LIBRES Library and Information Science Research Electronic Journal
Volume 17, Issue 2, September 2007
The role of causality and conceptual coherence in assessments of similarity
Louise F. Spiteri
Louise.Spiteri@dal.ca
School of Information Management
Dalhousie University
Halifax, Nova Scotia. Canada
Abstract
Conceptual coherence, which refers to concepts whose contents make sense to the perceiver, has
been associated traditionally with the notion of similarity, that is, objects, events, or entities form
a concept because they are similar to one another. An examination of traditional similarity-based
concept theories suggests that they do not provide an adequate account for conceptual coherence.
Library and Information Science needs to explore knowledge-based approaches to concept
formation, which suggest that one’s knowledge of a concept includes not just a representation of
its features but also an explicit representation of the causal mechanisms that people believe link
those features to form a coherent whole.
Introduction
Concepts are the glue that holds our mental world together …. Concepts tie our past
experiences to our present interactions with the world; the concepts themselves are
connected to our larger knowledge structures. Our concepts embody much of our
knowledge of the world, telling us what things there are and what properties they have
(Murphy, 2002, p. 1).
The standard psychological usage of concept is that of a mental representation individuated or
defined by its contents or features (Laurence & Margolis, 1999). Concepts are tied closely to
categories: Categorization involves characterizing something by means of concepts so, for
example, my concept of dog allows me to pick out a category of entities that I would call dogs
(Prinz, 2002).
Conceptual coherence refers to concepts whose contents “seem to hang together, a grouping of
objects that makes sense to the perceiver” (Murphy & Medin, 1999, p. 427). Conceptual
coherence has been associated traditionally with the notion of similarity, that is, objects, events,
or entities form a concept because they are similar to one another. Objects fall into natural
clusters of similar kinds (e.g., dogs) that are, at the same time, dissimilar to other kinds (e.g.,
cats). Concepts are pattern-recognition devices that enable us to classify novel entities and to
draw inferences about such entities (Smith & Medin, 1981). If I know something about the
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properties of the concept dog, for example, I am able to make inferences about Spot the
Dalmatian, even if I have never encountered a Dalmatian. Thus, "similarity may be the glue that
makes a category learnable and useful” (Murphy & Medin, 1999, p. 427). “Concepts give our
world stability in that they allow us to treat nonidentical things as equivalent” (Wisniewski,
2002, p. 467).
The notion of similarity, or likeness, underlies most approaches used in Library and Information
Science (LIS) in the design of bibliographic classification systems: “Classification is, in its
simplest statement, the putting together of like things, or more fully described, it is the arranging
of things according to likeness and unlikeness.” (Richardson, 1964, p. 1). The reliance upon
similarity assumes a shared or common understanding of the attributes or features that give a
concept its identity. Does similarity explain, however why a concept was formed or why it
makes sense to the perceiver? Will the same concept have the same degree of coherence
amongst different people, even within the same domain? Two recent studies that examined how
people within the domain of LIS inter-related seemingly similar concepts such as cataloguing
and authority control, for example, showed that although participants agreed that the two terms
were similar, they did not agree why they were similar. Some participants said that authority
control is a product of cataloguing, while others that cataloguing is a form of authority control
(Spiteri, 2004; Spiteri, 2005).
More recently, the influence of the prior theoretical knowledge that learners often contribute to
their representations of categories has also been a topic of study. For example, people not only
know that birds have wings and that they can fly and build nests in trees, but also that birds build
nests in trees because they can fly, and fly because they have wings. Many people even believe
that morphological features of birds such as wings are ultimately caused by the kind of DNA that
birds possess (Rehder, 2003). In comparison, however, with the development of models that
account for the effects of similarity and empirical observations, there has been relatively little
development of formal models to account for the effects of such prior knowledge.
This paper will examine how conceptual coherence is defined and explored in existing concept
theories. It will be argued that traditional similarity-based theories do not provide an adequate
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account for conceptual coherence, and that LIS needs to explore newer, knowledge-based
approaches to concept formation, which suggest that one’s knowledge of many concepts includes
not just a representation of a concept’s features but also an explicit representation of the causal
mechanisms that people believe link those features to form a coherent whole.
Similarity and cognition
In the cognitive sciences, similarity is thought to play an essential role in how people acquire and
categorize information. Conceptual knowledge involves “the representation of the information
required to interact successfully with the environment … the acquisition of conceptual
knowledge involves the construction of mental representations that can facilitate that interaction
(Hahn & Ramscar, 2001, p. 2). Once knowledge is acquired, similarity plays a
fundamental role in theories of knowledge and behaviour. It serves as an organizing
principle by which individuals classify objects, form concepts, and make generalizations
… it is employed to explain errors in memory and pattern recognition, and it is central to
the analysis of connotative meaning (Tversky, 1977, p. 327).
Categorization of acquired knowledge proceeds by “comparing new stimuli to previously
acquired knowledge representations, and classifying it according to which pre-existing
representation it most closely resembles, i.e., according to its similarity to some mental
representation” (Hahn & Ramscar, 2001, p. 2). Similarity is crucial to the process of
categorization:
Here is a simple and appealing idea about the way people decide whether an object
belongs to a category: The object is a member of the category if it is sufficiently similar
to known category members …If you want to know whether an object is a category
member, start with a representation of the object and a representation of the potential
category. Then determine the similarity of the object representation to the category
representation. If this similarity value is high enough, then the object belongs to the
category; otherwise, it does not (Rips, 1989, p. 21).
Categorization performs a fundamental function in the process of cognition: “By recognizing
similarities between potentially dissimilar entities, the individual is enabled to form theories, or
models, of his or her environment that allow him or her to extend to new encounters the
generalizations garnered from past experience” (Jacob, 1991, p. 78). Categorization is used also
to make predictions about new items that one encounters. If I observe several dogs that have fur,
four legs, and a wagging tail, for example, I can conclude that dogs as a category share these
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features. Similarity is the foundation of inductive thinking, since categories whose members
share similar properties have stronger inductive power than categories whose members are less
similar (Heit, 1997; Murphy, 2002). A shared understanding of the nature of a category can
facilitate communication. If I say “I have to go home because of my dog” (Murphy, 2002, p.
244), I may not need to explain what I mean by this, as it is generally known that dogs cannot be
left alone indefinitely.
Similarity and bibliographic classification
The concept of similarity, normally referred to as “likeness” in the LIS literature, is often stated
as being an important underlying principle of bibliographic classification. Shera posits four basic
assumptions of bibliographic classification: universal order of knowledge; hierarchical (genusspecies) division; differentiation; and permanence. More specifically, the principle of
differentiation “is derived from the likeness or unlikeness of the properties or attributes of the
component units of the classification” (Shera, 1965, p. 77).
Maltby defines classification as “not only the grouping of things which resemble one another and
the separation of those which do not, but the arrangement within each group of its components
according to their degree of resemblance” (Maltby, 1975, p. 16). Chan suggests that
classification is a process of “deciding on a property or characteristic of interest, distinguishing
things or objects that possess that property from those which lack it, and grouping things or
objects that have the property of characteristic in common into a class” (Chan, 1994, p. 259).
Richardson argues that likeness “is the universal principle of the order of things … Likeness is so
[much part of] the essence of all human thought, that literally there is no smallest part of the
human mind which cannot be analyzed into just this operation of distinguishing like and unlike
and either holding to or rejecting. Likeness, in particular, is the foundation of that systematic
thought carried to its ultimate which we call logic” (Richardson, 1964, p. 6).
Broadfield however, suggests that likeness indicates merely a relationship between things; it is
not a characteristic of things. “The quality of things that ‘unites’ them … is not a likeness nor
any kind of relation but a character … the genus, which, being variously differentiated in them,
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causes them to be related in various ways” (Broadfield, 1946, p. 2). Likeness thus does not
perform a unitary function, but works in concert with genus and species to form a coherent
concept. “Resemblance is really only a pointer, indicating the possibility that things might be
more profoundly related” (Broadfield, 1946, p. 6).
Similarity-based theories of concept formation
Classical Theory
The Classical Theory originates largely in classical Greek philosophy, and particularly in the
works of Plato and Aristotle, who believed that concepts have a definitional structure whereby
they contain necessary and sufficient conditions by which they can be defined. The concept
bachelor, for example, might be composed of a set of representations such as is not married, is
male, and is an adult. Each of these components, or features, specifies a condition that something
must meet in order to be a bachelor, and anything that satisfies them all counts as a bachelor,
namely, an adult male who is not married; any male who meets all the specified conditions will
be a bachelor. Categorization is a process of checking to see if the features that are part of a
concept are satisfied by the item being categorized. Presumably, then, entities that are
considered similar are members of the same category by virtue of the fact that they share the
same properties; thus, different species of the category mammals, although they may differ in the
sense of being different animals (e.g., dogs, cats, whales), are similar in that they share the
properties inherent to mammals. In the Classical Theory, therefore, all members of the same
category are equally similar to each other because they possess the same properties; similarity is
thus symmetrical, because what is true for one entity in the category is true also for another
(Laurence & Margolis, 1999; Rosch, 1999).
Prototype Theory
The 1970s saw a shift away from the definitional position of the Classical Theory to the notion
that concepts may have a looser structure consisting of features that may not apply equally to all
members of the concept. The Prototype Theory argues that all concepts show gradient degrees of
membership; for example, a sparrow is a better example of bird than is an emu, because a
sparrow is associated more readily with the features that one attributes to birds; likewise, fire
engine red is a better example of red than is red hair (Rosch, 1999; Rosch & Mervis, 1975).
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When an object is encountered, its features are mentally represented and compared to prototype
representations, which are those items that contain the largest number of typical features (Prinz,
2002). Items can be considered extremely typical, moderately typical, atypical, and borderline
concept members—typicality is thus a graded phenomenon. Prototype Theory is tied directly to
similarity measures, whereby the judged similarity of any two items is measured by comparing
the sets of shared and distinctive features that are associated with them. Prototype Theory is
sensitive also to context; for example, dogs or cats might be cited as prototypical pet animals,
while elephants and lions prototypical circus animals. Similarly, in Canada, for example, a
Canada goose is more likely to be cited as a prototypical bird than would be the case in, say,
Germany. The Classical Theory, by contrast, posits that the meaning or definition of a concept
should not change according to context (Rosch, 1999).
Exemplar Theory
The Classical and Prototype theories both focus upon unitary descriptions that capture the central
tendency of any given concept; the difference lies in the acceptance or rejection of a set of
necessary and sufficient features to create that description. The Exemplar Theory suggests that
people do not have a unitary definition of the concept dog, for example, nor is this concept
composed of a list of features that is found to varying degrees amongst dogs. Rather, one’s
concept of dog is composed only of the set of dogs that one has actually encountered and
remembered; so when I see a Doberman, I compare it to my stored memory of other Dobermans
I have encountered. If I encounter an unfamiliar entity, I consult my memory to see which
entities it is most similar to; I then add up the perceived similarities to conclude that this new
entity (e.g., a Havanese) is also a dog (Smith & Medin, 1999). The Exemplar Theory does not
require any defining characteristics; like the Prototype Theory, it deals with prototypes, but
rather than rely upon the matching of an entity to a list of salient features, my prototypes are
those exemplars with which I am most familiar. So, if I have encountered many Dobermans in
my life, this breed would form my prototypical image of the concept dog. This means also that
my definition of dog would be based not on a unitary description that would necessarily apply to
a majority of dogs, but to my exemplar of dog. In daily life, however, I may have encountered
several breeds of dogs, so I might, in fact, have more than one exemplar; for example, a
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Doberman may be my exemplar for a large breed of dog, while a Yorkshire Terrier my exemplar
for a small breed of dog.
The insufficiency of similarity to explain concept formation
In his seminal study of similarity, Goodman concluded that:
similarity, I submit, is insidious … Similarity, ever ready to solve philosophical problems
and overcome obstacles, is a pretender, an impostor, a quack. It has, indeed, its uses, but
is more often found where it does not belong, professing powers it does not possess
(Goodman, 1972, p. 437).
The crux of Goodman’s argument is that saying that two things are similar does not say very
much about them, since any two things can be regarded as similar or dissimilar, depending on
which features one selects for the purposes of comparison. As Hahn and Ramscar contend, a
chair and the pigeon outside the window could share numerous similarities with respect, say, to
their closeness to me: “Unless we specify the respects in which things are said to be similar, the
act of saying that they are similar is an empty statement” (Hahn & Ramscar, 2001, p. 3). Over
the years, several problems with similarity have been identified, as will be discussed next.
Circularity
As has been discussed previously, a coherent concept is one that makes sense to the perceiver:
The reason that bird is a useful concept is that birds are relatively similar to each other – most
birds have wings, lay eggs, and fly, for instance. As Hahn and Chater (1997) suggest, a
hypothetical concept drib which, grouped with a particular light bulb, Polly the pet parrot, the
English Channel, and the ozone layer, would seem to be a highly bizarre concept because the
items it groups are not at all similar. Similarity allows us to make generalizations about birds; if
we know that Polly is a bird and that she has a beak, it is reasonable to assume that other birds
may have beaks. On the other hand, since the constituent members of the drib concept are so
diverse, it is not reasonable to infer that if one drib has a beak, then so do other dribs.
Items are said to belong to the same concept if they share common properties. The problem is
that estimates of similarity may be influenced by people’s knowledge that the things being
compared are in the same, or different, concepts (Murphy & Medin, 1999). Rather than take the
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time to ascertain whether a canary and an emu are actually similar, I assume that they are so
because they are both grouped in the bird concept. In other words we often see things as being
similar because they belong to the same concept, rather than categorizing them because of their
similarity (Goodman, 1972; Hampton, 1998):
To say that birds are similar because, among other things, birds generally lay eggs, is the
same as saying that birds are similar because, among other things, they are grouped
together by the concept egg-layer. It seems that objects are similar because they fall
under the same concepts. The first point of view suggests that similarity can be used to
explain concepts; the second point of view suggests that concepts can be used to explain
similarity. This is dangerously circular at best (Hahn & Chater, 1997, p. 44).
Lack of constraints
The notion of similarity as a process of matching features or properties has been called into
question because it does not provide any constraints on what counts as a feature (Wisniewski,
2002). We say that objects are similar because they have many properties in common. Goodman
(1972) questions the number of properties that are required for statements of similarity; a plum
and a lawnmower, for instance, can share any number of properties, such as the fact that they
weigh less than 100 kilos. All entities can, in fact, have a potentially infinite number of
properties in common; likewise, the list of differences could be infinite. Furthermore, there are
some attributes that are true of only a small number of the category members – perhaps there are
some orange plums, or some lawnmowers run by robots.
Are two things similar, then, only if they have all their properties in common? This will
not work either, for of course no two things have all their properties in common.
Similarity so interpreted will be an empty and hence useless relation (Goodman, 1972, p.
443).
Any two entities can be deemed similar or dissimilar depending on how many features one uses,
and the relevance or salience that one attributes to these features: “Unless one can specify such
criteria, then the claim that categorization is based on attribute matching is almost entirely
vacuous” (Murphy & Medin, 1999, p. 428). The problem with determining salient features is
highlighted by Goodman’s “with respect” argument: X is similar to Y means nothing until it is
completed by ‘X is similar to Y with respect to property Z’ (Goodman, 1972). So, if I say that
Polly the parrot is similar to an emu, I must specify the properties by which I make this
determination.
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Goldstone (1994) suggests that a natural reply to the problem of constraints is that all logicallypossible properties are not salient or psychologically important. When we make similarity
judgments, therefore, we extract and compile features that we consider to be salient for the task
at hand; what is considered salient will vary according to the individual and the context.
Goodman (1972) uses baggage at an airport check-in as an example of how the salience of
features varies by individual. Spectators may notice shape, size, colour, and brand name; the
pilot is more concerned with weight, and the passenger with destination and ownership. Which
pieces of baggage are more alike than others depends not only upon what properties they share,
but upon who makes the comparison, and when.
Similarity is context dependent
Goodman’s “with respect” argument is overly simplistic, however, because he does not take into
account the process of combining properties. People do not usually compare objects only in a
single respect, such as size, but along multiple dimensions such as size, colour, shape, and so
forth. Given multiple respects, the question is how different factors are combined to give a single
similarity judgement. There will be many different similarity values between objects depending
on which respects are considered, such as expertise, cultural background, and so forth (Hahn &
Chater, 1997; Hampton, 1998; Medin, Goldstone & Gentner, 1993). Different types of similarity
can thus be distinguished, depending on the respects in question. The properties that are relevant
for a similarity comparison vary widely with age, environment, method of presentation, cerebral
hemisphere of processing, level of expertise, and goals; for example, expert and novice
physicists evaluate the similarity of physics problems differently, with experts basing similarity
judgments more on general principles of physics than on superficial features (Hardiman,
Dufresne, & Mestre, 1989; Suzuki, Ohnishi, & Shigemasu, 1992). Similarity may depend also on
the context in which a concept is presented (Roth & Shoben, 1983). Tversky (1977) found that
when choosing the most similar country to Austria from the set {Sweden, Poland, Hungary},
subjects chose Sweden more often than Hungary. In this case, the dimension form of government
is important because it highlights a difference between one of the choices (Sweden) and the other
two choices. When choosing the most similar country to Austria from the set {Sweden, Norway,
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Hungary}, subjects chose Hungary more often than Sweden, because now the feature
Scandinavian singles out Hungary from the other two candidates.
Similarity and conceptual coherence
Similarity-based theories of concept formation do not explain adequately the factors that affect
people’s perception of why things are alike. One of the underlying assumptions of these models
is people’s ability to base similarity assessments upon empirical or sensory observations of
features; thus, for example, I note that most dogs have a tail that wags, four legs, fur, the ability
to bark, and so forth. I can visually distinguish a dog from a cat because, although they may
share many of the same attributes (a tail, four legs, and fur), they look different. How reliable
are these perceptions of similarity, however? In a much-cited study, Keil (1989) presented
pictures of a raccoon that was modified to look like a prototypical skunk to subjects of various
ages and found that all subjects over the age of nine insisted that the animal was still a raccoon.
The saying “if it walks like a duck and quacks like a duck, it must be a duck” could be countered
with the argument that if a person can learn to both walk and quack like a duck, is this person
now a duck? The similarity-based theories do not take into account adequately what qualities are
essential to duckness, but rely only upon a tallying of attributes that constitute a duck. What
causes a dog to continue to be a dog, even if a number of typical dog features may not be
present? Is my understanding of dogness based upon my knowledge of the genetic structure of
dogs that makes them unique and different from other animals? How well can I define what
constitutes my coherent concept of dog? Would I be able to identify all the properties that are
unique to dogs, such that dogness survives, regardless of any physical alterations? If a dog were
genetically altered, would I still regard it as a dog? More importantly, is my understanding of the
essence of dogness the same as other people’s?
The Classical Theory does not account clearly for how we define the essence of an entity, nor, as
we have seen, does it account for the fact that some properties may be given different values and
weights by different people. The Prototype and Exemplar theories do a better job of
acknowledging that context affects what people perceive to be typical examples of a concept, but
still do not account for how people define the essence of a concept. Furthermore, the similaritybased theories do not account clearly for the inter-relationships among the properties that
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constitute dogness, nor how different people combine different features to understand the
concept dog. Thus, for example, my definition of a dog may rely upon a combination of physical
attributes (e.g., the ability to bark) and certain behavioural attributes (e.g., fetching, herding, or
retrieving). If I place a higher value on herding behaviour, based perhaps upon my contextual
experience and situation, I am likely to associate this attribute more closely with barking, than a
person who places a higher value on fetching behaviour, since barking is often an important
component of herding. Do some attributes thus have a closer causal relationship with dogness
than others? Similarity-based theories do not explain sufficiently how our underlying knowledge
or understanding of the essence of a concept affects which properties we choose as well as which
we combine in causal relationships to affect our understanding of a concept’s coherence.
Knowledge-based models of concept formation
Theory-Theory
Theory-Theory posits that the process of learning about most concepts involves noticing how
often properties or features occur and co-occur. Our perception of the salience of features thus
depends on how often we encounter them and their correlations, and on our understanding of
why these properties co-occur. In Theory-Theory, our formation of concepts is thus influenced
by our theories of how features are related; for example, blackness and roundness are both
frequently-occurring features of tyres, yet roundness seems to be more central to tyres since it is
so closely linked to the function of tyres (Keil, 2003).
Theory theorists reject similarity-based approaches because of their reliance upon counting
attributes whose salience and number cannot be constrained or controlled. Concepts are learned
as part of our overall understanding of the world around us. When we learn animal concepts, we
integrate this information with our general knowledge of biology, behaviour, and other relevant
domains. Concepts are influenced by what we already know, but may serve also to affect our
existing knowledge (Murphy, 2002; Rehder, 2003; Rips, 1989); so, for example, recent
experiments in the creation of self-replicating robots could cause us to question our current
understanding of the biological function of reproduction. Theory-Theory suggests that since
concepts should be consistent with what we already know, we use our prior knowledge to decide
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whether a new item we encounter belongs in an existing concept, or whether it is necessary to
create a new concept.
Theory-Theory believes that we do not rely only on simple observation or feature matching to
form concepts: We make inferences based upon our prior knowledge and experience and can add
information that we do not observe in the item itself. Returning to Keil’s modified raccoon; it
continues to be a raccoon even if it is transformed to look like a skunk because the true essence
of skunkhood lies deeper than superficial qualities. Subjects found that the essence of an entity
can include features that are not readily observed, and that even if observable features change,
the essence of the entity remains constant. Furthermore, although we may not be able to define
what, exactly, is the essence of a raccoon, we presume that it exists (Keil, 1989). This belief in
hidden essences is called psychological essentialism (Prinz, 2002). Essences are not simply
assumed to be defining features, but also the causal reason behind the manifestation of surface
features, thus the essence of tigers is responsible for all the properties of tigers.
The causal essentialist bias therefore attributes not only the assumption that many
categories have hidden essences, but also the belief that those essences are the reason
behind many of the features of a category. The causal essentialist bias does not usually
include any sense of how it is that the essence is causally linked to the surface, just the
notion that it is (Keil, 2003, p. 673).
Causal-Mode Theory
Like Theory-Theory, Causal-Mode Theory accounts for the effects of theoretical knowledge on
our understanding of concepts, but places greater emphasis upon causal knowledge, which
interrelates or links the features of many concepts that people possess. According to CausalModel Theory, people’s knowledge of many concepts includes not just a representation of a
concept’s features but also an explicit representation of the causal mechanisms that people
believe link those features. People use causal models to determine a new object’s category
membership (Rehder, 2003).
Causal-Mode Theory is designed to determine the importance, or weight, that individual features
have on establishing concept membership. The question of feature weighting is not new, as it has
been addressed by Prototype Theory since the 1970s, where weight is often determined by the
frequency with which features appear in concept members. Where Causal-Mode Theory differs
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is in its focus on how feature weights are determined by people’s domain theories; for example,
straight bananas are rated as better members of the category bananas than straight boomerangs
are of the category boomerangs, a result people attribute to the default feature curved occupying
a more theoretically-central position in the conceptual representation of boomerang as compared
with banana. Causal-Mode Theory posits also that particular combinations of features affect
people’s decision as to what makes for a coherent concept (Rehder, 2003). While both TheoryTheory and Causal-Mode Theory argue in favour of the effect of prior knowledge on perceptions
of concept formation, the latter theory places greater emphasis on the role of specific causal
knowledge.
Shortcomings of knowledge-based theories
A criticism of the knowledge approach is that it does not define clearly what it means by a
theory, nor how “attributes, similarity, or context could be derived from theories, either in the
abstract or from specific theories. Is this theory of categories as theories a new claim of
substance or only a battle cry? What is meant by a theory?” (Rosch, 1999, p. 69). Prinz (2002)
argues that with its reliance upon defining essences, the knowledge approach does not provide a
sufficient explanation of conceptual structure: If I cannot identify the essence of a raccoon, then
how can I have a coherent concept of a raccoon? Would I be able to differentiate, say, between a
raccoon and a stuffed raccoon toy? Saying that what makes a raccoon a raccoon is the essence of
being a raccoon is circular at best.
Another problem with people’s theories is that they could be incorrect or could change over time.
People’s concept of human evolution could be influenced by whether they accept the theories of
creationism versus natural selection; in this case, my concept of human evolution could vary
widely from that of another person. Furthermore, the knowledge approach suggests that my
understanding of airplanes, for example, could be limited or even incomplete unless I have an
understanding of physics, aerodynamics, and so forth. Knowledge-based theorists typically allow
that people can have rather sketchy theories, but this leads to the inevitable conclusion that they
will be unable to form clear concepts. Finally, people’s theories may be incorrect, as, for
example, the belief that AIDS is the result of divine retribution (Laurence & Margolis, 1999).
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The knowledge approach does not address how different people may or may not have the same
understanding of a concept. I may believe that animals have a soul, while my friend does not.
Theory theorists may counter that my friend and I posses the same concept of animals because
we share similar concept contents; this leads in turn to how we would define similarity without
comparing features, but since the knowledge approach does not define concepts in terms of
features, then how can we determine whether my friend and I share a common understanding of
the animals concept? All that the knowledge approach can claim is that my friend and I both
have our own animals concept.
The knowledge approach and conceptual coherence
The criticism that the knowledge approach does not explain adequately how we are able to
define the essence, say, of dog, is well founded. In my attempt to define a dog, it is probably
inevitable that I will list features, attributes, or behaviours that I associate with dogs. Does this
mean that I have captured the essence of a dog—perhaps, but that essence could, and is very
likely to be, peculiar to me. The similarity approach suggests that my understanding of the
concept of dog relies upon my ability to list the features that identify uniquely a dog and
distinguish it from other concepts. The knowledge approach does not argue against the process
of listing features; rather it suggests that one’s understanding of what constitutes a dog may
include not just a listing of attributes, but also an instinctual understanding of what a dog is, as,
for example, “I know a dog when I see one.” Furthermore, I may list X number of attributes I
associate with a dog, while my friend lists Y number of attributes; our attributes may differ, but
does this take away from the fact that both of us are capable of structuring a coherent concept of
dog? The knowledge approach does not suggest that my friend and I possess the same
understanding of dog, but states merely that we are capable of forming our own coherent
concepts, which reverts to the definition of conceptual coherence, namely, that a concept make
sense to the person who forms it.
The knowledge approach does not suggest that there is one, universal essence of doghood. As
was discussed above, criticism of the knowledge approach has focused upon its tolerance for (a)
individual interpretations of the essence of a concept, (b) the changing nature of one’s essence of
a concept over time, and (c) incorrect interpretations of the essence of a concept. This criticism
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highlights perhaps the most fatal flaw of the similarity approach, namely its assumption that
concepts are static, universal, and immutable. It is possible that my interpretation of doghood
may differ from other people’s in details (e.g., I may associate dogs with the chewing of slippers,
based on my prior experience, while others may associate dogs with chasing cars), but does this
mean that is impossible to achieve a degree of consensus over what constitutes a dog? Rather
than insist upon a unitary definition of doghood, the knowledge approach acknowledges the
existence of degrees of doghood that can be agreed upon, especially within a specific domain,
and the fact that these areas of consensus may vary across different domains; thus veterinarians’
degrees of doghood will likely differ from those of, say, animal activists. The knowledge
approach posits that we must be willing to accept a degree of uncertainty and some fuzzy
boundaries in the design of concepts, but that we can still find enough areas of commonalities to
make concepts coherent across a domain.
The fact that one’s understanding of a concept may change over time has been noted as a
weakness of the knowledge approach, which again points to similarity’s assumption that a
concept has an existence that is not open to context, environment, or even time itself. The
concept of marriage, for example, has recently undergone changes in both societal and legal
definitions in Canada, such that it no longer necessarily involves the civil union of a man and a
woman. Our understandings of concepts change with context, environment, and even personal
experience. If anything, the knowledge approach reflects the normal progression of concepts
over time that reflects changes in societal and cultural norms.
The third crux of the argument against the knowledge approach rests on its acceptance that one’s
essence of a concept may be wrong if it is based on erroneous theories of knowledge. Once
again, however, the knowledge approach may simply reflect the realities of life. It is possible for
one person to accept the tenets of creationism, even if scientific evidence suggests that this
theory may be unfounded. Because of its insistence upon personal knowledge, context, and
experience, the knowledge approach acknowledges that a person’s conceptual coherence may, be
fact, be founded upon erroneous information. It is questionable whether similarity can prevent
the formation of “wrong” concepts; a person can be provided with all the correct attributes of a
concept and still choose to define the concept incorrectly. The question of consensus, however,
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may act as a mitigating factor; although not all the members of a domain may agree on the
essence of a concept, to the point where some members’ interpretation of this concept may be
perceived as “wrong,” the knowledge approach suggests that it is still possible to establish a
baseline level of consensus common to the majority of the members.
Conclusion: The knowledge approach and bibliographic classification
The knowledge approach to conceptual coherence parallels recent discussions within LIS about
the structure of bibliographic classification systems. Hjørland and Albrechtsen (1999) and
Beghtol (2003) argue that classification research must be situated within specific contexts and
the domains in which the classification systems are designed to function. Rather than represent a
universal truth based upon unitary descriptions of concepts, classification systems represent only
particular points of view, whose creators “have to choose to represent one particular view of the
knowledge …. [therefore] a classification of a knowledge field … support[s] a given theoretical
viewpoint at the expense of other views” (Hjørland & Albrechtsen, 1999, pp. 134-135).
Any classification is relative in the sense that no classification can be argued to be a
representation of the true structure of knowledge … a classification is merely one
particular explanation of the relationships in a given field that satisfies a group of people
at a certain point in time (Mai, 2004, p. 41).
Classification is based more upon interpretation and judgment than upon logic and its ultimate
purpose is to suggest a view of the world that makes sense, or is coherent, to its users (Mai,
2004).
The importance of domain knowledge in the construction of classification systems is reflected in
recent LIS studies of domain analysis.
The domain-analytic paradigm in information science states that the best way to
understand information … is to study the knowledge-domains as thought or discourse
communities … Knowledge organization, structure, cooperation patterns, language and
communication forms, information systems, and relevance criteria are reflections of the
objects of the work of these communities and of their role in society (Hjørland &
Albrechtsen, 1995, p. 400).
The socio-cognitive view of domain analysis emphasizes how domains structure culturallyproduced signs and symbols and how its members mediate their cognitive processes into
coherent concepts that reflect shared cultural, historical, and social meanings. Members of a
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domain have both individual knowledge structures and biases, as well as shared views: “There is
an interplay between domain structures and individual knowledge, an interaction between the
individual and the social level” (Hjørland, 2004, p. 409).
The knowledge approach’s emphasis upon consensus has parallels also within LIS. Consensus in
classification can be traced to Henry Evelyn Bliss (1939), who believed that classification
systems should reflect how members of the scientific and educational communities structure
knowledge in their respective domains. Bliss believed that scientific and educational consensus
tends to become permanent and is therefore a sufficiently stable basis for a general bibliographic
classification system (Beghtol, 1995). Hjørland and Albrechtsen, however, argue that:
today, it is regarded as somewhat naive to think that consensus guarantees truth … [but]
this does not automatically reject consensus building as a method. In fact, an important
characteristic of a subject area might actually be its degree of stability, degree of
consensus among the researchers at a given time (Hjørland & Albrechtsen, 1995, p. 402).
There is an opportunity for consensus to play a potentially important role in the major
bibliographic subject access and classification systems. The Dewey Decimal Classification
(DDC) Editorial Policy Committee, for example, consults regularly with subject experts and
members of the LIS community “on matters relating to changes, innovation and the general
development of the DDC” (OCLC, 2005). The Library of Congress established the Subject
Authority Cooperative Program to provide a means for libraries to submit subject headings and
classification numbers to the Library of Congress via the Program for Cooperative Cataloging
(Library of Congress, 2005).
The dependence of many LIS bibliographic classification systems upon similarity-based
measures of conceptual coherence may result in systems that impose a unitary definition of
coherence on any given concept. As has been shown, similarity-based measures do not account
adequately for the effect of factors such as context, environment, time, culture, society,
knowledge, and expertise upon the definition and coherence of concepts, which is in stark
contrast to the knowledge approach, which assumes that these factors do, in fact, affect the
development of coherent concepts. The knowledge approach is based also on the assumption that
although a unitary definition of a concept is probably not possible, or even desirable, it may still
be possible to find enough consensus within knowledge domains to create concepts that are
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coherent to the majority of people, with the understanding that this consensus is prone to change
and may be reflective of only a given context in a given time.
Given the demonstrated shortcomings of the similarity approach, there is a need within LIS to
examine the impact of knowledge and causality upon people’s construction of concepts and to
see whether it is possible to achieve a consensus of coherence for these concepts within given
domains. The Causal-Model theory is the most developed working model of the knowledge
approach in its formal account of how causal knowledge influences the importance of features
and specific configurations of features in judgments of concept membership. This model’s ability
to provide a precise, quantitative account of both the differences in feature weights and the
importance of feature configurations induced by people’s knowledge thus makes it an attractive
candidate for integrating the knowledge approach into the construction of bibliographic
classification systems.
References
Beghtol, C. (1995). Domain analysis, literary warrant, and consensus: The case of
fiction studies. Journal of the American Society for Information Science, 46(1), 30-44.
Beghtol,C. (2003). Classification for information retrieval and classification for knowledge
discovery: Relationships between “professional” and “naïve” classifications. Knowledge
Organization, 30(2), 64-73.
Bliss, H. E. (1939). The organization of knowledge and the subject-approach to books. New
York: H. W. Wilson.
Broadfield, A. (1946). The philosophy of classification. London: Grafton & Co.
Chan, L. M. (1994). Cataloging and classification: An introduction (2nd ed.). New York:
McGraw-Hill.
Goldstone. R. (1994).The role of similarity in categorization: Providing a groundwork.
Cognition, 52, 125-157.
Goodman, N. (1972). Seven strictures on similarity. In N. Goodman (Ed.), Problems and
projects (pp. 437-447). Indianapolis: Bobbs-Merrill.
Hahn, U., & Chater, N. (1997). Concepts and similarity. In K. Lambert & D. Shanks (Eds.).
Knowledge, concepts and categories (pp. 43-92). Cambridge, MA: MIT Press.
Pg 18
LIBRES ISSN 1058-6768 Volume 17, Issue 2, September 2007
http://libres.curtin.edu.au/
Hahn, U., & Ramscar, M. (2001). Introduction: Similarity and categorization. In U. Hahn & M.
Ramscar (Eds.), Similarity and categorization (pp. 1-11). New York: Oxford University
Press.
Hampton, J. A. (1998). Similarity-based categorization and fuzziness of natural categories.
Cognition, 65, 137-165.
Hardiman, P. T., Dufresne, R., & Mestre, J. P. (1989). The relation between problem
categorization and problem solving among experts and novices. Memory & Cognition,
17, 627-638.
Heit, E. (1997). Features of similarity and category-based induction. In Ramscar, M.; Hahn, U.;
Cambouroplos, E.; & Pain, H. (Eds.), Proceedings of the interdisciplinary workshop on
categorization and similarity, University of Edinburgh (pp. 115-121). Retrieved January
11, 2007, from http://faculty.ucmerced.edu/eheit/simcat.pdf
Hjørland, B. (2004). Domain analysis: A socio-cognitive orientation for information science.
Bulletin of the American Society for Information Science and Technology, 30(3), 17-21.
Hjørland, B., & Albrechtsen, H. (1995). Toward a new horizon in information science:
Domain-Analysis. Journal of the American Society for Information Science, 46(6), 400425.
Hjørland, B., & Albrechtsen, H. (1999). An analysis of some trends in classification research.
Knowledge Organization, 27(1), 131-139.
Jacob, E. K. (1991). Classification and categorization: Drawing the line. In B. H. Kwasnik & R.
Fidel (Eds.), Advances in classification research, volume 2. Proceedings of the 2nd ASIS
SIG/CR classification research workshop (pp. 67-83). Medford, NJ: Learned
Information, Inc.
Keil, F. C. (1989). Concepts, kinds, and cognitive development. Cambridge, MA: MIT Press.
Keil, F. C. (2003). Categorisation, causation, and the limits of understanding. Language and
Cognitive Processes, 18(5/6), 663-692.
Laurence, S., & Margolis, E. (1999). Concepts and cognitive science. In E. Margolis & S.
Laurence (Eds.), Concepts: Core readings (pp. 3-81). Cambridge, MA: MIT Press.
Library of Congress. (2005). About the SACO program: Program for cooperative cataloging.
Retrieved January 11, 2007, from http://www.loc.gov/catdir/pcc/saco/sacopara.html.
Mai, J. E. (2004). Classification in context: Relativity, reality, and representation. Knowledge
Organization, 31(1), 39-48.
Maltby, A. (1975). Sayers’ manual of classification for librarians (5th ed.). London: André
Pg 19
LIBRES ISSN 1058-6768 Volume 17, Issue 2, September 2007
http://libres.curtin.edu.au/
Deutsch.
Medin, D. L., Goldstone, R. L., & Gentner, D. (1993). Respects for similarity. Psychological
Review, 100, 254-278.
Murphy, G. L. (2002). The big book of concepts. Cambridge, MA: MIT Press.
Murphy, G. L., & Medin, D. L. (1999). The role of theories in conceptual coherence. In E.
Margolis & S. Laurence (Eds.), Concepts: Core reading (pp. 425-458). Cambridge, MA:
MIT Pr ess.
OCLC. (2007). About DDC. Retrieved January 11, 2007, from
http://www.oclc.org/dewey/about/epc/.
Prinz, J. J. (2002). Furnishing the mind: Concepts and their perceptual basis. Cambridge, MA:
MIT Press.
Rehder, B. (2003). A causal-model theory of conceptual representation and categorization.
Journal of Experimental Psychology: Learning, Memory, and Cognition, 29(6), 11411159.
Richardson, E. C. (1964). Classification (3rd ed.). Hamden, CT: Shoe String Press.
Rips, L. J. (1989). Similarity, typicality, and categorization. In S. Vosnaidu & A. Ortony (Eds.),
Similarity and analogical reasoning (pp. 21-59). Cambridge: Cambridge University
Press.
Rosch, E. (1999). Reclaiming concepts. Journal of Consciousness Studies, 6: 61-77.
Rosch, E., & Mervis, C. B. (1975). Family resemblances: Studies in the internal structure of
categories. Cognitive Psychology, 7, 573-605.
Roth, E. M., & Shoben. E. J. (1983). The effect of context on the structure of categories.
Cognitive Psychology, 15, 346-378.
Shera, J. H. (1965). Libraries and the organization of knowledge. Melbourne: F. W. Cheshire.
Smith, E. E., & Medin. D. L. (1981). Categories and concepts. Cambridge, MA: Harvard
University Press.
Smith, E. E., & Medin. D. L. (1999). The exemplar view. In E. Margolis & S. Laurence (Eds.),
Concepts: Core readings (pp. 207-221). Cambridge, MA: MIT Press.
Spiteri, L. F. (2004). Word association testing and thesaurus construction. Libres, 14(2).
Retrieved January 11, 2007, from http://libres.curtin.edu.au/libres14n2/index.htm
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LIBRES ISSN 1058-6768 Volume 17, Issue 2, September 2007
http://libres.curtin.edu.au/
Spiteri, L. F. (2005). The use of word association in the construction of information retrieval
thesauri: A pilot study. Cataloging & Classification Quarterly, 40(1), 55-78.
Suzuki, H., Ohnishi, H., & Shigemasu, K. (1992). Goal-directed processes in similarity
judgment. Proceedings of the fourteenth annual conference of the Cognitive Science
Society (pp. 343-348). Hillsdale, NJ: Erlbaum.
Tversky, A. (1977). Features of similarity. Psychological Review, 84, 327–352.
Wisniewski, E. J. (2002). Concepts and categorization. In H. Pashler & D. Medin (eds.), Stevens’
handbook of experimental psychology. Third edition. Volume 2: Memory and cognitive processes
(pp. 467-530). New York: John Wiley & Sons.
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