International Journal
Transactionsof JapanofSociety
Affectiveof Engineering
Kansei Engineering
J-STAGE Advance Published Date: 2016.11.30
doi: 10.5057/ijae.IJAE-D-16-00009

ORIGINAL ARTICLE

Graphical Modeling Method of
Texture-related Affective and Perceptual Responses
Kensuke KIDOMA*, Shogo OKAMOTO*, Hikaru NAGANO** and Yoji YAMADA** Nagoya University, Nagoya-shi, Aichi 464-8601, Japan** Tohoku University, Sendai-shi, Miyagi 980-8577, Japan
Abstract: Techniques for modeling hierarchical and multidimensional human perceptual and affective experiences afford further
understanding of the affective values of humans with regard to products and services. In this study, we developed a method for thegraphical modeling of texture-related perceptual and affective experiences, which were expressed by adjectives. The method enables
the establishment of semantically causal relationships among the adjectives using the results of a standard sensory evaluation task basedon adjective rating. The initial model was simplified by covariance selection to enable the definition of the causalities based on
the assumption that the perception of the physical aspects of material surfaces produces affective and more personal experiences.
For the purpose of validation through an experiment, we applied the developed method to the textures of 46 different flat materials anddeveloped a model that was logically comparable to previously developed models. The proposed graphical modeling method promises
to facilitate the design of product surfaces and an analysis of the subjective feelings induced by touching them.
Keywords: Tactile sensations, Multilayered model, Covariance selection

1. INTRODUCTION
Various internal responses are produced when humans
touch material surfaces, which include psychophysicalresponses such as the perception of hard-soft or warmcold, and affective and emotional responses such as theperception of modern-classic, sharp-dull, or delicate-bold.
Figure 1 is a semantically multilayered causal model ofthe relationships among these texture-related responses.
The model constitutes a map of the causes and effects of
the human responses. An understanding of this causality
is beneficial to the tactile design of commercial products
in which the surface texture determines the affective value
and constitutes a design factor. Such products comprisethe packaging materials for food and general items [1],and seat fabrics [2]. Previous studies [1-8] agree that theupper layers of such a model contain the more affective
factors, which are affected by integrated multiple sensory
cues and individual knowledge, while the lower layers
contain responses that are largely dependent on theperception of physical stimuli.
However, there are insufficient previous studies onsuch hierarchical models. The most prominent issue inthis regard is that the approaches most commonly employedin affective engineering rely on multivariate analyses
based on correlation matrices (e.g., factor analysis andstructure equation modeling), and do not determine thecausalities among the related factors, although they may
Received: 2016.02.01 / Accepted: 2016.10.21

enable an estimation of the causalities based on the correlations among the factors.
Thus far, several studies have been conducted tobuild multilayered causality models of texture-related
adjectives [1-7]. However, a standard method is yet
to be established. Some research groups [2-5] haveclassified human internal responses into affective andpsychophysical layers and occasionally connected the
responses included in the two layers by multivariate
analyses. They, however, did not discuss the features ofthe layered structure, such as the number of layers.
Moreover, because the affective layer may have a complicated structure composed of a few sublayers [1, 6, 7],
the arbitrary design of the number of layers and items ineach layer is a risky approach. Chen et al. [1] performeda rating task in which material textures were scaled using

Figure 1: Multilayered causality model of perceptual, affective,
and preferential responses to tactile textures.
A directed arc indicates causality between two nodes.
Copyright © 2016 Japan Society of Kansei Engineering.
All Rights Reserved.

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adjectives as the criteria, and produced a three-layeredcausality model of adjectives based on the correlations
among them. However, the method was not elaborated
upon and might have involved a heuristic approach.
In contrast, Nagano et al. [6, 7] developed a causality
modeling method based on the mutual impacts of theadjectives instead of the correlations among them.
In these studies, the participants assessed the direction
and magnitude of the impact of one adjective on another.
Unfortunately, such an approach is not widely used in
the field of affective engineering. A similar approach
was also adopted by Munechika and Miwa [8], who
considered food quality. The challenge is therefore toautomatically build a hierarchical model using standard
sensory evaluation methods. To address this issue, wedeveloped a graphical method for modeling the hierarchical causalities among adjectives based on the resultsof a standard sensory evaluation task in which human
responses to stimuli were typically rated using five orseven adjective scale grades.
To the best of our knowledge, ours is the first study toundertake the development of a method for building ahierarchical causality model of texture-related human
responses based on adjective-rating tasks. The proceduresof the method are outlined in Section 2 of this paper, andthe underlying principles are described in Sections 3 and 4.
Finally, in Section 5, through an experiment involving
university students as participants, we present an exampleof the graphical model comprising 29 adjectives and 46
types of material samples and discuss the quantitative andqualitative validity of the model.
2. OUTLINE OF DEVELOPED METHOD
Here, we outline the procedures of the developedmodeling method, which is summarized in Fig. 2.

Users of this method perform a standard sensory evaluation task to rate target materials using adjectives. The
employed adjectives are those related to the perception ofphysical properties, such as rough-smooth and soft-hard(psychophysical descriptors), and those related to affective and personal experiences, such as modern-classic,
friendly-unfriendly, and exciting-boring. Each adjectivescore is treated as a characteristic variable of the material.
This sensory evaluation task yields a matrix that incorporates a partial correlation coefficient of an arbitrary pair ofadjectives. Hence, as shown in Fig. 2A, the results of thesensory evaluation task can be expressed as a completegraph in which the adjectives and the magnitudes of thepartial correlation coefficients are represented by nodes
and arcs.
Covariance selection is then used to simplify the relationships among the adjectives while macroscopicallyretaining their correlation matrix. As shown in Fig. 2B,
the arcs between weakly connected nodes are eliminated.
This process enables a focus on the strong direct relationships among the adjectives.
A correlation model simplified by the above covarianceselection (Fig. 2B) does not explicitly reflect the causal
relationships among the adjectives. We therefore determined the direction of the effect or causality between
two adjectives based on prior specific knowledge oftactile textures, namely that psychophysical adjectives
semantically influence affective adjectives [1-8]. We
allocated psychophysical adjectives to the bottom levelof the hierarchy and each of the other adjectives based on
the distance from the psychophysical adjectives at thebottom level. As shown in Fig. 2C, the impact of thepsychophysical layer on an adjective and the closeness of
the adjective to the bottom layer increases with decreasing
distance between the adjective and the psychophysicallayer.

Figure 2: Procedures for establishing the multilayered causal model of texture-related perceptual, affective, and preferential responses.
Each node corresponds to an adjective dyad used in the adjective-rating task. A) The correlation matrix is graphically
expressed as a complete graph. B) Some weak connections are eliminated by covariance selection. C) The causalities
between nodes are defined based on prior knowledge of texture-related perceptions and affections.

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Graphical Modeling Method of Texture-related Affective and Perceptual Responses

3. COVARIANCE SELECTION
The results of the sensory evaluation task provide thepartial correlation coefficients among all the possible
adjective dyads, and their graphical structure is exhaustive, as shown in Fig. 2A. Covariance selection [9, 10]
is used to obtain a simplified structure (Fig. 2B), thecorrelation matrix of which is not statistically different
from the sample correlation matrix. When the partial
correlation coefficient between two variables is nearlyzero, the variables are assumed to be independent of eachother, and the arc between them is removed. Because
each partial correlation matrix has a unique correlation
matrix, the correlation matrix is computed from thereduced model. Provided that the correlation matrix of the
reduced model is statistically consistent with the sample
correlation matrix, the model can be continuously reduced.
Typically, the model is continuously reduced until areasonably practicable form is achieved. The covarianceselection procedure is described below.
Here, Ω is a set of index pairs (i, j) of correlation or partial
correlation coefficients (1 ≤ i< j ≤ q), q is the number ofobserved variables (or adjectives), andis an estimateof a partial correlation coefficient between variables i and j.
Ω is divided into two exclusive subsets I and J. For (i, j) ∈ I,
= 0 and for (i, j) ∈ J,
has an arbitrary value.
According to the theorem presented by Dempster [9], themaximum likelihood estimate of a sample correlation
matrix R satisfies the following two conditions:where rij andare respectively a sample correlation
coefficient between variables i and j and its estimate. In
the present study, a correlation matrix that satisfies these
conditions was computed using the iterative algorithm of
Wermuth and Scheidt [11]. The corresponding partial
correlation matrix of the computed one was forced to set
some elements to zero.
In each step, one partial correlation coefficient with asmall magnitude is set to zero, and the corresponding
correlation matrix is computed and compared with thesample correlation matrix R to determine whether the
reduced model is statistically congruent with the observed
facts. By repeating this procedure, a simplified model that
captures the sample correlation matrix is obtained.
Two types of indices are used to test the deviance between
any two obtained models. Unless either type of index is
adjudged to be large, the model is continuously reduced.
The deviance between a reduced model RM and theoriginal full model FM is defined by R and :(1)
where n is the number of sample materials or products to beinvestigated. The distribution of dev(RM) asymptoticallyfollows a chi-square distribution. Its degree of freedom isthe number of partial correlation coefficients set to zero.
When a reduced model RMk+1 is derived from RMk by
setting the non-zero parameter of the latter to zero, theother deviance index is used to test the significance of theparameter:(2)
The value of this deviance index also asymptoticallyfollows a chi-square distribution with one degree of
freedom.
In the present study, we used reference p values of 0.50
and 0.20 to judge the above two types of deviance indices,
respectively. These p values are used in practice as criteria
for model selection [10]. If the calculated p values arelarger than the reference two values, the deviances are
deemed to be minute.
4. CAUSALITY MODELING BASED ON DISTANCESFROM PSYCHOPHYSICAL LAYERS
As shown in Fig. 2B, a reduced model is obtained bycovariance selection. This model is relatively simple andeasy to understand compared to the complete graph inFig. 2A. Nevertheless, while the partial correlation coefficients reflect the covariant properties of the variables,
their causality remains unknown. Typically, prior specificknowledge of the problem is used to further analyze
the causality. A temporal order of the events and thephysically or chemically evident causality may be used
for this purpose. However, this is clearly not applicableto human perceptual, affective, and preferential responses.
There is thus the question of what may be considered asprior knowledge of tactile textures.
Many researchers hold that psychophysical human
responses underlie the semantic space of perceptual andaffective responses [1-8]. Psychophysical responses involve
the perception of the sensory aspects of the physicalproperties of textured surfaces and can be expressed by
adjectives such as, rough, warm, and hard. From the viewpoint of product design, it is reasonable to position these
responses at the bottom of the model and to consider themas the causes of other affective and preferential responses.
This is because the physical qualities of a product are variableand directly influence human psychophysical responses.
In contrast, the affective or preferential responses that

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are positioned higher in the hierarchy are less affectedby physical stimuli of the product surface. Those
positioned in the upper layers are more weakly connectedto psychophysical responses. We therefore allocated thenon-psychophysical adjectives with respect to their respective distances from the bottom psychophysical layer.
With regard to tactile texture perception, the psychophysical aspects of a surface are characterized based on theperceptions of macroscopic and fine roughness, softness,friction, and warmness [12-16]. As described in Section 5,
uneven-flat, rough-smooth, hard-soft, sticky-slippery, wet-dry,
and warm-cold were the psychophysical adjective dyads of
our example application of the proposed modeling method.
As also shown in Fig. 3, we allocated these adjective dyads
to the bottom of the model and computed the strength of theconnections among the psychophysical adjectives and theother adjective dyads. We used the determination coefficientof a linear regression analysis, wherein the ratings assigned
to the non-psychophysical adjective dyads and the psychophysical ones were considered as objective and explanatory
variables, respectively. If the rating of an adjective dyad j isdenoted by yj, and those of the six types of psychophysicaladjective dyads are given by x = [x1, x2, x3, x4, x5, x6] T, theregression model can be expressed as(3)
where a = [a1, a2, a3, a4, a5, a6] T is the vector of the regression coefficients for the six types of psychophysicaladjectives. A larger determination coefficient indicatesthat the objective adjective dyad can be more directly
explained by the psychophysical layer. A smaller coefficient means that the objective adjective dyad is also
affected by factors other than the psychophysical ones, orthat there may be a nonlinear relationship between theobjective adjective dyad and the psychophysical layer.
The use of the determination coefficient to establish the
hierarchy of the adjective dyads enables an expression of
how the affective factors are deployed in the upper layer
and are affected by the psychophysical and other factors.

5. EXAMPLE OF GRAPHICAL MODEL OFTEXTURE-RELATED RESPONSES
We applied the proposed modeling method to the resultsof an adjective-rating task performed by Nagano [17] andvalidated the established causality model by comparison
with those of previous studies. The experimental protocol
was approved by the internal review board of the Engineering School, Nagoya University (Registration No. 12-6).
5.1 Material sample rating task
1) Methods:
The material surfaces were rated based on 29 perceptualand affective adjective dyads. Each of the adjective dyads
comprised two semantically opposite adjectives, as listed
in Table 1. The adjectives were selected from related
studies (e.g. [3]) with care taken to avoid the duplicationof similar descriptors. The adjectives were expected tocover the dimensions that form each semantic layer.
The psychophysical [12], emotional [1-7], and preferential layers comprise at least five, two or three, and one(like/dislike) dimensions, respectively. The 29 adjective
dyads used in the present study embodied these dimensions. Furthermore, they included typical adjectives suchas rich, modern, and beautiful, which are used to judge thevalues of materials and products.
Each participant was blindfolded and acousticallymasked with headphones that played pink noise, andTable 1: Adjective dyads used for the sensory evaluation task
Beautiful/Ugly

Clean/Dirty

Clear/Vague

Comfortable/Uncomfortable

Concrete/Abstract

Dangerous/Safe
Friendly/Unfriendly

Delicate/Bold

Exciting/Boring

General/Special

Good/Bad

Happy/SadHard/SoftInteresting/Uninteresting

Itchy/Not itchy
Natural/Artificial

Like/Dislike

Modern/Classic

Regular/Irregular

Rich/Poor

Rough/Smooth

Sharp/Dull

Significant/Insignificant

Simple/Complex

Sticky/Slippery

Strange/Usual

Uneven/Flat

Warm/Cold

Wet/Dry

Figure 3: Specification of the causal relationships based on the determination coefficient of
the regression analysis using six psychophysical adjectives as the explanatory variables

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Graphical Modeling Method of Texture-related Affective and Perceptual Responses

was required to touch and rate each material using 29
different seven-grade visual scales corresponding to the 29
considered adjective dyads. Each of the two semantically
opposite adjectives was written in Japanese and English ateither end of each visual scale. The allocation of the words
was randomized among the trials. The participant touchedeach material on a desk using his/her one hand and was notallowed to lift or bend the material. No time limit was set
for each trial because the number of adjectives to beconsidered for the rating was large. For the same reason,
the participants were allowed to touch the material sample
while doing the rating. They were nevertheless encouraged
to do the rating without contemplation. The participants
were not informed of any specific use of the materials.
The order in which the materials were presented to thedifferent participants was also randomized.
2) Material stimuli:
The experiment considered the 46 types of flat materials
listed in Table 2. Each material sample was cut into a
90 × 90 mm square and attached to a hard plastic plate.
The materials, which included wood, metal, cloth, ceramic,
and plastic, were selected to uniformly cover the entire
spectrum of tactile textures [12]. It should be noted thatthe results of this type of sensory evaluation task isdependent on the set of material samples, and it was
thus not our intension to draw a general conclusion or
establish a general model by the sample task.
3) Participants:
The participants were recruited by internal advertisements and comprised seven male and four female Japanese
university students without any known tactile disability.
Each signed an informed consent document.
Table 2: Forty-six materials used for the sensory evaluation task
Magnolia wood

Oak wood

Sapelli wood

Cork board

Fine woven straw

Coarse woven straw
Soft fake fur

Woven linen

Woven rush grass

Short hair fake furLong hair fake fur

Fake boa

Fake cowhide

Fake alligator hide

Fake woven leather

Fake suede

Felt

Towel

Satin

Pyramid rubber matting

Mirror plate

Iridescent sheet

Aluminum foil cloth

Mosaic tile

Glossy vinyl sheet

Glossless vinyl sheet

Cotton cloth
Crumped paper

Denim

Fine Japanese paper

Corrugated paper

Wall paper

Artificial grassSpongePerforated aluminum

Woven wire mesh

Glass-beadsfilled surface(7 mm diameter)

Glass-beadsfilled surface(5 mm diameter)

Glass-beadsfilled surface(3.5 mm diameter)

Glass-beadsfilled surface(1.5 mm diameter)

CottonGoose feathers

Steel wool

Stainless steel scrubber

Urethane resin

Ceramic tile

Although there were individual distinctions among thesemantic structures of the psychophysical and emotional
layers [7], the present discussion considers the average
results of all the participants, as was done in some previous
works [1, 2, 4-6]. Furthermore, one of the objectives of thisstudy was to validate the proposed graphical modelingmethod by comparing the average results of the present
experiments with those of previous studies, rather than
discussing individual uniqueness. This justifies the use of
the average results. However, a concern was whether the
average results for 11 participants were representative forthe local population. Hence, using the cross-validation
method, we conducted a post-hoc test to determine whether
the average covariance matrix computed from the 11
participants converged. Using the deviance index (1),we compared two types of average matrices: one was theaverage matrix for the 11 participants and the other was theaverage matrix for ten participants excluding an arbitrary
participant. No significant difference was observed between
the two matrices, indicating that the average matrix was not
significantly modified by any one of the 11 participants.
4) Data analysis:
The adjective dyad ratings by each participant weretransformed into z-scores and the average z-score for eachmaterial was then obtained.
5.2 Results: Graphical model of adjectives
We applied covariance selection to the results of theabovementioned sensory evaluation task to obtain asimplified model comprising 29 adjective nodes and 215
non-zero partial correlation coefficients. Overall, 191 arcs
were removed from the full model of all the 406 relationships among the adjective dyads. As described in Section 4,
the adjective nodes were positioned based on theirrespective distances from the bottom psychophysicallayer. We then directed the effect between two nodes
from the node in the lower layer to that in the upper layer.
The hierarchical model shown in Fig. 4 was thus obtained.
For visual clarity, the horizontal coordinates of the nodes
were determined as follows. We obtained the distancematrixin which each element was thecomplement of an absolute partial correlation coefficient.
Torgerson’s multidimensional scaling method [18] wasapplied to D, and the obtained coordinates of the mostprominent dimension were used as the horizontal coordinates of the adjective nodes. Hence, the partial correlation
coefficients between adjective nodes close along thehorizontal axis were relatively large.
The model in Fig. 4 shows only the major arcs between
nodes. The type of directed arc indicates the magnitude ofAdvance Publication

Figure 4: Graphical model of texture-related human responses.
The line type indicates the magnitude of the partial correlation coefficient. The solid, dotted, and fine dotted lines
respectively indicate the coefficient values greater than 0.4, 0.3, and 0.2. Those from the bottom (psychophysical),middle (attribute), and top (affective and preferential) layer are black, green, and blue, respectively.

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Graphical Modeling Method of Texture-related Affective and Perceptual Responses

the partial correlation coefficient. Arcs whose absolutecoefficients are greater than 0.3 and one of the threelargest coefficients related to a node are drawn with black,colored solid, and dotted lines. The fine dotted arcs arethose with absolute coefficients between 0.2 and 0.3 andare either the largest or second largest coefficients related
to a node. When two nodes are located at vertically close
positions, an undirected arc, which is equivalent to abidirectional arc, was used to indicate the vague causality.
5.3 Qualitative validity of model
1) Overall structure:
First, we focused on the overall distribution of theadjectives. By principle, the psychophysical adjectives
were in the bottom layer. The low and middle layers
contained the descriptors related to the physical attributesof the materials, such as simple-complex, dangerous-safe,
sharp-dull, regular-irregular, itchy-not-itchy, and naturalartificial. The middle-to-upper layers contained theaffective and qualitative adjectives such as modern-classic,
beautiful-ugly, rich-poor, exciting-boring, general-special,
happy-sad, and interesting-uninteresting. Preferential
words such as like-dislike and good-bad were also among
the affective adjectives but were not clearly separated
from the affective adjectives.
From another perspective, the lower layers included
the adjectives with clear physical causalities, while theupper layers embraced the adjectives that do not basicallycharacterize materials. For example, a dangerous-safe
rating significantly varied with a hard-soft rating, based on
which it is naturally inferred that the participants might
have adjudged harder materials as being more dangerous,and softer ones as being safe. The simple-complex rating

was strongly associated with that of uneven-flat, suggestingthat the macroscopic surface roughness might have been
perceived as being complex. Comfortable-uncomfortable
was associated with warm-cold, indicating that theperceived warmness of a material might have induced
comfort. In contrast, some adjectives in the upper level,such as exciting-boring and significant-insignificant,were weakly connected to the physical properties of thematerials. These adjectives were thus located far fromthe psychophysical layer. The propagations of the effects
were also depicted in the model. For example, uneven-flat
was linked to simple-complex, and then to regular-irregular,
and finally to general-special, indicating how the unevenness of material’s surface affects its being perceived as
special or general. Wet-dry affected rich-poor, which was inturn linked with like-dislike and good-bad. This indicatesthat materials such as artificial leather that feel wet werepreferred because of their premium feel. Nonetheless, all
the connections in Fig. 4 cannot be intuitively interpreted.
As mentioned above, the model indicates that the effectsof the psychophysical factors are propagated to the affective and more qualitative ones, suggesting the semantic
validity of the model.
2) Comparison with previous models:A. Consistency with earlier studies: To assess thevalidity of the proposed method and developed model in
the absence of a standard reference model, the present
results of the model were compared with those of previously developed models. Table 3 presents the classificationof the adjectives used in the previous studies. It should be
noted that these adjectives were contained in discrete
layers, as opposed to the continuous hierarchical structureof the present model.

Table 3: Comparison of the adjectives in each layer of the present and previous models.
The lower rows are lower layers. Because our model does not include discrete layers, the adjectives are groupedbased on the vertical allocations in the model. The representative adjectives for [3, 4] are listed.
Present model
Significant
Exciting, delicate,
general, interesting
Beautiful, rich, good
Clean, itchy, modern,regular, like, happy
Comfortable, strange,
friendly, concrete
Sharp, natural, clear,
dangerous, simple
Rough, uneven, hard,sticky, wet, warm

Nagano et al. [6,7]Rich, good, happy,
like
Beautiful, friendly,interesting,significant,comfortable

Concrete, general,dangerous, strange,
delicate, simple, clear,delicate, regular
Rough, uneven, hard,sticky, wet, warm

Chen et al. [1]Guest et al. [3]Ackerlay et al. [4]Matsuoka et al. [2]Enjoyable, irritating,comfortable,
exciting, sexy,relaxing, calming,
sensualRelaxing, calming,
pleasurable,
comfortable,
enjoyable,
uncomfortable,
irritating, sensual,arousing

Genuine, high-quality,
luxurious, elegant,calm, adult, simple,
casual, delicate,high-class

Hot-cold, rough(coarse)-smooth,sticky-slippery,
hard-soft

Bumpy, lumpy, firm,
hard, fuzzy, fluffy,
hairy, wet, greasy,
cold, slippery

Warm, hard, fluffy,
thick, elastic,
voluminous, bumpy,
smooth, slippery, dry

Playful, pleasurable,
sophisticated,premium, precious,
exciting

Natural, indulgent,sensual, relaxing,
delicate, simple

Warm, rough, hard,sticky, dry

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Although the present and previous studies adopted
different mathematical approaches and material specimens,
their allocations of adjectives in the layered structures werelargely consistent. However, detailed comparisons of theconnections among the adjectives were either difficult or
impossible because of the differences among the study
objectives. Nevertheless, it was impressive that completely
different methods involving rating tasks [1], word classification tasks [3], and semantic questionnaires [6, 7] produced
similar results. This suggests that the layered structures of
the adjectives were robust even though their detailed
connections variably depended on factors such as theparticipants and types of materials used for the experiments.
In other words, the layered structures were hardly affectedby the experimental methods and mathematical analyses,
suggesting a wide scope of potential applications.
B. Psychophysical layers: The locations of the psychophysical adjectives in the present model agree with thoseof previous studies [1-7]. This was due to an intentional
attempt on our part to achieve consistency.
C. Comparisons with specific previous studies:
Guest et al. [3] classified 168 adjectives used to describe
tactile experiences into two layers without the performance of psychophysical material stimuli experiments.
They found that the adjectives belonged to sensory or
emotional classes and established a two-layered model oftactile textures. The upper emotional layer comprised
comfortable-uncomfortable (e.g., pleasurable, calming, andirritating), arousing (e.g., exciting and relaxing), andstimulating (e.g., sensual, thrilling, and enjoyable) factors.
Ackerley et al. [4] performed an experiment and analysis
similar to those of Guest et al. [3]. Their upper layer comprised positive-negative emotions (e.g., relaxing, comfortable,
uncomfortable, and irritating) and arousing factors (e.g.,arousing, sensual, and exciting). The adjectives in thepresent model that are semantically close to the foregoing
factors or descriptors, such as exciting-boring, comfortableuncomfortable, happy-sad, and good-bad, were placed in
the upper or middle layers without exception.
Chen et al. [1] proposed a three-layered model, the upperand middle layers of which were largely consistent withthose of the present model. Their model was based on asensory evaluation experiment involving 37 flat samples ofmaterials that could be used to package food. The adjectives
exciting, sophisticated, pleasurable, premium, and precious
were placed in the top layer. In the present model, similar
adjectives such as exciting-boring, happy-sad, delicate-bold,
and rich-poor were placed in the middle or upper layer.
The middle layer of the model of Chen et al. contained theadjectives natural and simple, which were located in the

middle or lower layer of the present model. Conversely,
adjectives similar to sensual, indulgent, and relaxing, whichwere placed in the middle layer of the model of Chen et al.,were not used in the present experiment. Therefore, in termsof the locations of semantically comparable adjectives, thepresent model agrees well with that of Chen et al.
Nagano et al. [6, 7] built three- and four-layered models
by specifying the causalities among the adjectives. The
substantial similarity between their adjectives and thoseof the present study made comparison easy. Furthermore,they prepared the material samples in a manner similar tothat of the present study. As can be seen from Table 3,
there are no significant discrepancies between the resultsof the present study and those of Nagano et al., with the
exception of general-special and delicate-bold. These two
types of adjective dyads appeared in the second layer fromthe bottom in the model of Nagano et al., whereas they
were included in the upper layer of the present model.
Nagano et al. used the subjectively reported effects among
the adjectives, whereas our model was based on the adjective scores for each of the materials. The determination of
the consequences of this methodology difference requires
further study. The adjectives general-special and delicatebold have been rarely used in previous studies.
It is thus evident that the model developed in the present
study largely agrees with those of previous studies interms of the locations of the adjectives, and this validates
the presently proposed method.
5.4 Quantitative validity of present model
Although the deviation between the full and simplified
models was tested at every step of the covariance selection, it was beneficial to undertake the assessment fromanother perspective for validation purposes. The goodnessof fit index (GFI) is a similarity index for comparing
correlation matrices. The GFI value, which falls between
0 and 1 depending on the degree of match, with 1 indicating a perfect match, is given by(4)
The GFI value of the comparison of our simplified modelwith the original complete model was 0.77. Unfortunately,
the previous studies cited in this paper did not use any similar
index. However, the present GFI is fairly high, indicating thatthe original structure of the correlations among the adjectives
was well retained. It should nevertheless be noted that thecalculated GFI only validates the result of the covarianceselection, and not the hierarchical structure of the model.
This is because the index is based on the correlation matrices.

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Graphical Modeling Method of Texture-related Affective and Perceptual Responses

6. DISCUSSION
The modeling method developed in this study wasconfirmed to be valid for the data acquired from theexperiment. Nevertheless, the method has some limitations, some of which are discussed below.
6.1 Model describes semantic, static, and linear
causalities
Our graphical model does not describe neural subjective
experiences because it is based on the results of a sensory
evaluation task. It also does not consider the dynamics ofthe subjective experiences. For example, an upper layer
may transiently affect lower layers, and the latter may beconnected to the former in the response. Moreover, inthe experiments, the participants were allowed sufficient
perception time, which gives us reason to assume that the
subjective reports potentially converged to certain internal
states of the participants.
Considering the foregoing, we suggest that it would be
effective not to specify the direction between the nodes
when the restriction on a directional arc appears strong.
In the model in Fig. 4, some arcs remain bidirectional
and thus do not indicate causality between the nodes, butthat the two nodes connected by an undirected arc co-vary.
In the model, adjectives in the same layer are connectedby undirected arcs. This is an option for the designer tomitigate the constraint of the model.
The present model is based on covariance selection
and linear regression analysis of the adjective ratings.
Both procedures linearly connect multiple variables,
whereas human responses are intrinsically nonlinear.
The established model is thus a linear approximation of anonlinear system. Hence, the simultaneous application of
the model or method to products or objects belonging
to different categories should be avoided. Even broadly
similar products such as cooking utensils, cutlery, andtableware should not be dealt with using the same model.
Cutlery is typically made from metals, whereas tableware
are often ceramic. Ceramic chopsticks or forks feel strange
while ceramic dishes are widely acceptable. In other words,
different products are perceived with different sensevalues. The proposed model should thus be developed andapplied within good approximations.
6.2 Validity of causality model
The validation of the acquired causality model isdemanding. The purpose of graphical modeling is to help
analyze a system in which the relationships among
multiple factors are not easy to comprehend. The methods

for validating such models are limited. One effective
method involves checking whether or not the model issemantically valid. Another method involves comparisonof the model with previous models built by other experts.
The present model was validated by these methods, as
discussed in Section 5.3. It would be further convincing to
use the present causality model to solve specific problemsof real commercial products and services. This would
demonstrate the potency of the proposed method.
7. CONCLUSION
The graphical modeling of the causalities among
subjective human experiences, expressed by adjectives istypically done by performing sensory evaluation tasks.
For the specific case of tactile textures, several models
have been proposed [1-7], most of which comprised twolayers, namely, psychophysical and affective layers [2-5].
However, a fundamental problem of these models isthat the correlations among the experiences are not sufficient for adjudging the hierarchically causal structures of
the adjectives. To resolve this problem, some previous
studies investigated the subjective impacts amongdescriptive adjectives [6, 7]. However, a method basedon a typical adjective-rating task is desirable for generalapplication.
To summarize, in this study, we developed a method forbuilding a multilayered graphical model of adjective
dyads based on only the results of an adjective-rating task.
The acquired correlations among the adjectives were
simplified by covariance selection. The adjectives werethen respectively positioned in the different layers based on
the premise that psychophysical responses positioned in thebottom layer semantically propagate to other types of
responses in the upper layers. We applied the method to 46
types of materials and 29 adjective dyads. The establishedmodel was shown to be quantitatively valid in terms of its
good retention of the original correlation matrix, andsemantically valid in terms of the respective locations of
the adjectives.
The developed method is applicable to typical ratingtasks using adjectives, and generally does not require theperformance of special experiments. The method is
expected to contribute to the manufacturing of affective
products.

ACKNOWLEDGEMENTS
This work was partly supported by MIC SCOPE(142106003) and MEXT Shitsukan (15H05923).

Advance Publication

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Kensuke KIDOMA (Non-member)
Kensuke Kidoma received a B.S. degree from
Nagoya University in 2015. His research interests
comprise multivariate analysis.

Shogo OKAMOTO (Non-member)
Shogo Okamoto received MS and Ph.D degrees in
information sciences in 2007 and 2010, respectively,from the Graduate School of Information Sciences,
Tohoku University. Since 2010, he has been with the
Department of Mechanical Science and Engineering,
Nagoya University, Japan. His research interests
comprise haptics, human assistive technology, and wearable robots.

Hikaru NAGANO (Member)
Hikaru Nagano received B.S., M.S. and Ph.D
degrees in engineering from Nagoya University in2010, 2012, and 2015, respectively. Currently, he isan Assistant Professor in the Graduate School ofInformation Sciences, Tohoku University, Japan.
His research interests comprise human perception
and man-machine interfaces.

Yoji YAMADA (Non-member)
Yoji Yamada received a Ph.D. degree from theTokyo Institute of Technology in 1990. He has been
an associate professor at the Toyota Technological
Institute, Japan since 1983. In 2004, he joined the
National Institute of Advanced Industrial andScience Technology (AIST), as a group leader of
the Safety Intelligence Research Group at the Intelligent Systems
Research Institute. In 2008, he moved to the Graduate School ofEngineering, Nagoya University, as a professor.