[Paleopsych] Medin et al. Are There Kinds of Concepts?

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Are There Kinds of Concepts?
Annual Review of Psychology, Annual 2000 p121.
by Douglas L. Medin, Elizabeth B. Lynch, and Karen O. Solomon

Key Words categorization, cognitive processes, mental representation, domain

Abstract: Past research on concepts has focused almost exclusively on
noun-object concepts. This paper discusses recent research demonstrating that
useful distinctions may be made among kinds of concepts, including both object
and nonobject concepts. We discuss three types of criteria, based on
structure, process, and content, that may be used to distinguish among kinds
of concepts. The paper then reviews a number of possible candidates for kinds
based on the discussed criteria.


Many years ago, the cryptic but pointed comment of a collegue on a book by one
of the present authors (Smith & Medin 1981) said, "This is an excellent
overview but you two seem to think that concept is spelled, noun." The
commentator may have been generous at that, because natural object concepts
were the focus (with attempts made to justify the reasons). In this paper, we
do not distinguish questions about kinds of categories from questions about
kinds of concepts. Although the distinction between concepts and categories is
important (see Solomon et al 1999), where there are distinct kinds of
categories, the associated concepts will also be distinct. Although the
reviewer's barbed comment is still relevant today, since that time there has
been a continuous and substantial volume of research on categories and
concepts. This has served to greatly broaden the topic's empirical and
theoretical base, so that today there is a lot more to say about different
kinds of concepts than there was in 1981. Accordingly, this review is
organized around the question of whether there are distinct kinds of concepts.

On the surface it seems transparently true that there are kinds of
concepts--notions like democracy seem different from things like party or from
concepts such as "black-capped chickadee." But a little reflection suggests
that the notion of kinds of concepts must be evaluated relative to the
theoretical work a kind or domain is going to be asked to do. For example, if
one is interested in concept learning, the relevant issue might be whether
different kinds of concepts are acquired in the same way. Note that this
shifts but does not remove the explanatory burden: For the question to be
meaningful, criteria are needed for deciding whether concepts are "acquired in
the same way." In brief, questions about kinds of concepts should be answered
by theories rather than intuitions. In this paper, we attempt to bring
together candidates for kinds of concepts that have emerged across the
different theoretical perspectives of current research on concepts.

One motivation for the interest in kinds is that a number of scientists,
especially researchers in the area of cognitive development, have argued that
cognition is organized in terms of distinct domains, each characterized by
(usually) innate constraints or skeletal developmental principles (e.g.
Hirschfeld & Gelman 1994a). That is, cognition is said to be domain specific.
Some researchers object to the claims about innateness as well as the claims
about domain specificity (e.g. Jones & Smith 1993). To evaluate this debate,
one needs criteria for domains (or kinds).

A less-contentious reason to worry about kinds concerns trade-offs between
different levels of explanation and specificity or preciseness of
generalizations. To use an analogy with biological kinds, there are
interesting properties that all living things share, but there are further
interesting generalizations that may hold only for mammals or only for
primates or only for human beings. Treating all concepts as being of the same
type may be useful for some purposes but we may be missing important
principles that apply robustly only for subsets of concepts.

Another reason to care about kinds of concepts is that even universal aspects
of concepts may be more salient and easier to study in some concepts than in
others. Neurologists study the squid axon not because squids are the only
things that have axons but because the squid axon is large. Finally, the most
obvious reason to worry about kinds is that exploring different kinds allows
us to test the generality of our theories and models.

The rest of this review is organized as follows. First, a variety of criteria
for establishing distinct kinds of concepts is presented. Then some candidates
for kinds are discussed and the corresponding literature is evaluated with
respect to our criteria. Finally, a descriptive summary and prescriptive
advice are presented.

In evaluating the literature from a specific perspective, we take advantage of
a number of other recent review papers and edited volumes (e.g. Nakamura et al
1993, Van Mechelen et al 1993, Lamberts & Shanks 1997, Ward et al 1997a, Medin
& Heit 1999). Komatsu (1992) analyzes research on the role of intuitive
theories and other forms of knowledge versus the role of similarity in
categorization (for related analyses, see also Goldstone 1994a; Hahn & Chater
1997; Hampton 1997, 1998; Heit 1997; Malt 1995; Murphy 1993; Sloman & Rips
1998). Solomon et al (1999) focus on the role and implications of multiple
conceptual functions for concept theories. A review by Medin & Coley (1998)
traces relationships between laboratory studies using artificially created
categories and research using natural (lexical) concepts (see also Estes


We consider three types of interrelated criteria for distinguishing concept
types: (a) structural differences, (b) processing differences, and (c)
content-laden principles.

Structural Differences

A great deal of research on the psychology of concepts has been directed at
their componential structure, especially as it relates to categorization.
Virtually everyone believes that concepts should be analyzed in terms of
constituent attributes or features. For example, the concept of stallion may
be understood in terms of features such as animate, four-legged, male, adult,
and so on. Thus, criteria for kinds of concepts based on structural
differences would be based primarily on differences in the kinds of features
in a concept and the relations among these features. The 1970s were
characterized by a shift from the position that categories are organized in
terms of defining (singly necessary and jointly sufficient) features (the
so-called classical view) to the view that category membership is more graded
and structured in terms of features that are only typical or characteristic of
categories--the so-called probabilistic or prototype view (for seminal papers,
see Rosch & Mervis 1975, Smith et al 1974; for an early general review, see
Smith & Medin 1981).

As noted earlier, much of the discussion and research on conceptual structure
has employed object concepts (e.g. chair, bird, tool, etc). The possibility
remains that other categories conform to a classical view structure or exhibit
entirely novel structure.

Processing Differences

One might also distinguish among kinds of concepts based on the types of
processing that are done to develop and maintain them. For example, categories
formed through data-driven, bottom-up processes may be different from
categories formed through top-down categorical processes. It is an obvious but
important point that claims about either structure or processing cannot be
evaluated in isolation, that structure-process pairs must be considered (e.g.
Anderson 1978). For example, a hypothesis-testing mechanism for learning
classical view categories would likely fail to acquire probabilistic
categories. Researchers interested in processing principles have generally
assumed that differences in structure are associated with processing
differences. Of course, process may drive structure. For example, categories
created in the service of goals may be fundamentally different from natural
object categories. An alternative idea is that there may be multiple processes
that operate on the same structure.

It is fair to say that theories about conceptual structure and processing are
based primarily on research with object categories, though the conclusions
from this work are thought to apply to a wide range of concepts. Are object
categories analogous to the squid axon mentioned above? That is, are object
concepts just easy-to-study representatives of all concepts? One may also
wonder whether object concepts are themselves uniform in kind. Below we
discuss recent research that suggests that there are principles of conceptual
structure and processing that cannot be generalized across all concepts. We
then turn to the question of whether important variations exist among object

Content-Laden Principles

In contrast to the view that there are general, abstract principles of
conceptual structure and processing, advocates of domain specificity focus on
principles that apply uniquely to concepts with specific contents. For
example, in this view, kinds of concepts may be divided into domains of
concepts, such as naive biology, naive psychology, and naive physics. Given
that the contents of concepts in different domains are almost surely going to
be different, it is tempting to conclude that these advocates have created
kinds (or domains) simply by defining them into existence. As we shall see,
the domain specificity view does have empirical content. First, however, we
consider candidates for kinds based on structure and those based on


Nouns Versus Verbs

It appears that the distinction between nouns and verbs is universal (Sapir
1944). Gentner and colleagues (Gentner 1981, 1982; Gentner & France 1988;
Gentner & Boroditsky 1999) have marshaled theoretical and empirical arguments
for the view that nouns and verbs map onto ontologically distinct aspects of
the environment (see also MacNamara 1972). Although the contrast is not
without exception, the general idea is that nouns refer to clusters of
correlated properties that create chunks of perceptual experience. Languages
honor these perceptual discontinuities, as evidenced by good cross-cultural
consistency in the presence of lexical entries corresponding to these chunks.
In contrast, predicative concepts in general and verbs in particular focus on
relations among these entities involving such things as causal relations,
activity, or change of state. Given that relations presuppose arguments or
objects, it would seem that nouns are conceptually simpler than verbs and,
Gentner (1981) argues, more constrained by perceptual experience. If so, one
might expect that (a) (concrete) nouns should be learned before verbs (see
Bloom et al 1993, Choi & Gopnik 1995, Au et al 1994, Tardif et al 1999,
Tomasello 1992, Waxman 1998, Waxman & Markow 1995, Woodward & Markman 1997;
for review, see Gentner & Boroditsky 1999), (b) there should be more
cross-linguistic variability in verbs than in nouns (see Bowerman 1996;
Levinson 1994, 1999; Waxman et al 1997), and (c) linguistic (syntactic)
structure should play a greater role in verb learning than in noun learning
(see Naigles 1990, Choi & Bowerman 1991, Pinker 1994). Although there is not
universal agreement on any of these claims, the weight of evidence appears to
agree with each of them.

The distinction between nouns and verbs no doubt needs to be somewhat nuanced.
For example, motion is associated with both nouns and verbs (e.g. Kersten &
Billman 1995), but there is a bias for nouns to be associated with motion
intrinsic to an object and for verbs to be associated with motions involving
relations between objects (Kersten 1998a,b).

Count Nouns Versus Mass Nouns

Another lexical distinction that reveals differences in conceptual structure
is the mass/count distinction. For example, although you can say "a dog"
(count noun), you cannot say "a rice" or "a sand." Wisniewski et al (1996)
note that the mass/ count distinction applies to superordinate categories as
well: Some superordinate concepts are mass nouns (e.g. "some" furniture), and
others are count nouns (e.g. "an" animal). In a series of studies, Wisniewski
et al demonstrate that the linguistic distinction between mass and count
superordinates reflects conceptual differences as well. They found that
members of mass superordinates tend to co-occur and people tend to interact
with many members of a mass superordinate at one time, but they tend to only
interact with single members of count-noun superordinates. Furthermore, they
found that properties that characterize individuals are a more salient aspect
of count superordinates. Wisniewski et al conclude that mass superordinates
refer to unindividuated groups of objects, rather than to single objects, and
that, unlike count superordinates, mass superordinates are not true taxonomic
categories. Markman (1985) also noted conceptual differences between mass and
count superordinates. Specifically, she found that across languages, terms for
categories at more abstract levels of a hierarchy are more likely to be mass
nouns than are terms for categories at low levels of a hierarchy. She also
found that children learned concepts with the same extension faster when they
were referred to by a mass noun than by a count noun (Markman et al 1980).

Isolated and Interrelated Concepts

The structural difference between noun and verb concepts in terms of clusters
of features versus relational properties may also usefully distinguish among
kinds of nouns. Some noun concepts are intrinsically defined, whereas others
appear to be more relational in character (Barr & Caplan 1987, Caplan & Barr
1991). For example, the concept of grandmother seems to centrally involve the
relational notion of being a female parent of a parent. Barr & Caplan (1987)
found that relational concepts show more graded membership and smaller
differences between gradients of typicality and membership judgments than do
intrinsically defined concepts. Given that the literature has tended to focus
on intrinsic concepts, perhaps other phenomena associated with categorization
and other uses of concepts may not generalize to relational concepts. There is
not sufficient evidence to hazard a guess with respect to this possibility.

Goldstone (1996) has marshaled evidence for the distinction between isolated
and interrelated concepts where a concept is interrelated to the extent that
it is influenced by other concepts. He further showed that current models of
categorization (e.g. exemplar models) can account for some but not all of the
phenomena associated with interrelated concepts. However, he offers a
recurrent network model that successfully describes varying amounts of
intercategory influence. The fact that a unitary computational model accounts
for both isolated and interrelated concepts undermines the view that these are
distinct kinds of categories.

Objects Versus Mental Events

Although some researchers have focused on parallels between object and event
concepts (e.g. Rifkin 1985; for social events, see Morris & Murphy 1990), Rips
and his associates have demonstrated important differences between objects and
mental events (e.g. Rips & Conrad 1989, Rips & Estin 1998). For example,
part-whole relations seem to behave differently for objects and mental events.
The steering wheel of a car is not a kind of vehicle but a part of planning,
such as evaluating competing plans is a type of thinking (Rips & Estin 1998).
Evidence from other experiments suggests that parts of mental events (and, to
an intermediate degree, scripts) are less bounded (discriminable) and more
homogeneous than parts of objects (Rips & Estin 1998). Finally, if the
categories that describe mental events are less bounded, then they may be more
difficult to learn than object categories (see Keil 1983).

Artifacts Versus Natural Kinds

Numerous studies have shown that different kinds of features are important to
natural kind verses artifact categories (Barton & Komatsu 1989, Gelman 1988,
Keil 1989, Rips 1989). These studies indicate that functional features are
more important for artifacts, and features referring to internal structure are
more important for natural kinds. For example, Barton & Komatsu (1989)
presented participants with natural kind and artifact categories that had
changes either in molecular structure (e.g. a goat with altered chromosomes or
a tire not made of rubber) or in function (e.g. a female goat not giving milk
or a tire that cannot roll). Changes in molecular structure were more likely
to affect natural kind categories than artifact categories (e.g. a goat with
altered chromosomes is less likely to be a goat), whereas changes in function
were more likely to affect artifact categories (e.g. a tire that cannot roll
is less likely to be a tire). Later research does suggest some ambiguity with
regard to the criterial features of artifact categories. Malt & Johnson (1992)
found that artifact category membership decisions were more influenced by
physical than by functional features. (For another view on the nature of
artifact categories see Bloom 1996, 1998; Malt & Johnson 1998.) Overall, these
studies suggest that natural kind and artifact categories may differ on the
basis of the kinds of features that are criterial for membership in the

Research by Ahn (1998) may explain why different kinds of features are
criterial for natural kind and artifact categories. Ahn claims that the
centrality of a feature to a category depends on the causal status of that
feature relative to the other features in the category (see also Ahn 1999,
Sloman & Ahn 1999, Sloman et al 1998). Specifically, if a feature is thought
to give rise to other features in the category, removing that causal feature
affects category identity more than the removal of a noncausal feature does.
Ahn showed that the causal status hypothesis accounted for the results of
Barton & Komatsu (1989) and Malt & Johnson (1992). That is, the features in
these studies that had been judged as criterial to their categories were also
rated as the most causal. In artificial category studies, Ahn (1998) showed
that regardless of whether the category was a natural kind or an artifact,
when functional features caused compositional features, functional features
were considered more essential to category membership, whereas when
compositional features caused functional features, compositional features were
considered more essential to category membership. This suggests that the
differences between artifact and natural kind categories may result from the
fact that different kinds of features are causal in natural kind and artifact
categories (for further discussion, see Keil 1995). The original problem of
determining whether artifacts and natural kinds constitute distinct kinds of
categories thus becomes the problem of determining whether the causal status
of the features of a category can be determined independently of its status as
a natural kind or an artifact.

Abstract Concepts

Abstract concepts, such as truth and justice, seem different from object
concepts, such as dogs and boats. Yet little work has addressed how we
understand abstract concepts. One suggestion has been that abstract concepts
are understood through conceptual metaphors (Gibbs 1997, Lakoff & Johnson
1980). During this process, representations of concrete concepts are mapped
onto the abstract concepts to facilitate understanding. For example, justice
might be understood through a conceptual representation of a scale, and anger
might be understood through a conceptual representation of boiling water. If
abstract concepts are understood via a metaphorical representation of an
object concept, we might not expect to find structural differences between
these two types of concepts. Clearly more work needs to be done on how
abstract categories are formed and understood.

Basic Level Versus Subordinate and Superordinate Concepts

The observation by Markman (1985) that mass categories are likely to be
superordinate categories suggests that differences in taxonomic level may
correspond to differences in conceptual structure. Although most objects can
be described or named at a number of different levels of abstractness (e.g.
rocking chair, chair, furniture item, human artifact), the "best name" for
objects (Brown 1958) is at one particular level. In a classic paper, Rosch et
al (1976) argued that bundles of correlated properties associated with objects
form natural discontinuities or chunks that create a privileged level of
categorization. They showed that the basic level is the most inclusive level,
at which many common features or properties are listed, the most abstract
level at which category members have a similar shape (for a more detailed
analysis of comparability and levels, see Markman & Wisniewski 1997), and the
level above which much information was lost. Furthermore, the basic level is
preferred in adult naming, first learned by children, and the level at which
categorization is fastest. In short, these and other measures all converged on
a single level as privileged. The findings by Rosch et al (1976) presented a
powerful picture of a single taxonomic level as privileged across a wide range
of conceptual measures. The authors suggested that the basic level is the
level that provides the most cognitively accessible information about the
correlational structure of the environment. Are basic-level categories
different in kind from categories at other levels? Surprisingly, a number of
lines of research suggest that this may not be the case.

First of all, recent evidence suggests that, at least on some tasks, the basic
level may change as a function of expertise (e.g. Tanaka & Taylor 1991; Palmer
et al 1989; Johnson & Mervis 1997, 1998). For example, experts may prefer to
name at subordinate levels, and they verify category labels equally fast at
subordinate and basic levels. Although Rosch et al (1976) had contemplated
this possibility, this evidence compromises their explanation of the basic
level by suggesting that the cognitive accessibility of feature correlations
is expertise dependent, rather than universal and absolute. An interesting
possibility is that learning may modify the constituent features or attributes
of a concept. A number of recent findings and models provide support for this
possibility (e.g. Gauthier & Tarr 1997, Goldstone 1994b, Livingston et al
1998, Norman et al 1992, Schyns & Rodet 1997, Schyns 1998; for an overview and
commentary, see Schyns et al 1998; for an edited volume, see Goldstone et al
1997). In short, the salience of feature clusters may not be absolute and
invariant but rather variable as a function of learning.

Another complication is that although ethnobiologists (Berlin 1992) and
psychologists both find evidence for a privileged taxonomic level, they
disagree about where in the taxonomy this level is located. Berlin (1992)
pinpoints privilege at the level that would typically correspond to genus
(e.g. blue jay, bass, beech), whereas Rosch et al (1976) found the privileged
level to be a more abstract level, corresponding more nearly with class (e.g.
bird, fish, tree). One explanation is that this represents an expertise
effect. The people in traditional societies studied by ethnobiologists may be
biological experts relative to undergraduates in a technologically oriented
society--the population of choice for psychologists. Another possibility is
that ethnobiologists and psychologists use different measures of basicness and
that these measures do not converge (see also Barsalou 1991).

Coley et al (1997; see also Atran et al 1997, Medin et al 1997) did direct
cross-cultural comparisons of these two types of populations using a single
measure, inductive confidence. They assumed that if the basic level is the
most abstract level at which category members share many properties, then
inductive confidence (reasoning from one member having some novel property to
all members having that property) should drop abruptly for reference
categories above the basic level. Surprisingly, both the Itzaj of Guatemala
(members of a traditional society) and US undergraduates consistently showed
the same level as privileged, and this level corresponded to genus, consistent
with expectations derived from anthropology. This finding raises the
possibility that different levels within an object hierarchy are useful for
different kinds of tasks (different types of processing). At least for
novices, there is a disparity between the level privileged for induction and
that favored in naming and speeded category verification tests (though experts
may show a convergence across these three tasks). Despite the admirable
thoroughness of the original studies of Rosch et al (1976), evidence is
increasingly challenging their claim that a single taxonomic level is
privileged across the divergent processing demands of particular tasks.

Although Rosch et al have claimed that informativeness determines the basic
level, Barsalou (1991) has suggested that perceptual factors may be more
central. Barsalou argues that entities are categorized first by shape, because
the visual system extracts the low-spatial-frequency information that is used
to recognize shape faster than it extracts the high-spatial-frequency
information that is necessary to recognize more detailed information (e.g.
texture). For example, shape has fairly low variance across birds, making
shape a strongly predictive feature for the category bird. This argument is
strengthened by the fact that entities that do not share the same shape as
their fellow basic-level category members (defined by informativeness) are
usually not categorized initially at the basic level but instead are
categorized initially at the subordinate level (Jolicoeur et al 1984). For
example, a chicken is first categorized as a chicken rather than a bird,
presumably because it has an atypical shape for a bird. Barsalou (1991)
suggests that there may be a perceptual basic level, based primarily on shape
and used largely during perception, and a more informational basic level,
carrying more conceptual information and used for secondary categorizations
during reasoning and communication. This idea may help explain the discrepancy
between the privileged level discovered by Rosch et al 1976) on perceptual
tasks and that discovered by Coley et al (1997) on the induction task.

Murphy & Wisniewski (1989) present further evidence that different taxonomic
levels serve different functions. Specifically, superordinates may be used to
conceptualize scenes or other types of schemas where interconceptual relations
are important, whereas basic-level concepts may be used to conceptualize
entities in isolation. (For a recent review of research on hierarchical
category structure, see Murphy & Lassaline 1997.)

Another claim by Rosch et al (1976) that is under examination is the idea that
the basic level is the level at which categories are first learned by
children. Specifically, recent studies have raised the possibility that
superordinate categories may be learned as early as, or earlier than,
basic-level categories. For example, Mandler et al (1991) found that children
18 months old were able to distinguish between members of the superordinate
categories of animals and vehicles, but they were not able to distinguish
between members within each of these categories (such as dogs and rabbits, and
cars and boats). Mandler et al argued from this finding that children acquire
certain kinds of superordinate categories, which they call global categories,
prior to basic-level categories.

Other evidence suggests that the categories that a child first acquires are
not determined by their position within a taxonomic hierarchy but rather
depend on the particular objects to which the child has been previously
exposed. For example, infants 3-4 months old trained on domestic cats in a
habituation paradigm dishabituate to members of contrasting basic-level
categories (e.g. dogs, birds, tigers) but not to novel domestic cats. This
suggests that during training, the infants formed a representation of the
basic-level category "domestic cats" (Eimas & Quinn 1994, Eimas et al 1994).
However, infants also appear to be facile at learning categories at
superordinate levels. When infants 3-4 months old are trained on different
members of the superordinate category "mammal" (e.g. dogs, cats, tigers,
zebras), they dishabituate to nonmammal category members (birds, fish) but not
to novel mammals (e.g. deer, beavers) (Behl-Chadha 1996). Apparently, the
infants were able to form a representation of the superordinate category

These studies suggest that children can form both basic-level and global
concepts depending on the stimuli presented (see also Quinn & Johnson 1997.)
Although these findings appear to be robust, there may be less unanimity with
respect to their interpretation. A critical question concerns the criteria for
the claim that a child has learned a concept. For example, is sensitivity to
perceptual discontinuities that correspond to concepts equivalent to having a
concept? (For one point of view on this issue, see Mandler 1997, Mandler &
McDonough 1998.)

Overall, recent research tends to weaken the claim for a qualitative
distinction between the different levels of a taxonomic hierarchy. The
blurring of the distinction between levels undermines the notion that
basic-level concepts are special kinds of concepts that reflect the structure
of the world, independent of knowledge, expectations, goals, and experience.

Hierarchies Versus Paradigms

The previous discussion of levels is premised on categories being
hierarchically organized. But social categories based on factors such as race,
age, gender, and occupation (e.g. female teenager, Asian mail carrier)
represent more of a cross-classification or paradigm than a taxonomy. Is there
a notion of privilege for social categories, as there is for hierarchical
categories? It appears that a key factor in social information processing is
accessibility of categories (e.g. Smith & Zarate 1992, ER Smith et al 1996)
and that some social categories may be accessed automatically (e.g. Bargh
1994, Devine 1989, Banaji et al 1993, Greenwald & Banaji 1995, Zarate & Smith
1990). Some intriguing evidence even suggests that the activation of one
social category leads to the inhibition of competing social categories (Macrae
et al 1995). Although the structural difference between paradigms and
taxonomies is important, it is too early to tell if processing principles also
differ between social categories and taxonomic categories, mainly because
direct comparisons have not been done.

Ross & Murphy (1999) studied categories associated with foods and their
consumption, a context that is interesting because it allows one to study
relations between taxonomic categories (e.g. breads, meats, fruits) and script
categories that cut across taxonomic categories (e.g. snack foods, dinner
foods, junk foods). They report evidence that script categories are less
accessible than are common taxonomic categories. Both types of categories were
used in inductive reasoning, but their use varied with the type of inference
involved. This work points to the fact that even hierarchically organized
object categories may admit to other organizations.

Category Structure and the Brain

Studies of patients with selective cognitive impairments have often provided
important clues to normal functioning. One intriguing observation concerns
category-specific deficits, where patients may lose their ability to recognize
and name category members in a particular domain of concepts. Perhaps the most
studied domain difference has been living versus nonliving kinds. For example,
Nelson (1946) reported a patient who was unable to recognize a telephone, a
hat, or a car but could identify people and other living things (the opposite
pattern is also observed and is more common).

These deficits raise the possibility that living and nonliving things are
represented in anatomically and functionally distinct systems (Sartori & Job
1988). An alternative view (e.g. Warrington & Shallice 1984) is that these
patterns of deficits can be accounted for by the fact that different kinds of
information are needed to categorize different kinds of objects. For example,
sensory information may be relatively more important for recognizing living
kinds, and functional information more important for recognizing artifacts
(for computational implementations of these ideas, see Farah & McClelland
1991, Devlin et al 1998). Although the weight of evidence appears to favor the
kinds-of-information view (see Damasio et al 1996, Forde 1999, Forde &
Humphreys 1999), the issue continues to be debated (for a strong defense of
the domain-specificity view, see Caramazza & Shelton 1998).


Researchers are beginning to systematically explore a variety of structural
principles according to which conceptual representations vary. There is fairly
good support for the idea that nouns and verbs are different kinds of
concepts, or at least that the distinction serves to organize an interesting
body of research on linguistic and conceptual development. The lexical
distinction between nouns and verbs appears to be mirrored in conceptual
structure. Another factor that emerges across a number of candidates for kinds
of concepts is the difference between those that are composed of clusters of
features and those composed of relations. In the next section, we focus on
processing-related differences, but given that processing affects structure,
this can be seen as an addition to our list of structural distinctions.


Common Taxonomic Versus Goal-Derived Categories

Barsalou (1983, 1985) pointed out that many categories are created in the
service of goals and that these goal-derived categories may differ in
important ways from object categories. Examples of goal-derived categories
include "things to take out of your house in case of a fire" or "foods to eat
when on a diet." Goal-derived categories may activate context-dependent
properties of category members. For example, the fact that a basketball is
round is a stable property that should be accessed independent of context, but
the fact that basketballs float may only be accessed in contexts where a goal
relies on its buoyancy. Barsalou's research also shows that members of
goal-derived categories are not especially similar to one another and, thus,
that they violate the correlational structure of the environment that
basic-level categories are said to exploit. In addition, Barsalou has
determined that the basis for typicality effects differs for goal-derived
versus common taxonomic categories. Typicality or goodness of example is
generally assumed to be based on similarity relationships--a good example of a
category (e.g. robin for the category "bird") is similar to other category
members and not similar to nonmembers, whereas an atypical example (e.g.
penguin as a bird) shares few properties with category members and may be
similar to nonmembers. Barsalou (1985) found that typicality for goal-derived
categories was based on proximity to ideals rather than on central tendency.
For instance, the best example of diet foods is not one that has the average
number of categories for a diet food but one that meets the ideal of zero
categories. In short, it appears that goals can create categories and that
these categories are organized in terms of ideals.

Is this distinction between taxonomic and goal-derived categories fundamental?
It is difficult to say. Barsalou notes that the repeated use of goal-derived
categories (e.g. things to take on a camping trip for an experienced camper)
may lead to them being well established in memory. Perhaps more surprising are
recent observations that suggest that ideals play more of a role in organizing
common taxonomic categories than previously had been suspected. Atran (1998)
reports that for the Itzaj Maya of Guatemala, the best example of the category
"bird" is the wild turkey, a distinctive bird that is culturally significant
and prized for both its beauty and its meat. Lynch et al (1999) found that
tree experts based judgments of tree typicality on the positive ideal of
height and on the absence of undesirable characteristics or negative
ideals--central tendency played at most a minor role. It may be that
typicality is organized in terms of central tendency only for relative
novices. Actually, Barsalou's original investigation (1985) found that
although common taxonomic categories were most strongly based on central
tendency, proximity to ideals made a reliable and independent contribution to
goodness of example judgements. In short, common taxonomic and goal-derived
categories may be more similar than is suggested by first appearance.

Social Information Processing and Individuation

One could make a case for the view that processing associated with social
categories is different from the processing of object categories. For example,
there is the intriguing observation by Wattenmaker (1995) that linear
separability (separating categories by a weighted additive function of
features) is important for social categories but not for object categories.
More generally, people appear to be flexible in social information processing.
Fiske et al (1987) proposed a continuum model whereby people may form
impressions either by top-down, category-based processes or by bottom-up,
data-driven processes. (For a parallel constraint satisfaction model of
impression formation in which stereotypical and individuating information are
processed simultaneously, see Kunda & Thagard 1996.) Factors such as the
typicality of examples and the goals of the learner influence the relative
prominence of these two processes. This general framework has held up well and
serves to organize a great deal of research on social information processing
(for a review, see Fiske et al 1999). It is not clear, however, whether there
are corresponding processes that operate for nonsocial categories because this
question has been relatively neglected. The only relevant study we know of
(Barsalou et al 1998) did identify at least some conditions under which
individuation of examples took place. The dearth of comparisons derives in
part from the relative neglect of different kinds of processing associated
with object categories.

Stereotypes, Subtypes, and Subgroups

Although people clearly rely on stereotypes based on categories such as race,
gender, and age, increasing evidence suggests that people may be more likely
to use more specific social categories in their daily interactions. For
example, people appear to have and use several different subcategories for the
elderly, such as grandmother-type and elder statesman (Brewer et al 1981).

Do these subcategories share properties with subordinate object categories?
Some kinds of subcategories may operate similarly to subordinate object
categories, but others may operate differently. Fiske (1998) argues that
social subcategories can be divided into two different kinds based on the
goals of a perceiver. When a perceiver is trying to understand why a few
individuals differ from her stereotype of a group, she might form a subtype to
explain their aberrant behavior (Hewstone et al 1994, Johnston et al 1994).
For example, a person may form a subtype for black lawyers to explain why
several black individuals she knows speak differently and live in a different
part of town than her stereotype of blacks. Notably, forming a subtype allows
one to maintain his or her current stereotypes.

Fiske (1998) points out that the amount of experience one has with a group
also plays a role in whether a subtype is formed. When people have little
experience with a group, they tend to perceive less variability among
individuals, requiring subtypes to explain any aberrant behavior. With more
knowledge about a group, however, people tend to perceive more variability
among individuals, which in turn may lead them to form category subgroups.
Subgroups consist of category members who are more similar to one another than
category members of another subgroup. The key distinction between subgroups
and subtypes is that subtypes are made up of a group of people who disconfirm
the stereotype in some way, whereas subgroups are usually made up of people
who are consistent with the stereotype but in a different way from another
subgroup. For example, as Fiske (1998) notes, housewives and secretaries might
both be consistent with the stereotype of female, but in different ways.

The most common examples of subordinate object categories (e.g. rocking chair,
kitchen chair; song birds, birds of prey) seem to be more analogous to
subgroups than subtypes, although there may be some examples of subtype-like
object categories as well (e.g. birds that do not fly). A question that needs
to be addressed is why subtypes appear more common for social categories than
for object categories. (For an analysis of motivational processes aimed at
preserving stereotypes, see Kunda 1990.)

Category Processing and the Brain

Process dissociations have often been used as markers of distinct systems, and
recently this logic has been applied to categorization. Specifically, Knowlton
& Squire (1993; see also Squire & Knowlton 1995) have reported dissociations
between categorization and recognition in amnesic and normal individuals,
which they interpreted as indicating that multiple memory systems underlie
these two tasks. These findings pose challenges for categorization models that
assume that categorization and recognition are mediated by a common system.
This challenge has not gone unanswered. Nosofsky & Zaki (1998) showed that an
exemplar model of categorization can account for the Knowlton & Squire (1993)
dissociations, and a strong methodological critique has been made of the
Squire & Knowlton (1995) study (Palmeri & Flanery 1999). No doubt the debate
will continue.

Ashby et al (1998) offered a neuropsychological theory that assumes that
category learning involves both an explicit verbal system and an implicit
decision-bound learning system (see also Erickson & Kruschke 1998; for
multi-strategy category learning models, see Nosofsky et al 1994). The Ashby
et al model is promising in that it integrates neuropsychological and
computational modeling, but it is premature to evaluate either its success or
the illumination it might provide on kinds of categories.

Other Distinctions

We are necessarily limited in the scope and depth of our coverage; other
reviewers would no doubt highlight other differences. One intriguing idea that
should at least be mentioned is the proposition that categories are grounded
by emotional responses and that stimuli that trigger the same emotion category
are seen as similar and are categorized together (Niedenthal et al 1999).
Another idea is that different kinds of categories may be represented in
memory through different kinds of representational formats. For example,
although object categories may be organized in memory in a spatial format,
events may be organized in more of a temporal format (Barsalou 1999).


Our reading of the evidence is that the case for kinds of concepts based on
processing is somewhat weaker than the case for kinds based on structure. In
addition, the work on goal-derived categories serves to reinforce structural
distinctions. It could also be said that we have imposed something of an
artificial bound between structure and processing--the strongest case for
distinct kinds will require computational models that make concrete
assumptions about both structure and processing. We turn now to the third
candidate for kinds of concepts, those based on content.


A general trend in the cognitive sciences has been a shift from viewing human
beings as general-purpose computational systems to seeing them as adaptive and
adapted organisms whose computational mechanisms are specialized and
contextualized to our particular environment (Tooby & Cosmides 1992). In this
view, learning may be guided by certain skeletal principles, constraints, and
(possibly innate) assumptions about the world (e.g. see Keil 1981, Kellman &
Spelke 1983, Spelke 1990, Gelman 1990). In an important book, Carey (1985)
developed a view of knowledge acquisition as built on framework theories that
entail ontological commitments in the service of a causal understanding of
real-world phenomena. Two domains can be distinguished from one another if
they represent ontologically distinct entities and sets of phenomena. A
criterion used to determine whether two concepts refer to ontologically
distinct entities is that these concepts are embedded within different causal
explanatory frameworks (Solomon et al 1996, Inagaki & Hatano 1993). These
ontological commitments serve to organize knowledge into domains such as naive
physics (or mechanics), naive psychology, or naive biology (see Wellman &
Gelman 1992; Spelke et al 1995; Keil 1992, 1994; Au 1994; Carey 1995; Hatano &
Inagaki 1994; Johnson & Solomon 1997; Gopnik & Wellman 1994).

Researchers advocating domain specificity have suggested that concepts from
different domains are qualitatively different. Although it is difficult to
give a precise definition of domain (for a review, see Hirschfeld & Gelman
1994a), the notion of domain specificity has served to organize a great deal
of research, especially in the area of conceptual development. For example,
studies of infant perception and causal understanding suggest that many of the
same principles underlie both adults' and children's concepts of objects (e.g.
Baillargeon 1994, 1998; Spelke et al 1992). For example, common motion appears
to be a key determinant of 4-month-old infants' notion of an object. The
Gestalt principle of good continuation, however, plays no detectable role in
the concepts of object for infants at that age.

One of the most contested domain distinctions, and one that has generated much
research, is that between psychology and biology (e.g. Carey 1991; Johnson &
Carey 1998; Coley 1995; Hatano & Inagaki 1994, 1996, 1999; Inagaki 1997;
Inagaki & Hatano 1993, 1996; Kalish 1996, 1997; Gelman & Wellman 1991;
Rosengren et al 1991; Gelman & Gottfried 1996; Springer 1992, 1995; Springer &
Keil 1989,1991; Simons & Keil 1995; Keil 1995; Keil et al 1999; Au & Romo
1996, 1999). Carey (1985) argues that biological concepts like animal are
initially understood in terms of folk psychology. Others (Keil 1989, Springer
& Ruckel 1992) argue that young children do have biologically specific
theories, albeit more impoverished than those of adults. Ultimately, the
question breaks down to whether one accepts the criterion used to define
"ontologically distinct entities." For example, Springer & Keil (1989) show
that preschoolers think biological properties are more likely to be passed
from parent to child than are social or psychological properties. They argue
that this implies that the children have a biology-like inheritance theory.
Solomon et al (1996) claim that preschoolers do not have a biological concept
of inheritance because they do not have an adultlike understanding of the
biological causal mechanism involved. But is there really a single adult
understanding of biology? To address this question, one would need to examine
adult understandings from a variety of samples both within and across cultures
(Keil et al 1999).

What criteria should be used to define a particular domain? Domain-specificity
theorists claim that domain-defining framework theories are qualitatively
different from other theories in that "they allow and inspire the development
of more specific theories but do so by defining the domain of inquiry in the
first place" (Wellman & Gelman 1992:342). Do domains yield distinct kinds of
concepts? Of necessity, our concepts refer to different kinds of things in the
world. A fear is that domain-specificity theorists simply define kinds into
existence by stating a priori that certain kinds of content (e.g. physics,
biology, psychology) are important. In response, we point to the fact that
claims about constraints or contents are always subject to skepticism and
counter-attack in the form of both research and theory (e.g. for infant
perception, see Cohen 1998, Cohen & Amsel 1999, Needham 1998, Needham &
Baillargeon 1997, Xu & Carey 1996, Wilcox & Baillargeon 1998; for the role of
conceptual knowledge in naming and linguistic development, see Jones & Smith
1993; Soja et al 1991, 1992; LB Smith et al 1996; Landau et al 1998; Landau
1996; Diesendruck et al 1999; Gelman & Eberling 1998). In short, claims about
domains are anything but taken for granted.

It is one thing to stake out a domain and quite another to work out the
details of how the associated competencies develop, how they are manifest in
adults, and how cross-domain interactions emerge. Addressing these questions
sets a research agenda that promises to increase our understanding of concept
formation and use. For example, Gelman and her associates have been studying
the linguistic cues in parental speech that are correlated with distinct
ontological kinds (Gelman et al 1999, Gelman & Tardif 1998). In addition,
adult folkbiological models and associated reasoning strategies may differ
substantially both within and across cultures (Lopez et al 1997, Coley et al
1999) in a way that sharpens discussions of universal principles of biological
understanding (see Atran 1998).

To briefly mention cross-domain interactions, one key idea and candidate for a
universal principle in folkbiology has been psychological essentialism, the
theory that people act as if biological kinds have a (hidden) essence that
provides conceptual stability over changes in more superficial properties
(e.g. Atran 1990, Hall 1998, Medin & Ortony 1989; see also Margolis 1998). But
people also appear to essentialize social as well as biological categories
(Rothbart & Taylor 1992, Miller & Prentice 1999, Hirschfeld 1996), which
raises a number of further interesting questions. Does this essentialism bias
arise independently in these two domains, does it start in one and transfer to
the other, or is it possibly a bias that initially is highly general and only
later on is restricted to biological and social kinds (see Atran 1995; Gelman
et al 1994; Hirschfeld 1995; Gelman & Hirschfeld 1999; Kalish 1995; Gottfried
et al 1999; Gelman 1999; Keil 1994; Braisby et al 1996; Malt 1994; Malt &
Johnson 1992, 1998; Bloom 1998; Ghisilen 1999; Gelman & Diesendruck 1999; for
a related discussion and debate, see Rips 1994)?


Although we remain agnostic or even skeptical about some of the claims arising
from the domain-specificity framework, we believe that it is undeniable that
this framework has been enormously helpful in organizing a large body of
intriguing findings and observations, coupled with progress on the theoretical


One should not expect a definitive answer to the question of whether there are
distinct kinds of concepts. As suggested earlier, this question has to be
addressed relative to theories. What does seem clear, however, is that
sensitivity to kinds of concepts is quite an effective research strategy. Far
from creating insularity, questions about kinds are fostering richer theories
of conceptual behavior.

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Douglas L. Medin, Elizabeth B. Lynch, and Karen O. Solomon Department of
Psychology, Northwestern University, Evanston, Illinois 60208; e-mail:
medin at nwu.edu, elynch at nwu.edu, k-solomon at nwu.edu

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