This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/TOH.2014.2321575, IEEE Transactions on Haptics
1

Haptic Invitation of Textures: Perceptually
Prominent Properties of Materials Determine
Human Touch Motions
Hikaru Nagano, Shogo Okamoto, and Yoji Yamada, Member, IEEE
Abstract—In daily life, certain textures and materials invite our touch motions. To seek the nature of such haptic invitation, we
conducted a series of experiments consisting of sensory evaluations and ranking tasks for 36 materials to ascertain their perceptual
properties and their degrees of haptic invitation. In addition, we recorded the human touch motions elicited by these materials. The
results showed high degrees of haptic invitation for materials with perceptually prominent textures, which indicates that such textures
frequently invite human touch motions. We also developed a Bayesian network model that represented the probabilistic relationships
between invited touch motions and the properties of textures. The model substantiated the observation that different types of textural
prominence led to different types of invited touch motions. These results collectively suggest that materials with prominent textures
frequently encourage humans to touch them, using appropriate or specified touch motions.
Index Terms—Textural prominence, touch behavior, textural perception, material property.

F

1

I NTRODUCTIONERTAIN textures and materials in daily life invite humantouch motions. Some materials are more likely to elicit
this phenomenon, which we refer to as haptic invitation. Some
examples include the indented surfaces of embossed papers,
smooth-textured surfaces of furs, and high elastic siliconrubbers. Haptic invitation has the potential to be used forvarious purposes given that many researchers have reported
favorable effects of touch on consumers’ intentions when
making purchases [1], evaluating products [2], and evaluating
brands [3]. Furthermore, haptic invitation can be applied to
products that have the potential to attract a consumer’s touch,poster advertisements that seek to grab the attention of apassersby, and art works that seek to motivate the audience
to touch them. Thus, studies of haptic invitation were recently
conducted to investigate various materials and objects that are
conducive to touch [4], [5].
Several researchers have developed methods for using materials and objects that frequently invite human touch. Nagano
et al. [4], [6] revealed that linear combinations of the visualproperties of materials described degrees of haptic invitation
with a degree of accuracy ranging 70–80%. They found that
although glossiness and surface shapes strongly affect haptic
invitation, surface colors hardly affect it at all. Klatzky andPeck [5] reported that simple objects were more inviting tohuman touch than complex objects, and that humans wanted
to touch moderately textured objects more than rough objects.
Although methods have been investigated to design materials’ haptic invitation by adopting effective properties, thequestion remains of how people actually touch such materials.

C• The authors are with the Department of Mechanical Science and Engineering, Nagoya University, Nagoya, Japan.
E-mail: see http://www.mech.nagoya-u.ac.jp/asi/en
Manuscript received April xx, 20xx; revised January xx, 20xx.

Invited touch motions are likely related to the mechanism of
haptic invitation, but these have not yet been investigated.
This question of how people touch materials was partly
evoked by many research findings addressing differencesin touch motions or behaviors. Lederman and Klatzky [7]reported that hand movements varied during haptic object
exploration, depending on the desired properties of objects,
such as heaviness and hardness. In addition, hand dynamics
and contact forces for these prototypical hand movements were
quantitatively investigated by Jansen et al. [8]. Giboreau etal. [9] qualitatively assessed that textile experts used different
types of gestures to evaluate different properties of fabrics.
Whereas circular or linear finger movements are used toevaluate rough, relief, and slippery surfaces, localized pressure
applied through fingertips is used to evaluate the softness
and thickness of materials. In addition, other studies showedthat motions or behaviors were related to textural perception.
Gamzu and Ahissar [10] demonstrated that scanning velocities
are adapted to the grating frequency. Kaim et al. [11] reported
that both contact forces and velocities are affected by thepliability of objects. The relationships between the perceived
roughness of surfaces and contact forces [12], [13] and thosebetween surface friction and tangential forces [14] have also
been investigated. However, the relationships between textureinvited touch motions and textural properties have not beenthoroughly investigated. Finally, the present authors postulated
the idea that invited touch motions vary for different typesof materials [15]. We conjecture that this difference in touch
motions may be a clue to elucidating some of the mechanismsof haptic invitation. Therefore, in this study, we sought toexperimentally investigate one possible mechanism of haptic
invitation based on the following two propositions.
Proposition 1: Materials with perceptually prominenttextures frequently invite human touch motions. Human
touch motions may be effectively invited by materials with

1939-1412 (c) 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/TOH.2014.2321575, IEEE Transactions on Haptics
2

2 mm

2 mm

2 mm

2 mm

2 mm

Magnoliawood

Oak wood

Sapelli wood

Cork board

Fine woven
straw

2 mm

2 mm

2 mm

2 mm

2 mm

Short hair
fake furLong hair
fake fur

Fake boa

Fake cowhide Fake alligator
hide

2 mm

2 mm

2 mm

2 mm

Mirror plate

Iridescent
sheet

2 mm

2 mm

2 mm

Crumpled
paper

Corrugatedpaper

Wall paper

Satin

2 mm

Denim

Pyramidrubber matting

2 mm

Fine
Japanese paper

2 mm

Aluminum foil
cloth

2 mm

2 mm

2 mm

2 mm

Woven linen

Wovenrush grass

Soft fake fur

2 mm

2 mm

2 mm

2 mm

Fake woven
leather

Fake suede

2 mm

2 mm

2 mm

2 mm

Mosaic tile

Glossyvinyl sheet

Glosslessvinyl sheet

Cotton cloth

2 mm

2 mm

2 mm

2 mm

Perforated
aluminumWoven
wire mesh

Coarse woven
straw

Artificial grassSponge

Felt

Towel

Fig. 1. Thirty-six materials

prominence in textures such as roughness, hardness, andglossiness. Here, textural prominence indicates that one perceptual property is more conspicuous than other properties (seealso sec. 3.1). We conducted sensory evaluations to quantify
the textural properties of materials; then, from the quantified
properties we obtained, we calculated their degrees of textural
prominence. In addition, we conducted ranking tasks formaterials to specify their degrees of haptic invitation. To test
proposition 1, we compared the degrees of textural prominencewith the degrees of haptic invitation.
Proposition 2: Invited touch motions are influenced bydifferent types of prominence in textures. An example ofthis is that, apparently, soft materials may frequently invite
pushing, whereas other materials do not. Different types oftextural prominence may affect which type of touch motions
are likely to be induced, such as stroking or rubbing. To
test proposition 2, we observed the different touch motions
invited by different materials and investigated the relationships
between invited touch motions and textural properties.

2

E XPERIMENTS

Three experiments were performed, in which 14 volunteers (nine males and five females, aged 19–24 years, withno history of deficits in tactile processing, including 10 participants with corrected vision) participated in all three exper-

iments. In experiment 1, the participants visually evaluatedthe textural properties of materials that were consecutively
presented to them by using a semantic differential method. In
experiment 2, the degrees of haptic invitation of materials were
calculated based on ranking methods. In experiment 3, theparticipants touched materials that strongly invited their touch.
Human touch motions were measured using a camera and a
six-axis dynamic force sensor. All experimental procedures,
including the recruitment of participants, were approved by
the Ethics Committee of the Graduate School of Engineeringat Nagoya University.

2.1

Stimulus

Thirty-six materials, which are shown in Fig. 1, were usedin this study. These were selected from 40 materials throughpreliminary experiments. In these experiments, five volunteers (five males, aged 21–24 years) ranked the 40 materials,
and the degrees of haptic invitation were calculated as shownin supplementary figure 1. We excluded four materials with thehighest standard deviations of the degree of haptic invitation.
The remaining 36 materials, with relatively small individual
differences, were selected for the main experiment. A wide
variety of materials such as woods, papers, furs, and fabrics
were included.

1939-1412 (c) 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/TOH.2014.2321575, IEEE Transactions on Haptics
3

2.2 Experiment 1: Sensory evaluationmaterial specified by j ( j =1–36) was determined as follows:

The participants evaluated the materials, without touchingthem by using a seven-point scale in terms of six bipolar
adjective pairs: “rough-smooth,” “uneven-flat,” “hard-soft,”“warm-cold,” “sticky-slippery,” and “glossy-glossless,” which
we designate as percieved textures of the physical propertiesof material surfaces, including surface hardness and warmness.
The evaluation forms provided these terms in both English andJapanese. Five adjective pairs without “glossy-glossless” wereselected based on their commonality with regard to the tactile
dimensionality of textures [16]. In addition to these five pairs,
glossiness may be an important factor in the visual perceptionof textures. We instructed the participants that “uneven” and“rough” meant roughness perceived without and with lateral
hand motions, respectively.
As shown in Fig. 2a, a large white plate with a
140 mm × 140 mm square window was placed on a material
so that the participants could see only the surfaces and not thesides of the materials. At the beginning of each experiment,
we specified the positions of the material and the participant’shead. The distance and angle between the head and material
were set to be 600 mm and 45 degrees, respectively. In
addition, we instructed the participants to maintain their head
positions during experiments to retain their relative positions
between head and material. The materials and adjective pairs
were presented to each participant in random order.
Ratings from 1 to 7 were assigned on a seven-point adjective
scale that was used to perform measurements in the experiments. These ratings were normalized for a single participant
such that the mean and standard deviation became 0 and 1,
respectively. In supplementary table 1, the mean ratings of thesix textural properties and individual varieties are listed.
The degrees of textural prominence were calculated fromthe absolute values of six textural properties. Smaller absolutevalues of the textural property indicate that the materials
evaluated are neutral in terms of that property. In contrast,
larger absolute values indicate that materials take extreme
values on that bipolar axis. The degree of prominence for a

Side view
Material

140 mm2.3

Experiment 2: Ranking 36 Materials

We calculated the degrees of haptic invitation of the materials
on the basis of the ranking methods that were introduced ina previous study [4]. The participants ranked 36 materials
in order of the extent to which they felt inclined to touchthem. The materials were simultaneously presented to eachparticipant. In the earlier study [4], the majority of participants
answered that it was easier to choose materials with higher andlower degrees of haptic invitation and that it was not easy torank the other neutral materials. These opinions suggested thatthe ranks of haptic invitation should not be treated as equalinterval scales. We then converted the ranks to interval scales
as expected values of a standard normal distribution [17].
These values represent the degrees of haptic invitation of thematerials. The degree of the ith-ranked material was assigned
as the expected value of the ith largest observation in a sampleof size n from a standard normal population. The degree of
the ith-ranked material was determined as follows:∫ ∞n!
E(xi|n ) =x · a(x) · b(x) · ϕ (x)dx(2)(n − i)!(i − 1)! −∞
[
]i−1
(3)
a(x) = 12 − Φ(x)[1]n−ib(x) = 2 + Φ(x)
(4)
(
)
1
ϕ (x) = √ exp − 12 x2
(5)
2π
∫

b) Materials displayed in experiment 3
White plastic plate

140 mm
140 mm 20 mm

Fig. 2. Presentation method of materialsx0

ϕ (z)dz.

(6)

The degrees of haptic invitation for a given texture wereaveraged across the participants.
2.4

150 mm(1)

where x jk is the evaluation value of an adjective pair specified
by k (k =1–6) for material j. The degree of prominence
was assigned as the difference between the maximum value
among the six evaluation values and the averaged value ofthe remaining five evaluation values. Thus, the degree ofprominence represents the extent to which the most impressive
property among six properties is prominent, compared withthe other five properties. In supplementary table 2, the mean
prominence values of materials and their standard deviations
among the participants are listed.

Φ(x) =a) Material displayed in experiment 1
White plastic plate

140 mmPr j = max(|x jk |, k = 1, 2, · · · , 6)−ave(|x jk |, s.t.|x jk | ̸= max(|x jk |)).

Experiment 3: Observation of touch motions

Among the three materials offered to them, participants
touched the one material that they felt most strongly invited
their touch. The materials were placed under a white plastic
plate, as shown in Fig. 2b. If only one material was shown ineach experiment, the participant would only be able to touchthat one. Therefore, to avoid this unnatural situation, threematerials were shown to the participants.
We instructed the participants to close their eyes before theexperimenter arranged the three stimuli. They were instructed
to “keep your eyes closed until you hear a beep sound,

1939-1412 (c) 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/TOH.2014.2321575, IEEE Transactions on Haptics
4

CameraX
Z

Metal plate

Side view
Material

White plastic plate

the materials were placed.

3 P ROPOSITION 1: P ROMINENCE I NVITES
TOUCH
To test proposition 1, which posits that materials with prominent textures frequently invite human touch motions, twoanalyses were conducted. In the first analysis, degrees of haptic
invitation were compared with degrees of visual prominencein textures. These degrees of prominence were based on the
results of experiment 1. In the second analysis, materials wereclassified into two groups (touched and untouched materials)
based on the results of experiment 3. A comparison between
the two groups was then performed, in terms of degrees ofprominence.
3.1 First analysis: Correlation between textural
prominence and haptic invitation
The degrees of both textural prominence and haptic invitation
were averaged across the participants and then compared, as
shown in Fig. 4. The correlation coefficient was 0.54 (t(34) =3.7, p = 7.6 × 10−4 ). Materials with higher degrees of prominence more effectively invited human touch. This finding
supports proposition 1. However, because our finding is simply
correlation and its coefficient is not substantially high, weshould remember the possibility that the relation between
prominence and haptic invitation is mediated by other factors.
Some of the six ratings may have been correlated witheach other. In such a case, our definition of prominence value
ineffectively specifies the textural prominence. To organize a
set of uncorrelated ratings, we applied a factor analysis tothe six ratings. As a result, as listed in Table 1, the texturalproperties were effectively represented by three factors. The
degrees of textural prominence that were calculated fromthese uncorrelated variable sets were well correlated with
degrees of haptic invitation, with a correlation coefficient

1.5

Degree of prominence

after which freely touch the one you feel most inclined totouch,” in which a sound cue became a reference for theparticipants’ response times. Their hand movements were not
restricted at all by instructions; however, most participants
touched materials with their index finger. This was clearlydone because they knew that a camera captured the motion of
their index finger with an attached marker.
Combinations of three materials were determined based on
the average ranks of haptic invitation. One of three materials
was randomly selected from among the 12 materials thatexhibited the highest degrees of haptic invitation among 36
materials. At random, a second material was selected fromthe lowest 12 materials, and a third was selected from theremaining 12 materials. Hence, 12 combinations were presented to each participant in random order. Then, using the
24 materials that had not been touched in the first round,eight more combinations were determined through a processsimilar to that described above, and were then presented
to the participant. In total, each participant thus received aunique set of 20 combinations (12 and eight in the firstand second rounds, respectively). The material sets for the
first participant were only based on his or her ranking ofmaterials and those of the preliminary experiments involving
five volunteers (five males, aged 21–24 years). The degreesof haptic invitation were updated every time new degrees
were obtained in experiment 2. The ranking in the preliminary
experiment was not exactly same as the final rankings in themain experiment. For example, the rank of fine woven straw
changed most: from ninth to 23rd.
Human touch motions to materials were measured as shownin Fig. 3. The tip position of each participant’s index finger wasmeasured using a camera (Firefly MV, Point Grey ResearchInc., Richmond, Canada, 640 × 480 pix, 60 fps). This position
was detected from a red marker that was fixed on the metacarpophalangeal joint of the index finger. The contact forces were
measured using a six-axis dynamic force sensor (MINI 2/10,
BL AUTOTEC. LTD., Kobe, Japan) at a sampling frequencyof 60 Hz. The sensor was fixed under a metal plate on which

1.2

Corrugated paper

0.9

Mirror plate

Short hair fake fur

0.6
0.3

Soft fake fur
Fake sueder = 0.54

0-1.5 -1 -0.5

MarkerX

Six-axis dynamic force sensor

Y
Z
Materials

Fig. 3. Measurement of touch motions to textures

0

0.5

1

1.5

Degree of haptic invitation
Fig. 4. Correlation between textural prominence anddegree of haptic invitation. The solid line is a regressionline.

1939-1412 (c) 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/TOH.2014.2321575, IEEE Transactions on Haptics
5

3.2 Second analysis: Comparison between degrees
of prominence of touched versus untouched materials
The stimuli were divided into groups of touched and untouched materials. Because each of the 14 participants took
part in the 20 trials in experiment 3, the touched and untouched
TABLE 1Results of factor analysis: Factor loadings of six adjective
pairs

Micro roughness
Macro roughness
Hardness
Warmness
Friction
GlossinessContributing rate
Cumulative
contributing rate

Number of touches

20
15
10
5
0

Factor 1
0.96
0.73
−0.11
0.10
0.78
−0.21
0.56

Factor 2
0.00
0.06
−0.83
0.94
0.35
−0.48
0.27

Factor 3
−0.26
0.06
0.15
−0.33
−0.30
0.85
0.09

0.56

0.83

0.92

LHF
FWL
CoP PRMSHF
CWS SpGlVS WPSFIS
MP WL
FAH AGFJPFWSMW CC D r = 0.75
-1.5 -1 -0.5 0 0.5 1 1.5
Degree of haptic invitation

Fig. 5. Relationships between the number of touches anddegree of haptic invitation. LHF: Long hair fake fur, FWL:
Fake woven leather, PRM: Pyramid rubber matting, CoP:
Corrugated paper, SHF: Short hair fake fur, Sp: Sponge,
CWS: Coarse woven straw, WP: Wall paper, GlVS: Glossless vinyl sheet fur, SF: Soft fake fur, IS: Iridescent paper,
AG: Artificial grass, FAH: Fake alligator hide, WL: Woven
linen, MP: Mirror plate, FJP: Fine Japanese paper, FWS:
Fine woven straw, D: Denim, CC: Cotton cloth, MW:
Magnolia wood.

groups contained 280 and 224 samples, respectively. Fig. 5
shows the number of touches for all materials in comparison
with the degree of haptic invitation specified in experiment 2.
These numbers are well correlated with the degrees of haptic
invitation. The two groups of touched and untouched materials were compared in terms of their average degrees ofprominence. As shown in Fig. 6, the degrees of prominenceof touched materials were significantly greater than those of
untouched materials (t(502) = 6.7, p = 6.3 × 10−11 ). In otherwords, the materials that invited the participants’ touch had
more prominent textures; this finding is also consistent withproposition 1.

4 P ROPOSITION 2: TOUCH M OTION D EPENDS
ON P ROMINENCE
To test proposition 2, which posits that invited touch motions
are influenced by different types of textural prominence, weconstructed a Bayesian network model that offers conditional
probabilities between the invited touch motions and the properties of textures. The model simply connects the simultaneous
occurrence of events, which enabled us to quantitatively analyze statistical relationships while negating the need to discuss
any mathematical relationships between them. Using touch
motions as evidence, the model estimated the properties oftextures that frequently invite such motions. From the resultsof these estimations, we were able to address proposition 2.
4.1 Construction of a probabilistic model
From the results of experiments 1 and 3, we extracted nodes
that constructed a probabilistic network. There were three
types of nodes: properties of textures, touch motions, andtouch mode. A touch mode node is a meta node that represents types of touch behaviors. This node was determinedfrom the ensemble of touch motions. The primitiveness oftouch motions makes them helpful to interpret the physicalinteractions between touch and materials. In contrast, touchmodes are more intuitive in the exchange of a lack of detailed
information about motion dynamics. These nodes were all
discrete variables, and are described in detail below.

Degree of prominenceof 0.63 (t(34) = 4.8, p = 3.5 × 10−6 ). The visual prominence
values calculated by factors specified for a material set yieldedhigher correlation with the degrees of haptic invitation thanthose the general prominence values calculated by the six
textural properties. The resultant structure of factor analysis
depends on the stimuli set. With different material sets, wemay be led to other factorial structures. In the latter analysis,
to maintain generality, we used six textural properties rather
than the three factors acquired here.

1.2
0.9
0.6
0.3
0

Untouched Touched
Materials Materials

Fig. 6. Degrees of prominence of touched and untouchedmaterials. The error bar and asterisk symbols *** indicate
the standard deviation of prominence and significancelevel of p < 0.001, respectively.

1939-1412 (c) 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/TOH.2014.2321575, IEEE Transactions on Haptics
6

Micro
Roughness

Macro
Roughness

Hardness

Warmness

Friction

Glossiness

Textural properties

Touch motions

Touch mode

Fig. 7. Bayesian network model connecting textural properties, invited touch motions, and touch mode
4.1.1 Properties of textures
We created six nodes based on the adjective ratings “roughsmooth,” “uneven-flat,” “hard-soft,” “warm-cold,” “stickyslippery,” and “glossy-glossless,” which were taken from experiment 1. These ratings were quantified into three levels
with two boundaries (−0.43, 0.43) that divided the occurrence
probability equally into three. For example, for one participant,the standardized “rough-smooth” rating of soft fake fur was−1.1 (+: rough; −: smooth), which was lower than −0.43.
Therefore, the micro roughness node was labeled “smooth.”
The six adjective ratings were quantified in the following
manner.
Micro Roughness: Smooth, moderate, or rough.
Macro Roughness: Flat, moderate, or uneven.
Hardness: Soft, moderate, or hard.
Warmness: Cold, moderate, or warm.
Friction: Slippery, moderate, or sticky.
Glossiness: Glossless, moderate, or glossy.
4.1.2 Touch motions
Human touch motions were measured using a camera and aforce sensor. The measured data were normalized for eachparticipant, and we produced 11 nodes from the normalized
data. As in the case of the textural properties, the following
nodes were all discretized into their qualitative states with twoboundaries (−0.43, 0.43).
Time before touch: Short, moderate, or long. The time before
touch node was determined from the period between the timeat which a sound cue was presented to a participant, and thetime at which the force signal began to change. For example,
for one participant, the normalized value of time before touch
for cork board was 2.2 (+: long; −: short), which was higher
than 0.43. Therefore, the time before touch node of cork board
was assigned the label “long.”

Contact period: Short, moderate, or long. The contact
period node was determined as the period during which amaterial was being touched.
Maximum normal force: Weak, moderate, or strong. The
maximum normal force node was determined from the maximum Z-axial force exerted while the participant was in contact
with a material.
Average normal force: Weak, moderate, or strong. The
average Z-axial force during contact determined the average
normal force node.
Maximum tangential force: Weak, moderate, or strong. The
maximum tangential force node was the maximum resultant
force of the X and Y-axial forces applied to a material.
Average tangential force: Weak, moderate, or strong. The
average X-Y resultant force during contact determined theaverage tangential force node.

TABLE 2Average touch motion values of each touch mode. The
asterisk symbols ∗ ∗ ∗ indicate the significance level ofp < 0.001

Touch motion
Time before touch
Contact period
Max. normal force
Ave. normal force
Max. tangential force
Ave. tangential force
Max. force rate
Ave. force rate
Max. hand velocity
Ave. hand velocity
Travel distance

Touch mode
Push
Rub
Stroke Soft touch
−0.02
0.13
−0.08
0.06
0.10
0.11
0.70***−0.85***
1.74***−0.06−0.11−0.63***
1.83***−0.07−0.16−0.60***
0.74*** 1.35***−0.03−0.70***
0.27
1.65***−0.02−0.58***
1.46***−0.45*** 0.00
−0.51***
1.80***−0.62***−0.20***−0.39***−0.53***−0.02
0.35***−0.14−0.94***−0.31
0.25***
0.24−0.53***−0.18
0.77***−0.56***

1939-1412 (c) 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/TOH.2014.2321575, IEEE Transactions on Haptics
7

Maximum force rate: Low, moderate, or high. We defined
the ratio of the Z-axial force to the X-Y resultant force as
the force rate. This node was the maximum value of the force
rates while the participant was in contact with the material.
Average force rate: Low, moderate, or high. This node is
the average force rate.
Maximum hand velocity: Slow, moderate, or fast. We calculated the participants’ hand velocities from the time series
data of their hand positions that were captured on camera. The
maximum value of hand velocities during participants’ contact
with a material determined the maximum hand velocity node.
Average hand velocity: Slow, moderate, or fast. This nodeis the average hand velocity while a participant is in contact
with a material.
Travel distance: Short, moderate, or long. The travel distance node was determined from the total distance traveled bya hand while it was in contact with a material.
4.1.3 Touch mode
We defined the touch mode node as being representative ofthe typical modes of invited touch motions. Using a cluster
analysis that employed Ward’s method [18], we classified the
280 trials in experiment 3 into a number of groups on the basisof the 11 touch motions, as described above. The Euclidean
distances between trials were calculated from the values of
the 11 touch motions. Ward’s method adopts these Euclidean
distances as a dissimilarity matrix and merges the two trials
with the minimum dissimilarity into a new cluster. Repetitive
fusions decreased the number of clusters into four. Table 2 lists
the average values of touch motions for each touch mode. The
figures with asterisk symbols are significantly different fromzero, and represent the feature of the mode.
Touch mode: Push, rub, stroke, or soft touch. Based on
the features described in Table 2, the four types of touchmode were assigned as push, rub, stroke, and soft touch.
For example, the mode with strong normal forces and slow
hand velocities was called “push.” We next describe someexamples of touch motions that characterize these four modes:
push (max. and ave. normal force: strong, max. and ave. handvelocity: slow), rub (max. and ave. tangential force: strong,max. and ave. force rate: low), stroke (max. and ave. handvelocity: fast, travel distance: long), and soft touch (max. andave. normal force: weak, travel distance: short).
4.1.4 Model structure
The Bayesian network structure was determined from the discretized data of six properties, touch motions, and touch mode,by using a greedy search algorithm with Akaike Information
Criterion [19] as the evaluation score. The arcs of the network
were restricted such that they did not mutually connect texturalproperties. The same rule was applied to touch motions. In
addition, the arcs between textural properties and touch mode
were not permitted. The constructed model is presented inFig. 7.
4.2 Probabilistic estimation
To test proposition 2, we investigated the probabilistic relationships between the textural properties and the touch motions

or mode. The probabilities of the textural properties were
estimated using the touch motions and mode as evidence.
As shown in Fig. 7, two connected nodes have a statistical
relationship, and potentially a causal relationship. We do
not mention all of these connections, but we focus on theconnections with the largest probabilistic deviations for eachtextural property. Hence, we describe seven connections asfollows.
4.2.1 Micro roughness (rough/smooth) and travel distance
To investigate the relationships between the travel distance andmicro roughness nodes, we estimated the probabilities of the
micro roughness node from the travel distance node that wasgiven as evidence. The estimation results are listed in Table 3.
Cells with probabilities of 0.45 or higher are highlighted ingray. This value is the border of the statistically significant
interval (χ 2 (2) = 5.99, p = 0.05, N = 93) in the occurrence
number of events between the probability set (0.453, 0.273,
and 0.273) and the expected set (0.333, 0.333, and 0.333). The
probabilities of micro roughness being rough were the highest,
at 0.62 and 0.55, when the travel distance nodes were longand moderate, respectively. When the participants experienceapparently rough materials, it is reasonable for them to strokethese materials broadly to effectively produce vibrotactile cues.
Such skin vibration caused by exploratory movements plays
a significant role in the perception of micro roughness [20],[21].
4.2.2 Macro roughness (uneven / flat) and maximum forcerate
We estimated the probabilities of macro roughness by using
the maximum force rate as evidence. As shown in Table 4,
when the maximum force rate node was low, the probability of
TABLE 3
Probabilities of micro roughness when travel distance isgiven as evidence

Estimated result:
Micro roughness
SmoothMiddleRoughEvidence:
Travel distance
Short Middle Long
0.35
0.26
0.24
0.25
0.19
0.14
0.40
0.55
0.62

TABLE 4
Probabilities of macro roughness when maximum force rate
(normal / tangential forces) is given as evidence

Estimated result:
Macro roughness
Flat
MiddleUnevenEvidence:
Maximum force rate
(normal / tangential forces)
Low Middle High
0.53
0.32
0.25
0.15
0.24
0.17
0.32
0.44
0.58

1939-1412 (c) 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/TOH.2014.2321575, IEEE Transactions on Haptics
8

macro roughness being flat was 0.53. This trend implied that,for flat materials, the normal forces were relatively weaker
than the tangential ones. Flat materials were also accompaniedby large travel distances. Because overly large normal forces
hinder such hand movements, this low force rate is strategic
for the perception of flat surfaces. In addition, given that the
maximum force rate node was high, the network estimated thatthe probability of macro roughness being uneven was 0.58.
Participants tended to touch uneven materials with relatively
stronger normal forces than tangential ones. In the perceptionof macro roughness, lateral hand movements are not necessary
to a great extent, because surface unevenness is perceived as
the unevenness of pressure sensations rather than as vibrotactile information [20], [22]. Therefore, touch motions with highnormal to tangential force ratios are potentially appropriate forperceiving the surface unevenness of materials.
4.2.3 Hardness (hard/soft) and average force rate
As shown in Table 5, given the state of the average force rate
node as evidence, the network estimated the probabilities of
the hardness node. When the average force rate node washigh, the probability of hardness being soft was the highest,
at 0.61. In other words, the normal forces for apparently soft
materials were relatively stronger than the tangential forces.
The perception of softness or elasticity seems to be related to
information received from the contact area of the finger pads,
such as spatial pressure distributions, and the relationships
between the force and the contact area or the force and thedisplacement that takes place during pushing by fingers [23],[24], [25]. Therefore, in the experience of material softness,touch motions with a high average force rate were appropriate.

TABLE 5
Probabilities of hardness when average force rate (normal /tangential forces) is given as evidence

Estimated result:
HardnessSoft
Moderate
Hard

Evidence:
Average force rate
(normal / tangential forces)
Low Middle
High
0.33
0.44
0.61
0.25
0.21
0.19
0.42
0.35
0.19

TABLE 6
Probabilities of warmness when average normal force isgiven as evidence

Estimated result:
Warmness
Cold
Moderate
Warm

Evidence:
Average normal force
Weak Middle
Strong
0.39
0.25
0.17
0.30
0.33
0.35
0.31
0.42
0.48

4.2.4 Warmness (warm / cold) and average normal force
Table 6 lists the probabilities of the warmness node when
the average normal force is given as evidence. When the
average normal force node was strong, the probability ofwarmness being warm was the highest, at 0.48. In otherwords, participants tended to use strong normal forces for
touching apparently warm materials. Touch motions such as
pushing increase the contact area between fingers and material,
which allows human to effectively perceive surface warmness
because a large contact area enhances heat transfer. This
strategy is especially effective for warm-looking materials, andnot as effective for cold-looking ones. This is because when
the potential difference between the temperature of the skinand that of the material is large enough for humans to detect,a measure that fosters heat transfer is not required. On theother hand, because the heat transfer between the skin anda warm material is limited (here, being warm means that its
temperature is close to that of human skin), the reduction inthermal resistance owing to higher pressure and the resulting
large contact area is effective for detecting subtle differencesin temperature.
4.2.5 Friction (sticky / slippery) and maximum normalforce
We estimated the probabilities of the friction node from the
maximum normal force node that was given as evidence. As
shown in Table 7, when the maximum normal force node
was strong, the probability of friction being sticky was 0.56.
Furthermore, given that the maximum normal force node was
weak, the network estimated that the probability of friction
being slippery was 0.55. When participants touched slipperymaterials, they tended to use weak normal forces. The use ofstrong normal forces for sticky materials allowed participants

TABLE 7
Probabilities of friction when maximum normal force isgiven as evidence

Estimated result:
Friction
Slippery
Moderate
StickyEvidence:
Maximum normal force
Weak Middle Strong
0.55
0.31
0.27
0.12
0.32
0.17
0.33
0.37
0.56

TABLE 8Probabilities of glossiness when average tangential force isgiven as evidence

Estimated result:
Glossiness
GlosslessModerate
GlossyEvidence:
Average tangential force
Weak Middle Strong
0.60
0.44
0.39
0.12
0.13
0.06
0.28
0.43
0.55

1939-1412 (c) 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/TOH.2014.2321575, IEEE Transactions on Haptics
9

to experience their large frictional properties, and weak normal
forces were appropriate for them to effectively experience thesurface slipperiness of materials. These findings are supported
by previous studies [26], [27], which reported the relationships
between the shear deformations caused by tangential forces
applied to finger pads and the perception of friction.
4.2.6 Glossiness (glossy / glossless) and average tangential force
Table 8 lists the probabilities of the glossiness node when
the average tangential force is given as evidence. When the
average tangential force node was weak, the probability ofglossiness being glossless was the highest, at 0.60. On theother hand, when the average tangential force node was strong,the probability of the textures being glossy was 0.55. Because
glossy materials tend to look frictional, explanations similar
to those given in sec. 4.2.5 are also valid here. For glossy
and apparently frictional materials, the participants touched
them with large tangential forces. Such forces cause substantial
stretching of the skin, which enables humans to experience alarge degree of friction.
4.2.7 Touch mode and six properties of textures
The probabilistic relationships between the six properties andtouch mode are listed in Table 9. When the touch mode node
is “push,” the probability of hardness being soft is 0.55, andthat of it being hard is 0.18. Apparently soft materials arelikely to invite the push mode, whereas hard materials arenot. On the other hand, the rub mode is likely to be invited byhard materials. Stroke and soft touch modes are not strongly
related to the apparent softness of materials (hardness). As
shown in Table 9, each textural property, except for friction,is probabilistically linked with touch mode.

TABLE 9Probabilities of six properties of textures when touchmode is given as evidence

Estimated result:
Micro roughness
Estimated result:
Macro roughness
Estimated result:
Hardness
Estimated result:
Warmness
Estimated result:
Friction
Estimated result:
Glossiness

SmoothModerate
Rough
Flat
Moderate
UnevenSoft
Moderate
Hard
Cold
Moderate
Warm
Slippery
Moderate
StickyGlosslessModerate
Glossy

Push
0.28
0.30
0.42
0.23
0.22
0.55
0.55
0.27
0.18
0.15
0.40
0.45
0.28
0.28
0.44
0.43
0.32
0.25

Evidence:
Touch mode
Rub
Stroke0.33 0.20
0.16 0.17
0.51 0.63
0.42 0.44
0.20 0.14
0.38 0.42
0.25 0.39
0.18 0.30
0.57 0.31
0.43 0.33
0.24 0.32
0.33 0.35
0.42 0.37
0.20 0.23
0.38 0.40
0.33 0.54
0.12 0.14
0.55 0.32

Soft touch
0.38
0.20
0.42
0.44
0.17
0.39
0.43
0.20
0.37
0.45
0.27
0.28
0.41
0.19
0.40
0.44
0.33
0.23

4.2.8 Multiple evidences did not specify textural properties
We estimated the probabilities of textural properties given multiple touch motions as evidence, in addition to the estimations
from single evidence as described above. It is of interest to
us to determine whether the types of textures can be fully
identified from the observed touch motions. Unfortunately,
these probabilities were not significantly higher than thoseinferred by single evidence, which indicates that the textures
are unlikely to be fully identified by the motion parameters
used in the present study. However, the established network is
still valuable for discussing the trends of textures and touch
motions with moderate probabilistic deviations.
4.3

Probabilistic estimations support proposition 2

Table 10 summarizes Tables 3–9 and lists the likely connections between the touch mode and textural properties. As listed
in the table, the touch mode is probabilistically connectedto the prominent properties of texture. In addition, suchconnections are not accidental but are intuitively reasonable interms of the previously described human perceptual strategies.
These probabilistic trends fairly indicate that certain prominenttextures tend to invite certain hand movements.
The push mode is frequently invited by apparently uneven,soft, or warm materials. This mode is represented by strong
normal forces and small travel distances. As described earlier,such a motion is appropriate for testing these textural properties. A pushing motion allows us to experience a material’s
spatial unevenness within the contact area, its softness via thepressure of our finger pads, and its warmness via the heattransfer that is enhanced by the increased contact area. Further,lateral hand movements are dispensable to the experience ofunevenness, softness, and warmness.
The rub and stroke modes are often used for exploringrough materials. This is intuitively reasonable because thesemodes, which include lateral hand movements, produce theinformation needed to decode surface roughness. Meanwhile,
the rub mode is often invited by glossy materials, whereas
the stroke mode is invited by glossless ones. One of the

TABLE 10
Touch modes and textural properties that are likely toinvite touch motions. The representative materials in thetable satisfy all of the listed textural properties. For
example, the average ratings of fake woven leather were
uneven, soft, and warm.
Touch mode

Property of texture

Representative materials

Push

Macro roughness: uneven (55%),
Hardness: soft (55%)
Warmness: warm (45%)
Micro roughness: rough (51%),
Hardness: hard (57%),
Glossiness: glossy (55%)
Micro roughness: rough (63%),
Glossiness: glossless (54%)
Warmness: cold (45%)

Fake woven leather,SpongeRub

StrokeSoft touch

Perforated aluminum,
Woven wire mesh
Artificial grass,
Coarse woven straw
Satin

1939-1412 (c) 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/TOH.2014.2321575, IEEE Transactions on Haptics
10

major differences between these two touch modes lies in themagnitude of the tangential forces. The rub mode involves
larger tangential forces than the stroke mode. Tangential forces
are linked with the perception of friction. The rub mode isbetter suited to glossy materials than the stroke mode because
the large tangential forces of the rub mode are more relevantto the experience of potentially high coefficients of friction.
The soft touch mode has a probabilistic link with apparently
cold materials. This mode has weak normal forces and shortcontact periods. Such a motion is sufficient for heat transfer
between a person’s fingers and a cold material, a circumstancein which the temperature difference is large, and this heatcan thus be detected by the fingers’ temperature-sensitive
receptors. The soft touch mode is an economic motion for
feeling the coldness of materials.
The above mentioned results demonstrate that types ofprominent textures influence invited touch motions, a findingthat supports proposition 2.

5

C ONCLUSION

We conducted a series of experiments to investigate the potential mechanism of haptic invitation, which is a phenomenon inwhich the textures of materials invite human touch motions.
The many analyses of the results positively indicated two
propositions underlying haptic invitation. First, materials withvisually prominent textures frequently invite human touch
motions. Second, invited touch motions are influenced by thetypes of prominent textures.
With regard to the first proposition, a positive correlation
coefficient of 0.54 was observed between the degrees of
haptic invitation and textural prominence. This coefficientincreased to 0.63 in the case that the prominence values
were calculated based on the factorial values that integrated
the correlated textural properties. Furthermore, the degrees oftextural prominence of materials that invited touch motions
in experiment 3 were higher than those of materials thatwere not touched. These results support the first proposition;
namely, that materials with a prominent texture effectively
invite human touch. However, the coefficient values were notsubstantially large and the correlation always indirectly linked
two events. The prominence may be one contribution, andother factors may also pertain to haptic invitation.
With regard to our second proposition, we constructeda Bayesian network model that represented the probabilistic relationships between the invited touch motions and theproperties of textures. The model corroborated the idea that
invited touch motions and touch modes vary, depending on
the different types of prominence in textures.
As described above, perceptual prominence in textures tends
to invite human touch motions, and types of prominent textures
are likely to invite appropriate touch motions. Haptic invitation
increases the probability that people will make contact with
textures as haptic inputs, which can be sensed through economic motions for textures, unlike passive visual or auditory
stimuli. We interpret the haptic invitation of material as aphenomenon in which the textural prominence of a material
encourages us to feel it. One rational role of this phenomenon

is to maintain the system whereby individuals recognize tactile
textures, because the increase in the probability of touchingprominent textures may be instrumental in activating humanperceptual systems.

ACKNOWLEDGMENT
This work was in part supported by MEXT Kakenhi 24700192
and Shitsukan (23135514 and 25135717).

R EFERENCES[1]

J. Peck and T. L. Childers, “To have and to hold: The influence of haptic
information on product judgments,” Journal of Marketing, vol. 67, pp.
35–48, 2003.
[2] B. Grohmann, E. R. Spangenberg, and D. E. Sprott, “The influence oftactile input on the evaluation of retail product offerings,” Journal of
Retailing, vol. 83, no. 2, pp. 237–245, 2007.
[3] S. Littel and U. R. Orth, “Effects of package visuals and haptics onbrand evaluations,” European Journal of Marketing, vol. 47, no. 1/2,
pp. 198–217, 2013.
[4] H. Nagano, S. Okamoto, and Y. Yamada, “Physical and sensory factors of textures that appeal to human touch,” Proceedings of the 4thInternational Conference on Human System Interactions, pp. 324–329,
2011.
[5] R. L. Klatzky and J. Peck, “Please touch: Object properties that invite
touch,” IEEE Transactions on Haptics, vol. 5, no. 2, pp. 139–147, 2012.
[6] H. Nagano, S. Okamoto, and Y. Yamada, “Visual and sensory propertiesof textures that appeal to human touch,” International Journal of
Affective Engineering, vol. 12, no. 3, pp. 375–384, 2013.
[7] S. J. Lederman and R. L. Klatzky, “Hand movements: A window into
haptic object recognition,” Cognitive Psychology, vol. 19, pp. 342–368,
1987.
[8] S. E. M. Jansen, W. M. B. Tiest, and A. M. L. Kappers, “Identifying
haptic exploratory procedures by analyzing hand dynamics and contact
force,” IEEE Transactions on Haptics, vol. 6, no. 4, pp. 464–472, 2013.
[9] A. Giboreau, S. Navarro, P. Faye, and J. Dumortier, “Sensory evaluationof automotive fabrics: the contribution of categorization tasks and non
verbal information to set-up a descriptive method of tactile properties,”
Food quality and preference, vol. 5, no. 12, pp. 311–322, 2001.
[10] E. Gamzu and E. Ahissar, “Importance of temporal cues for tactile
spatial-frequency discrimination,” The Journal of Neuroscience, vol. 21,
no. 18, pp. 7416–7427, 2001.
[11] L. R. Kaim and Y. Drewing, “Finger force of exploratory movements is
adapted to the compliance of deformable objects,” Proceedings of the2009 IEEE World Haptics Conference, pp. 565–569, 2009.
[12] S. J. Lederman, “Tactile roughness of grooved surfaces: The touchingprocess and effects of macro- and microsurface structure,” Perception &Psychophysics, vol. 16, no. 2, pp. 385–395, 1974.
[13] A. M. Smith, C. E. Chapman, M. Deslandes, J. S. Langlais, and
M. P. Thibodeau, “Role of friction and tangential force variation in thesubjective scaling of tactile roughness,” Experimental Brain Research,
vol. 144, no. 2, pp. 211–223, 2002.
[14] A. M. Smith, G. Gosselin, and B. Houde, “Deployment of fingertip
forces in tactile exploration,” Experimental Brain Research, vol. 147,
no. 2, pp. 209–228, 2002.
[15] H. Nagano, S. Okamoto, and Y. Yamada, “Haptic invitation of textures:
an estimation of human touch motions,” Proceedings of the EuroHaptics
2012, pp. 338–348, 2012.
[16] S. Okamoto, H. Nagano, and Y. Yamada, “Psychophysical dimensionsof tactile perception of textures,” IEEE Transactions on Haptics, vol. 6,
no. 1, pp. 81–93, 2013.
[17] H. L. Harter, “Expected values of normal order statistics,” Biometrika,
vol. 48, pp. 151–165, 1961.
[18] J. H. Ward, “Hierarchical grouping to optimize an objective function,”
Journal of the American statistical association, vol. 58, no. 301, pp.
236–244, 1963.
[19] H. Akaike, “A new look at the statistical model identification,” IEEE
Transactions on Automatic Control, vol. 19, no. 6, pp. 716–723, 1974.
[20] M. Hollins and S. R. Rinser, “Evidence for the duplex theory of tactile
texture perception,” Perception & Psychophysics, vol. 62, no. 4, pp.
695–705, 2000.
[21] S. Bensmaı̈a and M. Hollins, “Pacinian representations of fine surface
texture,” Perception & Psychophysics, vol. 67, no. 5, pp. 842–854, 2005.

1939-1412 (c) 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/TOH.2014.2321575, IEEE Transactions on Haptics
11[22] M. Meenes and M. Zigler, “An experimental study of the perceptions
roughness and smoothness,” The American Journal of Psychology,
vol. 34, pp. 542–549, 1923.
[23] M. A. Srinivasan and R. H. LaMotte, “Tactual discrimination of softness,” Journal of Neurophysiology, vol. 73, no. 1, pp. 88–101, 1995.
[24] K. Fujita and H. Ohmori, “A new softness display interface by dynamic
fingertip contact area control,” Proceedings of the 5th World Multiconference on Systemics Cybernetics and Informatics, pp. 78–82, 2001.
[25] E. P. Scilingo, M. Bianchi, G. Grioli, and A. Bicchi, “Rendering softness:
Integration of kinesthetic and cutaneous information in a haptic device,”
IEEE Transactions on Haptics, vol. 3, no. 2, pp. 109–118, 2010.
[26] W. R. Provancher and N. D. Sylvester, “Fingerpad skin stretch increases
the perception of virtual friction,” IEEE Transactions on Haptics, vol. 2,
no. 4, pp. 212–223, 2009.
[27] Y. Kurita, S. Yonezawa, A. Ikeda, and T. Ogasawara, “Weight andfriction display device by controlling the slip condition of a fingertip,”
Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2127–2132, 2011.

Hikaru NAGANO received B.S. and M.S. degrees in engineering in 2010 and 2012, respectively, from Nagoya University. He is currently aPh.D. candidate in the Department of Mechanical Science and Engineering, Nagoya University,
as well as a Research Fellow with the JSPS,
Tokyo, Japan, from April 2013. His researchinterests include haptic and visual perception of
texture.

Shogo OKAMOTO received a B.S. degree inengineering from Kobe University in 2005, and
M.S. and Ph.D. degrees in information sciencesin 2007 and 2010, respectively, from the Graduate School of Information Sciences, Tohoku
University. Since 2010, he has been an assistantprofessor at the Graduate School of Engineering, Nagoya University. His research interests
include haptic interfaces and human-assist technology.

Yoji YAMADA (M’ 91) received a Ph.D. degreefrom the Tokyo Institute of Technology, Japan, in
1990. He has been an associate professor at the
Toyota Technological Institute, Nagoya, Japan
since 1983. In 2004, he joined the National Institute of Advanced Industrial and Science Technology (AIST), as a group leader of the Safety
Intelligence Research Group at the Intelligent
Systems Research Institute. In 2008, he moved
to the Department of Mechanical Science andEngineering, Nagoya University, as a professor.

1939-1412 (c) 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.