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What Appeals to Human Touch?
Effects of Tactual Factors and Predictability of Textures on Affinity to
Textures
Hikaru Nagano∗

Shogo Okamoto

Yoji Yamada

Nagoya University

Nagoya University

Nagoya University

A BSTRACTFor reasons that have not yet been investigated, some textures appeal to human touch. We investigate the relationships between thephysical and sensory factors, the predictability of textures, and theirappeal to human touch. We conduct sensory evaluation of 24 artificial clay textures, and the results are analyzed using factor analysis.
Experimental results identify five sensory factors. We reveal that70–80% of the textures’ appeal to human touch can be describedon the basis of the sensory and physical factors. We propose twomodels involving the predictability of textures. These models, aswell as models based on the sensory and physical factors, explain
the affinity to textures.
Keywords: sensory evaluation, textural dimension.
1

I NTRODUCTION

Certain textures and materials that are used on a daily basis appeal
to human touch. What appeals to human touch? Well-polishedmetal surfaces and finely woven clothes are examples of such materials. In addition, the textures whose haptic sensations are unpredictable may appeal to human touch. Some people may feellike touching such textures in order to investigate their haptic sensations. Discovering the reason for this affinity could enable us todesign products that attract customers. In this study, we investigate
the properties of materials that appeal to human touch.
To the best of our knowledge, human affinity to such materials
has not been investigated. We assume that our affinity is related tothe physical and sensory characteristics of the textures. In previous
studies, tactile sensations were described by the sensory characteristics of textures [1, 2, 3, 4]. Other studies have investigated the relationships between the physical and sensory characteristics of textures [5, 6]. From the viewpoint of product design, Winakor et al.
studied the effect of textiles’ physical characteristics on customers’
preferences [7]. In addition, the relationships between human impressions and the characteristics of some specific materials were
investigated. For example, some studies investigated relationshipsof this nature in regards to wrapping paper [8], sleeping wear [9]and car sheets [10, 11]. Kawabata et al. proposed a method of estimating sensory characteristics from the physical characteristics ofclothing fabrics [12]. However, the relationship between the physical and sensory factors of the textures and their appeal to humantouch has not been reported thus far.
The present study contains two hypotheses about the appeal tohuman touch. When we see a texture, our affinity to it is induced
by its appearances. Therefore, the physical factors of the textures
may directly induce human affinity. On the other hand, the affinity∗ This work was partly supported by MEXT KAKENHI 22800030 and
the Nitto Foundation. For e-mail address, see http://www.mech.nagoyau.ac.jp/asi/en/

IEEE World Haptics Conference 201121-24 June, Istanbul, Turkey
978-1-4577-0297-6/11/$26.00 ©2011 IEEEto textures may be determined through human sensory processing.
The first hypothesis is that the affinity is attributable to the physicaland sensory factors of the textures. In addition, the predictabilityof textures is potentially related to the affinity. For example, somepeople may want to touch textures whose haptic sensations are apparently unpredictable. On the other hand, some people may not
be interested in such textures. Therefore, the second hypothesis isthat the predictability of textures affect the relationships between
the affinity to textures and the sensory factors of textures.
We experimentally verify the above-mentioned hypotheses. The
objective of this study is to investigate how and to what extent thephysical and sensory factors of textures correlate with their appeal
to human touch and how and to what extent the predictability of
textures affects affinity to textures. This affinity may depend on
personal predispositions of participants, but we average individual
responses to investigate a potential trend in the relationships between the factors of the textures and their appeal to human touch.

a) 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

Side view

50 mm

Sample texture

Plastic plate

White plastic plate
Figure 1: Clay sample textures

203

2

D EFINITIONS OF A PPEAL TO H UMAN TOUCH

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 toprevent the variations in cultural background from affecting the intensity of the affinity, the sample textures should not be associatedwith 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 order of theintensity of their affinity to the textures, and we quantitatively assess the intensity of the affinity from the rank. In addition, to avoid
variations in the affinity caused by age and gender, participants arelimited to males in their twenties. Finally, “touch” does not imply
pushing, or holding an object; it simply implies stroking the surfaceof textures.
3

S TIMULATION : PHYSICAL FACTORS OF TEXTURE

Rather than finely investigating the effect of single physical factor,
we are going to see the possible influence of some physical factors.
We made 24 different textures controlled by four physical factors:
surface colors, surface gloss patterns, surface shape-types, and surface ridge and groove widths. We adopted light clay (Hearty SoftWhite, 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 before molding. The color variationof blue and orange is a complementary relationship. These colors
should cause a larger variation in sensory evaluations. The blue
(Phthalocyanine Blue, Liquitex, Ohio, USA) and orange (Scarlet
Red, Liquitex, Ohio, USA) paints were (4.0BP, 1.5, 7), and (8.0R,
5.0, 13), respectively, in the Munsell color presentation. The mixing ratio for the clay and paints was 100 g clay to 1.25 ml paint.
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, and dried. The JIS specular gloss values of the textures were 2.4%, 94.2%, 1.7%, and 85.1%
for the glossless and glossy blue and glossless and glossy orange,
respectively. As shown in Fig. 1, two shape types were used: gridand 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 by physicalfactor scores. The physical factor scores were normalized values
with a mean of 0 and a variance of 1 for each of the four physicalfactors: 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).
4

E XPERIMENTIn Task 1, the participants evaluated textures one after the otherusing the semantic differential (SD) method. Because we used adjective terms that included “predictable-unpredictable” in the experiments, we could quantify sensory properties that included thepredictability of the textures. We then applied factor analysis tothese properties (excluding “predictable-unpredictable”) to obtain
the independent sensory factors.
After Task 1, the participants ranked all 24 textures in order ofthe intensity of their affinity to the textures (Task 2). Based on
the ranks of the textures, we calculated the degrees of affinity toeach texture. Finally, we investigated the relationships between the
degrees of affinity and the physical and sensory factors using multiple regression analyses in section 6, and verified the validity of
two models that potentially describe the relationships between the204

predictability of perceived textures and the degrees of affinity to
textures through multiple regression analyses in section 7.
4.1 Task 1: Sensory evaluation of textures
In order to quantify the sensory properties of the textures, we conducted sensory evaluations using the SD method. The participantsevaluated the textures using five-point scales in terms of a pair ofadjective terms, such as “rough-smooth.” The adjective terms usedin the experiments were chosen in reference to studies on visualand haptic perception. In the experiments, we provided both English and Japanese terms on the evaluation sheets. We conductedpreliminary experiments to remove and merge the candidate terms
according to their appropriateness for the textures in this study. For
example, we removed the terms whose scores did not vary, such as“thick-thin.” Also, 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 1.
The participants were voluntary and included 12 students in their
twenties from the authors’ laboratory. As shown in Fig. 1b, a large
white plate with a 50 mm × 50 mm square window was placed on
the sample texture so that the participants would see only the textured surfaces and not the sides of the samples. The participants
were instructed to attempt to keep their head positions fixed in order to retain the relative position between the head and the texture
samples.
The textures and the adjective terms used to sort them were presented to each participant in random order. We conducted experiments in a laboratory environment where the temperature was approximately 24 ◦ C, and the textures were placed under an illumination of approximately 460 lx.
4.2 Factor analysis: Data analysis of Task 1
We assigned values of 1 to 5 on the five-point adjective scales obtained in Task 1. In order to reduce the effects of individual differences in sensory evaluation, the evaluation values of each adjective pairs were normalized within a single participant. Then, they
were averaged across all the participants. To decrease the numberof variables used in the later analysis, we applied factor analysis
to the evaluation values and extracted common factors as a synthesis of variables that were strongly correlated. xi was the vector ofevaluation values of p adjective term pairs for the texture specified
by i. The adjective term pairs were those listed in Table 1 excluding “predictable-unpredictable”; thus, p = 15. xi was broken down
into m common factors fi and unique factors ei :xi = A fi + ei (i = 1–n),p×m m×1 p×1
p×1

(1)

where the factor loadings A explain the strength of the relationships between common factors and adjective terms. n was the number of texture types (n = 24). We applied varimax rotation to facilitate interpretation of the relationships between factors and adjective
terms.

Table 1: Adjective terms

harshglossyvague
comfortable
dry
rough
slipperysharp-

not harsh
glosslessclear
uncomfortablewetsmoothstickyblunt

unevenelegant
dark
soft
vivid
warm
simplepredictable-flatinelegant
light
hard
colorlesscoldcomplexunpredictable4.3 Task 2: Texture ranking
2

Degree of Affinity

After Task 1, all sample textures were simultaneously presented
to each participant. The participants ranked the textures in termsof the intensity of their affinity to the textures. Each participant
was allowed to give the same rank to a few different textures if
he felt that it was difficult to rank all of the textures without any
duplicate ranks. Presenting the participants with all 24 different
textures at once allowed them to evaluate the relative differences inthe textures.

1.5
1
0.5
0
-0.5
-1
-1.5

Because the ranks of the textures are ordinal scales, we converted
the ranks to interval scales using the normalized-rank approach[13]. We defined these interval scales as the degrees of affinity.
The textures did not include any that might induce intense affinity. Hence, we assumed that the population of degrees followed anormal distribution.
The degree of the kth ranked texture was assigned to the expectedvalue of the kth largest observation in samples of size n from astandard normal population. The degree of the kth ranked texture
was determined by∫∞n!
E(xk|n ) = (n−k)!(k−1)!
−∞ x · a(x) · b(x) · ϕ (x)dx[]k−1
a(x) = 12 − Φ(x)[]n−kb(x) = 12 + Φ(x))(ϕ (x) = √12π exp − 12 x2∫x

Φ(x) = 0 ϕ (z)dz.

(2)
Figure 2: Degrees of affinity to textures

(3)
(4)
(5)
(6)

The degrees of affinity computed for each participant wereaveraged.
5

R ESULTS

5.1 Results of factor analysis: Factor loadings of 15 adjective terms

The factor loadings and each factor’s cumulative contributing rates calculated through the factor analysis are shown in
Table 2. Cumulative contributing rate is a percentage that
represents the degree to which the textures are described

Table 2: Results of factor analysis: Factor loadings of 15 adjective
terms
GlossyDry
RoughSlipperyHarsh
Comfortable
Elegant
Sharp
Dark
Cold
Complex
Uneven
Soft
Vivid
Vague
Contribut. rates
Cumulative
Contribut. rates-2(Blue, Glossless, Stripe, 2.0 mm)(Blue, Glossless, Stripe, 1.0 mm)(Blue, Glossless, Stripe, 0.5 mm)(Blue, Glossless, Grid, 1.0 mm)(Orange, Glossless, Grid, 1.0 mm)(Blue, Glossless, Grid, 2.0 mm)(Orange, Glossless, Stripe, 2.0 mm)(Orange, Glossless, Stripe, 1.0 mm)(Blue, Glossless, Grid, 0.5 mm)(Orange, Glossless, Grid, 2.0 mm)(Orange, Gloss, Stripe, 1.0 mm)(Orange, Glossless, Grid, 0.5 mm)(Blue, Gloss, Stripe, 0.5 mm)(Orange, Glossless, Stripe, 0.5 mm)(Blue, Gloss, Stripe, 1.0 mm)(Orange, Gloss, Stripe, 0.5 mm)(Blue, Gloss, Stripe, 2.0 mm)(Orange, Gloss, Stripe, 2.0 mm)(Orange, Gloss, Grid, 2.0 mm)(Blue, Gloss, Grid, 2.0 mm)(Orange, Gloss, Grid, 1.0 mm)(Blue, Gloss, Grid, 1.0 mm)(Orange, Gloss, Grid, 0.5 mm)(Blue, Gloss, Grid, 0.5 mm)

4.4 Normalized-rank approach: Data analysis of Task 2

Factor 1−0.9400.926
0.833−0.7580.734
0.124−0.0340.214
0.328−0.197−0.1100.186
0.270−0.559−0.0140.279

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
−0.3730.253−0.5150.186

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
0.169

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
0.132

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
0.103

0.279

0.465

0.634

0.767

0.870

by the obtained factors. We adopted the five-factor model(m = 5) because the cumulative rate was almost saturated
when m was 5. Cells with absolute factor loadings of 0.7 orabove are highlighted in gray. Because the adjective termswith large factor loadings represent the property of the factor, we named factor 1 the “glossless, rough, and dry factor,” factor 2 the “comfortable and elegant factor,” factor 3
the “cold and dark factor,” factor 4 the “complex factor,” andfactor 5 the “uneven factor.” Factor 1 was assumed to be affected by the fineness of the surface, which is associated withdryness, glossiness, and slipperiness. In contrast, factor 5 isaffected by the coarseness of the surface, which is related to
the groove and ridge widths of the surface. As indicated inTable 2, the cumulative contributing rate of the five factors isapproximately 0.87; thus, the dimension space of the texture
sensations was well established.
5.2 Degrees of affinity to textures

The degrees of affinity that resulted from Task 2 are shownin Fig. 2. The textures are arranged in descending order ofdegree. In terms of the surface gloss, the degree of glossless
textures was larger than that of glossy textures. The textureswith the ten largest degrees are glossless textures. Surface
gloss was assumed to affect the degrees much more significantly than the other physical factors. Regarding the effectsof the shape type, degrees of striped textures are larger thanthose of grid textures. Regarding the surface color, the degrees of blue textures are slightly larger than those of orange
textures; for the ridge and groove width, degrees slightly
increase in proportion to the width. Thus, the glosslessblue texture whose shape type is stripe and whose ridge andgroove width is 2.0 mm shows the highest degree of affinity.
The values of the standard deviations were high, though the205degrees of affinity were averaged across all the participantsin order to address general trends in this study.
6

Table 3: Regression coefficients of physical factor scores and degrees of affinity

R ELATIONSHIPS BETWEEN D EGREES OF A FFINITY AND
FACTORS OF T EXTURES

To investigate the relationships between the degrees of affinity and the factors of the textures, we performed multiple regression analysis. We performed the analysis in two different arrangements: first, objective variables are degrees andexplanatory variables are physical factor scores of textures,and second, 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 the correlation coefficients between
the physical and sensory factor scores and attempted to connect both factors using these coefficients.

Color(+:Blue/-:Orange)
Degrees of affinity to textures

0.087

Gloss
Shape Type(+:Glossless/ (+:Stripe/-:Glossy)-:Grid)0.741

Ridge andgroove wid.

0.376

0.147

Table 4: Regression coefficients of sensory factor scores and degrees of affinity

Degrees of affinity to textures

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

−0.2440.318

6.1 Relationships between degrees of affinity and physical factors

We conducted multiple regression analysis with the objective and explanatory variables being the degrees of affinityand physical factor scores of textures, respectively. The standard partial regression coefficients are shown in Table 3. We
found a significant negative correlation between the surface
gloss and the degrees. In other words, glossiness decreases
the degrees of affinity. Next, we determined the correlation
between the shape type (stripe/grid) of the textures and thedegrees: the degrees for striped textures were greater thanthe degrees for grid textures.
In contrast, there was little correlation between the degrees
and the surface color (blue/orange) or the ridge and groove
width (0.5/1.0/2.0 mm). Therefore, we estimated that the degrees of glossless and striped textures are high values. Thisestimate is consistent with the results shown in Fig. 2. In thiscase, R2 was 0.72; therefore, physical factors describe thedegrees of affinity as 72% of the variance in the data.
6.2 Relationships between degrees of affinity and sensory factors

We describe the relationships between the degrees of affinity
and sensory factor scores of textures (fi ) that resulted fromfactor analysis. We conducted the multiple regression analysis with the objective and explanatory variables being thedegrees of affinity and sensory factor scores of textures, respectively. In Table 4, we list the standard partial regression
coefficients that resulted from the multiple regression analysis. We found strong correlations between factors 1 (glossless, rough, dry) and 2 (comfortable, elegant) and the degrees
of affinity. Slight correlations between factors 4 (complex)
and 5 (uneven) and the degrees were also observed. Factor 3(cold, dark) did not have any effects on the degrees. Thus,
textures that were dry, glossless, comfortable, and elegant
showed high degrees of affinity. Human affinity to the textures was induced not only by the comfortable factor but also
the glossless, rough, dry factors. In this regression analysis,
R2 was 0.78; therefore the sensory factors described the degrees of affinity by 78% of the total. Interestingly, factor 3 is206

Table 5: Correlation coefficients between physical and sensory factor
scoresColor
GlossShape 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
−0.1340.408

the third factor that contributes to the recognition of textures,
though it does not contribute to the degrees. In contrast, factors 4 and 5 are minor factors for texture recognition, butthey more strongly influence the degrees of affinity.
6.3 Correlations between physical and sensory factors

The correlations between physical and sensory factors arepresented in Table 5. Factor 1 (glossless, rough, dry) is related to the surface gloss and shape type. Glossy and striped
textures have higher values in terms of factor 1. Factor 2(comfortable, elegant) is influenced by the shape type andridge and groove widths; this indicates that striped textureswith finer surface patterns are perceived as more comfortable and elegant. The correlations between factor 3 (cold,dark) and the surface color were strong, while the other physical factors had little effect on this factor. Factor 4 (complex) was strongly affected by the ridge and groove widths–
smaller widths lead to higher complexity. Also, grid texturestended to be more complex than striped textures. Finally,factor 5 (uneven) was dominantly affected by the ridge andgroove widths of the texture surfaces.
Fig. 3 shows the above mentioned relationships between
the degrees of affinity and the physical and sensory factorsof the textures. The line width represents the strength of therelationships, which corresponds to the absolute values inTables 3, 4, and 5. Although the figure is limited to the sample textures used in this study, it shows that the relationships
between the degrees of affinity and the physical and sensory
factors of the textures were quantitatively clarified.

Physical factor
Color

Gloss

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

0.087

0.741

0.376

0.576

0.503

0.069

-0.244

Factor 2
Comfortable,
Elegant

Factor 3
Cold,
Dark

Factor 4
Complex

Factor 5
Uneven-0.741-0.490

0.971

0.457

0.408

0.336

Color

Gloss

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

Ridge andGroove Width

Figure 3: Relationships between sensory factors, physical factors of
textures, and degrees of affinity

7-0.155

Factor 1
Factor 2
Glossless, Comfortable,
Rough, Dry
Elegant

Factor 3
Cold,
Dark

Factor 4
Complex

0.184

0.286

Factor 5
UnevenFactor 6
Predictable

Figure 4: Relationships between factors including predictability anddegrees of affinity-0.439

0.534

0.059

0.318

Sensory factor
Factor 1
Glossless,
Rough, Dry

0.542

0.147

Degree ofAffinity

0.591

Degree ofAffinity

Ridge andGroove Width

T WO MODELS DESCRIBING THE RELATIONSHIPS BE TWEEN PREDICTABILITY OF TEXTURES AND AFFINITY TO
TEXTURES

Here, we investigate how the predictability of texture is related to the affinity to textures. In this study, “predictability” means how predictable the haptic sensations of the textures are from their appearances. We propose the following two models that potentially describe the relationships
between the predictability of perceived textures and the degrees of affinity to textures. The participants evaluated thepredictability of textures using five-point scales in terms of a“predictable-unpredictable” pair in Task 1 without touchingthem. We verify the validity of the models through multiple
regression analyses.
7.1 First model: Predictability of textures affects eachof the other sensory factors

In the first model, the unpredictable textures minimally affectthe affinity to textures, whereas the sensory factors of predictable textures strongly affect the degrees of affinity. The
first model is
abk = c1 pk f1k + c2 pk f2k + · · · + c5 pk f5k ,(7)where f jk is the score of factor j for a texture specified byk. Degrees of affinity ab are estimated from predictabilities oftextures pk , standard regression coefficients c j , and sensory
factor scores of textures f jk . We assigned values of 1.0 to 0.2
on the five-point adjective scales obtained in Task 1 (1.0 =predictable, 0.2 = unpredictable). In (7), we multiply p andeach of the five sensory factor scores of textures f jk . The
degrees of affinity to unpredictable textures are reduced almost to zero to counteract the influence of the sensory factor
scores on the degrees of affinity. From the multiple regression analysis of (7), R2 was 0.79. This is as large as that of
the model in section 6. We expected R2 of the model specified in (7) to be higher than that of the model in section 6,
but (7) describes the degrees of affinity as well as the modelin section 6 does.
7.2 Second model: Predictability of textures is a factor
independent of the other sensory factors

In the second model, we assume linear relationships between
the predictability of textures and degrees of affinity to textures. The second model is′′′ab′k = c′1 f1k+ c′2 f2k
+ · · · + c′5 f5k+ c′6 p′k ,(8)where predictability of textures p′k affects degrees of affinity to textures as a factor independent of the other five sensory factors of the textures. If c′6 is positive, the more predictable textures show the higher degrees of affinity. If c′6 isnegative, the degrees of affinity to unpredictable textures are
high, meaning that people would like to touch the texturesin order to investigate their unpredictable haptic properties.
We verified the validity of this model through the multiple
regression analysis. Fig. 4 shows the results of the analysis. Values in the figure are the coefficients of the six factors. Factor 6 (predictable) is the third largest factor that
contributes to the degrees of affinity. The standard regression
coefficient between factor 6 and the degrees of affinity was
positive. Therefore, the degrees of affinity to predictable textures were higher than those to unpredictable textures. In thisregression analysis, R2 was 0.83. The model in (8) describes
the degrees of affinity as well as the model in section 6 does;
R2 for the model in section 6 was 0.78.
8

D ISCUSSIONS

8.1 Physical and sensory factors of textures effectively
described our affinity to the textures

We found that the physical and sensory factors of the texturesdescribed the degrees of affinity with accuracies of 72% and78%, respectively, indicating that these factors effectively

207

describe 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 this study,
the results support the argument that affinity to texture is generally determined by physical and sensory factors. Because
the linear combination of the physical factors accounted for
72% of the variations in affinity, the affinity may be intuitively driven by the visual appearances of the textures. On
the other hand, the sensory factors describe the affinity as
well as the physical factors do, which suggests that our internal sensory evaluation process mediates the affinity. The
remaining 20–30% of the variations that these factors could
not describe may be due to the physical and adjective terms
that this study did not consider. Individual differences in decision criteria in the two experimental tasks may also havecontributed to the residuals of the regression analyses.
8.2 Effect of predictability of textures on affinity to textures

In section 7, we constructed two types of models concerning the predictability of textures and their appeal to humantouch. These models were constructed based on the model
acquired in section 6. Thus, we expected that the models in
section 7 would describe the affinity to textures better thansection 6 model did. However, in terms of R2 , the models in
sections 6 and 7 described the affinity almost equally well.
The R2 of the models in section 7 were not significantly
higher than that of the model in section 6 (contrary to our expectations) for the following reasons: The model describedby (7) can not properly account for the textures whose haptic
sensations are unpredictable, but its affinity to human touch
is significant. On the other hand, the model described by (8)is not suitable for the predictable and unappealing textures.
However, the models in section 7 are not inferior to themodel in section 6. The models in section 7 describe thevariations of the affinity with an accuracy of 80% that is comparable the model in section 6. Also, according to the resultsof section 7.2, the predictability possibly influences the affinity of textures, followed by the factors of glossless, rough,dry and comfortable, elegant. Hence, the predictability is an
important factor, and it is expected that a novel model that
effectively involves the effects of the predictability will elucidate the causes behind texture’s appeal to human touch.
9

C ONCLUSIONSIn this study, we investigated the properties of specified textures that appeal to human touch. To determine the causeof this affinity, we investigated the relationships between thephysical and sensory factors, the predictability of the textures, and their appeal to human touch. We used textures insuch a way that we were able to control their physical factors.
The degrees of affinity were measured by a ranking systemand the normalized-rank method. In order to quantify thesensory factors of the textures, we conducted sensory evaluation and factor analysis. Consequently, five sensory factors
were identified: glossless, rough, dry; comfortable, elegant;208

cold, dark; complex; and uneven. Multiple regression analyses revealed that the physical and sensory factors effectively
described the degrees of affinity with accuracies of 72% and78%, respectively. Regarding the physical factors of the textures, their glossiness and surface shape patterns strongly affected the degrees of affinity. Regarding the influence of thesensory factors, glossless, rough, dry, comfortable, and elegant were observed to be strongly related to the degrees.
We constructed two models explaining the relationships
between the predictability of the perceived textures and thedegrees of affinity. In the first model, the predictability affected the other sensory factors. In the second model, thepredictability was a factor independent of the other sensory
factors. These models described our affinity to the textures
as well as the models that did not include the predictability.
The methodology of this study is useful for determining the best combinations among limited physical factors interms of texture’s appeal to human touch. In product design, limited physical factors are available; hence, the proposed method can enable us to design products or textures–in shops or amusement centers, for example–that appeal tohuman touch.
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