International Journal of Affective Engineering
J-STAGE Advance Published Date: 2024.02.14
doi: 10.5057/ijae.IJAE-D-23-00015

Original Article

Multi-layered and Multidimensional Structure of Evaluative,
Affective, and Psychophysical Impressions While Viewing
Hug Scenes
Yumeka OGURA 1, Shogo OKAMOTO 1* †and Yuki KOSUGE 1†

1 Tokyo Metropolitan University, 6-6, Asahigaoka, Hino, Tokyo 191-0065, Japan* Corresponding author, E-mail: okamotos@tmu.ac.jp† JSKE Member

Received: 2023.06.28
Accepted: 2023.12.20
Copyright: © 2023 by the authors. Submitted forpossible open access publication under the terms
and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/
by/4.0/).

Abstract: Psychophysical and affective experiences while watching audiovisualcontent can be antecedents to determine the overall evaluation of viewing
experiences. However, the layered relationships among these experiences havebeen hardly discussed. We investigated these experiences and their semantic
structure while watching hug scenes. We selected 38 adjectives describing hugs
and classified them into three layers: psychophysical, affective, and overall
evaluation. Participants scored each of the 24 videos containing hugging scenes
using these adjectives. The structure among the three layers was computed:
the four psychophysical factors affected the four affective factors, which in turnaffected the two overall evaluation factors, i.e., joyful and reassuring. The model
was confirmed to have statistical validity by structural equation modeling andsemantic validity by experiments using dummy links. The results will lead tothe determination of measures to enhance affective experiences when viewing
videos, and the formulation of criteria for measuring and evaluating affective
experiences.
Keywords: Affective experience, Emotion, Psychophysical factor, Structural
equation modeling

1. Introduction
Cognitive and affective judgments or experiences are involved in a variety of daily activities, such as viewing videos,reading, traveling, shopping, and playing sports (e.g., Schindler et al., 2017). Overall satisfaction with activities is basedon an ensemble of such experiences (Bao et al., 2019; Bartl et al., 2013; Guest et al., 2011; Hashim et al., 2017;
Hosany & Gilbert, 2010; Hosany & Witham, 2010; Kawabata & Niwa, 1989; Kidoma et al., 2017; Kumar et al., 2001;
Mano & Oliver, 1993; Matsumura et al., 2020; Nagano et al., 2018; Okamoto et al., 2016; Okamoto et al., 2021; Tu et al.,2018). For example, satisfaction with cruising can be synthesized by feelings of entertainment and aesthetics in travel
settings (Hosany & Witham, 2010). Product consumption satisfaction was analyzed from the viewpoint of the buyer’s
emotions and product utility (e.g., Mano & Oliver, 1993). Understanding how overall evaluation, such as satisfaction
with activities, can be broken down into affective and low-level cognitive (i.e., psychophysical) factors would lead toguidelines for designing affectively appealing activities for service providers. Furthermore, this will lead to the establishment of a measure for overall evaluation. Considering the generality and access to stimuli, we analyzed the structureof hedonic, affective, and psychophysical evaluations of short videos of hug scenes.
Some previous studies have decomposed satisfaction felt from activities and stimuli into multiple affective components(Bao et al., 2019; Bartl et al., 2013; Hosany & Gilbert, 2010; Kumar et al., 2001; Mano & Oliver, 1993; Matsumura et al.,2020; Schindler et al., 2017; Tu et al., 2018). These studies have not aimed to provide guidelines for creating commercial
services that evoke satisfaction. Other studies have estimated affective experiences from the psychophysical features ofactivities (Hosany & Witham, 2010; Okamoto et al., 2016; Pizzi et al., 2015; Thomson et al., 2010). These studies did not
aim to provide an understanding of the factors constituting overall satisfaction. In other words, expressing the overall

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satisfaction, affective factors, and psychophysical aspects linked in a model has not been pursued by many earlier studies.
Several researchers have established such models (Guest et al., 2011; Kawabata & Niwa, 1989; Kidoma et al., 2017;
Okamoto et al., 2021); however, they did not target affectively evocative stimuli, such as hug scenes with lengths ofseveral seconds, which were used in this study.
In this study, we collected subjective scores for overall satisfaction, affective, and psychophysical attributes fromparticipants who viewed the hug scenes. We aimed to determine the structures between the attributes of these three
categories, as shown in Figure 1, which leads to a guideline for designing affectively appealing audiovisual content andhelps understand the construct of overall satisfaction with them. This study is an extended version of the authors’
earlier work (Ogura et al., 2023) where fewer types of subjective evaluation items were collected than in this study andthe statistical and semantic validities of the model were not examined.
Overall evaluation (satisfaction) layer

Affective and emotional layer

Psychophysical features of hug scenes

Figure 1: Three-layered semantic structure of affective experience, i.e., viewing hug scenes.
Each layer is composed of multiple independent factors, i.e., white circles in the figure.

Further, a novel methodology to semantically validate multi-layered models is introduced in this study. Such models
are usually statistically validated by, for example, structural equation modeling method. However, the semantic validation of the models is also important and this process has largely been performed by referring to literature and experts’knowledge (Chen et al., 2009; Fujisaki et al., 2015; Guest et al., 2011; Kawabata & Niwa, 1989; Kidoma et al., 2017;
Okamoto et al., 2021). This study employs a user experiment to quantitatively examine the semantic validity of multilayered models. This experiment-based semantic validation approach can be a general method for multi-layered models.
As aforementioned, this study has two important aspects. The first is to establish and implement a method to computea multi-layered model comprising overall satisfaction, affective, and psychophysical factors, which helps understand anddesign affectively evocative contents. The second is to develop an experimental method for semantically validating suchmodels. We demonstrate these two features by adopting hug scenes as an example of affective stimuli.

2. Methods
2.1 Participants
Twelve university students (five females, mean age: 24.6 years) participated in Task 1, and 11 of them participated in
Task 2 after providing written informed consent. Task 2 was conducted on different days after Task 1. Participants were
unaware of the objectives of the study.
2.2 Video stimuli: Hug scenes
Twenty-four videos were selected by the authors and colleagues from a research group. All the videos were posted onvideo-sharing websites. The lengths of the videos ranged from 4 s to 23 s. The videos were limited to hugging scenes
between two people or a human and an animal. Videos with similar content were not included redundantly. Videos
featuring one or more of the four emotions (pleasant, unpleasant, activation, and deactivation) that form the basis of theAdvance Publication
Multi-layered Structure of Affective Impressions

circumplex model of emotion (Russell, 1980) were included. We included as many different types of videos as possible
in terms of the age, sex, and race of the characters. The movies include, for example, hug scenes of a mother and daughter
after a long separation; those between friends after arguing or separation; lovers; animals and owners; hugs to comfort
each other in sorrow; and hugs to greet close friends or non-close acquaintances.
2.3 Adjective attributes used to evaluate hug-scenes
Using search engines on websites, we selected 58 words related to hugging that could describe the attributes of hugging.
These include evaluative and affective words as well as those explaining the appearance of people and hugging motionsin the videos. Onomatopoeic words were excluded. Subsequently, from the 58 words, we selected those used to describe
the hug scenes using the check-all-that-apply method. In this method, participants watched 24 hug scenes and answered
yes/no to whether each word described those scenes. Finally, the 38 words that received more than two-thirds of theparticipants’ votes were adopted in a later experiment.
The adopted 38 attributes were as follows: reassuring, satisfying, joyful, attractive, relaxing, comforting, gentle,
physically warm, stressful, loving, arousing, full-hearted, sweet, sympathetic, exciting, enjoyable, possessive, sad, reliable,
awkward, passionate, respectable, intimate, cheering, close contact, protective, physically strong, dynamic, forcible,
violent, tight, sudden, embracive, soft, mannish, feminine, happy faces, and sad faces. Descriptions of the attributes
presented to the participants are listed in Table 1.
Table 1: Attribute words and description used in the experiment.
Attribute word
Reassuring
Relaxing
Comforting
GentlePhysically warm

Description
I feel peaceful, calm, and free from anxiety.
The hug scene relieves and unwinds my tension.
I feel comfortable in watching the scene.
The scene feels warm and compassion.
People hugging in the video feels the other person’s bodytemperature.
Satisfying
I feel satisfied and happy.
JoyfulI feel joy.
Stressful
I feel stressed by watching the hug scene.
LovingI feel that people in the video love each other.
Closely contacted People in the video are hugging in close contact.
Arousing
I feel aroused.
Full-hearted
The hug fills my heart with feelings.
Sweet
I feel enchanted.
Sympathetic
I feel people in the video share their feelings.
Exciting
I am excited by the hug.
Enjoyable
The hug is enjoyable and fun.
Protective
One of the people hugging appears to protect the other.
Possessive
One of the people hugging appears to monopolize the other.
Sad
The hug scene is heart-wrenching.

Attribute word
Description
Physically strong People strongly embrace each other.
Dynamic
The hugging motion is not still and dynamic.
Forcible
It appears that one hugs the other without his/her
consent.
Violent
The hugging motion is not kind.
Tight
The people are tightly hugging.
SuddenOne person suddenly gives a hug to the other.
Embracive
One puts his/her arms around the other.
SoftThe hugging motion is soft.
Reliable
I feel that people trust each other.
Awkward
I feel the hug scene is unnatural.
MannishI feel the hug is manly.
Feminine
I feel the hug is womanly.
Passionate
I feel strong feelings of love or friendship.
Respectable
I feel that people respect each other.
Intimate
I feel the intimacy of people in the video.
Attractive
The hug scene is attractive.
Cheerful
The hug scene is cheerful.
Happy faces
The people hugging are smiling and look happy.
Sad faces
The people hugging look sad.

2.4 Task 1: Assessment of hug-scenes to build a layered structure
2.4.1 Procedure
Individual participants watched 24 videos containing hugging scenes played on a computer screen, in random order.
After viewing each video, they rated it on a nine-grade visual scale for each of the 38 attributes. As shown in Figure 2,
two extremities of the scale were labeled “very” and “little,” whereas the center was “neutral.”Relaxing

Reassuring
LittleVery

LittleVery

Figure 2: Nine-graded visual scale of attribute words.

After evaluating the 24 videos, the participants assigned all the adjective attributes to one of the three layers in aforced-choice task. These three layers are psychophysical, affective, and evaluative. A description of each layer

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provided to the participants is as follows: The psychophysical category is the physical aspect of hugging, which can be
determined by observing the images. The affective layer is an attribute judged based on past experiences and feelings,
in addition to information from the psychophysical layer. The overall evaluation layer relates to the comprehensive
quality or preference of the hug based on these two layers. These layers were determined based on previous studies(Guest et al., 2011; Okamoto et al., 2021).
2.4.2 Data analysis
The layer to which the 38 adjectives belonged was determined by the participants’ votes. The results of the 9-grade
scaling were normalized within individuals for each attribute. A principal component analysis (PCA) was applied to theadjectives for each layer. Principal components with eigenvalues greater than one were employed according to the
Kaiser-Guttman criterion. Promax rotation was applied to the coefficient matrix to facilitate the interpretation of theprincipal components.
The structures of the lower (psychophysical), middle (affective), and upper (overall) principal components are investigated. Multiple regression analysis was applied to the upper and middle layers and the middle and lower layers using theprincipal components of the upper layer (category) as the objective variable and the principal components of the lower
layer (category) as the explanatory variables to determine the potential connections between the layers of the principal
components. Only regression coefficients with significant effects (p < 0.05) were adopted to construct a hypotheticallayered model for structural equation modeling. Subsequently, links determined to be significant (p < 0.05) by structural
equation modeling were adopted in the final model. For the structural equation modeling, lavaan (version 0.6) for R wasused. The sample size was 288 (12 participants × 24 replications), which is in the range of typical sizes for structural
equation modeling (Hoshino et al., 2005).
2.5 Task 2: Semantic validation of layered structure
2.5.1 Procedure
The participants viewed the layered model established as a result of Task 1, which showed a directed graph withnodes and arrows as the principal components and the influence between them, respectively, along with the coefficients
representing the strength of the influence. These coefficients were computed using structural equation modeling. The
participants were then asked, “Do you agree with the influence from one node to another?” for each arrow in the layered
model. Participants answered yes or no in a forced-choice manner.
As shown in Figure 3, the directed graph presented to participants randomly included dummy connections thatdiffered from the model obtained in Task 1. The dummy arrows have inverted signs for the coefficients of the arrows
obtained in Task 1. Dummy arrows were randomly generated for each participant and each participant was presented
with two dummy arrows. The participants were suggested the existence of the dummy arrows. Without such dummies,
the participants could be led to believe that all connections were valid, causing them to experience an implicit pressure
to respond affirmatively in all cases. Okada et al. (2019) used a similar method to examine the semantic validity of thecausal models. They presented an authentic model and a few modified models, of which arrows were randomly deleted
and added, and participants determined the model that they could agree. Their method could investigate whether theauthentic model would be entirely endorsed or not. In contrast, the method in this study can examine the semantic
validity of each arrow in the model. Furthermore, the introduction of dummy connections provided the basis for hypothesis tests for the proportions of “yes” or “no” responses to all the connections as described in Section 2.5.2.
Computed
arrow

Dummy
arrow

JoyfulJoyful+.64

Enjoyable−.64

Enjoyable

Figure 3: Dummy arrow presented to participants in Task 2.
The sign of the dummy arrow’s coefficient was inverted.

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Multi-layered Structure of Affective Impressions

2.5.2 Data analysis
For each directional link of the layered structure, the proportion of respondents who agreed with the meaning of thelink was compared to the proportion of respondents who agreed with the dummy links, supposing that the former should
be greater than the latter if the computed links were semantically valid. The rate of the latter proportion was 0.41 (9/22).
To compare the proportions, a hypothesis test (z-test) for two-sample proportions was used.

3. Results
Table 2 shows the results of the categorization of the 38 attributes. Four adjectives were classified in the overall
evaluation layer, 19 in the affective and psychological layers, and 15 in the psychophysical layer.
Table 2: Categorization of adjective.
Layers
Evaluative andpreferential

Description
The overall evaluation layer relates to the overall quality
or preference of the hug based on the other two layers.

Adjectives
reassuring, satisfying, joyful, attractive

Affective andpsychological

Layer of attributes judged based on one’s past experiences
and feelings, in addition to the psychophysical layer.

relaxing, comforting, gentle, stressful, loving, arousing, full-hearted,
sweet, sympathetic, exciting, enjoyable, possessive, sad, reliable,
awkward, passionate, respectable, intimate, cheering

PsychophysicalLayer of the physical aspects of hugging, which can be
judged by looking at the images.

closely contacted, protective, physically strong, dynamic, forcible,
violent, tight, sudden, embracive, soft, physically warm, mannish,feminine, happy faces, sad faces

Table 3 lists the coefficients of the principal component analysis computed for each layer. This table lists the four major
attributes and the coefficients for each component. The overall evaluation, affective/psychological, and psychophysicallayers comprised two, four, and four principal components, respectively.
Table 3: Coefficients of principal components after promax rotation.
Principal
component

Overall evaluation

Affective and psychologicalPsychophysical1

JoyfulSatisfying
Attractive
Reassuring

0.69
0.69
0.48
−0.08

Possessive
Sweet
LovingComforting

0.50
0.50
0.30
0.30

Physically strong
Tight
Closely contacted
Dynamic

0.53
0.46
0.40
0.39

2

Reassuring
JoyfulAttractive
Satisfying

0.96
−0.21
0.15
0.12

Enjoyable
Exciting
Sad
Cheering

0.51
0.49
−0.38
0.36

Feminine
Sad faces
MannishHappy faces

0.61
0.46
−0.41
−0.41

−

Relaxing
Kind
Comforting
Stressful

0.61
0.37
0.35
−0.29

Protective
EmbraciveSoft
Closely contacted

0.60
0.54
0.34
0.32

−

AwkwardRespectable
Sweet
Reliable

−0.47
0.44
−0.38
0.34

Forcible
SuddenViolent
Protective

0.65
0.52
0.43
0.24

3

4

The three-layered model computed is shown in Figure 4. The variables represented by the squares are the principal
components, and their names and main attributes are shown in the squares. Values next to the arrows indicate the intensityof influence. We adopted only significant coefficients (p < 0.05). R2 is the coefficient of determination that indicates how
well a principal component can be explained by the linear sum of the principal components in the lower layer.
The overall evaluation layer consists of two principal components: joyful and reassuring. The affective/psychological layer
consisted of four principal components: sweet, enjoyable, relaxing, and reliable. The psychophysical layer comprises four
principal components: physically strong, feminine, protective, and forcible. The results of the structural equation modelingshowed that the goodness of fit of the model was GFI = 0.99 (which is preferred to be greater than 0.90–0.95 (McDonald & Ho,2002)), the relative goodness of fit with the consideration of the degrees of freedom was CFI = 0.98 (which is preferred to begreater than 0.90 (McDonald & Ho, 2002)), and the significant probability for χ2 was p = 0.16 (which is preferred to be greaterthan 0.05 (McDonald & Ho, 2002)). Based on these indices, the model shown in Figure 4 was considered statistically valid.

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R² = 0.78

R² = 0.70[Enjoyable]
enjoyable,
exciting,
not-sad, cheering[Sweet]sweet,
possessive,
loving, comforting.79.33

Psychophysical
layer

.13

.64

.19

Affectivelayer

R² = 0.88[Reassuring]
reassuring

R² = 0.98[Joyful]
joyful, satisfying,
attractive

Overall
Evaluation
layer

−1.0

.29

[Physically strong]physically strong,tight, closely contacted,
dynamic,

.40

.49

.21

R² = 0.81[Relaxing]
relaxing,
kind,
comforting, not-stressful

−.28

.44

R² = 0.64[Reliable]
not-awkward,
respectable,
not-sweet, reliable
.49

.74

[Protective]
protective,
embracive,
soft, closely contacted[Feminine]
feminine, sad faces,
not-mannish,
not-happy faces

GFI = 0.99
CFI = 0.98
𝜒𝜒 2 46 = 55.5
(p = 0.16)

−.58

−.65

[Forcible]
forcible,sudden,violent, protective

Figure 4: Three-layered structure of impressions toward hug scenes. The values nearby the arrows are the coefficientsto determine the magnitudes of influences from the lower layer.

The results of the semantic validation experiment using dummy links are shown in Figure 5. Fourteen of the 16 links
exhibited p-values less than 0.05, indicating that they were semantically agreed upon by the participants. The two links
exhibited p-values between 0.05 and 0.10. Most links were semantically endorsed. No link had an agreement proportion
below 0.41, which favors the dummy links.
Overallevaluative
layer[Joyful]
joyful, satisfying,
attractive
1.0

1.0

[Reassuring]
reassuring

p < 0.01

p < 0.05

0.82

0.91

0.05 ≤ p < 0.10

0.73

0.82
Affective
layer[Sweet]sweet,
possessive,
loving, comforting
0.73
0.64

Psychophysicallayer[Physically strong]physically strong,tight, closely contacted,
dynamic[Relaxing]
relaxing,
kind,
comforting, not-stressful[Enjoyable]
enjoyable,
exciting,
not-sad, cheering
0.64

0.82

[Feminine]
feminine, sad faces,
not-mannish,
not-happy faces

1.0

0.91

0.91

[Reliable]
not-awkward,
respectable,
not-sweet, reliable
0.91

0.91

0.82

[Protective]
protective,
embracive,
soft, closely contacted[Forcible]
forcible,sudden,violent, protective

Figure 5: Proportions of semantic agreement for each link in the layered model. Numbers nearby the links are the proportions.
Rigid and bold arrows are significantly greater than 0.41 that is the proportion of agreement toward dummy links.

4. Discussion
Regarding the multi-layered model in Figure 4, goodness-of-fit indices above the standard criterion supported thestatistical validity of the model. For the individual variables in the upper and middle layers, the R2 value for the reliable
component was the smallest and R2 = 0.64, indicating that the correlation coefficient between the observed and estimatedvalues for this principal component variable was R = 0.80. Generally, this value indicates a strong correlation (Akoglu,
2018). Hence, the model is validated in terms of overall and local goodness-of-fit values.

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In contrast, in Task 2, the semantic validity of the model was supported. Most of the 16 directive links among thecomponents were semantically approved. Furthermore, all linkages were approved by the majority of participants.
Nonetheless, strong approval was not given for a few linkages in Task 2. The least approved relationships were thelinkage between feminine and enjoyable and between physically strong and sweet. As shown in Figure 4, regarding thefeminine-enjoyable linkage, its coefficient was negative, and more feminine (not-mannish, sad face, not-happy face)scenes felt less enjoyable. In general, being mannish or feminine in hug scenes may not determine the feeling ofenjoyment; hence, the link between feminine and enjoyable was not agreed upon by some participants. In the set of hug
scenes used in this study, a large proportion of the videos judged as feminine might have caused sadness. Similarly,
regarding the relationship between physically strong and sweet, no general influence exists, and it is reasonable thatsome participants did not approve of the linkage between these two attributes. In our video set, these two attributes may
have been correlated by coincidence. Furthermore, another potential reason that the semantics of these linkages were
not agreed upon by a large portion was the difference in the conditions between Task 1, in which the model wascomputed based on specific videos, and Task 2, in which the model was semantically tested based on the common senseof participants. In Task 2, general impressions of hugging were judged without viewing videos on a day different fromthat of Task 1. Such discrepancies in the conditions between the two tasks may have led to a few less-approved connections in the established model.
Although directly comparing our model and the literature is difficult, a partial comparison is possible. We consider
the connections between the top and middle layers represent emotional experiences well while viewing hug scenes. The
top layer of the structure, shown in Figure 4, includes the joyful and reassuring components. The joyful component islargely composed of enjoyable and relaxing components, and may capture the positive valence caused by viewing thevideos. The reassuring component mainly comprises the relaxing component and may express a less arousing status(Russell, 1980). These results suggest that the overall emotional value of hug scenes is judged along with emotional
valence and arousal in Russell’s circumplex emotional model. Thus far, models similar to those computed in the present
study for affectively evocative movies have not been reported; however, such concordance with Russell’s model
suggests its validity. Furthermore, as shown in Figure 5, all connections between the top and middle layers were
semantically supported by the participants. It may be meaningful to compare our results with those of studies based on
experiences other than hug scenes. We found similarities between the results of this study and those of Hosany and
Gilbert (2010). They reported that the emotional factors in travel experiences were joy, love, and surprise. Joy directly
corresponded to the joyful component in our study. Love corresponds to the sweet component (possessive, loving, andcomforting) in the middle layer of our model. Our evaluative attributes in Table 1 did not include those related tosurprise, and Hosany and Gilbert did not include feelings of reassurance or relief; hence, some differences in the results
exist between the two studies. Nonetheless, these differences mainly originate from variations in the attribute words
used in the two studies, and it must be noted that the disagreement is not substantial.
This study has some limitations. First, the three-layered linear model is the simplest form of a multilayered structure,
although it exhibits statistical and semantic validity. To further refine the model, we may employ other methods (Kidoma
et al., 2017; Okamoto et al., 2020) to produce flexible layered models in which the number of layers can vary according
to the complexity of the problem. Second, the model established in this study may fit people with cultural backgrounds
similar to those of the participants. For those with different backgrounds, the model should be recreated using datacollected from them. This is also true for audiovisual content. Currently, we must prepare for multilayered models
specified for each type of audiovisual content.

5. Conclusion
To date, the layered structure of the overall evaluative, affective, and psychophysical impressions of short video scenes
has not been investigated. Such structures help to understand how overall satisfaction with video-viewing experiences isdecomposed into affective and psychophysical antecedents. We exemplified hug scenes to establish a 2-4-4-factor modelin which the overall evaluation comprised two factors: joyful and reassuring. Structural equation modeling statisticallyvalidated the model. Furthermore, the semantic validation of individual relationships in the model was largely agreed
upon by the participants. The established model was specifically designed for hug scenes. However, the method employedin this study is potentially applicable to other types of emotionally appealing videos.

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Ethics Statement

This study was approved by the Institutional Review Board of Hino Campus, Tokyo Metropolitan University (H22-014).

Author Contributions

YO: conceptualization, stimulus preparation, experiment, statistical analysis, and writing of the manuscript.
SO: conceptualization, statistical analysis, manuscript editing, and supervision. YK: experiment, stimulus preparation,and manuscript review. All authors contributed to and approved the submitted manuscript.

Funding

This work was supported by the Institutional Research Fund of Tokyo Metropolitan University.

Conflict of Interest

The authors declare that they have no conflict of interest.

References

Akoglu, H. (2018). User’s guide to correlation coefficients. Turkish Journal of Emergency Medicine, 18(3), 91–93.
https://doi.org/10.1016/j.tjem.2018.08.001
Bao, S., Kuboki, R., Iijima, R., Minagawa, H., Yamanaka, K., & Mizuhiki, T. (2019). Printed book or e-book, which is
better? An investigation using manga and magazine. International Journal of Affective Engineering, 18(1), 9–16.
https://doi.org/10.5057/ijae.IJAE-D-17-00038
Bartl, C., Gouthier, M. H. J., & Lenker, M. (2013). Delighting consumers click by click: Antecedents and effects ofdelight online. Journal of Service Research, 16(3), 386–399. https://doi.org/10.1177/1094670513479168
Chen, X., Barnes, C. J., Childs, T. H. C., Henson, B., & Shao, F. (2009). Materials’ tactile testing and characterization
for consumer products’ affective packaging design. Materials and Design, 30(10), 4299–4310. https://doi.
org/10.1016/j.matdes.2009.04.021
Fujisaki, W., Tokita, M., & Kariya, K. (2015). Perception of the material properties of wood based on vision, audition,and touch. Vision Research, 109, 185–200. https://doi.org/10.1016/j.visres.2014.11.020
Guest, S., Dessirier, J. M., Mehrabyan, A., McGlone, F., Essick, G., Gescheider, G., Fontana, A., Xiong, R., Ackerley, R.,& Blot, K. (2011). The development and validation of sensory and emotional scales of touch perception. Attention,
Perception and Psychophysics, 73(2), 531–550. https://doi.org/10.3758/s13414-010-0037-y
Hashim, I. H. M., Kumamoto, S., Takemura, K., Maeno, T., Okuda, S., & Mori, Y. (2017). Tactile evaluation feedback
system for multi-layered structure inspired by human tactile perception mechanism. Sensors, 17(11), 2601. https://doi.org/10.3390/s17112601
Hosany, S., & Gilbert, D. (2010). Measuring tourists’ emotional experiences toward hedonic holiday destinations.
Journal of Travel Research, 49(4), 513–526. https://doi.org/10.1177/0047287509349267
Hosany, S., & Witham, M. (2010). Dimensions of cruisers’ experiences, satisfaction, and intention to recommend.
Journal of Travel Research, 49(3), 351–364. https://doi.org/10.1177/0047287509346859
Hoshino, T., Okada, K., & Maeda, T. (2005). Fit indices and model modification in structural equation modeling: A
review and new findings. Japanese Journal of Behaviormetrics, 32(2), 209–235. https://doi.org/10.2333/jbhmk.32.209
Kawabata, S., & Niwa, M. (1989). Fabric performance in clothing and clothing manufacture. Journal of the Textile
Institute, 80(1), 19–50. https://doi.org/10.1080/00405008908659184
Kidoma, K., Okamoto, S., Nagano, H., & Yamada, Y. (2017). Graphical modeling method of texture-related affective
and perceptual responses. International Journal of Affective Engineering, 16(1), 27–36. http://doi.org/10.5057/ijae.
IJAE-D-16-00009
Kumar, A., Olshavsky, R., & King, M. (2001). Exploring alternative antecedents of customer delight. Journal ofConsumer Satisfaction, Dissatisfaction & Complaining Behavior, 14, 14–26.
Mano, H., & Oliver, R. L. (1993). Assessing the dimensionality and structure of the consumption experience: Evaluation, feeling, and satisfaction. Journal of Consumer Research, 20(3), 451–466. https://doi.org/10.1086/209361
Matsumura, K., Nunokawa, H., & Sato, K. (2020). Potential factors and satisfaction of readers in social reading using
other’s comments. International Journal of Affective Engineering, 19(2), 119–125. https://doi.org/10.5057/ijae.IJAE-Advance Publication
Multi-layered Structure of Affective Impressions

D-19-00013
McDonald, R. P., & Ho, M. H. (2002). Principles and practice in reporting structural equation analysis. Psychological
Methods, 7(1), 64–82. https://doi.org/10.1037/1082-989x.7.1.64
Nagano, H., Okamoto, S., & Yamada, Y. (2018). Modeling semantically multilayered affective and psychophysicalresponses toward tactile textures, IEEE Transactions on Haptics, 11(4) 568–578.
Ogura, Y., Okamoto, S., & Kosuge, Y. (2023). Multi-layered and multidimensional structure explaining impressions of
hug scenes. International Symposium on Affective Science and Engineering. Proceedings of the 9th International
Symposium on Affective Science and Engineering, PM-1A-6. https://doi.org/10.5057/isase.2023-C000004
Okada, T., Okamoto, S., & Yamada, Y. (2019). Affective dynamics: Causality modeling of temporally evolving perceptual and affective responses. IEEE Transactions on Affective Computing, 13(2), 628–639. https://doi.org/10.1109/
TAFFC.2019.2942931
Okamoto, S., Kojima, H., Yamagishi, A., Kato, K., & Tamada, A. (2021). Layered-modeling of affective and sensory
experiences using structural equation modeling: Touch experiences of plastic surfaces as an example. IEEE Transactions on Affective Computing, 12(2), 429–438. https://doi.org/10.1109/TAFFC.2018.2879944
Okamoto, S., Nagano, H., Kidoma, K., & Yamada, Y. (2016). Specification of individuality in causal relationships
among texture-related attributes, emotions, and preferences. International Journal of Affective Engineering, 15(1),11–19. http://doi.org/10.5057/ijae.IJAE-D-15-00018
Okamoto, S., Wakamatsu, K., Nakauchi, S., Kwon, J., & Sakamoto, M. (2020). Computation of sensory-affective
relationships depending on material categories of pictorial stimuli. IEEE Transactions on Affective Computing,14(1), 498–508. https://doi.org/10.1109/TAFFC.2020.3039684
Pizzi, G., Marzocchi, G. L., Orsingher, C., & Zammit, A. (2015). The temporal construal of customer satisfaction.
Journal of Service Research, 18(4), 484–497. https://doi.org/10.1177/1094670515584752
Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178.
https://psycnet.apa.org/doi/10.1037/h0077714. https://doi.org/10.1037/h0077714
Schindler, I., Hosoya, G., Menninghaus, W., Beermann, U., Wagner, V., Eid, M., & Scherer, K. R. (2017). Measuring
aesthetic emotions: A review of the literature and a new assessment tool. Plos One, 12(6), Article e0178899. https://doi.org/10.1371/journal.pone.0178899
Thomson, D. M. H., Crocker, C., & Marketo, C. G. (2010). Linking sensory characteristics to emotions: An example
using dark chocolate. Food Quality and Preference, 21(8), 1117–1125. https://doi.org/10.1016/j.foodqual.2010.04.011
Tu, J.-C., Xu, Y., & Chang, Y.-S. (2018). Analyzing affective factors of writing stationery under office workers’ lifestyles. International Journal of Affective Engineering, 17(3), 171–181. https://doi.org/10.5057/ijae.IJAE-D-16-00047

Yumeka OGURA (Non-member)
Yumeka Ogura is a software engineer who received the B.S. degree in computer sciencefrom Tokyo Metropolitan University in 2023. Her research interest includes affective
computing.

Shogo OKAMOTO (Member)
Shogo Okamoto received the M.S. and Ph.D. degrees in information science fromthe Graduate School of Information Sciences, Tohoku University, in 2007 and 2010,
respectively. He is currently an Associate Professor with the Department of Computer
Science, Tokyo Metropolitan University. His research interests include haptics, affectiveengineering, human-assistive technology, and extended reality.
Yuki KOSUGE (Member)
Yuki Kosuge is a graduate student of Tokyo Metropolitan University. She received the B.S. degree incomputer science from Tokyo Metropolitan University in 2023. Her research interest includes affective
computing and emotional haptics.