 24 different textures at once, participants could
evaluate the relative differences in textures.
D. Normalized-rank approach: data analysis of Task 2
Because the ranks of the textures are ordinal scales, weconverted the ranks to interval scales using normalizedrank approach [13]. We defined these interval scales as thedegrees of affinity. The textures did not include any thatmight include intense affinity. Hence, we assumed that the
population of degrees followed a normal distribution.
The degree of the 𝑘th ranked texture was assigned to the
expected value of the 𝑘th largest observation in samples ofsize 𝑛 from a standard normal population. The degree ofthe 𝑘th ranked texture was determined by∫∞𝑛!
𝑥 ⋅ 𝑎(𝑥) ⋅ 𝑏(𝑥) ⋅ 𝜙(𝑥)𝑑𝑥 (6)𝐸(𝑥𝑘∣𝑛 ) = (𝑛−𝑘)!(𝑘−1)!
−∞
]𝑘−1
[1
(7)
𝑎(𝑥) = 2 − Φ(𝑥)[1]𝑛−𝑘𝑏(𝑥) = 2 + Φ(𝑥)(8)
( 1 2)
1√(9)
𝜙(𝑥) = 2𝜋 exp − 2 𝑥∫𝑥
Φ(𝑥) = 0 𝜙(𝑧)𝑑𝑧.
(10)

VI. R ELATIONSHIPS BETWEEN D EGREES OF A FFINITY
AND FACTORS OF T EXTURES
To investigate the relationships between the degrees ofaffinity and factors of textures, we performed multiple regression analysis. We performed analysis in two ways: objective variables are degrees, and explanatory variables are
physical factor scores of textures; and objective variables
are degrees, and explanatory variables are sensory factor
scores of textures. Multiple regression analysis was applied
to the standardized values. In addition, we obtained thecorrelation coefficients between the physical and sensory
factor scores and attempted to connect both factors using
these coefficients.
A. Relationships between degrees of affinity and physicalfactors
We conducted the multiple regression analysis with theobjective and explanatory variables being the degrees of

The degrees of affinity computed for each participant wereaveraged.

TABLE IIR ESULT OF FACTOR ANALYSIS : FACTOR LOADINGS OF ADJECTIVE

V. R ESULTS

TERMS

A. Results of factor analysis
The factor loadings and each factor’s eigenvalues andcumulative contributing rates calculated through the factor
analysis are shown in Table II. Eigenvalues mean the sizeof the factors’ effects. Cumulative contributing rate is apercentage that represents the degree to which the textures
are explained by the obtained factors. We adopted the fivefactor model (𝑚 = 5) because the cumulative rate wasalmost saturated when 𝑚 was 5. Cells showing 0.7 orlarger absolute factor loadings were painted gray. Because
the adjective terms with large factor loadings represent theproperty of the factor, we named factor 1 as the “glossless,rough, and dry factor,” factor 2 as the “comfortable andelegant factor,” factor 3 as the “cold and dark factor,”

GlossyDry
RoughSlipperyHarsh
Comfortable
Elegant
Sharp
Dark
Cold
Complex
Uneven
Soft
Vivid
Vague
Eigenvalue
Cumulative
Contribut. rates

326

Factor 1−0.9400.926
0.833−0.7580.734
0.124−0.0340.214
0.328−0.197−0.1100.186
0.270−0.559−0.014
4.657

Factor 2−0.1830.250−0.152
0.048
0.199
0.893
0.869
0.747
−0.028
0.079
0.199
0.040
−0.3730.253−0.515
3.667

Factor 3−0.115−0.1170.091−0.0140.109−0.029
0.026
0.104
0.941
0.938
−0.0090.121−0.523−0.6490.087
2.561

Factor 4
0.210−0.144
0.012
0.030
0.454
0.019
0.222
0.480
0.002
0.136
0.960
−0.145−0.556
0.387
0.122
1.913

Factor 5
0.024
0.067
0.159
−0.388−0.2590.231−0.015−0.0960.009
0.130−0.146
0.903
0.210
0.065
−0.574
0.850

0.279

0.465

0.634

0.767

0.870

Degree ofaffinity

2
1
0
-1
-2

Fig. 2.

(Blue, Gloss, Grid, 0.5 mm)(Orange, Gloss, Grid, 0.5 mm)(Blue, Gloss, Grid, 1.0 mm)(Orange, Gloss, Grid, 1.0 mm)(Blue, Gloss, Grid, 2.0 mm)(Orange, Gloss, Grid, 2.0 mm)(Orange, Gloss, Stripe, 2.0 mm)(Blue, Gloss, Stripe, 2.0 mm)(Orange, Gloss, Stripe, 0.5 mm)(Blue, Gloss, Stripe, 1.0 mm)(Orange, Glossless, Stripe, 0.5 mm)(Blue, Gloss, Stripe, 0.5 mm)(Orange, Glossless, Grid, 0.5 mm)(Orange, Gloss, Stripe, 1.0 mm)(Orange, Glossless, Grid, 2.0 mm)(Blue, Glossless, Grid, 0.5 mm)(Orange, Glossless, Stripe, 1.0 mm)(Orange, Glossless, Stripe, 2.0 mm)(Blue, Glossless, Grid, 2.0 mm)(Orange, Glossless, Grid, 1.0 mm)(Blue, Glossless, Grid, 1.0 mm)(Blue, Glossless, Stripe, 0.5 mm)(Blue, Glossless, Stripe, 1.0 mm)(Blue, Glossless, Stripe, 2.0 mm)-3

Degrees of affinity to textures. Error bars are standard deviations among the participants.

affinity and physical factor scores of textures, respectively.
The standard partial regression coefficients are shown in
Table III. We found a significant negative correlation between the surface gloss and degrees. In other words, being
glossy decreases the degrees of affinity. Next, we determined the correlation between the shape type (stripe/grid)of textures and the degrees: the degrees for striped textures
were greater than the degrees for grid textures.
In contrast, there was not much of a correlation between
the degrees and surface color (blue/orange) or ridge andgroove width (0.5/1.0/2.0 mm). Therefore, we estimatedthat the degrees of glossless and striped textures are highvalues. This estimate is consistent with the results shown in
Fig. 2. In this case, 𝑅2 was 0.72: therefore, physical factors
explain the degrees of affinity as 72% of the variance inthe data.

and the degrees were observed. Factor 3 (cold, dark) did
not show any effects on the degrees. Thus, textures thatare dry, glossless, comfortable, and elegant showed highdegrees of affinity. The degrees of affinity were not determined only by the evaluation of comfort. In this regression
analysis, 𝑅2 was 0.78; therefore sensory factors explain
the degrees of affinity as 78% of the total. Interestingly,
factor 3 is the third factor that contributes to the recognitionof textures, while it does not contribute to the degrees. In
contrast, while factors 4 and 5 are minor factors for texture
recognition, they more strongly influence the degrees ofaffinity.

TABLE IVS ENSORY FACTOR SCORES

B. Relationships between degrees of affinity and sensory
factors

Blue, Gloss, Stripe, 0.5mm
Blue, Glossless, Stripe, 0.5mm
Blue, Gloss, Stripe, 1.0mm
Blue, Glossless, Stripe, 1.0mm
Blue, Gloss, Stripe, 2.0mm
Blue, Glossless, Stripe, 2.0mm
Blue, Gloss, Grid, 0.5mm
Blue, Glossless, Grid, 0.5mm
Blue, Gloss, Stripe, 1.0mm
Blue, Glossless, Stripe, 1.0mm
Blue, Gloss, Stripe, 2.0mm
Blue, Glossless, Stripe, 2.0mm
Orange, Gloss, Stripe, 0.5mm
Orange, Glossless, Stripe, 0.5mm
Orange, Gloss, Stripe, 1.0mm
Orange, Glossless, Stripe, 1.0mm
Orange, Gloss, Stripe, 2.0mm
Orange, Glossless, Stripe, 2.0mm
Orange, Gloss, Grid, 0.5mm
Orange, Glossless, Grid, 0.5mm
Orange, Gloss, Grid, 1.0mm
Orange, Glossless, Grid, 1.0mm
Orange, Gloss, Grid, 2.0mm
Orange, Glossless, Grid, 2.0mm

We describe the relationships between the degrees ofaffinity and sensory factor scores of textures that resulted
from factor analysis. We conducted the multiple regression
analysis with the objective and explanatory variables being the former and latter types of variables, respectively.
Sensory factor scores are degrees of correlations between
each factor and texture, as shown in Table IV. We show
standard partial regression coefficients in Table V that resulted from multiple regression analysis. We found strong
correlations between factors 1 (glossless, rough, dry) and2 (comfortable, elegant) and the degrees of affinity. Slightcorrelations between factors 4 (complex) and 5 (uneven)

Factor 1
0.564
0.535
0.393
0.084
0.764
0.160
−0.6490.321−1.9760.689−1.950
0.418
0.294
0.789
0.458
0.021
0.641
0.621
−1.5400.591−1.1050.631−2.111
1.357

Factor 2
0.517
1.135
−0.4921.821−1.4470.417−1.834
0.283
0.220
0.810
−0.689−0.5771.474−0.1620.493
1.350−0.622−0.6630.002
0.232−0.6350.195
0.074−1.901

Factor 3
1.138
1.054
1.197
0.963
0.732
1.062
1.394
1.099
0.430
0.449
0.770
1.070
−0.671−0.937−1.040−0.993−0.682−0.731−0.929−1.080−1.298−1.049−0.920−1.029

Factor 4
0.756
0.702
0.045
−0.742−0.991−2.150
1.276
1.134
0.185
1.191
−0.9280.034−0.0011.008−0.537−0.573−1.644−1.587
0.474
0.641
1.231
1.002
−0.482−0.042

Factor 5−0.1560.461−0.2250.088−0.196−0.675−1.754−0.525
1.340
1.758
0.439
1.002
−1.269−0.031−0.5620.529−0.1080.641−1.585−1.866
0.811
0.668
0.356
0.860

TABLE VR EGRESSION COEFFICIENTS OF SENSORY FACTOR SCORES ANDTABLE IIIR EGRESSION COEFFICIENTS OF PHYSICAL FACTOR SCORES AND

DEGREES OF AFFINITY

DEGREES OF AFFINITY

Degrees of affinity to textures

Color(+:Blue/-:Orange)

Gloss(+:Glossless/-:Glossy)

Shape Type(+:Stripe/-:Grid)Ridge andgroove wid.

0.087

0.741

0.376

0.147

Degrees of affinity to textures

327

Factor 1(Rough,Glossless,
Dry)

Factor 2(Elegant,
Comfort.)Factor 3(Cold,
Dark)

Factor 4(Complex)

Factor 5(Uneven)

0.591

0.503

0.069

−0.244

0.318

TABLE VI
C ORRELATION COEFFICIENTS BETWEEN PHYSICAL AND SENSORY

Physical factor

FACTOR SCORES

Color
Gloss
Shape type
R & G width

Factor 1−0.0550.534
0.457−0.038

Factor 2
0.014
0.258
0.336
−0.439

Factor 3
0.971−0.0100.093−0.018

Factor 4
0.044
0.053
−0.490−0.741

Factor 5
0.139
0.260
−0.134
0.408

Color

Gloss

Shape Type(+:Blue/-:Orange)(+:Glossless/-:Glossy)(+:Stripe/-:Grid)

0.087

0.741

0.376

Ridge andGroove Width

0.147

Degree ofAffinity

C. Correlations between physical and sensory factors

0.591

We show the correlations between physical and sensory
factors in Table VI. Factor 1 (glossless, rough, dry) isrelated to the surface gloss and shape type. Glossy andstriped textures had higher values in terms of factor 1.
Factor 2 (comfortable, elegant) is influenced by the shape
type and ridge and groove widths; this indicates that striped
textures with finer surface patterns are perceived to be
more comfortable and elegant. The correlations between
factor 3 (cold, dark) and the surface color was strong,while the other physical factors had little effect on thisfactor. Factor 4 (complex) was strongly affected by theridge and groove widths in that smaller widths cause
higher complexity. Further, grid textures tended to be more
complex than the striped textures. Finally, factor 5 (uneven)was dominantly impacted by the ridge and groove widthsof the texture surfaces.
Fig. 3 shows the above mentioned relationships between
the degrees of affinity and physical and sensory factorsof textures. The line width represents the strength of therelationships, which correspond to the absolute values ofthe standard partial regression coefficients and correlation
coefficients. As shown by this figure, although it is limited
to the sample textures used in this study, the relationships
between the degrees of affinity and physical and sensory
factors of textures were quantitatively clarified.

0.503

0.069

-0.2440.318

Sensory factor
Factor 1
Glossless,
Rough, Dry

Factor 2
Comfortable,
Elegant

Factor 3
Cold,
Dark

Factor 4
Complex-0.741-0.4390.534-0.490
0.971

0.457

Factor 5
Uneven

0.408

0.336

Color

Gloss

Shape Type(+:Blue/-:Orange)(+:Glossless/-:Glossy)(+:Stripe/-:Grid)

Ridge andGroove Width

Fig. 3. Relationships between sensory factors, and physical factors of
textures, and degrees of affinity to textures

B. Textural dimensions: comparison with related studies
In this study, five factors/axes were identified as the
textural dimensions. Several researchers also attempted
to extract these haptic dimensions in material or texture
recognition. Here, we compare our results with those of
the related studies. Table VII shows a reference table forthe studies on haptic-textural dimensions.
Yoshida [1], [2] investigated the dimensions for haptic
perception of 50 materials by multidimensional scaling(MDS). Interestingly, he studied the differences of suchdimensions between the cases of haptic-only, visual-only,and visuo-haptic conditions. For the visuo-haptic condition,
he found that three dimensions, heaviness-coldness, wetsmoothness, and hardness, well established the dissimilarity space of the materials. Hollins et al. [3] concluded
that the textural dimensions of materials are composedof smooth-rough, hard-soft, and sticky-slippery axes using
MDS. Shirado et al. [5] used a factory analysis method
and identified four dimensions for 20 materials: roughness,coldness, moisture, and hardness. Yoshioka et al. [6] foundthree dimensions using MDS for haptic exploration by barefingers: roughness, hardness, and stickiness. Although it
was limited to car sheet materials, Picard et al. [11] found
four textural dimensions: soft-harsh, thin-thick, relief, andhardness. Tanaka et al. [14] found two dimensions using
factor analysis: moist and downy-warm.
Because the dimensions of roughness/harshness, softness/hardness, and warmth/coldness were reported mostoften, these three factors should be major factors in textural recognition. Our results included the roughness andcold factors, while the softness/hardness factor was not

VII. D ISCUSSIONS
A. Physical and sensory factors of textures effectively
explained our affinity to the textures
We found that the physical and sensory factors of the
textures explained the degrees of affinity by 72% and 78%,
respectively, which indicates that these factors effectively
explain our affinity to the textures. Although the acquired
relationships between the intensity of the affinity and thesefactors were limited to the sample textures used in thisstudy, the results support the argument that affinity to
the textures is generally determined by their physical andsensory factors. Because the linear combination of thephysical factors accounted for 72% of the deviations inaffinity, the affinity may be intuitively driven by the visualappearances of the textures. On the other hand, the sensory
factors explain the affinity as well as the physical factors,
which suggest that our internal sensory evaluation process
mediates the affinity. The remaining 20%–30% of thedeviations that these factors could not describe may be dueto the physical and adjective terms that this study did notconsider. Individual differences in decision criteria at thetwo experimental tasks may also have led to the residualsof the regression analyses.
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TABLE VII
R EFERENCE TABLE OF TEXTURAL FACTORS
AuthorM. Yoshida [1], [2]M. Hollins
et al. [3]
H. Shirado
et al. [5]T. Yoshioka
et al. [6]
D. Picard
et al. [11]Y. Tanaka
et al. [14]H. Naganoet al.

Year
1968

MethodMDS

Texture50 materials

Factor 1
Heavy, Cold

Factor 2
Smooth, Wet

Factor 3
Hard

2000

MDS

17 materials

Smooth/RoughHard/Soft

Sticky/Slippery

2007

Factor analysis

20 materials

Rough

Cold

Moist

2007

MDS

17 materials

Rough

Hard

Sticky2003

MDS

24 car seatmaterials

Soft/Harsh

Thin/Thick

Relief (Uneven)

Hard

2006

Factor analysis

13 fabrics

Moist

Downy,Warm

2011

Factor analysis

24 textures

Glossless
Rough,Dry

Comfortable,Elegant

Cold,Dark

Complexclearly observed. This may be because in our study, thesample textures were made of the same materials and justtheir surface geometric patterns varied. In addition, theparticipants did not touch the textures but visually judged
their properties. These specific experimental conditions
may have hidden the factor of softness/hardness.
Another major difference between this study and related studies was the presence of comfortable-elegant(factor 2) and complex (factor 4). None of the related studies reported such textural dimensions. These
two factors may have been found because we adopted
the adjective terms of elegant-inelegant, comfortableuncomfortable, and complex-simple, which are usuallyused for explaining visual impressions of textures.
Finally, in this study, two types of roughness were extracted as different dimensions: harshness and unevenness.
Because harshness was coupled to dry and glossless infactor 1, harshness may indicate the fine surface roughnessof the textures. On the other hand, unevenness (factor5) was correlated with the ridge and groove widths ofthe surfaces. Hence, in this study, unevenness may havebeen recognized as an adjective term for coarse roughness.
Fine and coarse roughness were perceived by different
dimensions. The same tendency was suggested by Picard
et al. [11] as harsh and relief factors.

Factor 4

Factor 5

Hard

Unevenand comfortable-elegant were observed to strongly relatewith the degrees.
The methodology of this study is useful for determining
the best combinations among limited physical factors interms of the textures’ appeal to human touch. In productdesign, limited physical factors are available; hence, theproposed method can potentially enable us to design products or textures for shops or amusement centers, whichappeal to human touch.
R EFERENCES[1] M. Yoshida, “Dimensions of Tactual Impressions (1),” Japanese
Psychological Research, vol. 10, pp. 123–137, 1968.
[2] M. Yoshida, “Dimensions of Tactual Impressions (2),” Japanese
Psychological Research, vol. 10, pp. 157–173, 1968.
[3] M. Hollins, S. Bensmaia, K. Karlof and F. Young, “Individual
Differences in Perceptual Space for Tactile Textures: Evidence from
Multidimensional Scaling,” Perception and Psychophysics, vol. 62,
pp. 1534–1544, 2000.
[4] W.M.B. Tiest and A.M.L. Kappers, “Analysis of Haptic Perceptionof Materials by Multidimensional Scaling and Physical Measurements of Roughness and Compressibility,” Acta Psychologica, vol.
121, no. 1, pp. 1–20, 2006.
[5] H. Shirado and T. Maeno, “Modeling of Human Texture Perception
for Tactile Displays and Sensors,” Proc. World Haptics Conference,
pp. 629–630, 2005.
[6] T. Yoshioka, S. J. Bensmaia, J. C. Craig and S. S. Hsiao, “Texture
Perception through Direct and Indirect Touch: An Analysis ofPerceptual Space for Tactile Textures in Two Modes of Exploration,”
Somatosensory and Motor Research, vol. 24, pp. 53–70, 2007.
[7] G. Winakor, C. J. Kim, and L. Wolins, “Fabric Hand: Tactile
Sensory Assessment,” Textile Research Journal, vol. 50, pp. 601–
610, 1980.
[8] X. Chen, F. Shao, C. Barnes, T. Childs, and B. Henson, “Exploring Relationships between Touch Perception and Surface Physical
Properties,” International Journal of Design, vol. 3, pp. 67–76, 2009.
[9] C. J. Kim, and K. Piromthamsiri, “Sensory and Physical Hand Properties of Inherently Flame-Retardant Sleepwear Fabrics,” Textile
Research Institute, vol. 54, no. 1, pp. 61–68, 1984.
[10] T. Matsuoka, H. Kanai, H. Tsuji, T. Shinya and T. Nishimatsu,“Predicting Texture Image of Covering Fabric for Car Seat by
Physical Properties,” Journal of Textile Engineering, vol. 54, no.
3, pp. 63–74, 2008.
[11] D. Picard, C. Dacremont, D. Valentin and A. Giboreau, “Perceptual
Dimensions of Tactile Textures,” Acta psychologica, vol. 114, pp.
165–184, 2003.
[12] S. Kawabata, and M. Niwa, “Fabric Performance in Clothing andClothing Manufacture,” Journal of the Textile Institute, vol. 80, no.
1, pp. 19–50, 1989.
[13] H. L. Harter, “Expected Values of Normal Order Statistics,”
Biometrika, vol. 48, no. 1 and 2, pp. 151–165, 1961.
[14] Y. Tanaka, M. Tanaka and S. Chonan, “Development of a Sensor
System for Measuring Tactile Sensation,” Proc. IEEE Sensors, pp.
554–557, 2006.

VIII. C ONCLUSIONSIn this study, we investigated the properties of specified
textures that appeal to human touch. To determine thecause for such affinity, we investigated the relationshipbetween the physical and sensory factors of the textures
and their appeal to human touch. The textures were fabricated using clay, paints, textured molds, and sealers whichenabled us to control their physical and sensory factors.
The intensity of the affinity was measured by a ranking
system and normalized-rank method. In order to quantify
the sensory factors of the textures, we conducted sensory
evaluation using 15 adjective term pairs, and the results
were analyzed via factor analysis. Consequently, five orthogonal sensory factors were identified: glossless-roughdry, comfortable-elegant, cold-dark, complex, and uneven.
Multiple regression analyses, revealed that the physical andsensory factors effectively explained the degrees of affinity
by 72% and 78%, respectively. In terms of the physicalfactors of the textures, their glossiness and surface shapepatterns strongly affected the degrees of affinity. In termsof the influence of the sensory factors, glossless-rough-dry
329