What is an abstract word

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noun. The quality of being abstract, especially the quality of existing or being presented in abstract form, rather than with reference to concrete details or particular instances (frequently opposed to concreteness). Also occasionally: an example of this; an abstraction.

What does abstractness mean?

Definitions of abstractness. the quality of being considered apart from a specific instance or object.

Can abstract be used as a verb?

The verb abstract is used to mean “summarize,” as in “abstracting an academic paper.” This meaning is a figurative derivative of the verb’s meanings “to remove” or “to separate.”

How do you use the word abstract?

Abstract in a Sentence ?

What is abstract verb?

Abstract verb refers to a verbal aspect in verbs of motion that is multidirectional (as opposed to unidirectional), an indirect motion, or a repeated action or series of actions (instead of a single, completed action).

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What this handout is about

This handout provides definitions and examples of the two main types of abstracts: descriptive and informative. It also provides guidelines for constructing an abstract and general tips for you to keep in mind when drafting. Finally, it includes a few examples of abstracts broken down into their component parts.

What is an abstract?

An abstract is a self-contained, short, and powerful statement that describes a larger work. Components vary according to discipline. An abstract of a social science or scientific work may contain the scope, purpose, results, and contents of the work. An abstract of a humanities work may contain the thesis, background, and conclusion of the larger work. An abstract is not a review, nor does it evaluate the work being abstracted. While it contains key words found in the larger work, the abstract is an original document rather than an excerpted passage.

Why write an abstract?

You may write an abstract for various reasons. The two most important are selection and indexing. Abstracts allow readers who may be interested in a longer work to quickly decide whether it is worth their time to read it. Also, many online databases use abstracts to index larger works. Therefore, abstracts should contain keywords and phrases that allow for easy searching.

Selection

Say you are beginning a research project on how Brazilian newspapers helped Brazil’s ultra-liberal president Luiz Ignácio da Silva wrest power from the traditional, conservative power base. A good first place to start your research is to search Dissertation Abstracts International for all dissertations that deal with the interaction between newspapers and politics. “Newspapers and politics” returned 569 hits. A more selective search of “newspapers and Brazil” returned 22 hits. That is still a fair number of dissertations. Titles can sometimes help winnow the field, but many titles are not very descriptive. For example, one dissertation is titled “Rhetoric and Riot in Rio de Janeiro.” It is unclear from the title what this dissertation has to do with newspapers in Brazil. One option would be to download or order the entire dissertation on the chance that it might speak specifically to the topic. A better option is to read the abstract. In this case, the abstract reveals the main focus of the dissertation:

This dissertation examines the role of newspaper editors in the political turmoil and strife that characterized late First Empire Rio de Janeiro (1827-1831). Newspaper editors and their journals helped change the political culture of late First Empire Rio de Janeiro by involving the people in the discussion of state. This change in political culture is apparent in Emperor Pedro I’s gradual loss of control over the mechanisms of power. As the newspapers became more numerous and powerful, the Emperor lost his legitimacy in the eyes of the people. To explore the role of the newspapers in the political events of the late First Empire, this dissertation analyzes all available newspapers published in Rio de Janeiro from 1827 to 1831. Newspapers and their editors were leading forces in the effort to remove power from the hands of the ruling elite and place it under the control of the people. In the process, newspapers helped change how politics operated in the constitutional monarchy of Brazil.

From this abstract you now know that although the dissertation has nothing to do with modern Brazilian politics, it does cover the role of newspapers in changing traditional mechanisms of power. After reading the abstract, you can make an informed judgment about whether the dissertation would be worthwhile to read.

Indexing

Besides selection, the other main purpose of the abstract is for indexing. Most article databases in the online catalog of the library enable you to search abstracts. This allows for quick retrieval by users and limits the extraneous items recalled by a “full-text” search. However, for an abstract to be useful in an online retrieval system, it must incorporate the key terms that a potential researcher would use to search. For example, if you search Dissertation Abstracts International using the keywords “France” “revolution” and “politics,” the search engine would search through all the abstracts in the database that included those three words. Without an abstract, the search engine would be forced to search titles, which, as we have seen, may not be fruitful, or else search the full text. It’s likely that a lot more than 60 dissertations have been written with those three words somewhere in the body of the entire work. By incorporating keywords into the abstract, the author emphasizes the central topics of the work and gives prospective readers enough information to make an informed judgment about the applicability of the work.

When do people write abstracts?

• when submitting articles to journals, especially online journals
• when applying for research grants
• when writing a book proposal
• when completing the Ph.D. dissertation or M.A. thesis
• when writing a proposal for a conference paper
• when writing a proposal for a book chapter

Most often, the author of the entire work (or prospective work) writes the abstract. However, there are professional abstracting services that hire writers to draft abstracts of other people’s work. In a work with multiple authors, the first author usually writes the abstract. Undergraduates are sometimes asked to draft abstracts of books/articles for classmates who have not read the larger work.

Types of abstracts

There are two types of abstracts: descriptive and informative. They have different aims, so as a consequence they have different components and styles. There is also a third type called critical, but it is rarely used. If you want to find out more about writing a critique or a review of a work, see the UNC Writing Center handout on writing a literature review. If you are unsure which type of abstract you should write, ask your instructor (if the abstract is for a class) or read other abstracts in your field or in the journal where you are submitting your article.

Descriptive abstracts

A descriptive abstract indicates the type of information found in the work. It makes no judgments about the work, nor does it provide results or conclusions of the research. It does incorporate key words found in the text and may include the purpose, methods, and scope of the research. Essentially, the descriptive abstract describes the work being abstracted. Some people consider it an outline of the work, rather than a summary. Descriptive abstracts are usually very short—100 words or less.

Informative abstracts

The majority of abstracts are informative. While they still do not critique or evaluate a work, they do more than describe it. A good informative abstract acts as a surrogate for the work itself. That is, the writer presents and explains all the main arguments and the important results and evidence in the complete article/paper/book. An informative abstract includes the information that can be found in a descriptive abstract (purpose, methods, scope) but also includes the results and conclusions of the research and the recommendations of the author. The length varies according to discipline, but an informative abstract is rarely more than 10% of the length of the entire work. In the case of a longer work, it may be much less.

Here are examples of a descriptive and an informative abstract of this handout on abstracts.
Descriptive abstract:

The two most common abstract types—descriptive and informative—are described and examples of each are provided.

Informative abstract:

Abstracts present the essential elements of a longer work in a short and powerful statement. The purpose of an abstract is to provide prospective readers the opportunity to judge the relevance of the longer work to their projects. Abstracts also include the key terms found in the longer work and the purpose and methods of the research. Authors abstract various longer works, including book proposals, dissertations, and online journal articles. There are two main types of abstracts: descriptive and informative. A descriptive abstract briefly describes the longer work, while an informative abstract presents all the main arguments and important results. This handout provides examples of various types of abstracts and instructions on how to construct one.

Which type should I use?

Your best bet in this case is to ask your instructor or refer to the instructions provided by the publisher. You can also make a guess based on the length allowed; i.e., 100-120 words = descriptive; 250+ words = informative.

How do I write an abstract?

The format of your abstract will depend on the work being abstracted. An abstract of a scientific research paper will contain elements not found in an abstract of a literature article, and vice versa. However, all abstracts share several mandatory components, and there are also some optional parts that you can decide to include or not. When preparing to draft your abstract, keep the following key process elements in mind:

• Reason for writing: What is the importance of the research? Why would a reader be interested in the larger work?
• Problem: What problem does this work attempt to solve? What is the scope of the project? What is the main argument/thesis/claim?
• Methodology: An abstract of a scientific work may include specific models or approaches used in the larger study. Other abstracts may describe the types of evidence used in the research.
• Results: Again, an abstract of a scientific work may include specific data that indicates the results of the project. Other abstracts may discuss the findings in a more general way.
• Implications: What changes should be implemented as a result of the findings of the work? How does this work add to the body of knowledge on the topic?

(This list of elements is adapted with permission from Philip Koopman, “How to Write an Abstract.”)

All abstracts include:

• A full citation of the source, preceding the abstract.
• The most important information first.
• The same type and style of language found in the original, including technical language.
• Key words and phrases that quickly identify the content and focus of the work.
• Clear, concise, and powerful language.

Abstracts may include:

• The thesis of the work, usually in the first sentence.
• Background information that places the work in the larger body of literature.
• The same chronological structure as the original work.

How not to write an abstract:

• Do not refer extensively to other works.
• Do not add information not contained in the original work.
• Do not define terms.

If you are abstracting your own writing

When abstracting your own work, it may be difficult to condense a piece of writing that you have agonized over for weeks (or months, or even years) into a 250-word statement. There are some tricks that you could use to make it easier, however.

Reverse outlining:

This technique is commonly used when you are having trouble organizing your own writing. The process involves writing down the main idea of each paragraph on a separate piece of paper–see our short video. For the purposes of writing an abstract, try grouping the main ideas of each section of the paper into a single sentence. Practice grouping ideas using webbing or color coding.

For a scientific paper, you may have sections titled Purpose, Methods, Results, and Discussion. Each one of these sections will be longer than one paragraph, but each is grouped around a central idea. Use reverse outlining to discover the central idea in each section and then distill these ideas into one statement.

Cut and paste:

To create a first draft of an abstract of your own work, you can read through the entire paper and cut and paste sentences that capture key passages. This technique is useful for social science research with findings that cannot be encapsulated by neat numbers or concrete results. A well-written humanities draft will have a clear and direct thesis statement and informative topic sentences for paragraphs or sections. Isolate these sentences in a separate document and work on revising them into a unified paragraph.

If you are abstracting someone else’s writing

When abstracting something you have not written, you cannot summarize key ideas just by cutting and pasting. Instead, you must determine what a prospective reader would want to know about the work. There are a few techniques that will help you in this process:

Identify key terms:

Search through the entire document for key terms that identify the purpose, scope, and methods of the work. Pay close attention to the Introduction (or Purpose) and the Conclusion (or Discussion). These sections should contain all the main ideas and key terms in the paper. When writing the abstract, be sure to incorporate the key terms.

Highlight key phrases and sentences:

Instead of cutting and pasting the actual words, try highlighting sentences or phrases that appear to be central to the work. Then, in a separate document, rewrite the sentences and phrases in your own words.

Don’t look back:

After reading the entire work, put it aside and write a paragraph about the work without referring to it. In the first draft, you may not remember all the key terms or the results, but you will remember what the main point of the work was. Remember not to include any information you did not get from the work being abstracted.

Revise, revise, revise

No matter what type of abstract you are writing, or whether you are abstracting your own work or someone else’s, the most important step in writing an abstract is to revise early and often. When revising, delete all extraneous words and incorporate meaningful and powerful words. The idea is to be as clear and complete as possible in the shortest possible amount of space. The Word Count feature of Microsoft Word can help you keep track of how long your abstract is and help you hit your target length.

Example 1: Humanities abstract

Kenneth Tait Andrews, “‘Freedom is a constant struggle’: The dynamics and consequences of the Mississippi Civil Rights Movement, 1960-1984” Ph.D. State University of New York at Stony Brook, 1997 DAI-A 59/02, p. 620, Aug 1998

This dissertation examines the impacts of social movements through a multi-layered study of the Mississippi Civil Rights Movement from its peak in the early 1960s through the early 1980s. By examining this historically important case, I clarify the process by which movements transform social structures and the constraints movements face when they try to do so. The time period studied includes the expansion of voting rights and gains in black political power, the desegregation of public schools and the emergence of white-flight academies, and the rise and fall of federal anti-poverty programs. I use two major research strategies: (1) a quantitative analysis of county-level data and (2) three case studies. Data have been collected from archives, interviews, newspapers, and published reports. This dissertation challenges the argument that movements are inconsequential. Some view federal agencies, courts, political parties, or economic elites as the agents driving institutional change, but typically these groups acted in response to the leverage brought to bear by the civil rights movement. The Mississippi movement attempted to forge independent structures for sustaining challenges to local inequities and injustices. By propelling change in an array of local institutions, movement infrastructures had an enduring legacy in Mississippi.

Now let’s break down this abstract into its component parts to see how the author has distilled his entire dissertation into a ~200 word abstract.

What the dissertation does
This dissertation examines the impacts of social movements through a multi-layered study of the Mississippi Civil Rights Movement from its peak in the early 1960s through the early 1980s. By examining this historically important case, I clarify the process by which movements transform social structures and the constraints movements face when they try to do so.

How the dissertation does it
The time period studied in this dissertation includes the expansion of voting rights and gains in black political power, the desegregation of public schools and the emergence of white-flight academies, and the rise and fall of federal anti-poverty programs. I use two major research strategies: (1) a quantitative analysis of county-level data and (2) three case studies.

What materials are used
Data have been collected from archives, interviews, newspapers, and published reports.

Conclusion
This dissertation challenges the argument that movements are inconsequential. Some view federal agencies, courts, political parties, or economic elites as the agents driving institutional change, but typically these groups acted in response to movement demands and the leverage brought to bear by the civil rights movement. The Mississippi movement attempted to forge independent structures for sustaining challenges to local inequities and injustices. By propelling change in an array of local institutions, movement infrastructures had an enduring legacy in Mississippi.

Keywords
social movements
Civil Rights Movement
Mississippi
voting rights
desegregation

Example 2: Science Abstract

Luis Lehner, “Gravitational radiation from black hole spacetimes” Ph.D. University of Pittsburgh, 1998 DAI-B 59/06, p. 2797, Dec 1998

The problem of detecting gravitational radiation is receiving considerable attention with the construction of new detectors in the United States, Europe, and Japan. The theoretical modeling of the wave forms that would be produced in particular systems will expedite the search for and analysis of detected signals. The characteristic formulation of GR is implemented to obtain an algorithm capable of evolving black holes in 3D asymptotically flat spacetimes. Using compactification techniques, future null infinity is included in the evolved region, which enables the unambiguous calculation of the radiation produced by some compact source. A module to calculate the waveforms is constructed and included in the evolution algorithm. This code is shown to be second-order convergent and to handle highly non-linear spacetimes. In particular, we have shown that the code can handle spacetimes whose radiation is equivalent to a galaxy converting its whole mass into gravitational radiation in one second. We further use the characteristic formulation to treat the region close to the singularity in black hole spacetimes. The code carefully excises a region surrounding the singularity and accurately evolves generic black hole spacetimes with apparently unlimited stability.

This science abstract covers much of the same ground as the humanities one, but it asks slightly different questions.

Why do this study
The problem of detecting gravitational radiation is receiving considerable attention with the construction of new detectors in the United States, Europe, and Japan. The theoretical modeling of the wave forms that would be produced in particular systems will expedite the search and analysis of the detected signals.

What the study does
The characteristic formulation of GR is implemented to obtain an algorithm capable of evolving black holes in 3D asymptotically flat spacetimes. Using compactification techniques, future null infinity is included in the evolved region, which enables the unambiguous calculation of the radiation produced by some compact source. A module to calculate the waveforms is constructed and included in the evolution algorithm.

Results
This code is shown to be second-order convergent and to handle highly non-linear spacetimes. In particular, we have shown that the code can handle spacetimes whose radiation is equivalent to a galaxy converting its whole mass into gravitational radiation in one second. We further use the characteristic formulation to treat the region close to the singularity in black hole spacetimes. The code carefully excises a region surrounding the singularity and accurately evolves generic black hole spacetimes with apparently unlimited stability.

Keywords
gravitational radiation (GR)
spacetimes
black holes

Works consulted

We consulted these works while writing this handout. This is not a comprehensive list of resources on the handout’s topic, and we encourage you to do your own research to find additional publications. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial. We revise these tips periodically and welcome feedback.

Belcher, Wendy Laura. 2009. Writing Your Journal Article in Twelve Weeks: A Guide to Academic Publishing Success. Thousand Oaks, CA: Sage Press.

Kilborn, Judith. 1998. “Writing Abstracts.” LEO: Literacy Education Online. Last updated October 20, 1998. https://leo.stcloudstate.edu/bizwrite/abstracts.html.

Koopman, Philip. 1997. “How to Write an Abstract.” Carnegie Mellon University. October 1997. http://users.ece.cmu.edu/~koopman/essays/abstract.html.

Lancaster, F.W. 2003. Indexing And Abstracting in Theory and Practice, 3rd ed. London: Facet Publishing.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License.
You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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Introduction

Probably the best known theorizing on the organization of the semantic system within the embodied or grounded cognition approach is that of Barsalou (2008). While the theory has not been implemented, it would appear that the systems involved in the representation and use of abstract concepts, in particular the perceptual system and those responsible for frames and simulations, are the same as those required for the representation and use of concrete concepts (but see Adams and Campbell, 1999; Mitchell and Clement, 1999; Ohlsson, 1999). In this paper we will discuss neuropsychological and functional imaging evidence which suggests that the representation of abstract concepts, in fact, involves a system additional to those involved in the semantic representation of concrete concepts. We will then discuss what computationally could give rise to this separability between abstract and concrete words within the functional architecture of the semantic system.

As far as neuropsychological evidence is concerned, we will specifically discuss two syndromes—deep dyslexia and the selective preservation of abstract concepts in the so-called reversed concreteness effect found in some semantic dementia and herpes encephalitis patients. The first of these functional syndromes—deep dyslexia—appears to provide evidence for at least partial separability of the semantic representations of concrete and abstract words. The prototypic characteristic of the deep dyslexic patient, and generally why their reading difficulty was analyzed, is the making of semantic errors when reading aloud. However, there are also a variety of other characteristics in their reading that such patients have in common (Coltheart et al., 1987; Plaut and Shallice, 1993). One is that the patients are much more able to read aloud words with a concrete, or better an imageable, meaning than those with an abstract meaning (Shallice and Warrington, 1975; Coltheart et al., 1987). Face can be read but not faith. Moreover, for many of these patients the difference is very large. Thus Shallice (1988) considered the performance of the first four deep dyslexics whose reading was analyzed in detail; the smallest difference between the reading of concrete and abstract words was in patient GR of Marshall and Newcombe (1966) who read aloud 50% of the former but only 10% of the latter.

Since it is standardly accepted that the phonological route or routes for reading are inoperative in these patients, it would seem straightforward to produce an explanation for their inability to read abstract words which is based on the assumption that there are different systems for holding the semantic representations of abstract and imageable/concrete words. Thus, if one assumes that there is an at least partial separability between the semantic systems holding representations of imageable and abstract words, then it is simple to assume that in this functional syndrome the latter subsystem is no longer directly accessible from a visual word-form system, while the former subsystem is. This, indeed, was the explanation for this aspect of the deep dyslexia functional syndrome given by Morton and Patterson (1980).

There are, however, two reasons to be cautious about this interpretation. The first is that the abstract-to-concrete difference is only one of the many characteristics of deep dyslexia, and this type of explanation of the functional syndrome as a whole is not very economical; thus Morton and Patterson (1980) require five separate functional impairments to explain all aspects of the functional syndrome. Secondly, it is possible to provide an explanation of the superiority of concrete over abstract word reading assuming that exactly the same set of systems are involved in reading for both types of word, but that the semantic representations of abstract words are in some sense quantitatively weaker than those of concrete words. Thus in the connectionist model of Plaut and Shallice (1993), the attractor structure leads to an abstract-concrete difference; the reduced number of features of abstract words by comparison with concrete words make the processes involved in the reading of abstract words less able to support a clean-up process than can the richer semantic representations of concrete words.

A second possible type of explanation, which is also compatible with abstract and concrete words being represented semantically in the same system is given by Newton and Barry (1997). Deep dyslexia can be present in either an input, central or output form, depending where the impairment lies in the semantic route for reading, it being assumed that the phonological route or routes are inoperative (Shallice and Warrington, 1980). Newton and Barry studied a patient, LW, with an output form of deep dyslexia—his comprehension of the written word was intact—but who showed a large effect of concreteness on his ability to read aloud (highly concrete 48%; abstract 8%). These authors hold that, when using only the semantic route for reading, the contrast in performance between concrete and abstract words in LW could be due to the greater difficulty that the abstract words produce for the lexicalization process by which an output phonological word-form or lemma is produced from a semantic representation. Indeed, Barnard et al. (1982) showed that it was much easier for normal subjects to name from definition concrete words, such as barrel (77% correct), than abstract words, such as betray (23% correct). Newton and Barry plausibly argue that concrete words have a higher degree of specificity in the lexicalization process than abstract words like idea, for which they claim “there will be a great deal of spreading activation to many… related concepts” (p. 502). More generally it has been argued that accessing abstract and concrete words involves the same semantic system but that access also requires a network of prior knowledge, and abstract words are more heavily dependent on this network (Schwanenflugel, 1991). So loss of access to the network could give rise to the deep dyslexic pattern of concrete word superiority.

These two examples of explanations of better performance on concrete than abstract words both depend on concrete words being higher on some quantitative dimension—number of features or degree of specificity—than abstract words. Moreover, intuitively there are no apparent processes where on a relevant dimension abstract words would be easier to operate on than concrete words. There is one model—that of Plaut and Shallice (1993)—where higher performance on abstract than concrete words can occur, but this requires a rather specific set of assumptions. It is clear that if a much stronger case for the separability of systems underlying concrete and abstract semantic representations in the relative preservation of abstract concepts compared with concrete ones can be found, then an explanation in terms of their different placings on an underlying continuous dimension is much more difficult to produce.

The Reversed Concreteness Effect

The first patient to be described with the reversed concreteness effect—the better processing of abstract rather than concrete (or imageable) words—was AB of Warrington (1975). AB was asked to provide the meaning of a set of abstract and concrete words. He was rated as producing an appropriate description of the meanings of 85% of the abstract words but only 24% for the concrete ones. Thus he described a pact as “friendly agreement” and arbiter as “He is a man who tries to arbitrate. Produce a peaceful solution.” But to hay and needle, he said he had forgotten the meaning. AB suffered from what would now be known as semantic dementia. Later patients showing the reversed concreteness effect have also been described with semantic dementia (see e.g., Breedin et al., 1994; Cipolotti and Warrington, 1995; Bonner et al., 2009; Macoir, 2009; Papagno et al., 2009a).

A second aetiology in which the reversed concreteness effect was obtained is herpes simplex encephalitis. Warrington and Shallice (1984) described patient SBY who was 94% correct at giving the meaning of abstract words, but only 50% correct at giving the meaning of concrete words. Further patients with the reversed concreteness effect following herpes simplex encephalitis have since been described by Sirigu et al. (1991) and Mattioli (2008).

One critical property of these two aetiologies—semantic dementia and herpes simplex encephalitis—is that they are both conditions generally giving rise to so-called semantic degradation rather than semantic access difficulties, when they affect the semantic system (see Warrington and Shallice, 1979, 1984; Warrington and Cipolotti, 1996). In particular, there tends to be high consistency across sessions in whether or not a patient with one or other of these two conditions knows the meaning of a word (Warrington and Shallice, 1984; Warrington and Cipolotti, 1996). Thus, it is argued that the deficit is of the semantic representations themselves rather than in accessing or retrieving them.

A second critical property is that both have primary lesion sites in the anterior temporal lobes. In semantic dementia the critical lesion site is thought to be in the inferior parts of the anterior temporal cortex (Mummery et al., 2000; Mion et al., 2010) and this would be the site of any hypothetical semantic “hub” as on the theory of Rogers et al. (2004). There are some suggestions that the critical lesion site is more lateral than medial (e.g., Binney et al., 2010), but this is less clear in other studies (e.g., Mion et al., 2010). For the semantic deficits characteristic of herpes simplex encephalitis, where category specificity within the semantics of concrete entities is more typical (Capitani et al., 2003), the critical lesion site is again inferior anterior temporal cortex, but in this case potentially more medial than lateral (e.g., Tyler et al., 2004).

The reversed concreteness effect, as discussed so far, has been demonstrated only in individual patients selected for study because they show this characteristic. However, recently there have been criticisms of drawing inferences from individual case studies to the organization of the normal cognitive system, in particular with respect to category specificity, of which the reversed concreteness effects is one example (Laws, 2005; Laws and Sartori, 2005). It is possible that patients showing a reversed concreteness effect are premorbidly biased, with respect to the average of the population, in how well abstract concepts are represented by comparison with concrete ones (Hoffman and Lambon Ralph, 2011). This makes studies using a case series methodology, in which patients are selected because of their aetiology and not because of their behavioral characteristics, particularly important (Schwartz and Dell, 2010; Shallice and Buiatti, 2011). Three research studies have been carried out. One study, that of Yi et al. (2007), was only concerned with the comprehension of verbs, which in general we will not deal with in this paper. Unfortunately the results of the other two point in opposite directions.

In the first of the other two studies, Hoffman and Lambon Ralph (2011) recruited seven patients with a diagnosis of semantic dementia and gave them all seven tests, each of which compared comprehension of abstract and concrete words. Two involved only verbs. The other five involved synonym judgments, description-to-word matching, picture-to-word matching and word-to-related word matching. No patient performed significantly better on the abstract words on any test. Three of the patients performed at a very similar level on the concrete and abstract words, but three performed significantly better overall on the concrete words, if the two verb processing tasks are included. Hoffman and Lambon Ralph draw the conclusion that the reversed concreteness effect is an artifact of the selection of premorbidly atypical patients. There is, however, a major problem with their study. There is no control group. As discussed above most people in most tasks find abstract words more difficult to process than concrete ones. We do not know whether the pattern of performance shown by the semantic dementia patients in this case series produced the typical level of difference between abstract and concrete words that an impaired general-purpose semantic system would show or whether the relative difference between the two types of words was in fact less than that normally found, especially for the three patients who showed very similar levels of performance between the two types of word.

By contrast, the study of Loiselle et al. (2012) did have a control group; in fact it had two. It compared 7 patients having unilateral removals of the anterior temporal cortex with 15 patients having unilateral removals of the amygdala and the hippocampus and 15 healthy controls. One experimental test given was of synonym judgments for 50 matched abstract and concrete words. Z-scores were derived from the performance of the healthy controls. The mean z-score for the anterior temporal patients was −1.06 for the abstract words but −3.53 for the concrete ones, significantly worse; by comparison the amygdala-hippocampal group scores virtually identically across the two types of word: −2.24 and −2.23, respectively. This supports the position that systems lying within the anterior temporal cortex are particularly important for processing the semantics of concrete by comparison with abstract words. This implies that the semantic processing of abstract words is in part dependent on other systems, a position originally put forward by Breedin et al. (1994) to explain the preservation of abstract word comprehension in their semantic dementia patient. Where might this other system be?

Functional Imaging Studies

Functional imaging research has also led to the proposal that distinct systems may underlie the representations of abstract and concrete concepts (Binder et al., 2005). There is an extensive literature on neuroimaging studies of semantic processing (see Binder et al., 2009, for review). When processing of abstract words is contrasted with that of concrete words it tends to produce higher activation particularly in the left inferior frontal gyrus. Thus in a meta analysis of Wang et al. (2010), the left inferior frontal region was much the largest area that was consistently more activated for abstract than for concrete words.

However, the functional imaging evidence needs to be considered cautiously for a number of reasons. Firstly, many of the studies involve tasks, such as lexical decision, which make relatively small demands on semantic processing. This, however, means that the estimates of areas selectively involved in one or other type of semantic processing would be conservative. Two early studies that used lexical decision found somewhat surprising results. One using PET did find left inferior frontal gyrus to be more activated for abstract than concrete words (Perani et al., 1999) but many other regions in the right hemisphere were also involved. Kiehl et al. (1999) found only a right hemisphere region, namely the right superior temporal gyrus. However, neither used a random effects analysis and the Kiehl et al. study only had six subjects. Two later fMRI studies found effects much more limited to the left inferior frontal gyrus. In a study of Fiebach and Friederici (2004) only the left inferior frontal gyrus was involved, while in that of Binder et al. (2005) a somewhat larger left inferior frontal gyrus activation spread into the left precentral gyrus and to a small part of the left superior temporal gyrus.

One recent study did, however, not find any left inferior frontal gyrus activation when comparing abstract words with concrete ones, that of Vigliocco et al. (2013). The study was very impressive in that many nuisance variables were controlled between the abstract and concrete words sets. Altogether 14 variables were controlled including ones concerned with the orthography and phonology of the words, age and mode of acquisition, in addition to familiarity and frequency. However, another variable that was controlled was imageability. So the abstract word set included words such as angel, demon, fury, and grief, while the concrete words included ones like product, relic, estate, and object (Vigliocco, pers. commun.). Now neuropsychologically, where it has been examined in deep dyslexia, the key variable differentiating words easy and difficult for the patient to read was not concreteness (C) but imageability (I) (Shallice and Warrington, 1975). Thus for nouns relatively high in imageability or concreteness, 67% were read correctly by deep dyslexic patient, KF, if for the word I > C, but only 39% if I < C − 0.5¹. The interpretation given at the time was that imagery was not itself the critical process, but whether the meaning of the word had been primarily learnt from visual experience. This is just the concept that was later used to explain what had been lost in semantic dementia patients showing a reversed concreteness effect (Breedin et al., 1994; Papagno et al., 2009a). Thus the Vigliocco et al. (2013) results are not relevant if one conceives of as abstract what cannot be learnt from sensory experience alone.

There is, however, a second problem with respect to the role of the left inferior frontal gyrus in activation by abstract concepts in lexical decision. The region is found to be activated in other lexical decision contrasts, in particular with low frequency words compared with high frequency ones, when concreteness is controlled (Fiebach et al., 2002). Thus, the region may be involved because of other processes, such as subvocalisation, especially as Fiebach et al. also found that in lexical decision pseudo words activated the region more than words (see also Fiebach et al., 2007). However, in the main Fiebach and Friederici (2004) study, reaction times to abstract and concrete words were virtually identical, so it is less plausible that additional mediation by subvocal rehearsal is occurring more for abstract words.

If, however, one moves to more demanding tasks, such as synonym judgments, there is yet another process, in addition to subvocalisation, which could be involved and which could lead to activation of left inferior frontal gyrus, namely working memory maintenance (Petrides, 1994). Intuitively, these two processes seem more likely to be involved in decisions on abstract words, as these tend to be the more difficult ones. Yet, when difficulty was specifically assessed in synonym judgment, it was not found to be the critical variable in the abstract-concrete contrast. Thus in the study of Noppeney and Price (2004), difficulty had a much weaker effect than abstraction per se on activation in the left inferior frontal gyrus. In the study of Sabsevitz et al. (2005) there was an area of overlap between difficulty and abstraction in left Brodmann area 45 but there were other parts of the left inferior frontal gyrus which were just activated by abstract rather than difficult concepts. Thus both with tasks making small demands on semantic processing and those making larger ones, the imaging findings are broadly consistent with the idea that a specific system in the left inferior frontal gyrus is involved in compiling the representations of abstract words.

A left inferior frontal gyrus localization also fits with inferences from other methodologies. The left inferior frontal lobe is a region which tends to be spared in semantic dementia prior to the later stages of the disease (Papagno et al., 2009a). Moreover in the three herpes encephalitis cases referred to above, the lesion appears not to extend to the left inferior frontal lobe; instead temporal cortices and the limbic system were held to be damaged. In none of the other cases of reversed concreteness effect reviewed by Papagno et al. (2009a,b) was the left frontal lobe held to be involved. A study using rTMS and lexical decision has also been carried out by Papagno et al. (2009b). They found that lexical decision to abstract words was less accurate after stimulation of the left inferior frontal gyrus instead of control sites, while no such effects were found for concrete words. A similar effect was also found for the left superior temporal gyrus. Overall, however, the inferior frontal gyrus appears to be critical for the semantic processing of abstract words.

Contrasting Properties of Semantic Representations of Abstract and Concrete Terms

There is other neuropsychological evidence that the processing of abstract and concrete words differs qualitatively. This is shown in two studies of Crutch and Warrington 2005, 2007) on two patients. One patient, FBI, was a deep dyslexic. The other, AZ, had a semantic access/refractory disorder (see Warrington and Shallice, 1979; Warrington and McCarthy, 1987; Warrington and Cipolotti, 1996). Two types of similarity effects were examined to see if they differed between concrete and abstract words. The first was between semantically related members of a superordinate category, such as yacht, dinghy, canoe, ferry, and barge for concrete words or fury, anger, rage, annoyance, and wrath for abstract words. The contrasting situation was one in which the words differ in their superordinate semantic category but are linked by semantic association such as dagger, blood, ambulance, policeman, and handcuffs for concrete words or democracy, republic, freedom, politics, and election for abstract ones. For FBI two tasks were used: 4 and 5-alternative spoken word to written word matching and reading aloud the words in these sets. For each of the two types of word only one of the two kinds of similarity has a major effect, but it does so for both tasks. However, the other kind of similarity had little effect. For both tasks the critical effect for concrete words was belonging to the same category but for abstract words it was being within a group of associated words. Analogous findings were obtained with the two patients. This is evidence that the underlying semantic representations of concrete words and abstract words differ qualitatively not just quantitatively in their structure. Crutch and Ridgway (2012) prefer to see the semantics of the two types of word as both represented in a single distributed network. However, to us the contrasting semantic properties of the two types of word makes it at least as plausible that their semantic representations involve separable processing systems with different underlying micro-structure. To make this more plausible we need to consider how a semantic system or systems for concrete and abstract words might work computationally.

The “Hub” as a Possible Model of the Semantic System

In order to consider whether the semantic representations of abstract and concrete concepts involve the same system or not, it is necessary to consider how each of them is composed. The computational model of the semantic system that provides currently the most plausible account of the semantic representations of concrete words is the “hub” model in which a central amodal semantic “hub” has a number of “spokes” representing different aspect of the concept (Rogers et al., 2004; Patterson et al., 2007). In the version of Jefferies and Lambon Ralph (2006) the spokes are verbal descriptors, visual, auditory, somatasensory and olfactory/gustatory features and “praxis”. The hub learns to transform input corresponding to one aspect of a concept derived from one of the spokes to produce an output to a different spoke, corresponding to another aspect. The concepts and features used to train the Rogers et al. net are derived from a study of Garrard et al. (2001). If we leave on one side superordinate concepts, then the typical more dominant features of the 32 living thing and 32 artifact concepts Garrard et al. studied are indeed codeable in representations in one of these spoke systems e.g., visual—alligator: has tail, barrel: is made of wood; auditory—aeroplane: can make a noise, dog: can bark; somatosensory—axe: is sharp, cat: can scratch; olfactory/gustatory—apple: is sweet, pineapple: is juicy; praxis—basket: can be filled, bicycle: can be ridden.

The hub model is not without its internal difficulties. In particular it is unclear how one form of category specificity—the superior performance with artifact knowledge compared with living thing knowledge quite frequently reported in herpes simplex encephalitis patients—can occur with very similar lesion sites to semantic dementia where such category specificity is very rare (Garrard et al., 1998; see Lambon Ralph et al., 2007; Shallice and Cooper, 2011, for discussion). However, we consider it a plausible model of the semantics of concrete words as it can account for many striking phenomena with respect to semantic dementia itself (Patterson et al., 2007).

Conceptual Limitations of the Hub Model with Respect to Abstract Words

In the hub model a concrete concept like sparrow is represented by a list of features: isa bird, is small, has wings, is brown, chirps, etc. To a non-expert the presence or absence of features are apparently independent of each other; that a sparrow is small, drab and chirps and a parrot is larger, highly colored and squawks appear to be just two possibilities in a three dimensional space where any of eight possibilities are equally likely. But what does a feature mean? The features listed above have two parts—what one might call an operator e.g., potential action (can be …), and an argument e.g., filled or ridden (as in basket: can be filled, or bicycle: can be ridden). Thus, within the hub model the content of a concept such as bicycle may be represented in more formal terms as a conjunction of features with each feature comprising an operator and an argument:

In the features given above the operator is specified by the spoke subsystem so in the case of bicycle: can be ridden it derives from the spoke system being the praxis one. The set of operators available is therefore limited by the set of spoke systems and these are highly restricted in number even if one considers subcategories of feature; for vision, examples would be the operators has a X or made of X derived from object-form or texture representations, respectively. Thus on the hub model a concrete concept has a list structure of features and the operator part of an individual feature is specified by the specific spoke that activates the feature.

Consider instead an abstract concept like tendency or hope. Tendency does have visual or spatial aspects, such as a 10° angled line approaching the horizontal, but they are few in number, far from being distinctive to tendency and cannot without additional information specify the concept. Hope too has visual aspects, such as a generally positive expression but they are as little distinctive to hope as a 10° line is to tendency, and distinctiveness is a key property for learning a concept². Moreover unlike a concrete concept, their core semantic representation is not well captured by a list of independent features with access to the representation requiring that only a subset of the full list of features be activated. Instead, the concepts tendency, in its WordNet (Fellbaum, 1998) sense of “a characteristic likelihood of or natural disposition toward a certain condition or character or effect”, and hope, in its WordNet sense of “to intend with some possibility of fulfilment”, need to be captured by something equivalent to:

For such concepts a much wider set of operators like desire, believe, and possible, as well as representational abilities related to probability are required. Even more critically, the logical relations between the different elements of the whole representation are much more complex than the simple list structure that, say, the hub model provides. This is both reflected in the recursive embedding of operators (e.g., believe (possible (X))) and by the fact that the X in (2) and (3) is an event or state of the world, in contrast to (1) where it corresponds to a physical object. Moreover, in the former cases the state of the world referred to by X is not the current or actual state of the world but a hypothetical, possible state of the world.

What specific representational abilities might be required for these concepts? Within the fields of mathematical logic and formal semantics, providing an account of the meaning of statements such as “it is possible that X” led to the development by Lewis (see Lewis and Langford, 1932) of so-called “modal logics” (specifically logics of necessity and possibility, and logics of knowledge and belief) and in particular to the development by Kripke (1959) and others of “possible world semantics”. The central idea behind semantic theories of this general kind is that the meaning of a statement X is determined with respect to a model or “world”. Modal logics augment traditional predicate logic with modal operators such as necessary and possible, or know and believe, while possible world semantics provides a semantic theory in which the meaning of these operators is provided via the abstract concept of a “possible world”.

A possible world may be thought of as a set of atomic tokens and relations between those tokens where the relations are internally consistent. Thus, if the possible world includes a relation such as larger-than then this relation must be transitive within the possible world. Tokens may correspond to concrete objects in the real world or to abstract entities (such as “a job”). Informally a possible world can be thought of as similar to a mental model (Johnson-Laird, 1983) [see in particular, Perner (1988) for discussion of possible world semantics in the representation of mental states]. Formally, the requirement of internal consistency means that a possible world is closed with respect to the deductions that it supports. Thus, if A is true in a world W and A implies B then B must also be true in W. A statement of the form possible (X) is true if and only if there exists at least one possible world in which X is true, while necessary (X) is true if and only if X is true in all possible worlds³. Critically, while the core meaning of bicycle as in definition (Equation 1) can plausibly be provided as a set of features or within predicate logic (with a standard so-called extensional semantics), the meaning of tendency and hope cannot—additional machinery such as modal logic and possible world semantics is required⁴.

We are not suggesting that all abstract words require modal logic in order to adequately characterize their core meaning, or that modal logic alone can capture the core meaning of all abstract words. Rather, the claim is that the meanings of abstract words cannot be adequately captured purely in terms of a list of perceptually grounded features, as provided by the hub model. As a further example, consider democracy, which is defined in WordNet as “a political system in which the supreme power lies in a body of citizens who can elect people to represent them”. This sense of democracy is not readily characterizable either as a set of perceptually grounded features or as a proposition in a modal logic. At the very least it is related to concepts of statehood, government and election in a way that is qualitatively different from the relation between, for example, bicycle and wheels.

The Separable Systems Approach

In Shallice and Cooper (2011) we argued that differences from concrete concepts in the computational requirements for how they are represented in an underlying semantic system, such as those discussed above, make it plausible that representing the meanings of abstract concepts involves a different computational system than that involved in representing the meanings of concrete concepts. Functionally, this system would need to incorporate the ability to abstract over events or situations rather than just individuals, to apply modal operators recursively, and to allow the representation of hypothetical as well as actual events or situations. Moreover if we consider how the representation of an event might be realized computationally, then the binding of argument roles to arguments is required (see Shastri, 2002); thus representing an event like the giving of a gift requires filling the roles of the gift giver, the gift recipient and the gift object.

We should make two qualifications to this position. The first qualification is that the evidence we have reviewed does not distinguish between two possibilities. One is that the left inferior frontal gyrus is the location of the semantic representations of abstract words. The second is that it is critically involved in processes necessary to access or construct these representations.

Secondly, we presume that one type of representation of an event, including binding, can take place in parieto-temporal systems, namely perceptual representations of the current world or of a sensory (e.g., visual) image, loosely what at the psychological level (with premotor systems) is we assume to be carried by the concept embodied cognition. However, using Shastri’s example, what is represented at this level of processing is person A handing a concrete object (e.g., a book) to person B. What is not represented is that the object is a gift, and all the many culturally dependent implications this has for the giver and the recipient. Thus even though parieto-temporal systems can capture the representation that a particular glittering object is gold (or not as the case might be!), impairments in understanding the abstract meaning of a proverb such as all that glitters is not gold—that appearance does not necessarily correspond to essence—instead involves prefrontal cortex (Murphy et al., submitted). In particular, left lateral patients produce more than four times more concrete interpretations of such proverbs than do healthy controls. It is compatible with their lacking such representations. For representations at such higher non-perceptual levels, binding would, we assume, not be available in parietal cortex.

At the very least an abstract concept semantic system would need the power to implement recursion and argument role filling, neither of which is, for instance, available in the architecture of the hub system. We further argued in Shallice and Cooper (2011) that given requirements such as these, it would be plausible that the computational microstructure of the region of the human cortex supporting the representation and processing of abstract concepts would be different from that of the anterior temporal cortex held to support the representation of concrete concepts, and proposed on the basis of functional imaging and patient studies that this abstract representational system was located in the left ventrolateral prefrontal cortex.

In the psychological literature, the idea that word meaning involves more than just a list of semantic features is, of course, old. Indeed, Miller and Johnson-Laird (1976) argue that the meanings are represented by mini-programs. Thus they represent the meaning of LOSE(x, w) by⁵ :

Someone x loses something w if there is a time t such that Q_t(POSSESS(x, w)) and:

(i) R_t(notINTEND(x, notPOSSESS(x, w)))

(ii) HAPPEN_t(NotPOSSESS(x, w))

Miller and Johnson-Laird (1976, p. 568)

The basic difference in our position from this earlier perspective is that in our view such program-like entities are critical for representing the meaning of abstract words, but while they coexist with feature-based ones, they are in a functionally and anatomically separable system.

The role that we have assigned to the left inferior frontal gyrus has another if slightly more indirect precursor. The computational machinery which we have proposed for this region with respect to the compiling of the meaning of abstract words has many similarities to that presupposed for compiling syntax. In particular our position on abstract words, too, requires that unification links be made between the arguments of two or more operators, as in the example of hope above (see, e.g., Pollard and Sag, 1987, 1994, for unification in syntactic operations). Hagoort (2003), too, has argued that the left inferior frontal gyrus contains the necessary computational machinery for implementing unification processes. In his account chunks of syntactic structure (e.g., S, NP, VP, N, and V) of an utterance are stored in memory. In a unification workspace the feet of one syntactic chunk are potentially linked to the root of another. In the computational model of Vosse and Kempen (2000), which he adopts, rival sets of unification links for spanning a whole utterance (e.g., a sentence) compete by lateral inhibition until one reaches threshold. In Hagoort’s account this process of forming provisional sets of links which compete by lateral inhibition takes place in the left inferior frontal gyrus. In later papers (e.g., Hagoort, 2005) he extends this idea to consider semantics, with semantic unification being held to take place in a region a little more inferior and anterior than that for syntactic unification. The form of semantic unification he considers is the integration of word meaning into an unfolding discourse representation of the preceding context, for instance in the selection of the appropriate meaning of a homonym. Our proposal is that an analogous process may underlie the semantic representations of individual abstract words.

Of course it may be argued that unification as a concept is little more than binding which is widely postulated to occur in many cognitive processes, as in episodic memory encoding in the hippocampus (Marr, 1971; Gardner-Medwin, 1976) or perceptual feature-binding in parietal cortex (Treisman, 1998). The critical formal difference between unification and binding is that the former combines multiple potentially overlapping sources of information. Unification will fail if overlapping elements of the to-be-combined representations are inconsistent. Moreover unification is typically used in building complex structures (e.g., where multiple arguments serve different functional roles) out of parts, and where the parts place constraints on each other. Thus what we assume distinguishes the unification process taking place in left inferior frontal cortex is that the item or element is being bound to a node within a more complex structure representing an abstract general property such as propositional phrase or type of mental state.

How does this position relate to the cognitive neuroscience evidence just discussed? If the computational properties of an abstract concept semantic system were designed in part to allow events to be represented, Crutch and Warrington’s findings that associations are critical in the representations of abstract words would seem to follow. A set of words like gamble, casino, poker, and chance, ones used in Crutch and Warrington’s (2005) experiment on interference from associated sets, almost inevitably creates a characteristic situation or set of events related to playing poker, as does the example democracy, republic, freedom, politics, and election discussed earlier, redolent of the 2012 American election; so the individual abstract semantic representations would be linked to each other through it.

A second phenomenon which has been held to support the idea that the semantics of abstract words can be represented in the hub and hence to present difficulties for an abstract semantic system account comes from a rTMS study of Hoffman et al. (2010). They followed Schwanenflugel and Shoben (1983) in assuming that the same semantic system is involved for abstract and concrete words but the precise meaning of an abstract concept is heavily dependent on context. (It is not clear whether or how this would apply to concepts like neutron or checkmate.) They gave subjects a 3-alternative synonym judgment task together with a target word presented altogether at the same time or also preceded for 6 s by a 2-sentence context. Without the context, slower responding to abstract words occurred with rTMS to left Brodmann area 45 than without it. However, with context no such effect occurred. With concrete words, rTMS has no effect in either case.

Hoffman et al. (2010) explain their result as occurring through left ventrolateral prefrontal cortex having an executive regulation role with respect to the processing of abstract words, and this becomes less necessary when context is provided. It is not, though, clear, what computational function executive regulation plays in understanding an abstract word in a context-free situation. Moreover there are possible alternative explanations of the result. rTMS does not lead to any increase in errors with abstract words, it just leads to slowing in the no-context condition. Thus in the context situation the subject will have already understood the word, which has already been presented in the context, at least in the example given, so the subject will just have to comprehend one critical word instead of two, and so at worst will presumably be slowed up only half as much. As the no-context effect was only just significant at the 0.05 level one would not therefore predict a significant effect in the context case even if the left ventrolateral PFC was as critical there. Moreover, even if full abstract comprehension of the three choice words is slowed, the 6 s of context presentation will have left a rich set of concrete images from parieto-temporal regions available to facilitate the choice between the three alternatives, at least on some trials, so this again would be expected to reduce any effect in the context condition compared to the no-context one. The study, by itself does not resolve the issue.

Conclusions

Our primary conclusion is a negative one. It is that the computational capacities provided by embodied cognition, on the one hand, and the feature-based representation of semantics on other hand (and more specifically the “hub” system), are insufficiently powerful to adequately capture the semantics of abstract concepts. Moreover we have argued that patients with reversed concreteness effects on the one hand and deep dyslexia on the other provide some evidence that the semantic representations of abstract and concrete words are at least partially separable in the cognitive system. This position is further supported by the different patterns of interference and facilitation found by Crutch and Warrington in their single case studies. Neuroimaging evidence, too, suggests that the left inferior frontal cortex plays a more important role in the compiling of the semantics of abstract than concrete words.

The computational characteristics that we have ascribed to an abstract representational system have a very similar conceptual basis to—but are different from—those involved in grammatical/syntactic operations in language. If the microstructure of cortex is critical for the computational properties of the functional systems it supports, then it is plausible that systems with similar computational requirements are supported by overlapping or adjacent regions of cortex. It is therefore not surprising that a similar region of cortex would be involved in the representation of abstract concepts to that damaged in agrammatism (e.g., Tyler et al., 2005, 2010, 2011). Moreover deduction, which also requires similar computational properties in the construction of abstract structures in premise integration, also involves a very similar region (Reverberi et al., 2012). Two qualifications should be made. The first is that the operational definitions of abstraction used in empirical studies have typically been made apophatically or negatively, by the absence of concreteness in the entity, or as we have argued neuropsychologically more appropriately, the absence of imageability of the concept. Ideally on our approach, one ought to be able to produce an operationalization of abstraction which is positive rather than negative. Until this is done, direct empirical support for a position such as ours will be difficult to obtain.

The second qualification relates to the way that the general thrust of this paper may be interpreted as suggesting that representations of abstract concepts are held in the left inferior frontal gyrus. Moreover, the link we have made between our approach and Hagoort’s unification concept tends to reinforce that view. However, the direct empirical cognitive neuroscience evidence is open to a second interpretation. This is that the representations of abstract concepts are carried in a more distributed fashion, possibly more generally in prefrontal cortex. In this case the left inferior frontal region would be crucial in performing appropriate computations to compile the more distributed representations. Which of these two possibilities is to be preferred empirically remains in our view an open question. In either case, though, there would be more to the mind than embodied cognition.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

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What are abstract nouns? You probably can recall that nouns are words that name people, animals, places, things, and ideas. Here, we’ll define abstract nouns, provide abstract noun examples, and give you the information you need for using an abstract noun to write interesting sentences.

What are Abstract Nouns

Abstract nouns are words that name things that are not concrete. Your five physical senses cannot detect an abstract noun – you can’t see it, smell it, taste it, hear it, or touch it. In essence, an abstract noun is a quality, a concept, an idea, or maybe even an event.

Abstract nouns and concrete nouns are usually defined in terms of one another. Something that is abstract exists only in the mind, while something that is concrete can be interacted with in a physical way. Qualities, relationships, theories, conditions, and states of being are some examples of the types of things abstract nouns define.

Types of Abstract Nouns

It’s not always easy to determine if a noun is abstract or concrete. Many grammar experts argue over whether certain terms, making things even worse. The line separating abstract nouns from concrete nouns is often quite blurry. For example, many abstract noun lists include the word laughter, but others leave it out, as it’s something that can be heard, seen, and physically felt.

Abstract Noun Examples

The following lists contain different types of abstract nouns. Certain abstract nouns, especially the ones describing feelings and emotions, easily fit into multiple categories, as they can be used in different ways. Get to know them, and it’ll be easier for you to spot an abstract noun when you see one.

More Examples

Although you may not realize it, you experience abstract nouns every day and in many different types of situations. Once you’ve read these abstract noun examples, you’ll probably find it very easy to come up with some abstract nouns of your own.

• Love, fear, anger, joy, excitement, and other emotions are abstract nouns.

• Courage, bravery, cowardice, and other such states are abstract nouns.

• Desire, creativity, uncertainty, and other innate feelings are abstract nouns.

These are just a few examples of non-concrete words that are sensed. The following sentences contain abstract noun examples which have been italicized for easy identification. Notice that although the ideas expressed are real, they are things you can’t see, touch, taste, smell, or hear.

• I want to see justice served.

• I’d like the freedom to travel all over the world.

• Joe felt a nagging sense of doom.

• Love is a kind of irresistible desire; it’s hard to define.

• When Sarah jumped into the lake to rescue a drowning cat, her bravery astonished onlookers.

Abstract Nouns Exercises

Many abstract nouns are formed from adjectives, though some are formed from verbs or nouns. You’ll find one of these words in parenthesis at the end of each sentence. Use it to form an abstract noun to fill in the blank.

1. _______________ is something almost everyone appreciates. (kind)
2. The wrestlers exhibited immense ___________________. (strong)
3. As the sun dipped below the horizon, _______________ came over the city. (dark)
4. It is my _______________ to welcome the mayor. (please)
5. Our ________________ will last forever. (friend)

Answer Key: 1 – Kindness 2 – Strength  3 – Darkness 4 – Pleasure 5 – Friendship