1.2 Perceptual Fluency
Lindgaard et al. (2006) studied visual appeal from
the viewpoint of aesthetics; studies on aesthetic expe-
rience can provide helpful insights into understand-
ing the impressions formed by consumers. Aesthetic
pleasure is explained by perceptual fluency, which is
defined as the ease with which stimuli can be phys-
ically identified (e.g., symmetry, contrast, prototypi-
cality); fluent processing emotionally evokes positive
responses to the stimuli (Reber et al., 2004). For ex-
ample, a figure with high-contrast components is ex-
pected to evoke high fluency, and the stimulus that
is fluently perceived elicits a positive reaction from
the perceiver. Therefore, perceptual fluency can serve
as a useful indicator for exploring impressions. We
can see the effects of perceptual fluency in the early
stages of a model of aesthetic appreciation (Leder
et al., 2004). This model has five stages (perceptual
analyses, implicit memory integration, explicit clas-
sification, cognitive mastering, and evaluation), and
the first two stages are related to perceptual fluency.
In the stage of perceptual analyses, processing of per-
ceptual variables such as contrast, complexity, color,
and symmetry proceeds quickly, and in the stage of
implicit memory integration, aesthetic preferences are
affected by features such as familiarity and prototyp-
icality. In this paper, we regard the positive reactions
of perceivers facilitated by perceptual fluency during
the early stages of human informational processing as
a measure of their impressions of Web pages.
1.3 Ontological Engineering
Ontological engineering is one of the methodologies
used to describe knowledge systematically. From the
viewpoint of knowledge base, “ontology is defined
as a theory (system) of concepts/vocabulary used as
building blocks of an information processing system”
(Mizoguchi et al., 1995). Ontologies are classified
into two types according to the nature of the knowl-
edge described (Mizoguchi, 2003). One of them is re-
ferred to as domain ontology, which describes domain
knowledge, and the other is referred to as task ontol-
ogy, which describes knowledge about processes.
In the ontology development environment Hozo
†
,
each node represents a whole concept and has some
slots, each of which represents a part-concept. Each
whole concept consists of one or more part concepts
with part-of or attribute-of links (Figure 1). Hozo
supports the description of role concepts which repre-
sent a role that depends on the contents of each whole
concept. For example, a human being plays the role
†
http://www.hozo.jp/
Figure 1: Whole concept and part concepts.
of a teacher only in the context of school and not out-
side the school. In other words, every part concept in
the whole concept has a role to play within a given
context. In the context, a class of instances that can
play a role is defined by a class constraint, and it is
called a role-holder (Kozaki et al., 2000).
Figure 2 shows the whole structure of a top-level
ontology YAMATO
‡
. According to YAMATO, an en-
tity is divided into three classes: physical, abstract,
and semi-abstract. While instances of physical class
need 3D space and time to exist, instances of an
abstract class need neither of them. Instances of a
semi-abstract class need only time to exist, and the
class contains a mind, representation, content, and
representation form. Representations such as nov-
els, poems, paintings, music, and symbols are dis-
tinguished from their proposition and form of repre-
sentation (Mizoguchi, 2004). A representation has a
content role played by a proposition and a form role
played by a representation form (Figure 3).
Although it is crucial in design tasks to manage
knowledge on impressions, no common method for
achieving this purpose has been established. In re-
lated research, a model of idea explanation styles
for a designer has been proposed; this model en-
ables designers to share their ideas about a new prod-
uct by adopting the ontological engineering approach
(Ogawaet al., 2009). The study did not directly model
vague ideas themselves, but modeled the explanation
style for these ideas. According to Ogawa’s frame-
work, we can practically model consumers’ impres-
sions of products, even though they are as vague as
ideas are.
In this paper, we introduce a framework to de-
scribe consumers’ impressions by adopting the onto-
logical engineering approach. As noted above, posi-
tive responses to irritations are derived from percep-
tual fluency; therefore, we model perception and its
related concepts: awareness of an object and self-
report of impressions. We first ontologically describe
awareness, perception, and self-report on the basis of
the Hozo and YAMATO environments to construct a
framework for sharing impressions. Then, by adapt-
ing our framework, we demonstrate an instance of im-
pression derivation during the task of appreciation of
Web pages that contains some designed elements.
‡
http://www.ei.sanken.osaka-u.ac.jp/hozo/onto library/
upperOnto.htm
PROPOSAL OF A FRAMEWORK TO SHARE KNOWLEDGE ON CONSUMER'S IMPRESSIONS
389