Content Differences
Content Differences for Abstract and Concrete Concepts
Katja Wiemer-Hastings & Xu Xu
Northern Illinois University, DeKalb
Running head: Abstract and Concrete Concepts
Please address correspondence to:
Katja Wiemer-Hastings
Department of Psychology
Northern Illinois University
DeKalb, IL 60115
Phone: 815.753.5227
Fax:
815.753.8088
Email: katja@niu.edu
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Content Differences
Abstract
The content of 36 abstract and concrete
noun concepts was explored and compared in a
feature generation task with 31 participants.
Abstract concepts had significantly fewer entity
properties, more properties expressing subjective
experiences, and overall less specific features.
Situation properties generated for abstract and
concrete concepts differed in kind, but not in
number. Abstract concepts were predominantly
related to social aspects of situations, including
agents. Abstractness emerges as a function of types
and specificity of conceptual components.
Systematic relations of conceptual content were
revealed with context availability and imageability.
The implications of the findings are discussed with
respect to cognitive processes involving abstract
concepts.
Content Differences for Abstract and Concrete
Concepts
Abstract words, like indecision, difference, or
consideration, are abundant in daily conversation.
We use such words to describe, interpret, and explain
events or circumstances in our social and physical
environment. Abstract concepts contain a variety of
categories each of which has sparked its own domain
of research, including personality traits, behaviors
and social cognition, emotions, cognitive processes,
and events. As such, there is considerable interest in
abstract concepts across disciplines within and
beyond psychology. In spite of this, very little is
known about their representation. Abstract entities
are not experienced directly: they are not spatially
constrained entities that we can see, or touch, or
interact with. Instead, abstract concepts represent
complex entities such as behaviors, scenes, or
subjective experiences. Thus, they are more aptly
described as constructs of the mind which represent
and structure experiences. The instances represented
by an abstract concept vary considerably. This is
reflected in ratings of contextual variety which tend
to be higher for abstract concepts (Galbraith &
Underwood, 1973). For example, difference can
refer to sensory units, such as the result of adding a
bit of salt to a soup or a height difference, or to
mental units such as two opinions. At first glance,
abstract (e.g., difference) and concrete entities (e.g.,
bucket) are easily distinguished.
However,
researchers have struggled to outline exactly in what
respect they differ. The obvious difference is that
only the concrete things are physical entities with
defined by spatial boundaries. This ontological
difference has important implications.
We
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experience abstract and concrete things as
qualitatively different – they seem to belong to
different realms. As salient as this distinction may
be, however, it mostly reveals what abstract concepts
lack (i.e., physical substance, spatial boundaries).
This leaves open the interesting question what
abstract concepts do represent.
The distinction of physical and nonphysical
concepts is unsatisfying in a second regard: there are
graded differences in concreteness. For example,
people perceive scientist to be more abstract than
milkbottle, and notion as more abstract than climate.
Physicality, being a dichotomous variable, cannot
easily explain the more subtle differences. What
makes concrete concepts vary in concreteness?
These questions motivate the research presented here.
We explored the content of abstract versus concrete
concepts by systematically analyzing participantgenerated features, to identify differences at a
componential level. The analysis focused on the
kinds of features that characterize people’s
knowledge of concrete versus abstract concepts, the
quantity of these features, and their relations to
perceived concreteness and associated variables, such
as imagery. Since a majority of concept processing
models are based on the notion of conceptual
components, this analysis is an important step in
advancing our understanding of abstract concepts.
Variables Associated with Concreteness
Differences between abstract and concrete
concepts have been studied in detail in form of
concreteness effects on different kinds of processes.
Generally, abstract concept processing is more
challenging than concrete concept processing.
Concreteness effects have been reported in a variety
of tasks, including studies of learning, memory
retrieval, comprehension, lexical decision, translation
and semantic deficits.
Imageability and context availability. The
most influential theories that have been put forward
to explain concreteness effects are the dual-coding
theory and context availability theory. Both make
distinct assumptions about storage of and access to
abstract and concrete concepts in memory. Put in
very simple terms, the dual-code theory assumes that
concreteness effects are due to abstract concepts
lacking a perceptual representation (Paivio, 1971;
1986), whereas the context-availability theory
attributes concreteness effects to more relevant
information stored in memory for concrete concepts,
which facilitates their processing (Bransford &
Johnson, 1972; Kieras, 1978).
Ratings of
Content Differences
imageability and context availability tend to be
highly correlated with concreteness (Paivio, 1986;
Rubin, 1980; Schwanenflugel, Harnishfeger, &
Stowe, 1988). However, the foundations for imagery
and the nature of context effects in memory are not
well understood. The basis for both is likely linked
to conceptual content. The present analysis may thus
shed light on why abstract concepts evoke less or no
imagery, and why less information may be stored in
memory for abstract concepts.
A conceptual content analysis may also be
able to resolve the interesting recent finding that
concreteness and imageability, and concreteness and
context availability are not consistently correlated for
the entire range of concreteness (Altarriba, Bauer, &
Benvenuto, 1999; Wiemer-Hastings, Krug, & Xu,
2001). While high correlations were found for word
samples that span the entire concreteness range, the
correlation patterns differed when calculated
separately for concrete versus abstract words. In
particular, imageability ratings were correlated with
concreteness ratings only for abstract words (N=155;
r=0.57, p<0.05), but not for concrete words (N=100;
r=0.02) (Altarriba, Bauer, & Benvenuto, 1999). In
contrast, the association between rated context
availability and concreteness was much stronger for
concrete words (r=0.68, p<0.05) than for abstract
ones (r=0.25, p<0.05). Thus, context availability
increases strongly with concreteness of physical
items, but only little with concreteness of nonmaterial
items. In a similar study with 18 abstract and 18
concrete items, we replicated this pattern for context
availability (Wiemer-Hastings, Krug, & Xu, 2001).
However, in this sample, neither ratings for
imageability (r=0.33) nor ratings for context
availability (r=0.17) were significantly associated
with concreteness of abstract concepts. Both were
significantly correlated with concreteness of physical
items (imageability: r=0.48, p<0.05; context
availability: r=0.58, p<0.05).
The findings suggest a qualitative difference
between abstract and concrete concepts, which
coincides with the dichotomous physicality
dimension.
This difference may be the main
contributor to the strong correlations that are
observed for samples spanning both concrete and
abstract items. Further, it seems that the main factor
underlying ratings of imageability and context
availability is predominately varying among concrete
concepts, and that imageability ratings may further be
influenced by a separate factor that predominately
varies for abstract concepts. These hypotheses can be
tested through correlations between these measures
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and measures based on the features generated for
abstract and concrete concepts.
Ease of predication. Jones (1985) suggested
in the context of dyslexia that imageability effects
may be mediated by a semantic factor which he calls
ease of predication, which is the ease with which
individuals can access predicates of a concept. He
found that rated imageability and ease of predication
were highly correlated (r=0.88). However, this
account is also unsatisfactory as an explanation for
concreteness effects, because it is unclear how people
rate ease of predication. It is possible that people
base their ratings of ease of predication on the ease
with which they can access an image (a related
argument is made by de Mornay Davies & Funnell,
2000). Jones instructed his participants to ignore
other factors than ease of predication in their ratings,
but imagery may be an automatic process that
mediates the participant ratings without their
knowledge. Thus, ease of predication measures, too,
require explanation at a fundamental conceptual
level.
Overview
Knowledge of differences in the conceptual
content due to varying concreteness could better
explain concreteness effects, as well as provide a
better understanding of imageability, context
availability, ease of predication, and other variables
related to concreteness. Further, a componential
analysis of abstract concepts may allow for more
sophisticated predictions in research involving such
concepts, including encoding and recall, semantic
relations, semantic access in priming studies, and
categorical organization. What types of information
may be involved in the representation of abstract
entities like a thought or a goal? While our research
is largely exploratory, we are building on a few
earlier studies from which we have derived a few
hypotheses regarding the content of abstract
concepts.
Psychological
situations
and
action
schemata. A large majority of abstract concepts are
agent-centered; that is, they are strongly related to a
person or group. This is true of large categories of
abstract concepts, including emotions, cognition,
actions and interaction, communication, character
traits and attitudes, and others.
Such abstract
concepts would likely be associated with aspects of
the internal and external context of the agent, such as
behaviors, mental processes, another person, and
states of affairs. The extent to which agents and
agents’ contextual properties are part of the content
of abstract concepts has not yet been subject of
Content Differences
systematic investigation. However, related analyses
suggest that they likely are. For example, Hampton
(1981) investigated the prototypicality structure of
abstract concepts using a property generation task.
Many of the generated properties describe a social
situation involving an agent and agent-related aspects
of the situation. Hampton suggests that abstract
concepts would commonly involve behaviors, agent
characteristics such as goals, and other aspects of a
situation, consistent with our reasoning.
Similarly, researchers on personality traits
have proposed agents, agent characteristics, and
agent experiences as abstract concept components
(Chaplin, John, & Goldberg, 1988). Research on
psychological situations has revealed quite similar
components as the properties of situation prototypes,
such as agents, their behaviors, and dispositions, as
well as physical attributes such as states, and objects
(Cantor, Mischel, & Schwartz, 1982). On the basis
of these studies, we predicted that descriptions of
abstract concepts would show a strong focus on an
agent and agent-related properties, including
cognitive and emotional states and behaviors.
Subjective experiences. There is evidence
that abstract concepts are linked closely to subjective
experiences that are only accessible through
introspection. Introspective features have recently
been proposed as a necessary component of abstract
item representation (Barsalou, 1999). For example, a
concept like decision cannot be understood without
having experienced the mental process of weighing
two or more choices against each other in terms of
their good and bad sides. A large number of abstract
concepts involve such elements of subjective
experience, which may be mental, emotional, or even
physiological in nature. Recent data also show that
such components directly affect the perceived
concreteness of an item. The larger the proportion of
an item’s introspective elements is (i.e., goals,
interpretations, evaluations, or emotions), the higher
people judge its concreteness (Wiemer-Hastings,
Krug, & Xu, 2001). Based on this finding, we
predict that people will list significantly more
properties for abstract concepts that express
subjective experiences of an event or a situation.
Such a finding would further support the finding that
concreteness is in part a function of introspective
components, and it would indeed be stronger because
the measure for the proportion of introspective
content would be based on properties generated by
the participants, whereas in the above study, it was
based on the experimenters’ coding schema for
abstract concepts.
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Context variability. Abstract words occur in
a larger variety of contexts, just as abstract entities
can occur in a large variety of situations (Galbraith &
Underwood, 1973). Contextual variability may be a
factor underlying context availability differences for
abstract and concrete concepts. Context may be less
accessible in memory because the large number
contexts stored with an abstract concept are
competing for activation, or because contexts need to
be actively generated during concept processing.
Since abstract concepts represent instances from a
larger variety of contexts, their features may place
low constraint on context generation.
A likely consequence of contextual
variability is that abstract concepts involve
components that are relatively unspecified, i.e.,
features that act like slots in a script or schema
(Minsky, 1975; Schank & Abelson, 1977) that allow
for a variety of specific attributes. For example, the
restaurant script contains a slot for an agent bringing
the menu to the table, for the type of food served in
the restaurant, etc. Abstract concepts resemble
scripts in that they refer to actions, goals, and agents
at a general level. Thus, we expected that abstract
concepts would be characterized by unspecific
features. For example, an intuitive analysis suggests
that difference occurs in contexts that contain any two
or more items that can be compared on some
dimension. At the extreme, perhaps some abstract
concepts can be described as content-free schemata
(Fiske & Taylor, 1991) in that they only specify
abstract components and relations between them.
Distinct versus graded differences. Abstract
versus concrete items are typically defined
dichotomously, as physical and non-physical items.
Concreteness ratings are consistent with this view
only to some extent. The consistent finding is that
concreteness ratings tend to form a clearly bimodal
distribution (Nelson & Schreiber, 1992; WiemerHastings, Krug, & Xu, 2001) with each mode
centered in one of the two halves of the concreteness
scale. This suggests a qualitative difference in
representation which may be associated with distinct
content elements that form part exclusively of either
concrete or abstract concepts. This is in line with the
finding that imageability and context availability
correlate to different extents with concreteness
ratings for abstract versus concrete concepts
(Altarriba, Bauer, & Benvenuto, 1999). On the other
hand, there are many degrees of concreteness, which
suggests that there are additional concept
characteristics that vary gradually across sections of
the concreteness range. Thus, we expected to find
semantic components which were exclusively named
Content Differences
for abstract or concrete items, as well as components
which varied gradually with concreteness.
Participants in our experiments received a
property generation task for 36 nouns spanning the
entire scale of concreteness. Property generation
tasks have proved useful in concept research.
Perhaps the best known example of this is the
prototype research by Rosch and colleagues (e.g.,
Rosch & Mervis, 1975). Our focus of interest was on
proportions of different knowledge domains in the
content of abstract and concrete concepts, and
whether these varied in a graded fashion, or whether
there were realms of experience exclusively
applicable to just either concrete or abstract concepts.
While participant-generated features unlikely
represent exact conceptual content, they should
accurately reflect these types of knowledge and
systematic differences in knowledge domains across
the concreteness dimension. Features provide insight
into only one aspect of representation.
We
acknowledge that by focusing on features, we are
ignoring more complex aspects such as relations
among features, or organizations of features
according to individuals’ assumptions about entities,
which play an important role for concept processing.
However, our hope is that providing this groundwork
will facilitate more complex research of this kind.
Method
Participants
Thirty-one undergraduate students at
Northern Illinois University participated in this
experiment for course credit. All participants were
native speakers of English.
Materials
We constructed a random set of thirty-six
nouns, varying in concreteness across the
concreteness scale. Words were sampled from an
initial sample of 1993 nouns retrieved from the
MRC2 database (Coltheart, 1981; Wilson, 1988).
The database provided for all of these nouns
familiarity ratings and concreteness ratings from a
variety of norms. The concreteness scale was divided
into six equidistant subsections to include words of
all levels of concreteness. From each section, six
nouns were sampled randomly but matched in
familiarity across the concreteness levels. The
complete list is shown in Appendix A. Our sample
consists of items representing a variety of categories,
including plants (tree), animals (mackerel), nonliving
things (beehive), substances (venom), actions
(removal), emotions (happiness), social processes
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(emancipation), states (inaction), temporal concepts
(day), communication concepts (story), and so on.
The word sample was deliberately chosen at random
across the concreteness range because our concern
was with general trends of feature components as a
function of concreteness, rather than types of
concepts. A systematic comparison of instances of
each of these categories would likely reveal further
differences.
Design
Three sets of 12 items were constructed of
the 36 words. Each set contained two words from
each concreteness level, matched in familiarity. Each
set was presented to a participant with one of three
tasks. One task asked participants to generate
features for each item.
Another task asked
participants to generate context elements that always
occur with the given item. This second task was
included to address the argument that abstract
concepts are more strongly anchored in context
information, whereas concrete concepts have a
component that is independent of context. The two
instructions allowed us to compare, between subjects,
the number and kinds of features generated for these
two conditions. To the extent that context is a natural
part of a concept’s content, the instructions should
not have a significant effect on the resulting
properties. A third task was a response time measure
for a related variable which we will not report here.
Each word set was presented as first, second
or third word set equally often, and was presented
equally often with each of the tasks. Finally, task
order was also counterbalanced. Within each set,
words were presented in different random orders.
The design thus included, as independent variables,
word type (with six concreteness levels) and
instruction (item versus context feature), both varied
within participants. Analyses averaged effects across
the word sets. The smaller sets of twelve items each
were formed so that the task was doable in a
reasonable amount of time.
Ten participants
generated features for each item in each condition.
Procedure
Data were collected in individual sessions.
The experimenter read the instructions aloud.
Instructions included examples ranging in
concreteness that were not used in the study.
Participants were given a practice trial with three
words: one abstract, one concrete, and one
intermediate word.
After clarifying questions,
participants started on the 12 words for this block. A
standard list of near-synonyms for unknown target
Content Differences
words was provided to participants to not bias their
answers by describing the items. All descriptions
were tape-recorded with the participants’ consent.
The experiment took between 20 and 40 minutes to
complete.
Participants worked on the two tasks in
succession, with twelve of the items presented in
each. One task asked participants to list properties
for each item. Part of the instructions was repeated
before every new item to remind participants of the
main task. The intent was to reduce carry-over of
processing strategies such as exhaustive scene
descriptions to subsequent items. The instructions
for the item feature generation task read as follows:
“Please describe as exhaustively as you can what
aspects characterize this object: (item)”.
The
instructions for contextual features were: “Please
describe as exhaustively as you can aspects of a
situation that MUST be true for this object to occur in
it: (item)”.
Results
Analysis of Transcripts
The recorded data were transcribed manually
and then coded for types of knowledge. Only
twenty-six of the transcripts were used in the
analyses. The data from five participants were
unusable for a variety of reasons, including
misunderstanding of the instructions, experimenter
error in the presented items, and most commonly, too
many item omissions to include a transcript without
biasing the results. Omissions were not necessarily
due to unfamiliarity with words; a few participants
struggled with descriptions of the more abstract items
(e.g., aspect and exception).
Coding of transcripts. The transcripts were
parsed into feature units, which consisted for the
most part of individual words. For a systematic
evaluation and comparison of conceptual content, all
features were coded using a coding schema that was
developed based on feature occurrences in previous
data analyses (Wu & Barsalou, 2004). The Wu and
Barsalou coding schema was developed for coding
features of concrete objects. This coding schema was
chosen for two reasons. First, it allows coding of
features for both concept types because it covers
several domains of concept knowledge: item
properties, situation properties, and introspective
properties, which are aspects of a person’s subjective
experience related to an item. As such, the schema
covers all knowledge domains which were
hypothesized to be of relevance for abstract concepts.
Second, this coding schema has been successfully
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applied in previous studies involving feature
representations and has been shown to cover relevant
aspects of conceptual knowledge (e.g., McRae &
Cree, 2002; Cree & McRae, 2003).
Entity properties describe the item’s
characteristics such as parts, appearance, material,
and characteristic behaviors. These properties cover
the internal structure of a concept, as opposed to
knowledge of associated items, actions and the like.
For example, a tree’s item properties include roots,
branches and leaves, green and brown colors, wood,
growing, producing oxygen, etc. Situation properties
cover knowledge of the context in which an item is
used. This includes animate beings, actions, physical
and social states, functions, locations, and other
aspects. Situation properties generated for tree
typically include animals such as birds and squirrels;
water and soil; actions such as climbing or felling a
tree; and functions such as producing furniture,
making fire, or offering shade. Finally, introspective
properties are produced when participants access
personal experiences with an object. This can
include emotional or evaluative responses, negation,
representational states and more complex features
such as contingencies and causal relations. Some of
these properties are procedural in that they organize
the feature descriptions (e.g., “if then”, or “not”).
Others are more informative by themselves,
especially emotions and representations. For tree,
example experiential features may include
evaluations such as valuable or beautiful.
Strictly speaking, only entity properties are
actual features of a concrete item. The other domains
express knowledge surrounding the use and typical
occurrence of an item. For example, when an agent
is mentioned in describing a basketball (e.g., kids
throw them through a basket), then kids are not a
feature of the basketball. However, features such as
situational and introspective ones indicate that a
participant accesses knowledge about how the item is
typically used, rather than just information confined
to the item itself.
This coding schema also accommodates
taxonomic properties, such as exemplars, category
membership, coordinates, synonyms, and the like.
By listing features falling into this domain,
participants describe an item’s characteristics by
positioning it in relation to other items. Such
statements reflect predicative knowledge about items,
but they do not specifically reflect an item’s content,
but rather how it is organized in semantic memory.
For example, listing joy, a synonym, for happiness
does not reveal any of its content, but only shows that
the participant correctly understands the target.
Content Differences
Another indication for taxonomic properties not
strictly expressing conceptual content is that many of
the properties that could be classified as taxonomic
could be double-classified in one of the other
domains. For example, when generating fork for the
target knife, fork is both a coordinate in the same
category, and it is also an object that is found in the
same context (thus, a situation property).
Because of this, we coded features as item,
situation or experience properties wherever possible.
The items that were coded as taxonomic were
analyzed separately.
Slightly more taxonomic
properties were mentioned for abstract items than for
concrete items, but this difference was only
marginally significant in a subject analysis (F1(1,
25)=4.21, MSE=0.19, p=0.051). Individual analyses
of the taxonomic properties reveal that this is mostly
due to more coordinate terms and synonyms being
listed for abstract items.
The feature lists for all words were coded by
one of the authors. Only one person used the coding
schema so that the codes would be used consistently.
Beforehand, a subset of features were coded by both
authors to discuss the use of the schema categories
and to establish clear coding guidelines. Many
features were coded by assigning a code to individual
words; in other cases, a code was applied to a
proposition. Items belonging to a proposition were
marked with a “\”, and only the last word belonging
to that feature was coded using the feature code.
Examples for one coded concrete and one coded
abstract item are shown in Appendices B and C,
respectively.
Feature summary scores. We evaluated
differences between abstract and concrete conceptual
content based on type and token measures for the
broader knowledge domains (i.e., object vs. situation
vs. experience features) and for the subcategories
within the situation domain. Types refer to the
different feature categories mentioned for a word;
tokens refer to the number of instances that a
particular feature category is mentioned for a word.
Thus, the type scores show how many kinds of
features were used for a word, but not how often.
This score is useful in evaluating the complexity with
which participants flesh out the features related to an
item in each of the domains. For example, a situation
type score of “1” would indicate that people on
average only refer to one type of situation property
(e.g., an action), whereas scores of “3” and higher
suggest that a scenario is described (e.g., a person, an
action, and an object).
The token score reflects more accurately the
proportion of features generated for a word that fall
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into the different categories. Variation in these
scores can be quite informative. For example, people
might list fewer parts for a less concrete object, or a
situation for pity is likely to involve two persons, so
that the person code would justly apply twice.
Tokens did not count an item more than once when it
was evident that it referred to an identical element;
e.g., “an object”, “it”, “it” may all refer to the same
object and were not counted as three occurrences of a
situation object, but as one. The type scores depict
more schematic, ontological characteristics of the
items.
Category scores summed up the number of
its subcategories used to code the feature list for a
word. Category type scores counted just the number
of different subcategories mentioned, whereas
category token scores also counted individual
instances for each. Likewise, the subcategory token
score counted how often each subcategory was used
for a given word and participant, whereas the
subcategory type only counted whether or not a
subcategory was used, resulting in scores of either 1
or 0.
Category Type Analysis
The mean number of situation, entity and
introspective features are listed in Table 1. These
scores are absolute scores, meaning that they do not
present the proportions of features mentioned overall
that fall into each category. Proportion scores do not
make much sense here because the tokens were not
counted, and thus the type measures do not accurately
reflect the actual proportion of features generated for
each domain.
A weighed type score was calculated
additionally which divided all scores by the number
of subcategories in their respective knowledge
domain. As can be seen from Table 1, the scores in
the situation domain are much larger (if not weighed)
than those in the other two domains. In fact,
participants used altogether 16 types of situation
properties, 11 types of entity properties, and only 6
types of experience-related properties. The weighed
scores presented in Table 1 indicate the proportion of
the kinds of features in a domain that were, on
average, presented by the features generated by a
participant in the different instruction conditions.
Analyses were performed on the original type scores.
How did abstract and concrete concepts
differ with respect to the kinds of features generated?
Domain differences were confined to entity and
experience-related properties.
There were no
concreteness effects on situation properties.
Instructions to generate relevant context features (as
Content Differences
opposed to item features) increased the number of
different kinds of situation properties only marginally
(subject analysis only: F1(1,25)=3.51, MSE=1.66,
p=0.07). Thus, only few participants used additional
situation properties to the ones used to describe item
features when their direction was focused on the
context.
This suggests that participants access
roughly the same number of different types of
situation knowledge for abstract and concrete
concepts. Whether these types are the same for
concrete and abstract concepts is not evident from
this analysis; we will address this question below.
Further, the data suggest that shifting
participants’ focus from items and their features to
features of the context does not appear to increase the
number of different kinds of situation knowledge.
Again, this does not show whether participants use
the same types of situation properties in each case, or
whether they switch them. It does show, though, that
the complexity of situation properties that are
generated does not increase.
As expected, significant differences were
observed between abstract and concrete concepts
both in the number of types of entity and experiential
features. First, participants generated significantly
more kinds of entity properties for concrete than for
abstract concepts (F1(1,25)=27.67, MSE=0.36,
p<0.001; F2(1,34)=21.31, MSE=1.16, p<0.001).
Instructions to generate item features increased the
number of kinds of entity properties, but only
significantly for concrete items: a significant
interaction was found for item concreteness and
instruction, F1(1,25)=22.90, MSE=0.33, p<0.001;
F2(1,34)=8.97, MSE=0.51, p<0.005.
Thus, in
general, when participants focus on an object’s
context instead of on the object itself, they
appropriately list fewer item properties. At the same
time, they still make significantly more mention of
entity properties when asked to describe the context,
suggesting that the concrete target object may
dominate in the process of activating situation
knowledge.
This difference is critical for our
understanding of abstract items, since entity
properties are the only internal features of items.
The data show that for abstract items, predominantly
situational features are mentioned, suggesting that
these concepts are firmly entrenched and not
separable from events and states in a situation and
subjective experiences associated with these. This is
of course consistent with the typical definition of
abstract items as nonmaterial entities. In fact, it is
surprising that any entity properties are listed for
abstract concepts at all. A closer look at the kinds of
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entity properties shows that such properties were only
listed for items of the “somewhat abstract” group.
For many of these, it may be argued that the features
coded as entity properties may be coded as situation
properties more appropriately. These include “parts”
listed for story and saga such as beginning, climax
and ending, plot, etc., all of which could alternatively
be considered social artifacts. Clearly, these are as
little material as the targets themselves. Possession,
too, yielded some entity properties such as “my
initials on the bottom”, where possession was treated
as a specific object.
A reverse pattern of differences was obtained
for experience-related features. Participants listed
significantly more different introspective features for
abstract than for concrete items (F1(1,25)=76.99,
MSE=0.24, p<0.001; F2(1,34)=20.09, MSE=0.64,
p<0.001). This difference, too, was robust across
instructions.
Interestingly, the proportion of
experience-related
features
was
increased
significantly when participants were instructed to
generate context features (F1(1,25)=4.72, MSE=0.48,
p<0.05; F2(1,34)=10.58, MSE=0.17, p<0.005).
Thus, as participants focus on an item’s context, they
appear to access more differentiated information
about mental processes and states, such as goals,
emotions and so on.
An average of one experience-related
property was generated for each abstract target. This
suggests that subjective experience is a regular aspect
of such concepts, rather than knowledge that is
occasionally activated when processing the concept.
We also looked through the codes to examine more
closely what kinds of experience-related properties
were generated by participants for concrete items.
Such properties were listed predominantly for a few
specific targets, most notably, for labyrinth and prize.
A mental state like confusion was listed quite
regularly for labyrinth; likewise, emotions were
frequently mentioned for prize. About half of the
features coded as introspective were more procedural
in nature, indicating feature contingencies and
negated features.
Entity and experiential properties across the
concreteness scale. Do different knowledge domains
apply dichotomously either to abstract or to concrete
concepts, as suggested by simplified definitions along
the physical / not physical dimension, or are there
gradual differences that may account for the large
variation in perceived concreteness? In previous
work, it was found that particularly the proportion of
introspective (or experiential) properties is
significantly correlated with rated concreteness of
abstract items, suggesting that abstract items vary
Content Differences
gradually on this dimension. Similarly, an object
with many internal features may be perceived as
more concrete. Thus, we may expect to see an
increase in the proportion of experience features
within the abstract target sample as items get more
abstract, and an increase in the proportion of entity
features for concrete items as these get more
concrete. We observed before that there are items
that seem to be somewhat concrete and somewhat
abstract. It may be that there is a grey range in the
middle of the scale where items would be described
by a mix of features that are typically part of abstract
or concrete items.
We plotted the proportions of entity and
introspective features across the six concreteness
levels for easier comparison. They are displayed in
Figure 1. The Figure does not suggest a dichotomous
break in the types of features predominantly activated
for abstract versus concrete concepts. Rather, it
seems that only concepts at the two most concrete
and the two most abstract levels have clearly
distinctive conceptual content.
Very concrete
concepts contain a large amount of item properties
and evoke few if any experiential properties. In
contrast, experiential features are regularly part of
very abstract concepts, for which few if any concrete
item properties are produced.
The relation between the number of
properties (tokens) expressing knowledge about
entities (r=0.74) and experience (r=-0.69) to
concreteness ratings was significant in a correlation
analysis (p<0.01 for both). That is, the more concrete
a concept is, the more entity properties and the fewer
experience properties are generated. The concepts
rated as somewhat concrete and somewhat abstract
have a lower proportion of both entity and
experiential features. By exclusion, this means that
their conceptual content may be largely dominated by
situation properties.
Further, the amount of
experiential and entity features is almost balanced for
these concepts: they contain a few concrete
properties, but also knowledge that is experiential in
nature.
Subcategory type analysis for situation
properties. The analyses at the category level did not
reveal a difference in the number of different
situation properties generated for abstract versus
concrete concepts. This could indicate that situation
properties do not distinguish between abstract and
concrete concepts. On the other hand, differences
may be hidden at the finer level of subcategories. To
test for this possibility, we examined whether there
were specific subcategories within the situation
knowledge domain that would be mentioned
9
significantly more often for one of the two concept
types. For abstract concepts, we expected to see
frequent mention of a person, actions and events, and
social categories. In contrast, we expected that more
concrete situation elements such as objects,
buildings, locations, physical states and the like
would be more central to concrete concepts.
Table 2 shows the type scores obtained for
concrete and abstract concepts for the situation
subcategories. All these scores vary between 0 and 1.
They indicate whether, on average, a feature of each
given subcategory was generated. Seven out of the
14 categories listed (two categories were not included
in this analysis: quantity, because it is not readily
interpretable, and manner because of no occurrences)
revealed significant differences in the extent to which
the feature was mentioned for abstract versus
concrete concepts, across items and participants. For
the most part, the patterns are consistent with our
expectations. In particular, abstract concepts are
clearly centered around a person most of the time,
whereas concrete concepts only mention an agent a
third of the time (F1(1,25)=37.03, MSE=0.11,
p<0.001; F2(1,34)=14.13, MSE=0.29, p<0.01). In
terms of the scores, actions and a person are the most
frequently mentioned situation properties for abstract
concepts (M=0.84, averaged across instructions).
This means that on average, participants mentioned
an agent and an action for 8 out of 10 abstract items.
Actions were also mentioned considerably
more often for abstract than for concrete concepts,
but this difference only approached significance in
the subject analysis (p=0.11 or p=0.07 for item and
subject analysis, respectively). Less frequently used
properties mentioned more often for abstract
concepts were social states (M=0.23) and social
artifacts (M=0.16). Social states are mentioned close
to never for concrete concepts (M=0.02;
F1(1,25)=46.23,
MSE=0.03,
p<0.001;
F2(1,34)=11.63, MSE=0.07, p<0.005), neither were
social artifacts (M=0.02; F1(1,25)=25.24, MSE=0.02,
p<0.001; F2(1,34)=6.52, MSE=0.05, p<0.05). Thus,
social concepts can be considered quite distinctly
associated with the contents of abstract concepts. All
these situation elements are consistent with
suggestions made in previous research (e.g.,
Hampton, 1981) on the content of abstract concepts,
such as agents, behaviors, and social aspects.
For concrete concepts, the most frequently
mentioned situation properties that were mentioned
significantly more often than for abstract concepts
were objects (F1(1,25)=29.56, MSE=0.09, p<0.001;
F2(1,34)=5.09,
MSE=0.33,
p<0.05),
location
(F1(1,25)=16.19, MSE=0.05, p<0.001; F2(1,34)=4.92,
Content Differences
MSE=0.14, p<0.05), living things (F1(1,25)=56.81,
MSE=0.08, p<0.001; F2(1,34)=10.27, MSE=0.24,
p<0.005), and a function (F1(1,25)=12.10,
MSE=0.02, p<0.005; F2(1,34)=4.05, MSE=0.05,
p=0.05). Thus, out of the 14 situation properties,
seven are mentioned significantly more often for
either abstract or concrete items. Three of the
remaining items, actions, social organizations, and
spatial relations approached significance. Buildings
were mentioned so rarely that no differences
emerged.
The analysis at the level of individual
properties shows that while both abstract and
concrete concepts are described in terms of
situations, they seem to activate aspects of context
that are to some extent distinct. If we were to
summarize these, the best description for abstract
item specific situation properties would be socially
relevant aspects (agents, behavior, social states,
social artifacts), whereas those for concrete items
would be item predicates (e.g., function and location)
and co-occurring concrete items (living things,
objects).
These separate aspects reflect the
ontologically different domains of things material
and nonmaterial. The data suggest that abstract and
concrete conceptual content does not converge on the
level of situation knowledge, but that each are
associated with (or contain) different aspects of
situations.
Category Token Analysis
The patterns for types were repeated for
tokens. As a reminder, tokens measure the overall
number of features generated for an item in a domain,
regardless of the number of different kinds of
properties. Different features for a type of property
are counted separately in this analysis; so, for
example, if a participant lists eight different parts for
an object, then the token for parts will be 8. The
average number of tokens generated within each
knowledge domain, entity, situation, and experience,
are summarized in Table 3. Consistent with the
results for types, there was no effect of the
concreteness level on the number of situation
properties. Instructions to describe the context
increased this count. This difference was significant
only in the subject analysis (F1 (1.25)=4.32,
MSE=2.30, p<0.05).
As before, there were main effects for
concreteness on the number of entity and experiential
features generated. Participants listed significantly
more entity properties for concrete items regardless
of instruction (F1(1,25)=72.13, MSE=0.64, p<0.001;
F2(1,34)=24.08, MSE=1.28, p<0.001). A main effect
10
was also observed for instruction, but as the data
clearly show, this was due to a large change only for
the concrete items. The interaction was significant,
with more entity properties listed for concrete than
abstract items, and more for concrete items that were
listed under feature instructions (F1(1,25)=20.90,
MSE+0.34, p<0.001; F2(1,34)=7.82, MSE=0.56,
p<0.01). So, when asked to list features that are true
of an abstract concept, participants list on average
one entity property for every fifth item, whereas they
list an average of two per concrete item.
For experiential features, we once again
observed the reverse pattern. Abstract concepts
evoked significantly more knowledge involving
subjective experiences than concrete concepts across
both conditions (F1(1.25)=69.72, MSE=0.34,
p<0.001; F2(1,34)=22.15, MSE=0.77, p<0.001).
Furthermore, instructions to focus on context
increased the number of experiential properties for
both abstract and concrete concepts. This effect was
significant only in the item analysis (F2(1,34)=9.09,
MSE=0.19, p<0.01), the subject analysis revealed a
marginal effect, p=0.08.
Subcategory token analysis for situation
properties.
The token scores showed stronger
differentiation for situation properties of abstract
versus concrete concepts, particularly in the subject
analyses. Items were analyzed in a mixed ANOVA
with item type (abstract vs. concrete) as betweenitem and instruction (features vs. context) as withinitem factor. The subject analyses were a withinsubject analysis with item type (abstract vs. concrete)
and instruction (features vs. context) as repeated
measures. Table 4 shows the mean number of tokens
obtained for each situation property, separate by item
type and instruction.
The first five situation
properties listed in the table were all mentioned
significantly more frequently for abstract than for
concrete targets, including person (F11,23)=29.12,
MSE=0.37, p<0.001; F2(1,34)=14.82, MSE=0.64,
p<0.001), action (F1(1,23)=5.36, MSE=0.22, p<0.05;
F2 marginal effect, p=0.08), social state
(F1(1,23)=38.56, MSE=0.03, p<0.001; F2(1,34)=9.79,
MSE=0.09,
p<0.01),
and
social
artifacts
(F1(1,23)=19.95, MSE=0.02, p<0.001; F2(1,34)=6.39,
MSE=0.06, p<0.05). These results are consistent
with the type analysis except for actions. These were
mentioned more repeatedly for abstract concepts,
whereas its type score had not differed as a function
of concreteness.
Concreteness effects were observed for six
further situation properties, which were mentioned
significantly more often for concrete concepts. These
were object (F1(1,23)=12.54, MSE=0.12, p<0.005; F2
Content Differences
only marginal, p=0.11), location (F1(1,23)=15.34,
MSE=0.07, p<0.005; F2(1,34)=4.42, MSE=0.16,
p<0.05), living things (F1(1,23)=35.86, MSE=0.10,
p<0.001; F2(1,34)=9.88, MSE=0.29, p<0.01), time
(F1(1,23)=26.09, MSE=0.02, p<0.001; F2 not
significant), function (F1(1,23)=10.08, MSE=0.03,
p<0.01; F2 marginal, p<0.06, and physical state
(F1(1,23)=6.24, MSE=0.04, p<0.05; F2 not
significant). There was also a marginal effect for
spatial relations in the subject analysis (p=0.06).
Time and physical state scores were only
significantly higher in the token analysis. Given that
abstract concepts are more temporal in nature, since
they include processes and events, it is somewhat
surprising that time was mentioned more often for
concrete concepts. We explain this effect by the
effects of random sampling of our word materials,
which put two time concepts (day, daybreak) into the
low-concrete sample. Especially day triggered a lot
of time features such as week, 24 hours, etc.
Meanwhile, temporal aspects could also be expressed
differently, such as in the mention of actions, events,
or emotions.
Overall, the differences in the use of situation
properties in the description of abstract versus
concrete concepts were more pronounced in the token
analysis, involving more kinds of situation properties.
Again, the data show that situation knowledge
features strongly in both types of concepts, but that
there are aspects of situations that are predominantly
relevant for either abstract or concrete concepts. In
fact, the situation properties for which no significant
differences were observed had token scores close to
zero (with the exception of events). It is quite
possible that in a larger sample, some of these would
also be used differently.
A small number of instructional effects were
observed. When participants were instructed to list
features of the relevant context rather than of the
target itself, they mentioned a person significantly
more often (F1(1,23)=29.12, MSE=0.37, p<0.001;
F2(1,34)=4.80, MSE=0.19, p<0.05), and social
organizations less often (F1(1,23)=9.78, MSE=0.05,
p<0.01; F2(1,34)=6.03, MSE=0.01, p<0.05). For
social organizations, the interaction approached
significance in an item analysis (p=0.10); tokens
were mostly high for abstract concepts when asked to
generate item features. Living things, events, objects
and physical state were marginally affected by
instructions (with p values between 0.06 and 0.10);
all of them tended to be generated more often when
describing context. Closer analysis shows that for all
but events, the increase in token scores happens
predominantly for concrete concepts.
11
The analyses so far have shown that abstract
and concrete concepts both contain knowledge about
the situations in which they occur, and that entity
properties are predominantly part of concrete
concepts, whereas experiential properties are mostly
found in abstract concepts.
Situational and
experiential features are rarely considered parts of a
concept’s internal structure. Our analysis suggests
that abstract concepts (at least those rated as highly
abstract) have a weak internal structure (cf. Gentner,
1981). That is, in place of the internal structure that
is common to concrete concepts, abstract concepts
are combinations of specific situation properties
combined with subjective experiences. As such, they
are more akin to abstract schemata for scenes and
events than to concrete concepts.
Features versus Associative Knowledge
One possible confound in our data is that
participants may have generated features that are not
really part of the target’s conceptual representation.
In particular, participants may first list some features,
and then start to give examples, which could result in
the high number of situation properties. The first
features mentioned may be used as cues by
participants to mention related knowledge, which
may be more related to the first features rather than to
the target itself (comparable issues have been
discussed for associative sets, cf. Nelson &
Schreiber, 1992). Since our participants could end
their feature lists anytime, we were not strongly
concerned about this, however, quite a few
participants generated very lengthy lists for some
targets. To estimate whether such processes affected
our data, we therefore repeated the token analysis just
for the first five coded properties listed for each
target. We expected that if situational knowledge is
mostly activated as an afterthought through
associations, then their counts may be lowered in this
analysis, and entity (and perhaps experiential)
features may emerge as more central sources of
conceptual knowledge.
The analysis was performed only on features
generated under feature instructions since these are
supposed to yield the actual conceptual knowledge.
Table 5 shows the resulting average numbers of
features. Overall, the numbers resemble the results
from the full analysis. However, the differences
between abstract and concrete items appear
somewhat stronger in these first features. There are
close to no entity properties listed for abstract
concepts, and close to no experiential features listed
for concrete concepts. Both of these were highly
significant in both item and subject analyses (Entity:
Content Differences
F1(1,24)=141.25, MSE=0.01, p<0.001, t(34)=4.97,
p<0.001; Experience: F1(1,24)=45.45, MSE=0.01,
p<0.001, t(34)=4.69, p<0.001).
Interestingly, the difference in situation
properties was significant in a subject analysis, with
abstract concepts evoking significantly more situation
properties early on than concrete concepts
(F1(1,24)=34.86, MSE=0.02, p<0.001, t approaching
significance, p<0.1). That is, situation properties
seem to be accessed earlier on for abstract concepts,
whereas for concrete concepts, about an even number
of features accessed first are entity and situation
properties. This makes sense given that abstract
conceptual content appears to be largely constituted
of situational knowledge. Thus, this analysis shows
that situation knowledge is part of even the first
knowledge people access about a concept; at the
same time, differences between abstract and concrete
concepts are more pronounced for these features,
suggesting that later activated knowledge, part of
which may be associative in nature, may be more
similar for both.
Feature Specificity
We compared the specificity of features
generated for abstract versus concrete concepts. For
this analysis, we coded the specificity of features for
properties generated under the feature instructions
only. The analysis was constrained on properties of
situations, entities, and experiences. A total of 3120
features were analyzed in this manner. Features were
sorted by feature type and coded blindly by one of
the authors, i.e., without information about the target
item and its concreteness. Specific features were
those that identified a particular object, action,
person, location, emotion, etc., whereas unspecific
features were those that identified a property but did
not mention a specific one. This distinction is akin to
the difference between a slot and a filler within a
frame (Minsky, 1975). An unspecific feature is a
slot, in that it can be filled in a situation by a variety
of specific manifestations. Specific features are
fillers for the respective feature type. Example pairs
of specific versus unspecific properties of the same
kind are listed in Table 6. For example, a person is a
slot; Lincoln is a filler for this slot.
An average of 70% of the features were
specific overall. As expected, more specific features
were found for concrete (84.4%) than for abstract
concepts (57.2%), t(34)=7.11, p<0.001.
The
proportion of specific features was highly correlated
with rated concreteness, r=0.73, p<0.001. Thus, not
surprisingly, feature specificity plays an important
role in the perception of concreteness.
One
12
possibility is that this difference is due to experiential
features being more unspecific and entity features
being specific. To examine this possibility, we
calculated t-tests and correlations for the specificity
of the properties in all domains separately. Because
many abstract concepts did not contain any entity
properties and many concrete concepts no
experiential properties, the variance of their
specificity proportions was not equivalent. Sign tests
show significant differences in the proportion of both
(Experience: 91% for abstract, 100% for concrete
concepts; Entities: 67% for abstract, 94% for
concrete concepts). Again, abstract concepts have
more unspecific features for both. Furthermore, the
data suggest that experience-related properties are not
less specific than entity properties (in fact, overall,
they were specific more often). The correlations
between concreteness and the specificity of entity
properties (r=0.30) and experience properties
(r=0.26) were not significant, thus, variation in
specificity in these domains did not seem to be
systematically related to concreteness.
Situation properties were significantly more
specific for concrete (81%) than for abstract concepts
(48%), t(34)=6.85, p<0.001. The specificity score for
these properties was also highly correlated with
concreteness, r=0.77, p<0.001: the higher the
proportion of specific features, the more concrete the
concept was rated (see Table 7). Thus, specificity of
situation properties was the only significant predictor
of concreteness among these three knowledge
domains. Since situation properties constitute the
largest percentage of features generated for concrete
and abstract concept types, this is an important
finding. The finding is consistent with the contextual
variety hypothesis: the situation components of
abstract concepts are more abstract (or unspecific),
enabling the concept to apply to a wider range of
different situations. This suggests that a concept is
more abstract, the more different situations it applies
to.
The results also confirm parallels between
abstract concepts and scripts or very abstract
schemata, as they share a significant proportion of
features that represent a flexible variety of different
instances. Abstract concepts emerge in this analysis
as social abstract schemata, most of whose features
are represented as feature slots rather than in terms of
specific fillers. This shows that a sizeable component
of abstract concepts is represented at a more abstract
level (in terms of schema or frame levels, Minsky,
1975) than concrete concepts, which suggests that
their characteristics may in some ways be parallel to
Content Differences
superordinate object categories, such as furniture or
animals.
Concreteness-Related Variables and Conceptual
Content
In the following, the results of the content
analysis will be related to different variables that
have been associated with concreteness in previous
research. The goal is to identify links between these
variables and conceptual content as reflected by type
and token scores for knowledge domains, as well as
specificity of conceptual content.
Context availability. Ratings of context
availability were significantly correlated with the
number of entity tokens generated for a concept
(r=0.55, p<0.01).
They were not significantly
correlated with any other feature scores. Thus,
context availability was most strongly associated
with an item feature that does not occur frequently
for abstract concepts. This finding may explain why
we did not observe a correlation of concreteness and
context availability ratings for the range of abstract
concepts previously (Wiemer-Hastings et al., 2001).
On the other hand, context availability is slightly
negatively correlated with the number of experience
properties (r=-0.25). That is, the more introspective
properties are part of a concept, the lower are its
ratings of context availability.
However, this
correlation was not significant and much weaker than
the one observed for entity properties. Our findings
raise the interesting question why context may be
more easily available for concepts with many entity
properties. One possibility is that relevant contexts
for concrete objects are closely related to their
features, and that larger numbers of features put more
constraints on typical contexts.
Context availability ratings were also weakly
correlated with the specificity of features (r=0.34,
p<0.05). This correlation is easier to interpret – if
many properties, especially situation properties, are
unspecific, then it should be more difficult to
generate a context for the item. Unspecific properties
pose much less constraint on the kind of context that
could be activated, thus, individuals may need to put
more effort in accessing or generating a specific
instance. It is actually somewhat surprising that the
correlation of these measures was not stronger. The
correlation coefficients for context availability and
the specificity scores separate by knowledge domain
show that entity properties and situation properties
weighed into the relation most strongly. Neither
individual correlation was significant, however.
Imageability. Rated imageability of the target
concepts was significantly correlated with the
13
number of entity tokens (r=0.62, p<0.01) and,
somewhat weaker, the number of experience
properties (r=-0.42, p<0.05).
Thus, higher
imageability of more concrete concepts may be
related to the concreteness of their feature
components: entity properties tend to be perceptual,
so they may evoke imagery; experiential features
may not evoke any imagery and may in fact impede
the formation of imagery. Imageability was also
significantly associated with specificity of properties
overall, and in particular with the specificity of entity
properties (r=0.36, p<0.05) and situation properties
(r=0.49, p<0.01). This makes sense, since an image
needs to be constrained by specific features of the
object. One can imagine, for example, the parts and
external surface features of a truck, but one can’t
imagine equally well “the pieces of some object”.
Thus, the entity properties, which are close to unique
to object concepts, seem closely linked to the
likelihood that imagery is generated, and the presence
of experience-related properties appears to inhibit
imagery generation.
Predication. An approximate measure for
ease of predication may be the total number of
properties that are generated for an item. A more
accurate measure would also include some measure
of the time frame within which these properties are
produced. The correlation of the number of features
overall and concreteness was small, r=0.24. This
suggests that in general, individuals list more
properties for more concrete concepts, which is
consistent with the ease of predication hypothesis.
However, the association was quite weak. Perhaps,
then, ease of predication is not so closely related to
characteristics of abstract versus concrete concepts,
but more closely related to variables like imagery and
context availability.
Complexity. Barsalou has suggested that
abstract concepts are essentially not abstract, but
simply more complex concepts than concrete
concepts (Barsalou, 1999).
One way to
operationalize complexity is the number of different
types of properties involved in a concept. For
example, a concept that contains parts, surface
features, emotions, a living thing, a location and a
function would likely be perceived as more complex
than a concept that simply contains an agent and an
action, or a shape and a function. Cree and McRae
(2003) calculated a score for visual complexity based
on entity properties, which they conceptualized as the
sum of all external properties of an item (including its
parts and surface features) and found it to
systematically vary across different item categories
(e.g., nonliving items overall tended to be less
Content Differences
complex than creatures). For the current analysis,
complexity needs to be conceptualized more broadly
to apply to both abstract and concrete concepts. The
type score presents a ready estimate of complexity: it
indicates the number of different concept features that
individuals list, on average, for a given concept. We
expected that more complex concepts would contain
a larger variety of features. If complexity is a
characteristic of abstract concepts, then this simple
measure of complexity should be correlated with
perceived concreteness. We found marginal support
for this hypothesis, r=-0.32, p<0.06. Thus, more
abstract concepts tend to have larger numbers of
different types of features. Separate analyses by
feature domains show that this tendency was unique
to situation properties: only the number of different
types of situation properties was marginally
correlated with concreteness, r=-0.32, p<0.06. Our
data suggest that more abstract items tend to be
related to more different aspects of situations;
however, this relation does not seem to be strong as
estimated by our measure.
There may be a variety of other measures for
complexity, such as the extent to which features from
situation, experience, and entity properties are
involved in a concept. It should further be noted that
some features by themselves are more complex than
others. For example, a person is relatively little
complex in comparison to a social state, which
implies at least one person (and typically a group of
people), and various conditions such as freedom,
specific relationships such as competition, and so on.
A more accurate measure of complexity may use
features weighted for rated complexity of each
individual feature, and may result in a stronger
correlation with concreteness.
Discussion
Although much is known about differences
in the way abstract and concrete concepts are
processed, accounts for such differences have rarely
addressed the building blocks of such concepts. In
particular, there has been no systematic investigation
of factors that underlie variation of concreteness
within the concrete vs. the abstract range of
concreteness. We think that the key to concreteness
can only be found by exploring systematic
differences in the components of concepts. While
participant-generated features are certainly not
equivalent to a concept’s content, one can
nevertheless argue that they reflect important aspects
of its nature (cf. Cree & McRae, 2003). For example,
in our study, distinctions could be made between
knowledge related to an item, a situation, and
14
subjective experiences. The kinds of knowledge
reflected in individuals’ descriptions of concepts can
further serve as a basis for systematic comparisons
across concepts of different concreteness.
How do abstract and concrete concepts
differ, qualitatively and quantitatively, in their
content as described by participant-generated
features? The most important findings from our
study were that abstract concepts do not have any (or
very few) item properties in the traditional sense –
such as parts, functions, etc. Instead, they describe
on an abstract level scenes, events, and experiences
in situations, which are quite frequently social
situations. In contrast to concrete concepts, they can
be characterized by a high proportion of features that
reflect introspective processes and states, both mental
and emotional, that are associated with our
interpretations of situations. Further, distinctive
situation properties for abstract concepts are agents
and social aspects. In contrast, concrete concepts
elicited a high number of item properties, as well as a
few distinct situation properties such as functions and
locations.
While our word sample was quite limited for
practical reasons, the results for concrete entities
resonate well with the recent study by Cree and
McRae (2003) which explored distinctive features of
categories of 541 concrete items. This study used the
same coding schema by Wu and Barsalou, allowing
for a direct comparison of their results to the present
study. Distinctive features were established by a
cluster analysis and included functions, entity
behaviors, external components, material, internal
surface properties, external surface properties,
locations, and internal components. This list contains
6 item properties as well as two situation properties
(function, location), all of which also featured
centrally in our participants’ feature lists for concrete
concepts. This suggests that in spite of some
variation in features that is probably due to
idiosyncratic word effects, the pattern of data
obtained in our study is fairly reliable.
Our sample was too small to establish
distinctive features for abstract concepts. In the
introduction we mentioned the large variety of
abstract concepts, such as emotions, personality
traits, behaviors, attributes, attitudes, cognitive
processes, relations, and so on. We think that the
feature generation task may be a promising approach
to try to identify systematic differences in the
conceptual make-up of these different kinds of
concepts. However, we suspect that overall abstract
categories will be less distinct because of their
relative unspecific features (more on this below).
Content Differences
Perceptual Representations of Abstract Concepts
It seems that the results of our study replace
the puzzling dichotomy of abstract versus concrete by
a puzzling dichotomy of experience versus entity
properties. Considering that concrete concepts are
mostly described in terms of concrete features (i.e.,
parts, appearance, material), it is not surprising that
abstract concepts have a sizeable proportion of
abstract components. This is consistent with a
previous finding in which we showed that
concreteness of abstract concepts is best predicted by
the proportion of its components that are related to
introspective experiences, as established by a coded
database (Wiemer-Hastings et al., 2001). Based on
these findings, we think that any attempt to ultimately
decompose abstract concepts into a neat set of
concrete components is doomed to fail. Many
situation components are ultimately concrete, and so,
perhaps abstract concepts can be visualized in the
mind to a good extent.
However, subjective
experiences are as abstract as abstract concepts get,
and perhaps as challenging to cognitive scientists as
the mind itself. Subjective experiences appear so
central to abstract concepts that we are quite
confident that any complete account of abstract
concepts will have to integrate this component.
It is a challenge that some of the unique
components of abstract concepts tend to be just as
abstract as the concepts, especially in regards to
theories that argue that abstract concepts may be
represented perceptually (e.g., Barsalou, 1999).
However, assumptions about the nature of perceptual
representations have been made that can handle the
abstract features.
One of Barsalou’s critical
assumptions with respect to abstract concepts is that
their representation would involve perceptual
simulations of introspective features, such as
representational states of the mind, emotions, and
evaluations.
Thus, properties reflecting mental
experiences originating in the mind itself can be
simulated, i.e., activated neurologically and
integrated into an active representation of an abstract
concept just like sensory-motor experiences that
originate in sensory organs. Thus, in principle, the
approach seems feasible. Of course, details remain to
be fleshed out both theoretically and empirically.
Second, we have identified several situation
properties that appear to be central to abstract
concepts when compared to concrete ones. They
include agents, actions, and a variety of social
aspects. Situation properties are more central to
abstract concepts than they are to concrete concepts
because they have more proportionate weight (given
15
that entity properties are mostly absent).
Accordingly, abstract concepts may be based to a
large extent on quite observable situations, actions
and agents therein, fleshed out with introspective
experiences and evaluations.
Situating Abstract and Concrete Concepts:
Difference in Focus
In related work, Barsalou and WiemerHastings (in press) have suggested that while both
abstract and concrete concepts are embedded in
situational knowledge, the focus may be on specific
items for concrete concepts, and on specific situation
aspects for abstract concepts. Our findings are
consistent with such a view. Figures 2 and 3
illustrate this focus difference. The outer frame
symbolizes the boundaries of a situation. Both
abstract and concrete concepts are situated in the
same kinds of contexts, thus the general components
within the frame are identical for all examples. We
have largely simplified situations by only including
two individuals, two objects, a person and an object
attribute, and one action. The lines between these
indicate relations. Circles in the frames are examples
of a specific focus. In Figure 2, the focus is on an
object along with its attributes, actions performed on
or with the object, and relations to other objects.
Objects and individuals may further be associated
with specific locations, which would be included in
the focus frame. This focus essentially resembles a
semantic or propositional network model that
represents information associated with an entity.
Our data suggest that the focus frame for
objects does not vary much – it will include the
object and its attributes, and to some extent actions
and functions that are immediately related to the
object in a context. The main factor that varies is the
amount of information stored with the object, as
measured, for example, by the number of entity
properties that participants generated. To the extent
that typical actions are closely linked to a specific
person, that person may also be part of the focus. For
example, if the object is a syringe, then the situation
may include a nurse or a doctor.
Figure 3 shows a variety of foci for abstract
concepts. These are different from object foci in
several ways. First, the focus is not on objects and
their properties (although an object may be part of the
focus, such as shown in the lower right). Instead, the
focus typically involves a person, along with some
attribute, action, or other person. Different types of
abstract concepts are probably associated with typical
context foci; for example, emotions and attitudes will
typically involve a person and an introspective
Content Differences
attribute; attributes will involve a characteristic and
perhaps entities it is associated with. Second, many
abstract concepts will not have a center entity within
the frame of focus as concrete objects (Figure 2).
Typically, network models symbolize concepts by
nodes, and relations to attributes, actions, and related
entities are shown as links or arcs. For abstract
concepts, this is not always correct, as a variety of
nodes and links are part of the entire concept.
Exceptions are abstract concepts that are concurrent
with a specific situation component, such as an
attribute or an action. However, for many abstract
concepts the focus is not on an entity and its
relations, but on various entities and relations, all of
which are related to the concept. For example,
indifference would imply a person, a mental state, a
relation to some state of affairs, and a state of affairs.
Thus, while the majority of object properties are
contained in the object and its attributes, the
properties of abstract entities are part of the situation.
If features were rated for importance for
different kinds of concepts, we would probably see
that within the frame of focus, most emphasis would
be given to particular parts. For example, personality
traits, attitudes, beliefs, and other mental states and
processes would probably highlight the person and
related characteristics, whereas events and processes
would probably emphasize actions.
A third
difference in the context focus for abstract and
concrete concepts concerns the size of the focus
frame, which varies among abstract concepts. The
focus may include as little as an attribute (e.g., size),
or it may include a complex network of individuals,
goals, and actions (e.g., strategy, discussion).
Finally, abstract concepts often critically involve a
temporal dimension. For simplicity, we did not
include a dimension for time in the Figure.
One thing that is quite obvious from these
Figures is that they represent conceptual frames at a
very abstract level (cf. Minsky, 1977). It is clear that
individuals know a lot more about concepts than
what situation components they are linked to.
Figures 2 and 3 compare abstract and concrete
concepts with respect to their relations to situation
knowledge, rather than providing exhaustive
schemata for their conceptual content. The Figures
show that situation knowledge is more intricately
related to abstract concepts, since it constitutes a
central part of them, whereas it is involved in object
concepts only to the extent that typical actions,
functions and locations are involved.
The effects of instructions in our study are
interesting in the context of this discussion. When
participants are instructed to describe features that are
16
true of the target item, the focus should be on the
conceptual content; when asked to describe relevant
context, the focus should shift away from the concept
“core” to situational knowledge. We found that
instructions to focus on context increased the number
of tokens for experience-related properties, as well as
for a number of situation properties: person, and
marginally, events, and (for concrete concepts) living
things and objects. At the same time, a decrease was
observed in the number of entity properties (for
concrete concepts only) and social organizations (for
abstract concepts only). Thus, for concrete concepts,
participants appropriately (but not completely) reduce
the focus on the properties of the items themselves,
and shift the focus to associated situation elements,
predominantly concrete entities (person, object,
living things). For abstract concepts, the shift affects
the tokens of situation and experience properties
only, with a focus (for situation properties) on person
and event properties. Overall, the make-up of
abstract concepts did not qualitatively differ by
instruction, consistent with the finding that abstract
concepts essentially represent elements, relations and
processes that are typically referred to as context.
This is also in line with the finding that a shift from a
context focus to a target focus did not increase the
number of entity properties generated for abstract
concepts.
Variables Associated with Concreteness
We have found systematic relations of
imageability and context availability to concept
features, in particular, to the number of entity versus
experiential properties. One question in this context
is what determines a concept’s context availability.
Correlation studies show that this variable is mostly
associated with concreteness ratings for concrete
items, as well as with items across the entire
concreteness range. Within the concrete items, we
also observed significant correlations between the
rated context availability and the number of entity
properties. This can be interpreted in two ways.
Perceptual features may be accessing conceptrelevant information in memory.
The other
possibility is that the knowledge reflected by entity
properties is the knowledge that needs to be accessed
in memory during concept processing. If there are
more entity properties, then a richer representation
would be accessed, resulting in facilitation of concept
processing. Interestingly, our data suggest that it
may not be the mere amount of relevant knowledge
in memory that matters, but that this effect seems to
be related only to perceptual features.
Content Differences
Abstract concepts do not tend to have fewer
properties in our analysis; likewise, no systematic
differences have been found in the number of
associates that are produced for abstract and concrete
nouns (Altarriba et al., 1999; Nelson & Schreiber,
1992). This further supports the view that perceptual
features are the driving force behind context
availability effects.
Imageability is also
predominantly related to entity properties, and to a
lesser extent also to experience properties. Since
imagery (particularly visual imagery) seems to be
strongly limited to perceptual entity properties, this
makes sense. The results from the content analysis
suggest that imageability and context availability
predominantly account for processing and
concreteness differences that are due to the amount of
perceptual features that concepts possess; they do not
explain the variance in concreteness for abstract
concepts. Other variables, such as specificity or
complexity, may be more important variables in this
lower range of concreteness.
Feature-Based Concept Research
To what extent do the data we presented
advance our knowledge of abstract concepts? We
will argue that the results can be used to inform and
advance abstract concept research in several domains,
just like feature lists have given rise to one of the
most influential representational models in the
concept literature, the prototype model (Rosch &
Mervis, 1975). A review of the concept literature
reveals that features (or semantic components) have
been used widely as building blocks of models for
concept representation, organization, and other
processes. For example, Rosch and colleagues used
feature lists to reveal the organization of concepts
around prototypes, and levels of categorization, both
of which have been very influential and productive
approaches in the study of concepts. Most similarity
models are based on a computation of feature overlap
(Tversky, 1977). Categories are assumed to form,
among other factors, based on feature similarities
amongst their members. In short, the notion of
feature components of concepts is firmly entrenched
in many areas of research. Specific models vary in
methodology, background assumptions and proposed
format, but many if not most have the assumption of
conceptual features in common (Barsalou & Hale,
1993).
We think that one likely reason that our
knowledge of abstract concepts is scarce is that there
has not been a systematic account of abstract concept
features. The features we identified in our study may
be considered as a first important step towards
17
establishing a basis of knowledge that allows us to
test conceptual models for abstract concepts. In what
follows, we will outline hypotheses regarding
cognitive processes involving abstract concepts,
based on our findings. We will focus on similarity, a
central process closely linked to a variety of
cognitive processes, such as category formation and
reasoning.
Abstract Concept Similarity
Comparison of objects entails the comparison
of features. Thus, knowledge of an item’s features
allows us to predict similarity and to identify how
similarity of is determined. A feature shared by two
objects increases their similarity in some respect, e.g.,
perceptually (both round), functionally (both used to
eat), or ontologically (both alive). Similarity for a
pair of items tends to increase with the number of
features that they share (Tversky, 1977). Abstract
concepts refer to situations varying in scope and
complexity. In this context, "situation" refers to a
small subset of components and relations in a context
which are relevant to the concept, rather than the
context in its entirety. For example, an abstract
concept may refer to a person-action situation, or a
person-person-relation situation, etc. (compare Figure
3). Thus, we use the term situation here to refer to
abstract situations that are a subset of all the features
found in a complex environment.
Comparing
situations is qualitatively different from comparing
objects, which should have important implications.
Less distinctiveness.
Abstract concepts
should be more similar to one another than concrete
concepts, regardless of category, because overall they
have more common and fewer distinctive features.
This is mostly due to their most important features
being situational properties which tend to be
unspecific. We have shown that situation properties
associated with abstract concepts are roughly half of
the time (52%) unspecified features like some person,
some object, do something, or something happens.
The most commonly mentioned feature, person, was
left unspecified in 92% of its mention, suggesting
that a majority of abstract concepts have an
unspecified agent in common.
The level of
vagueness about abstract concept features is very
systematic. The immediate effect is that concepts
will not be further differentiated at the level of
specific features. For example, concepts like pity,
hope, jeopardy, happiness, inaction all have a
frequent property (person) in common. In contrast, a
frequently used feature for concrete concepts,
external component, will vary for every individual
item: branches and leaves for tree, walls and corners
Content Differences
for labyrinth, and so on. Thus, for concrete concepts,
individual feature types offer a high level of
differentiation.
Of course, this will also affect the
distinctiveness of categories of abstract items. Many
concrete item categories are distinguished based on
specific features for feature types such as function
(e.g., tools versus vegetables), systemic properties
(e.g., living things versus non-living things) or
material (e.g., plants versus furniture). Since abstract
concepts share a limited set of properties, categories
can be expected to overlap more. For example,
communication concepts will share verbal
components with some mental concepts such as
worry or thought. In a first exploration of categorical
similarity for abstract versus concrete concepts,
higher similarity was found for typical abstract
category exemplars of different categories than for
typical concrete exemplars of categories (WiemerHastings, Barnard, & Felnar, under review). For both
general categories, such as action and event, as well
as narrower categories such as social offenses and
character trait, the most typical exemplars for the
categories were rated as more similar across
categories than for concrete categories.
There are some indications that social
categories are not organized around an average
prototype, but around ideal exemplars (Barsalou,
1985). In particular, structures consistent with such
organization have been reported for traits (Borkenau,
1990) and traits versus states (Chaplin, John, &
Goldberg, 1988). Chaplin et al. further suggest that
the types of attributes that distinguish such categories
are qualitatively different for social categories. As
one such attribute, they suggest dimensional
intensity, where exemplars vary in the intensity of
some feature (e.g., somewhat angry). Ideal-based
categories are typically related to goals (Barsalou,
1985), so these findings further emphasize the social
character of abstract concepts.
Higher variability. Abstract concepts that
refer to unspecific situation elements, such as
someone, doing something, are constituted of a
limited set of features, resulting generally in higher
likelihood of feature overlap. However, unspecific
features offer no reliable basis for comparison
because they include a large variety of instances.
Accordingly, individuals may access specific
instances to estimate the similarity of the concepts,
which would result in high variance in their
judgments depending on the overlap of the specific
instances. Also, the similarity instructions may lead
people to adopt specific processing strategies for
abstract concepts with unspecific features. For
18
example, they may estimate similarity by activating
instances that are maximally similar to each other, or
by activating instances that are maximally dissimilar.
One way to empirically assess this would be by
varying instructions to either rate similarity or
differences, and to test if the focus on similar aspects
versus different aspects would result in higher
asymmetries in ratings as for concrete concepts.
More influence of context. Features do not
influence into a similarity computation with a fixed
weight. Instead, the item that it is compared to, as
well as the context of previous items, have a strong
influence. For example, a mouse and a lion seem
more similar in a context of tools and vegetables than
in the context of other animal pairs. Similarly, when
comparing a knife and scissors, the function of
cutting may be emphasized; when comparing a knife
and a pan, the material and the location may be
foregrounded. The context makes specific features
more salient than others. In the case of abstract
concepts, this may have more far-reaching
consequences than simply shifts in focus. As we
mentioned, abstract concepts quite often involve
unspecific features. Because they vary so much
across contexts, it is possible that the activation for a
given concept will be more strongly influenced by
the context of the other abstract concept in the pair.
Given the low constraint exercised by the unspecific
features on access to specific instances, people may
use the context as an additional constraint to guide
and facilitate access to the concept.
Higher rates of thematic integration.
Recently, much attention has been given to the
observation that participants at times base high
similarity ratings on thematic relations. Two items
have a thematic relation when they play
complementary roles in a scenario or event (e.g.,
knife, meat: the knife cuts meat). The thematic
relation itself can vary. Concrete concepts are often
integrated around locations, functions, and uses. For
abstract concepts, the most common thematic
relations are temporal (happens after, happen at the
same time) and causal (leads to). The curious effect
of integrations in similarity judgment tasks is that
items that have very little in common (e.g., cow,
milk) are rated as being very similar (Wisniewski &
Bassok, 1999). For concrete concepts, this happens
only if the item pair shares a salient thematic relation
and no taxonomic similarity. For example, for a pair
like bear, fish individuals are able to reject the “X eat
Y” thematic relation in favor of the taxonomic “both
are animals” as grounds for their similarity ratings.
However, for abstract concepts individuals can be
observed to predominantly use thematic relations and
Content Differences
frequently to ignore taxonomic relations.
For
example, jealousy and anger are rarely judged based
on their categorical similarity (emotions) but instead
are regularly integrated through a causal thematic
relation “X leads to Y” (Wiemer-Hastings & Xu,
2003).
Similarity ratings based on thematic
integrations violate instructions because thematic
relations do not cause similarity in the usual sense of
the word – instead they reflect associative relations
between two items. For example, soup is not really
similar to spoon, neither is an explosion similar to a
shock. Thus, the question is what explains the high
frequency with which participants refer to thematic
relations when judging abstract item similarity.
It is possible to link the results of this paper
to these findings. Concrete concepts are mostly
described by perceptual and functional properties
which also have been shown to be distinctive features
that differentiate category membership (Cree &
McRae, 2003).
In contrast, when processing
similarity of abstract concepts, participants would
identify commonalities such as an agent, a social
situation, and subjective experiences.
These
properties may be little distinctive (see above).
Furthermore, social components (e.g., person,
behavior, social state, etc.) likely activate individuals'
schemata about people and behaviors. Our analysis
shows that social situations are frequently and quite
distinctly part of abstract concepts; so social
schemata likely influence many similarity judgments.
Research in the area of social cognition has shown
that individuals interpret and evaluate behaviors,
interpersonal relations, and people in order to make
predictions.
Many abstract concepts may thus
automatically activate associated goals, outcomes,
and causes (support for this hypothesis may be the
goal-derived structure of some social categories
described previously). This may put the abstract
concepts to be compared in a specific, uniting context
in which they are related.
For example, mischief typically activates
knowledge about typical agents (children) and likely
outcomes (getting in trouble). Inaction is typically
described in terms of a person, a situation that
requires some action, and absence of action. Mere
comparison of features may result in a judgment of
dissimilar because one entails an action, while the
other explicitly entails no action. However, when
people judge the similarity of these concepts, the
terms may conjointly activate a relevant social
schema which would put mischief in the slot of the
situation that requires an action, and inaction would
be interpreted as the failed response to someone's
mischief. This processing may be automatic, which
19
would make sense considering how important the
appropriate interpretation of social behaviors and
events is in our lives. If a good fit is obtained for the
concepts within a schema, this information may
override schema-independent feature comparisons,
resulting in some degree of perceived similarity.
The integration frequency may also be
increased by the large number of unspecific features
for abstract concepts.
Individuals have much
flexibility in interpreting unspecific properties.
Processing may be simpler by assuming feature
identity across concepts. For example, hope and
happiness both involve a person. Instead of creating
a different activation for each concept, it may be
more economical to assume that both refer to the
same individual. Since humans are quite proficient at
relating a person’s actions, dispositions and emotions
to each other, it is likely that there is then a strong
bias to relate the concepts to each other causally. In
fact, it has even been shown that individuals can
make connections between dispositions that seem
contradictory (Asch & Zukier, 1984). Thus, there
may be a strong bias to relate abstract concepts to
each other causally, and to integrate them. Thus,
abstract concepts may accordingly be integrated as
readily as concrete concepts are compared
taxonomically.
Recursive Structures
Abstract concepts do not occur independently
of other concepts. Hampton (1981) suggests that
“effects” of some behaviors are likely involved in
abstract concepts. In any social situation, we make
inferences about the people around us based on their
behaviors, including their goals, their character, their
attitudes, and their relations to other people.
Behaviors and events are analyzed with respect to
possible causes, consequences, and possible
responses. Based on our inferences, we make
predictions about future events, behaviors, and the
development of relationships. Acting and responding
intelligently in fact depends on our ability to make
such inferences. In our data, such processing may be
revealed in a variety of ways. First, many features
coded as situation and experience properties may in
fact be causes, associates, and effects of a concept.
This includes emotions, representative states,
evaluations, social states, actions, and events.
Second, coordinates and synonyms of a concept were
coded separately and not included in the analysis,
because they are unlikely part of the conceptual
content.
Abstract concepts are relational concepts,
which have been argued to have a weak internal
Content Differences
structure, paired with many external links to related
concepts (Gentner, 1981). Accordingly, the rich
connections between these concepts are likely to be
reflected in descriptions of the individual concepts’
characteristics. As we have seen, abstract concepts
are largely described in terms of feature types that are
themselves abstract concepts, including three
experience properties (emotion, representation,
evaluation) and frequent situation properties (social
state, social artifact, action, event). Such features
may reflect external links of the target concepts to
related concepts. For example, the target concept
emancipation may be described as a transition from
one social state to another. The first social state may
be described as a state of social oppression, the
second as a state of freedom or autonomy. The
process leading from the first to the second may be
described as a process of liberation. Notice that all
these components of the concept of emancipation are
themselves abstract concepts.
It is impossible at present to decide whether
such related concepts are merely associates or actual
components of abstract concepts. At first glance,
having an integrated network of abstract concepts
constituting the content of an abstract concept is
disturbing. If such concepts build the content of one
abstract concept, what builds their concept,
respectively? If the components of abstract concepts
themselves consist of integrated networks of abstract
concepts, then it becomes difficult to see where the
meaning comes in. However, it is possible that a
number of abstract concepts have components that
are not themselves abstract, such as agents and
specific behaviors. Perhaps the description of an
abstract concept in terms of other abstract concepts
reflects a cognitive economy where we use abstract
concepts to reflect our knowledge of more complex
abstract concepts. For example, oppression in the
above example may be represented by a specific
relation between two people, one of who constrains
the liberty of the other person. Emancipation may
then be represented by a simulation (Barsalou, 1999)
of the transition from this state to one in which these
constraints are removed. This process would be quite
similar to the transition between frames for object
rotation or movement as conceptualized by Minsky
(1975).
Another reason that we are not too concerned
about recursive use of abstract concepts in each
others’ descriptions is that a similar process can be
observed in concrete concepts. When an individual
describes their object knowledge in terms of parts
and materials, they essentially describe their
knowledge of an object through other objects. For
20
example, tree is often described with the features
leaves, bark, trunk, roots, and wood. All of these are
concrete objects themselves. Just like we may
describe leaf as a part of a tree, we may describe
freedom or autonomy as a result of emancipation, as
a state where no oppression is present. Networks of
abstract concepts may thus be quite similar in
structure and function as those of concrete ones.
Thus, to the extent that recursive structures present a
problem, we argue that it is not a problem that is
unique to abstract concepts. At the same time,
independent of this issue, the value of componential
analysis is obvious in the many areas of concept
research that have built on the notion of features.
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Content Differences
Appendix A.
Word materials by concreteness level (concreteness measures are on a 7-point scale, with
1=most abstract, 7 = most concrete).
Items
Rated Concreteness (MRC)
Highly Abstract
Aspect
2.2
Desperation
2.6
Exception
2.6
Hope
2.6
Ingratitude
2.5
Jeopardy
2.7
Medium Abstract
Emancipation
3.0
Happiness
3.0
Inaction
3.0
Mischief
3.3
Pity
3.0
Removal
3.5
Little Abstract
Formation
3.8
Morass
4.2
Possession
3.8
Saga
3.7
Story
4.3
Zone
3.9
Little Concrete
Day
4.8
Daybreak
5.1
Pest
4.8
Prize
4.7
Sedative
4.6
Venom
4.8
Medium Concrete
Bass
5.5
Blossom
5.6
Hairpin
5.8
Labyrinth
5.2
Lace
5.5
Nectar
5.6
Highly Concrete
Beehive
6.1
Insect
5.9
Mackerel
6.4
Owl
6.1
Tree
6.0
Vine
6.0
22
Content Differences
Appendix B.
Excerpt from Coded Transcripts for Concrete Concept. (Item: Tree; Instructions: Item Feature
Generation.)
Features
Domain
Domain Property
Wood,
Entity
Material
has
\
branches,
Entity
External Component
has
\
different
Entity
Quantity
colored
Entity
External Surface Feature
leaves,
Entity
External Component
animals
Situation
Living Thing
to_live
Entity
Systemic
in_them,
Situation
Spatial
like
Meta
Meta
squirrels,
Situation
Living Thing
owls
Situation
Living Thing
kids
Situation
Person
climb
Situation
Action
them,
Meta
Cue Repetition
build
Situation
Action
tree
Meta
Cue Repetition
houses
Situation
Build
23
Content Differences
Appendix C.
Excerpt from Coded Transcripts for Abstract Concept. (Item: Hope; Instructions: Item Feature
Generation.)
Features
Domain
Domain Property
mmm
Meta
Hesitation
um
Meta
Hesitation
maybe
hope
Meta
Cue Repetition
could
have
the_hope_that
Meta
Cue Repetition
something
\
will
\
happen
Situation
Event
good,
Experience
Evaluation
you
Situation
Person
really
Situation
Quantity
want
Experience
Representation
something
\
to_happen,
Situation
Event
24
Content Differences
25
Author Note
Katja Wiemer-Hastings, Department of Psychology, Northern Illinois University; Xu Xu,
Department of Psychology, Northern Illinois University.
The authors gratefully acknowledge Jan D. Krug and Patricia Fernandez for participation in the
data analysis. We are also grateful to Larry Barsalou for insightful questions and comments which have
contributed to our interpretations of this research, and for much help in the proper use of the coding
schema used in the evaluation of our data.
Correspondence regarding this article may be addressed to Katja Wiemer-Hastings, the
Department of Psychology, Northern Illinois University, DeKalb, IL 60115 (email: katja@niu.edu).
Content Differences
Table 1
Average Number of Types of each Knowledge Domain
Situations
Entities
Instruction
Item
Types
Weighed
Types
Weighed
Features
Abstract
3.44
0.22
0.33
0.03
Concrete
3.62
0.23
2.09
0.19
Context
Abstract
3.81
0.24
0.25
0.02
Concrete
4.19
0.26
0.93
0.08
Types
1.09
0.33
1.47
0.54
Experience
Weighed
0.18
0.06
0.25
0.09
26
Content Differences
Table 2
Type Scores of Situation Properties for Abstract and Concrete Concepts
Item Features
Context Features
Situation Property
Concrete
Abstract
Concrete
Abstract
More for Abstract
Person**
0.34
0.71
0.38
0.96
Social State**
0.01
0.19
0.02
0.26
Social Artifact*
0.01
0.18
0.02
0.13
More for Concrete
Object*
0.61
0.48
0.92
0.43
Location*
0.41
0.19
0.39
0.22
Living Thing**
0.35
0.01
0.44
0.03
Function*
0.18
0.04
0.08
0.01
No significant differences
Action
0.65
0.83
0.65
0.84
Event
0.26
0.24
0.32
0.39
Time
0.24
0.13
0.23
0.11
Physical State
0.21
0.14
0.32
0.19
Building
0.01
0.00
0.04
0.02
Social Organization
0.03
0.10
0.02
0.02
Spatial Relation
0.1
0.06
0.10
0.03
Note. Situation properties for which a significant main effect of concreteness was obtained are marked
with * (p<0.05) or ** (p<0.01).
27
Content Differences
Table 3
Average Number of Features (Tokens) Generated for Each Knowledge Domain
Instruction Item
Situations
Entities
Experience
Features
Abstract
3.95
0.22
1.28
Concrete
3.88
2.02
0.36
Context
Abstract
4.49
0.19
1.65
Concrete
4.29
1.01
0.62
28
Content Differences
29
Table 4
Token Scores of Situation Properties for Abstract and Concrete Concepts
Item Features
Context Features
Situation Property
Concrete
Abstract
Concrete
Abstract
More for Abstract
Person**
0.38
0.99
0.49
1.34
Action*
0.66
0.87
0.69
0.93
Social State**
0.01
0.19
0.02
0.29
Social Artifact**
0.01
0.18
0.02
0.13
More for Concrete
Object**
0.64
0.56
0.95
0.51
Location**
0.41
0.20
0.40
0.22
Living Thing**
0.36
0.01
0.48
0.03
Time**
0.27
0.13
0.24
0.11
Function**
0.19
0.04
0.08
0.01
Physical State*
0.22
0.14
0.32
0.14
No Difference
Event
0.26
0.25
0.32
0.42
Building
0.01
0.00
0.04
0.01
Social Organization
0.03
0.10
0.02
0.02
Spatial Relation
0.10
0.06
0.10
0.04
Note. Situation properties for which a significant main effect of concreteness was obtained in the subject
analysis are marked with * (p<0.05) or ** (p<0.01).
Content Differences
Table 5
Knowledge Domain Tokens Among first Five Features
Situation
Abstract
0.66
Concrete
0.42
Entity
0.02
0.40
Experience
0.19
0.04
30
Content Differences
Table 6
Examples for Specific and Unspecific Features for Different Types of Concept Knowledge
Property
Specific Feature
Unspecific Feature
Entity – component
Branch, Leg
Part, Aspect, Pieces
Entity – appearance
Large, dark
Characteristic
Entity – material
Wood, Metal
Some Material
Situation – person
Doctor, Kid, A. Lincoln
Someone, You, A Person
Situation – object
Road, Sign, Water
Something, Things
Situation – action
Make, Collect, Grow, Run
Activity, Doing Something
Situation – event
Gain, Game, Celebration
Something Happens, Change
Situation – living thing Bees, Birds
Animals, A Creature
Situation – location
Jail, Forest
Place, Area, Background, Anywhere
Experience – emotion
Anger, Sadness
Emotion, Feeling
31
Content Differences
32
Table 7.
Correlations of Token Scores, Type Scores, and Proportions of Specific Features with Concreteness and
Related Variables.
Concreteness
Context Availability
Imageability
Token Scores
Entity Tokens
0.74**
0.55**
0.62**
Experience Tokens
-0.69**
-0.25
-0.42*
Situation Tokens
-0.03
0.14
0.10
Type Scores
Entity Types
0.13
0.05
0.00
Experience Types
-0.2
-0.16
-0.14
Situation Types
-0.32
-0.06
-0.25
Proportion of Specific
Features
Specificity
0.73**
0.34*
0.51**
Entity Specificity
0.26
0.29
0.36*
Experience Specificity
0.30
-0.09
0.13
Situation Specificity
0.77**
0.31
0.49**
Note. Significant correlations are marked with an asterisk; * signifies p<0.05, ** signifies p<0.01.
Content Differences 33
Figure 1: Proportions of entity and experiential properties generated for items of six concreteness levels
(numbers increase with higher concreteness).
Figure 2: Situation with focus on an object. The focus is illustrated by the circle.
Figure 3: Situations illustrating different foci for abstract concepts.
Frequency of Mention
Content Differences
0.6
0.5
0.4
0.3
0.2
0.1
0
Experience
Entity
1
2
3
4
5
Concreteness
6
34
Content Differences
35
Content Differences
36