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Writing Research Papers

• Research Paper Structure

Whether you are writing a B.S. Degree Research Paper or completing a research report for a Psychology course, it is highly likely that you will need to organize your research paper in accordance with American Psychological Association (APA) guidelines.  Here we discuss the structure of research papers according to APA style.

Major Sections of a Research Paper in APA Style

A complete research paper in APA style that is reporting on experimental research will typically contain a Title page, Abstract, Introduction, Methods, Results, Discussion, and References sections. 1  Many will also contain Figures and Tables and some will have an Appendix or Appendices.  These sections are detailed as follows (for a more in-depth guide, please refer to " How to Write a Research Paper in APA Style ”, a comprehensive guide developed by Prof. Emma Geller). 2

What is this paper called and who wrote it? – the first page of the paper; this includes the name of the paper, a “running head”, authors, and institutional affiliation of the authors.  The institutional affiliation is usually listed in an Author Note that is placed towards the bottom of the title page.  In some cases, the Author Note also contains an acknowledgment of any funding support and of any individuals that assisted with the research project.

One-paragraph summary of the entire study – typically no more than 250 words in length (and in many cases it is well shorter than that), the Abstract provides an overview of the study.

Introduction

What is the topic and why is it worth studying? – the first major section of text in the paper, the Introduction commonly describes the topic under investigation, summarizes or discusses relevant prior research (for related details, please see the Writing Literature Reviews section of this website), identifies unresolved issues that the current research will address, and provides an overview of the research that is to be described in greater detail in the sections to follow.

What did you do? – a section which details how the research was performed.  It typically features a description of the participants/subjects that were involved, the study design, the materials that were used, and the study procedure.  If there were multiple experiments, then each experiment may require a separate Methods section.  A rule of thumb is that the Methods section should be sufficiently detailed for another researcher to duplicate your research.

What did you find? – a section which describes the data that was collected and the results of any statistical tests that were performed.  It may also be prefaced by a description of the analysis procedure that was used. If there were multiple experiments, then each experiment may require a separate Results section.

What is the significance of your results? – the final major section of text in the paper.  The Discussion commonly features a summary of the results that were obtained in the study, describes how those results address the topic under investigation and/or the issues that the research was designed to address, and may expand upon the implications of those findings.  Limitations and directions for future research are also commonly addressed.

List of articles and any books cited – an alphabetized list of the sources that are cited in the paper (by last name of the first author of each source).  Each reference should follow specific APA guidelines regarding author names, dates, article titles, journal titles, journal volume numbers, page numbers, book publishers, publisher locations, websites, and so on (for more information, please see the Citing References in APA Style page of this website).

Tables and Figures

Graphs and data (optional in some cases) – depending on the type of research being performed, there may be Tables and/or Figures (however, in some cases, there may be neither).  In APA style, each Table and each Figure is placed on a separate page and all Tables and Figures are included after the References.   Tables are included first, followed by Figures.   However, for some journals and undergraduate research papers (such as the B.S. Research Paper or Honors Thesis), Tables and Figures may be embedded in the text (depending on the instructor’s or editor’s policies; for more details, see "Deviations from APA Style" below).

Supplementary information (optional) – in some cases, additional information that is not critical to understanding the research paper, such as a list of experiment stimuli, details of a secondary analysis, or programming code, is provided.  This is often placed in an Appendix.

Variations of Research Papers in APA Style

Although the major sections described above are common to most research papers written in APA style, there are variations on that pattern.  These variations include: 

• Literature reviews – when a paper is reviewing prior published research and not presenting new empirical research itself (such as in a review article, and particularly a qualitative review), then the authors may forgo any Methods and Results sections. Instead, there is a different structure such as an Introduction section followed by sections for each of the different aspects of the body of research being reviewed, and then perhaps a Discussion section. 
• Multi-experiment papers – when there are multiple experiments, it is common to follow the Introduction with an Experiment 1 section, itself containing Methods, Results, and Discussion subsections. Then there is an Experiment 2 section with a similar structure, an Experiment 3 section with a similar structure, and so on until all experiments are covered.  Towards the end of the paper there is a General Discussion section followed by References.  Additionally, in multi-experiment papers, it is common for the Results and Discussion subsections for individual experiments to be combined into single “Results and Discussion” sections.

Departures from APA Style

In some cases, official APA style might not be followed (however, be sure to check with your editor, instructor, or other sources before deviating from standards of the Publication Manual of the American Psychological Association).  Such deviations may include:

• Placement of Tables and Figures  – in some cases, to make reading through the paper easier, Tables and/or Figures are embedded in the text (for example, having a bar graph placed in the relevant Results section). The embedding of Tables and/or Figures in the text is one of the most common deviations from APA style (and is commonly allowed in B.S. Degree Research Papers and Honors Theses; however you should check with your instructor, supervisor, or editor first). 
• Incomplete research – sometimes a B.S. Degree Research Paper in this department is written about research that is currently being planned or is in progress. In those circumstances, sometimes only an Introduction and Methods section, followed by References, is included (that is, in cases where the research itself has not formally begun).  In other cases, preliminary results are presented and noted as such in the Results section (such as in cases where the study is underway but not complete), and the Discussion section includes caveats about the in-progress nature of the research.  Again, you should check with your instructor, supervisor, or editor first.
• Class assignments – in some classes in this department, an assignment must be written in APA style but is not exactly a traditional research paper (for instance, a student asked to write about an article that they read, and to write that report in APA style). In that case, the structure of the paper might approximate the typical sections of a research paper in APA style, but not entirely.  You should check with your instructor for further guidelines.

Workshops and Downloadable Resources

• For in-person discussion of the process of writing research papers, please consider attending this department’s “Writing Research Papers” workshop (for dates and times, please check the undergraduate workshops calendar).

Downloadable Resources

• How to Write APA Style Research Papers (a comprehensive guide) [ PDF ]
• Tips for Writing APA Style Research Papers (a brief summary) [ PDF ]
• Example APA Style Research Paper (for B.S. Degree – empirical research) [ PDF ]
• Example APA Style Research Paper (for B.S. Degree – literature review) [ PDF ]

Further Resources

How-To Videos     

• Writing Research Paper Videos

APA Journal Article Reporting Guidelines

• Appelbaum, M., Cooper, H., Kline, R. B., Mayo-Wilson, E., Nezu, A. M., & Rao, S. M. (2018). Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board task force report . American Psychologist , 73 (1), 3.
• Levitt, H. M., Bamberg, M., Creswell, J. W., Frost, D. M., Josselson, R., & Suárez-Orozco, C. (2018). Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology: The APA Publications and Communications Board task force report . American Psychologist , 73 (1), 26.  

External Resources

• Formatting APA Style Papers in Microsoft Word
• How to Write an APA Style Research Paper from Hamilton University
• WikiHow Guide to Writing APA Research Papers
• Sample APA Formatted Paper with Comments
• Sample APA Formatted Paper
• Tips for Writing a Paper in APA Style

1 VandenBos, G. R. (Ed). (2010). Publication manual of the American Psychological Association (6th ed.) (pp. 41-60).  Washington, DC: American Psychological Association.

2 geller, e. (2018).  how to write an apa-style research report . [instructional materials]. , prepared by s. c. pan for ucsd psychology.

Back to top  

• Formatting Research Papers
• Using Databases and Finding References
• What Types of References Are Appropriate?
• Evaluating References and Taking Notes
• Citing References
• Writing a Literature Review
• Writing Process and Revising
• Improving Scientific Writing
• Academic Integrity and Avoiding Plagiarism
• Writing Research Papers Videos

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Chapter 11: Presenting Your Research

Writing a Research Report in American Psychological Association (APA) Style

Learning Objectives

• Identify the major sections of an APA-style research report and the basic contents of each section.
• Plan and write an effective APA-style research report.

In this section, we look at how to write an APA-style empirical research report , an article that presents the results of one or more new studies. Recall that the standard sections of an empirical research report provide a kind of outline. Here we consider each of these sections in detail, including what information it contains, how that information is formatted and organized, and tips for writing each section. At the end of this section is a sample APA-style research report that illustrates many of these principles.

Sections of a Research Report

Title page and abstract.

An APA-style research report begins with a  title page . The title is centred in the upper half of the page, with each important word capitalized. The title should clearly and concisely (in about 12 words or fewer) communicate the primary variables and research questions. This sometimes requires a main title followed by a subtitle that elaborates on the main title, in which case the main title and subtitle are separated by a colon. Here are some titles from recent issues of professional journals published by the American Psychological Association.

• Sex Differences in Coping Styles and Implications for Depressed Mood
• Effects of Aging and Divided Attention on Memory for Items and Their Contexts
• Computer-Assisted Cognitive Behavioural Therapy for Child Anxiety: Results of a Randomized Clinical Trial
• Virtual Driving and Risk Taking: Do Racing Games Increase Risk-Taking Cognitions, Affect, and Behaviour?

Below the title are the authors’ names and, on the next line, their institutional affiliation—the university or other institution where the authors worked when they conducted the research. As we have already seen, the authors are listed in an order that reflects their contribution to the research. When multiple authors have made equal contributions to the research, they often list their names alphabetically or in a randomly determined order.

In some areas of psychology, the titles of many empirical research reports are informal in a way that is perhaps best described as “cute.” They usually take the form of a play on words or a well-known expression that relates to the topic under study. Here are some examples from recent issues of the Journal Psychological Science .

• “Smells Like Clean Spirit: Nonconscious Effects of Scent on Cognition and Behavior”
• “Time Crawls: The Temporal Resolution of Infants’ Visual Attention”
• “Scent of a Woman: Men’s Testosterone Responses to Olfactory Ovulation Cues”
• “Apocalypse Soon?: Dire Messages Reduce Belief in Global Warming by Contradicting Just-World Beliefs”
• “Serial vs. Parallel Processing: Sometimes They Look Like Tweedledum and Tweedledee but They Can (and Should) Be Distinguished”
• “How Do I Love Thee? Let Me Count the Words: The Social Effects of Expressive Writing”

Individual researchers differ quite a bit in their preference for such titles. Some use them regularly, while others never use them. What might be some of the pros and cons of using cute article titles?

For articles that are being submitted for publication, the title page also includes an author note that lists the authors’ full institutional affiliations, any acknowledgments the authors wish to make to agencies that funded the research or to colleagues who commented on it, and contact information for the authors. For student papers that are not being submitted for publication—including theses—author notes are generally not necessary.

The  abstract  is a summary of the study. It is the second page of the manuscript and is headed with the word  Abstract . The first line is not indented. The abstract presents the research question, a summary of the method, the basic results, and the most important conclusions. Because the abstract is usually limited to about 200 words, it can be a challenge to write a good one.

Introduction

The  introduction  begins on the third page of the manuscript. The heading at the top of this page is the full title of the manuscript, with each important word capitalized as on the title page. The introduction includes three distinct subsections, although these are typically not identified by separate headings. The opening introduces the research question and explains why it is interesting, the literature review discusses relevant previous research, and the closing restates the research question and comments on the method used to answer it.

The Opening

The  opening , which is usually a paragraph or two in length, introduces the research question and explains why it is interesting. To capture the reader’s attention, researcher Daryl Bem recommends starting with general observations about the topic under study, expressed in ordinary language (not technical jargon)—observations that are about people and their behaviour (not about researchers or their research; Bem, 2003 [1] ). Concrete examples are often very useful here. According to Bem, this would be a poor way to begin a research report:

Festinger’s theory of cognitive dissonance received a great deal of attention during the latter part of the 20th century (p. 191)

The following would be much better:

The individual who holds two beliefs that are inconsistent with one another may feel uncomfortable. For example, the person who knows that he or she enjoys smoking but believes it to be unhealthy may experience discomfort arising from the inconsistency or disharmony between these two thoughts or cognitions. This feeling of discomfort was called cognitive dissonance by social psychologist Leon Festinger (1957), who suggested that individuals will be motivated to remove this dissonance in whatever way they can (p. 191).

After capturing the reader’s attention, the opening should go on to introduce the research question and explain why it is interesting. Will the answer fill a gap in the literature? Will it provide a test of an important theory? Does it have practical implications? Giving readers a clear sense of what the research is about and why they should care about it will motivate them to continue reading the literature review—and will help them make sense of it.

Breaking the Rules

Researcher Larry Jacoby reported several studies showing that a word that people see or hear repeatedly can seem more familiar even when they do not recall the repetitions—and that this tendency is especially pronounced among older adults. He opened his article with the following humourous anecdote:

A friend whose mother is suffering symptoms of Alzheimer’s disease (AD) tells the story of taking her mother to visit a nursing home, preliminary to her mother’s moving there. During an orientation meeting at the nursing home, the rules and regulations were explained, one of which regarded the dining room. The dining room was described as similar to a fine restaurant except that tipping was not required. The absence of tipping was a central theme in the orientation lecture, mentioned frequently to emphasize the quality of care along with the advantages of having paid in advance. At the end of the meeting, the friend’s mother was asked whether she had any questions. She replied that she only had one question: “Should I tip?” (Jacoby, 1999, p. 3)

Although both humour and personal anecdotes are generally discouraged in APA-style writing, this example is a highly effective way to start because it both engages the reader and provides an excellent real-world example of the topic under study.

The Literature Review

Immediately after the opening comes the  literature review , which describes relevant previous research on the topic and can be anywhere from several paragraphs to several pages in length. However, the literature review is not simply a list of past studies. Instead, it constitutes a kind of argument for why the research question is worth addressing. By the end of the literature review, readers should be convinced that the research question makes sense and that the present study is a logical next step in the ongoing research process.

Like any effective argument, the literature review must have some kind of structure. For example, it might begin by describing a phenomenon in a general way along with several studies that demonstrate it, then describing two or more competing theories of the phenomenon, and finally presenting a hypothesis to test one or more of the theories. Or it might describe one phenomenon, then describe another phenomenon that seems inconsistent with the first one, then propose a theory that resolves the inconsistency, and finally present a hypothesis to test that theory. In applied research, it might describe a phenomenon or theory, then describe how that phenomenon or theory applies to some important real-world situation, and finally suggest a way to test whether it does, in fact, apply to that situation.

Looking at the literature review in this way emphasizes a few things. First, it is extremely important to start with an outline of the main points that you want to make, organized in the order that you want to make them. The basic structure of your argument, then, should be apparent from the outline itself. Second, it is important to emphasize the structure of your argument in your writing. One way to do this is to begin the literature review by summarizing your argument even before you begin to make it. “In this article, I will describe two apparently contradictory phenomena, present a new theory that has the potential to resolve the apparent contradiction, and finally present a novel hypothesis to test the theory.” Another way is to open each paragraph with a sentence that summarizes the main point of the paragraph and links it to the preceding points. These opening sentences provide the “transitions” that many beginning researchers have difficulty with. Instead of beginning a paragraph by launching into a description of a previous study, such as “Williams (2004) found that…,” it is better to start by indicating something about why you are describing this particular study. Here are some simple examples:

Another example of this phenomenon comes from the work of Williams (2004).

Williams (2004) offers one explanation of this phenomenon.

An alternative perspective has been provided by Williams (2004).

We used a method based on the one used by Williams (2004).

Finally, remember that your goal is to construct an argument for why your research question is interesting and worth addressing—not necessarily why your favourite answer to it is correct. In other words, your literature review must be balanced. If you want to emphasize the generality of a phenomenon, then of course you should discuss various studies that have demonstrated it. However, if there are other studies that have failed to demonstrate it, you should discuss them too. Or if you are proposing a new theory, then of course you should discuss findings that are consistent with that theory. However, if there are other findings that are inconsistent with it, again, you should discuss them too. It is acceptable to argue that the  balance  of the research supports the existence of a phenomenon or is consistent with a theory (and that is usually the best that researchers in psychology can hope for), but it is not acceptable to  ignore contradictory evidence. Besides, a large part of what makes a research question interesting is uncertainty about its answer.

The Closing

The  closing  of the introduction—typically the final paragraph or two—usually includes two important elements. The first is a clear statement of the main research question or hypothesis. This statement tends to be more formal and precise than in the opening and is often expressed in terms of operational definitions of the key variables. The second is a brief overview of the method and some comment on its appropriateness. Here, for example, is how Darley and Latané (1968) [2] concluded the introduction to their classic article on the bystander effect:

These considerations lead to the hypothesis that the more bystanders to an emergency, the less likely, or the more slowly, any one bystander will intervene to provide aid. To test this proposition it would be necessary to create a situation in which a realistic “emergency” could plausibly occur. Each subject should also be blocked from communicating with others to prevent his getting information about their behaviour during the emergency. Finally, the experimental situation should allow for the assessment of the speed and frequency of the subjects’ reaction to the emergency. The experiment reported below attempted to fulfill these conditions. (p. 378)

Thus the introduction leads smoothly into the next major section of the article—the method section.

The  method section  is where you describe how you conducted your study. An important principle for writing a method section is that it should be clear and detailed enough that other researchers could replicate the study by following your “recipe.” This means that it must describe all the important elements of the study—basic demographic characteristics of the participants, how they were recruited, whether they were randomly assigned, how the variables were manipulated or measured, how counterbalancing was accomplished, and so on. At the same time, it should avoid irrelevant details such as the fact that the study was conducted in Classroom 37B of the Industrial Technology Building or that the questionnaire was double-sided and completed using pencils.

The method section begins immediately after the introduction ends with the heading “Method” (not “Methods”) centred on the page. Immediately after this is the subheading “Participants,” left justified and in italics. The participants subsection indicates how many participants there were, the number of women and men, some indication of their age, other demographics that may be relevant to the study, and how they were recruited, including any incentives given for participation.

After the participants section, the structure can vary a bit. Figure 11.1 shows three common approaches. In the first, the participants section is followed by a design and procedure subsection, which describes the rest of the method. This works well for methods that are relatively simple and can be described adequately in a few paragraphs. In the second approach, the participants section is followed by separate design and procedure subsections. This works well when both the design and the procedure are relatively complicated and each requires multiple paragraphs.

What is the difference between design and procedure? The design of a study is its overall structure. What were the independent and dependent variables? Was the independent variable manipulated, and if so, was it manipulated between or within subjects? How were the variables operationally defined? The procedure is how the study was carried out. It often works well to describe the procedure in terms of what the participants did rather than what the researchers did. For example, the participants gave their informed consent, read a set of instructions, completed a block of four practice trials, completed a block of 20 test trials, completed two questionnaires, and were debriefed and excused.

In the third basic way to organize a method section, the participants subsection is followed by a materials subsection before the design and procedure subsections. This works well when there are complicated materials to describe. This might mean multiple questionnaires, written vignettes that participants read and respond to, perceptual stimuli, and so on. The heading of this subsection can be modified to reflect its content. Instead of “Materials,” it can be “Questionnaires,” “Stimuli,” and so on.

The  results section  is where you present the main results of the study, including the results of the statistical analyses. Although it does not include the raw data—individual participants’ responses or scores—researchers should save their raw data and make them available to other researchers who request them. Several journals now encourage the open sharing of raw data online.

Although there are no standard subsections, it is still important for the results section to be logically organized. Typically it begins with certain preliminary issues. One is whether any participants or responses were excluded from the analyses and why. The rationale for excluding data should be described clearly so that other researchers can decide whether it is appropriate. A second preliminary issue is how multiple responses were combined to produce the primary variables in the analyses. For example, if participants rated the attractiveness of 20 stimulus people, you might have to explain that you began by computing the mean attractiveness rating for each participant. Or if they recalled as many items as they could from study list of 20 words, did you count the number correctly recalled, compute the percentage correctly recalled, or perhaps compute the number correct minus the number incorrect? A third preliminary issue is the reliability of the measures. This is where you would present test-retest correlations, Cronbach’s α, or other statistics to show that the measures are consistent across time and across items. A final preliminary issue is whether the manipulation was successful. This is where you would report the results of any manipulation checks.

The results section should then tackle the primary research questions, one at a time. Again, there should be a clear organization. One approach would be to answer the most general questions and then proceed to answer more specific ones. Another would be to answer the main question first and then to answer secondary ones. Regardless, Bem (2003) [3] suggests the following basic structure for discussing each new result:

• Remind the reader of the research question.
• Give the answer to the research question in words.
• Present the relevant statistics.
• Qualify the answer if necessary.
• Summarize the result.

Notice that only Step 3 necessarily involves numbers. The rest of the steps involve presenting the research question and the answer to it in words. In fact, the basic results should be clear even to a reader who skips over the numbers.

The  discussion  is the last major section of the research report. Discussions usually consist of some combination of the following elements:

• Summary of the research
• Theoretical implications
• Practical implications
• Limitations
• Suggestions for future research

The discussion typically begins with a summary of the study that provides a clear answer to the research question. In a short report with a single study, this might require no more than a sentence. In a longer report with multiple studies, it might require a paragraph or even two. The summary is often followed by a discussion of the theoretical implications of the research. Do the results provide support for any existing theories? If not, how  can  they be explained? Although you do not have to provide a definitive explanation or detailed theory for your results, you at least need to outline one or more possible explanations. In applied research—and often in basic research—there is also some discussion of the practical implications of the research. How can the results be used, and by whom, to accomplish some real-world goal?

The theoretical and practical implications are often followed by a discussion of the study’s limitations. Perhaps there are problems with its internal or external validity. Perhaps the manipulation was not very effective or the measures not very reliable. Perhaps there is some evidence that participants did not fully understand their task or that they were suspicious of the intent of the researchers. Now is the time to discuss these issues and how they might have affected the results. But do not overdo it. All studies have limitations, and most readers will understand that a different sample or different measures might have produced different results. Unless there is good reason to think they  would have, however, there is no reason to mention these routine issues. Instead, pick two or three limitations that seem like they could have influenced the results, explain how they could have influenced the results, and suggest ways to deal with them.

Most discussions end with some suggestions for future research. If the study did not satisfactorily answer the original research question, what will it take to do so? What  new  research questions has the study raised? This part of the discussion, however, is not just a list of new questions. It is a discussion of two or three of the most important unresolved issues. This means identifying and clarifying each question, suggesting some alternative answers, and even suggesting ways they could be studied.

Finally, some researchers are quite good at ending their articles with a sweeping or thought-provoking conclusion. Darley and Latané (1968) [4] , for example, ended their article on the bystander effect by discussing the idea that whether people help others may depend more on the situation than on their personalities. Their final sentence is, “If people understand the situational forces that can make them hesitate to intervene, they may better overcome them” (p. 383). However, this kind of ending can be difficult to pull off. It can sound overreaching or just banal and end up detracting from the overall impact of the article. It is often better simply to end when you have made your final point (although you should avoid ending on a limitation).

The references section begins on a new page with the heading “References” centred at the top of the page. All references cited in the text are then listed in the format presented earlier. They are listed alphabetically by the last name of the first author. If two sources have the same first author, they are listed alphabetically by the last name of the second author. If all the authors are the same, then they are listed chronologically by the year of publication. Everything in the reference list is double-spaced both within and between references.

Appendices, Tables, and Figures

Appendices, tables, and figures come after the references. An  appendix  is appropriate for supplemental material that would interrupt the flow of the research report if it were presented within any of the major sections. An appendix could be used to present lists of stimulus words, questionnaire items, detailed descriptions of special equipment or unusual statistical analyses, or references to the studies that are included in a meta-analysis. Each appendix begins on a new page. If there is only one, the heading is “Appendix,” centred at the top of the page. If there is more than one, the headings are “Appendix A,” “Appendix B,” and so on, and they appear in the order they were first mentioned in the text of the report.

After any appendices come tables and then figures. Tables and figures are both used to present results. Figures can also be used to illustrate theories (e.g., in the form of a flowchart), display stimuli, outline procedures, and present many other kinds of information. Each table and figure appears on its own page. Tables are numbered in the order that they are first mentioned in the text (“Table 1,” “Table 2,” and so on). Figures are numbered the same way (“Figure 1,” “Figure 2,” and so on). A brief explanatory title, with the important words capitalized, appears above each table. Each figure is given a brief explanatory caption, where (aside from proper nouns or names) only the first word of each sentence is capitalized. More details on preparing APA-style tables and figures are presented later in the book.

Sample APA-Style Research Report

Figures 11.2, 11.3, 11.4, and 11.5 show some sample pages from an APA-style empirical research report originally written by undergraduate student Tomoe Suyama at California State University, Fresno. The main purpose of these figures is to illustrate the basic organization and formatting of an APA-style empirical research report, although many high-level and low-level style conventions can be seen here too.

Key Takeaways

• An APA-style empirical research report consists of several standard sections. The main ones are the abstract, introduction, method, results, discussion, and references.
• The introduction consists of an opening that presents the research question, a literature review that describes previous research on the topic, and a closing that restates the research question and comments on the method. The literature review constitutes an argument for why the current study is worth doing.
• The method section describes the method in enough detail that another researcher could replicate the study. At a minimum, it consists of a participants subsection and a design and procedure subsection.
• The results section describes the results in an organized fashion. Each primary result is presented in terms of statistical results but also explained in words.
• The discussion typically summarizes the study, discusses theoretical and practical implications and limitations of the study, and offers suggestions for further research.
• Practice: Look through an issue of a general interest professional journal (e.g.,  Psychological Science ). Read the opening of the first five articles and rate the effectiveness of each one from 1 ( very ineffective ) to 5 ( very effective ). Write a sentence or two explaining each rating.
• Practice: Find a recent article in a professional journal and identify where the opening, literature review, and closing of the introduction begin and end.
• Practice: Find a recent article in a professional journal and highlight in a different colour each of the following elements in the discussion: summary, theoretical implications, practical implications, limitations, and suggestions for future research.

Long Descriptions

Figure 11.1 long description: Table showing three ways of organizing an APA-style method section.

In the simple method, there are two subheadings: “Participants” (which might begin “The participants were…”) and “Design and procedure” (which might begin “There were three conditions…”).

In the typical method, there are three subheadings: “Participants” (“The participants were…”), “Design” (“There were three conditions…”), and “Procedure” (“Participants viewed each stimulus on the computer screen…”).

In the complex method, there are four subheadings: “Participants” (“The participants were…”), “Materials” (“The stimuli were…”), “Design” (“There were three conditions…”), and “Procedure” (“Participants viewed each stimulus on the computer screen…”). [Return to Figure 11.1]

• Bem, D. J. (2003). Writing the empirical journal article. In J. M. Darley, M. P. Zanna, & H. R. Roediger III (Eds.),  The compleat academic: A practical guide for the beginning social scientist  (2nd ed.). Washington, DC: American Psychological Association. ↵
• Darley, J. M., & Latané, B. (1968). Bystander intervention in emergencies: Diffusion of responsibility.  Journal of Personality and Social Psychology, 4 , 377–383. ↵

A type of research article which describes one or more new empirical studies conducted by the authors.

The page at the beginning of an APA-style research report containing the title of the article, the authors’ names, and their institutional affiliation.

A summary of a research study.

The third page of a manuscript containing the research question, the literature review, and comments about how to answer the research question.

An introduction to the research question and explanation for why this question is interesting.

A description of relevant previous research on the topic being discusses and an argument for why the research is worth addressing.

The end of the introduction, where the research question is reiterated and the method is commented upon.

The section of a research report where the method used to conduct the study is described.

The main results of the study, including the results from statistical analyses, are presented in a research article.

Section of a research report that summarizes the study's results and interprets them by referring back to the study's theoretical background.

Part of a research report which contains supplemental material.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Lab Report Format: Step-by-Step Guide & Examples

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In psychology, a lab report outlines a study’s objectives, methods, results, discussion, and conclusions, ensuring clarity and adherence to APA (or relevant) formatting guidelines.

A typical lab report would include the following sections: title, abstract, introduction, method, results, and discussion.

The title page, abstract, references, and appendices are started on separate pages (subsections from the main body of the report are not). Use double-line spacing of text, font size 12, and include page numbers.

The report should have a thread of arguments linking the prediction in the introduction to the content of the discussion.

This must indicate what the study is about. It must include the variables under investigation. It should not be written as a question.

Title pages should be formatted in APA style .

The abstract provides a concise and comprehensive summary of a research report. Your style should be brief but not use note form. Look at examples in journal articles . It should aim to explain very briefly (about 150 words) the following:

• Start with a one/two sentence summary, providing the aim and rationale for the study.
• Describe participants and setting: who, when, where, how many, and what groups?
• Describe the method: what design, what experimental treatment, what questionnaires, surveys, or tests were used.
• Describe the major findings, including a mention of the statistics used and the significance levels, or simply one sentence summing up the outcome.
• The final sentence(s) outline the study’s “contribution to knowledge” within the literature. What does it all mean? Mention the implications of your findings if appropriate.

The abstract comes at the beginning of your report but is written at the end (as it summarises information from all the other sections of the report).

Introduction

The purpose of the introduction is to explain where your hypothesis comes from (i.e., it should provide a rationale for your research study).

Ideally, the introduction should have a funnel structure: Start broad and then become more specific. The aims should not appear out of thin air; the preceding review of psychological literature should lead logically into the aims and hypotheses.

• Start with general theory, briefly introducing the topic. Define the important key terms.
• Explain the theoretical framework.
• Summarise and synthesize previous studies – What was the purpose? Who were the participants? What did they do? What did they find? What do these results mean? How do the results relate to the theoretical framework?
• Rationale: How does the current study address a gap in the literature? Perhaps it overcomes a limitation of previous research.
• Aims and hypothesis. Write a paragraph explaining what you plan to investigate and make a clear and concise prediction regarding the results you expect to find.

There should be a logical progression of ideas that aids the flow of the report. This means the studies outlined should lead logically to your aims and hypotheses.

Do be concise and selective, and avoid the temptation to include anything in case it is relevant (i.e., don’t write a shopping list of studies).

USE THE FOLLOWING SUBHEADINGS:

Participants

• How many participants were recruited?
• Say how you obtained your sample (e.g., opportunity sample).
• Give relevant demographic details (e.g., gender, ethnicity, age range, mean age, and standard deviation).
• State the experimental design .
• What were the independent and dependent variables ? Make sure the independent variable is labeled and name the different conditions/levels.
• For example, if gender is the independent variable label, then male and female are the levels/conditions/groups.
• How were the IV and DV operationalized?
• Identify any controls used, e.g., counterbalancing and control of extraneous variables.
• List all the materials and measures (e.g., what was the title of the questionnaire? Was it adapted from a study?).
• You do not need to include wholesale replication of materials – instead, include a ‘sensible’ (illustrate) level of detail. For example, give examples of questionnaire items.
• Include the reliability (e.g., alpha values) for the measure(s).
• Describe the precise procedure you followed when conducting your research, i.e., exactly what you did.
• Describe in sufficient detail to allow for replication of findings.
• Be concise in your description and omit extraneous/trivial details, e.g., you don’t need to include details regarding instructions, debrief, record sheets, etc.
• Assume the reader has no knowledge of what you did and ensure that he/she can replicate (i.e., copy) your study exactly by what you write in this section.
• Write in the past tense.
• Don’t justify or explain in the Method (e.g., why you chose a particular sampling method); just report what you did.
• Only give enough detail for someone to replicate the experiment – be concise in your writing.
• The results section of a paper usually presents descriptive statistics followed by inferential statistics.
• Report the means, standard deviations, and 95% confidence intervals (CIs) for each IV level. If you have four to 20 numbers to present, a well-presented table is best, APA style.
• Name the statistical test being used.
• Report appropriate statistics (e.g., t-scores, p values ).
• Report the magnitude (e.g., are the results significant or not?) as well as the direction of the results (e.g., which group performed better?).
• It is optional to report the effect size (this does not appear on the SPSS output).
• Avoid interpreting the results (save this for the discussion).
• Make sure the results are presented clearly and concisely. A table can be used to display descriptive statistics if this makes the data easier to understand.
• DO NOT include any raw data.
• Follow APA style.

Use APA Style

• Numbers reported to 2 d.p. (incl. 0 before the decimal if 1.00, e.g., “0.51”). The exceptions to this rule: Numbers which can never exceed 1.0 (e.g., p -values, r-values): report to 3 d.p. and do not include 0 before the decimal place, e.g., “.001”.
• Percentages and degrees of freedom: report as whole numbers.
• Statistical symbols that are not Greek letters should be italicized (e.g., M , SD , t , X 2 , F , p , d ).
• Include spaces on either side of the equals sign.
• When reporting 95%, CIs (confidence intervals), upper and lower limits are given inside square brackets, e.g., “95% CI [73.37, 102.23]”
• Outline your findings in plain English (avoid statistical jargon) and relate your results to your hypothesis, e.g., is it supported or rejected?
• Compare your results to background materials from the introduction section. Are your results similar or different? Discuss why/why not.
• How confident can we be in the results? Acknowledge limitations, but only if they can explain the result obtained. If the study has found a reliable effect, be very careful suggesting limitations as you are doubting your results. Unless you can think of any c onfounding variable that can explain the results instead of the IV, it would be advisable to leave the section out.
• Suggest constructive ways to improve your study if appropriate.
• What are the implications of your findings? Say what your findings mean for how people behave in the real world.
• Suggest an idea for further research triggered by your study, something in the same area but not simply an improved version of yours. Perhaps you could base this on a limitation of your study.
• Concluding paragraph – Finish with a statement of your findings and the key points of the discussion (e.g., interpretation and implications) in no more than 3 or 4 sentences.

Reference Page

The reference section lists all the sources cited in the essay (alphabetically). It is not a bibliography (a list of the books you used).

In simple terms, every time you refer to a psychologist’s name (and date), you need to reference the original source of information.

If you have been using textbooks this is easy as the references are usually at the back of the book and you can just copy them down. If you have been using websites then you may have a problem as they might not provide a reference section for you to copy.

References need to be set out APA style :

Author, A. A. (year). Title of work . Location: Publisher.

Journal Articles

Author, A. A., Author, B. B., & Author, C. C. (year). Article title. Journal Title, volume number (issue number), page numbers

A simple way to write your reference section is to use Google scholar . Just type the name and date of the psychologist in the search box and click on the “cite” link.

Next, copy and paste the APA reference into the reference section of your essay.

Once again, remember that references need to be in alphabetical order according to surname.

Psychology Lab Report Example

Quantitative paper template.

Quantitative professional paper template: Adapted from “Fake News, Fast and Slow: Deliberation Reduces Belief in False (but Not True) News Headlines,” by B. Bago, D. G. Rand, and G. Pennycook, 2020,  Journal of Experimental Psychology: General ,  149 (8), pp. 1608–1613 ( https://doi.org/10.1037/xge0000729 ). Copyright 2020 by the American Psychological Association.

Qualitative paper template

Qualitative professional paper template: Adapted from “‘My Smartphone Is an Extension of Myself’: A Holistic Qualitative Exploration of the Impact of Using a Smartphone,” by L. J. Harkin and D. Kuss, 2020,  Psychology of Popular Media ,  10 (1), pp. 28–38 ( https://doi.org/10.1037/ppm0000278 ). Copyright 2020 by the American Psychological Association.

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The abstract is the first section in a psychological report or journal. It includes a summary of the aims, hypothesis, method, results and conclusions, and thus provides an overview of the entire report.

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How to Write an Abstract?

• Open Access
• First Online: 24 October 2021

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• Samiran Nundy 4 ,
• Atul Kakar 5 &
• Zulfiqar A. Bhutta 6  

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An abstract is a crisp, short, powerful, and self-contained summary of a research manuscript used to help the reader swiftly determine the paper’s purpose. Although the abstract is the first paragraph of the manuscript it should be written last when all the other sections have been addressed.

Research is formalized curiosity. It is poking and prying with a purpose. — Zora Neale Hurston, American Author, Anthropologist and Filmmaker (1891–1960)

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Writing the Abstract

Abstract and Keywords

Additional Commentaries

1 what is an abstract.

An abstract is usually a standalone document that informs the reader about the details of the manuscript to follow. It is like a trailer to a movie, if the trailer is good, it stimulates the audience to watch the movie. The abstract should be written from scratch and not ‘cut –and-pasted’ [ 1 ].

2 What is the History of the Abstract?

An abstract, in the form of a single paragraph, was first published in the Canadian Medical Association Journal in 1960 with the idea that the readers may not have enough time to go through the whole paper, and the first abstract with a defined structure was published in 1991 [ 2 ]. The idea sold and now most original articles and reviews are required to have a structured abstract. The abstract attracts the reader to read the full manuscript [ 3 ].

3 What are the Qualities of a Good Abstract?

The quality of information in an abstract can be summarized by four ‘C’s. It should be:

C: Condensed

C: Critical

4 What are the Types of Abstract?

Before writing the abstract, you need to check with the journal website about which type of abstract it requires, with its length and style in the ‘Instructions to Authors’ section.

The abstract types can be divided into:

Descriptive: Usually written for psychology, social science, and humanities papers. It is about 50–100 words long. No conclusions can be drawn from this abstract as it describes the major points in the paper.

Informative: The majority of abstracts for science-related manuscripts are informative and are surrogates for the research done. They are single paragraphs that provide the reader an overview of the research paper and are about 100–150 words in length. Conclusions can be drawn from the abstracts and in the recommendations written in the last line.

Critical: This type of abstract is lengthy and about 400–500 words. In this, the authors’ own research is discussed for reliability, judgement, and validation. A comparison is also made with similar studies done earlier.

Highlighting: This is rarely used in scientific writing. The style of the abstract is to attract more readers. It is not a balanced or complete overview of the article with which it is published.

Structured: A structured abstract contains information under subheadings like background, aims, material and methods, results, conclusion, and recommendations (Fig. 15.1 ). Most leading journals now carry these.

Example of a structured abstract (with permission editor CMRP)

5 What is the Purpose of an Abstract?

An abstract is written to educate the reader about the study that follows and provide an overview of the science behind it. If written well it also attracts more readers to the article. It also helps the article getting indexed. The fate of a paper both before and after publication often depends upon its abstract. Most readers decide if a paper is worth reading on the basis of the abstract. Additionally, the selection of papers in systematic reviews is often dependent upon the abstract.

6 What are the Steps of Writing an Abstract?

An abstract should be written last after all the other sections of an article have been addressed. A poor abstract may turn off the reader and they may cause indexing errors as well. The abstract should state the purpose of the study, the methodology used, and summarize the results and important conclusions. It is usually written in the IMRAD format and is called a structured abstract [ 4 , 5 ].

I: The introduction in the opening line should state the problem you are addressing.

M: Methodology—what method was chosen to finish the experiment?

R: Results—state the important findings of your study.

D: Discussion—discuss why your study is important.

Mention the following information:

Important results with the statistical information ( p values, confidence intervals, standard/mean deviation).

Arrange all information in a chronological order.

Do not repeat any information.

The last line should state the recommendations from your study.

The abstract should be written in the past tense.

7 What are the Things to Be Avoided While Writing an Abstract?

Cut and paste information from the main text

Hold back important information

Use abbreviations

Tables or Figures

Generalized statements

Arguments about the study

8 What are Key Words?

These are important words that are repeated throughout the manuscript and which help in the indexing of a paper. Depending upon the journal 3–10 key words may be required which are indexed with the help of MESH (Medical Subject Heading).

9 How is an Abstract Written for a Conference Different from a Journal Paper?

The basic concept for writing abstracts is the same. However, in a conference abstract occasionally a table or figure is allowed. A word limit is important in both of them. Many of the abstracts which are presented in conferences are never published in fact one study found that only 27% of the abstracts presented in conferences were published in the next five years [ 6 ].

Table 15.1 gives a template for writing an abstract.

10 What are the Important Recommendations of the International Committees of Medical Journal of Editors?

The recommendations are [ 7 ]:

An abstract is required for original articles, metanalysis, and systematic reviews.

A structured abstract is preferred.

The abstract should mention the purpose of the scientific study, how the procedure was carried out, the analysis used, and principal conclusion.

Clinical trials should be reported according to the CONSORT guidelines.

The trials should also mention the funding and the trial number.

The abstract should be accurate as many readers have access only to the abstract.

11 Conclusions

An Abstract should be written last after all the other sections of the manuscript have been completed and with due care and attention to the details.

It should be structured and written in the IMRAD format.

For many readers, the abstract attracts them to go through the complete content of the article.

The abstract is usually followed by key words that help to index the paper.

Andrade C. How to write a good abstract for a scientific paper or conference presentation? Indian J Psychiatry. 2011;53:172–5.

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Squires BP. Structured abstracts of original research and review articles. CMAJ. 1990;143:619–22.

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Preparing a manuscript for submission to a medical journal. Available on http://www.icmje.org/recommendations/browse/manuscript-preparation/preparing-for-submission.html . Accessed 10 May 2020.

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Samiran Nundy

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Institute for Global Health and Development, The Aga Khan University, South Central Asia, East Africa and United Kingdom, Karachi, Pakistan

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Nundy, S., Kakar, A., Bhutta, Z.A. (2022). How to Write an Abstract?. In: How to Practice Academic Medicine and Publish from Developing Countries?. Springer, Singapore. https://doi.org/10.1007/978-981-16-5248-6_15

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How We Use Abstract Thinking

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

MoMo Productions / Getty Images

• How It Develops

Abstract thinking, also known as abstract reasoning, involves the ability to understand and think about complex concepts that, while real, are not tied to concrete experiences, objects, people, or situations.

Abstract thinking is considered a type of higher-order thinking, usually about ideas and principles that are often symbolic or hypothetical. This type of thinking is more complex than the type of thinking that is centered on memorizing and recalling information and facts.

Examples of Abstract Thinking

Examples of abstract concepts include ideas such as:

• Imagination

While these things are real, they aren't concrete, physical things that people can experience directly via their traditional senses.

You likely encounter examples of abstract thinking every day. Stand-up comedians use abstract thinking when they observe absurd or illogical behavior in our world and come up with theories as to why people act the way they do.

You use abstract thinking when you're in a philosophy class or when you're contemplating what would be the most ethical way to conduct your business. If you write a poem or an essay, you're also using abstract thinking.

With all of these examples, concepts that are theoretical and intangible are being translated into a joke, a decision, or a piece of art. (You'll notice that creativity and abstract thinking go hand in hand.)

Abstract Thinking vs. Concrete Thinking

One way of understanding abstract thinking is to compare it with concrete thinking. Concrete thinking, also called concrete reasoning, is tied to specific experiences or objects that can be observed directly.

Research suggests that concrete thinkers tend to focus more on the procedures involved in how a task should be performed, while abstract thinkers are more focused on the reasons why a task should be performed.

It is important to remember that you need both concrete and abstract thinking skills to solve problems in day-to-day life. In many cases, you utilize aspects of both types of thinking to come up with solutions.

Other Types of Thinking

Depending on the type of problem we face, we draw from a number of different styles of thinking, such as:

• Creative thinking : This involves coming up with new ideas, or using existing ideas or objects to come up with a solution or create something new.
• Convergent thinking : Often called linear thinking, this is when a person follows a logical set of steps to select the best solution from already-formulated ideas.
• Critical thinking : This is a type of thinking in which a person tests solutions and analyzes any potential drawbacks.
• Divergent thinking : Often called lateral thinking, this style involves using new thoughts or ideas that are outside of the norm in order to solve problems.

How Abstract Thinking Develops

While abstract thinking is an essential skill, it isn’t something that people are born with. Instead, this cognitive ability develops throughout the course of childhood as children gain new abilities, knowledge, and experiences.

The psychologist Jean Piaget described a theory of cognitive development that outlined this process from birth through adolescence and early adulthood. According to his theory, children go through four distinct stages of intellectual development:

• Sensorimotor stage : During this early period, children's knowledge is derived primarily from their senses.
• Preoperational stage : At this point, children develop the ability to think symbolically.
• Concrete operational stage : At this stage, kids become more logical but their understanding of the world tends to be very concrete.
• Formal operational stage : The ability to reason about concrete information continues to grow during this period, but abstract thinking skills also emerge.

This period of cognitive development when abstract thinking becomes more apparent typically begins around age 12. It is at this age that children become more skilled at thinking about things from the perspective of another person. They are also better able to mentally manipulate abstract ideas as well as notice patterns and relationships between these concepts.

Uses of Abstract Thinking

Abstract thinking is a skill that is essential for the ability to think critically and solve problems. This type of thinking is also related to what is known as fluid intelligence , or the ability to reason and solve problems in unique ways.

Fluid intelligence involves thinking abstractly about problems without relying solely on existing knowledge.

Abstract thinking is used in a number of ways in different aspects of your daily life. Some examples of times you might use this type of thinking:

• When you describe something with a metaphor
• When you talk about something figuratively
• When you come up with creative solutions to a problem
• When you analyze a situation
• When you notice relationships or patterns
• When you form a theory about why something happens
• When you think about a problem from another point of view

Research also suggests that abstract thinking plays a role in the actions people take. Abstract thinkers have been found to be more likely to engage in risky behaviors, where concrete thinkers are more likely to avoid risks.

Impact of Abstract Thinking

People who have strong abstract thinking skills tend to score well on intelligence tests. Because this type of thinking is associated with creativity, abstract thinkers also tend to excel in areas that require creativity such as art, writing, and other areas that benefit from divergent thinking abilities.

Abstract thinking can have both positive and negative effects. It can be used as a tool to promote innovative problem-solving, but it can also lead to problems in some cases:

• Bias : Research also suggests that it can sometimes promote different types of bias . As people seek to understand events, abstract thinking can sometimes cause people to seek out patterns, themes, and relationships that may not exist.
• Catastrophic thinking : Sometimes these inferences, imagined scenarios, and predictions about the future can lead to feelings of fear and anxiety. Instead of making realistic predictions, people may catastrophize and imagine the worst possible potential outcomes.
• Anxiety and depression : Research has also found that abstract thinking styles are sometimes associated with worry and rumination . This thinking style is also associated with a range of conditions including depression , anxiety, and post-traumatic stress disorder (PTSD) .

Conditions That Impact Abstract Thinking

The presence of learning disabilities and mental health conditions can affect abstract thinking abilities. Conditions that are linked to impaired abstract thinking skills include:

• Learning disabilities
• Schizophrenia
• Traumatic brain injury (TBI)

The natural aging process can also have an impact on abstract thinking skills. Research suggests that the thinking skills associated with fluid intelligence peak around the ages of 30 or 40 and begin to decline with age.

Tips for Reasoning Abstractly

While some psychologists believe that abstract thinking skills are a natural product of normal development, others suggest that these abilities are influenced by genetics, culture, and experiences. Some people may come by these skills naturally, but you can also strengthen these abilities with practice.

Some strategies that you might use to help improve your abstract thinking skills:

• Think about why and not just how : Abstract thinkers tend to focus on the meaning of events or on hypothetical outcomes. Instead of concentrating only on the steps needed to achieve a goal, consider some of the reasons why that goal might be valuable or what might happen if you reach that goal.
• Reframe your thinking : When you are approaching a problem, it can be helpful to purposefully try to think about the problem in a different way. How might someone else approach it? Is there an easier way to accomplish the same thing? Are there any elements you haven't considered?
• Consider the big picture : Rather than focusing on the specifics of a situation, try taking a step back in order to view the big picture. Where concrete thinkers are more likely to concentrate on the details, abstract thinkers focus on how something relates to other things or how it fits into the grand scheme of things.

Abstract thinking allows people to think about complex relationships, recognize patterns, solve problems, and utilize creativity. While some people tend to be naturally better at this type of reasoning, it is a skill that you can learn to utilize and strengthen with practice. 

It is important to remember that both concrete and abstract thinking are skills that you need to solve problems and function successfully. 

Gilead M, Liberman N, Maril A. From mind to matter: neural correlates of abstract and concrete mindsets . Soc Cogn Affect Neurosci . 2014;9(5):638-45. doi: 10.1093/scan/nst031

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American Psychological Association. Convergent thinking .

American Psychological Association. Critical thinking .

American Psychological Association. Divergent thinking .

Lermer E, Streicher B, Sachs R, Raue M, Frey D. The effect of abstract and concrete thinking on risk-taking behavior in women and men . SAGE Open . 2016;6(3):215824401666612. doi:10.1177/2158244016666127

Namkoong J-E, Henderson MD. Responding to causal uncertainty through abstract thinking . Curr Dir Psychol Sci . 2019;28(6):547-551. doi:10.1177/0963721419859346

White R, Wild J. "Why" or "How": the effect of concrete versus abstract processing on intrusive memories following analogue trauma . Behav Ther . 2016;47(3):404-415. doi:10.1016/j.beth.2016.02.004

Williams DL, Mazefsky CA, Walker JD, Minshew NJ, Goldstein G. Associations between conceptual reasoning, problem solving, and adaptive ability in high-functioning autism . J Autism Dev Disord . 2014 Nov;44(11):2908-20. doi: 10.1007/s10803-014-2190-y

Oh J, Chun JW, Joon Jo H, Kim E, Park HJ, Lee B, Kim JJ. The neural basis of a deficit in abstract thinking in patients with schizophrenia . Psychiatry Res . 2015;234(1):66-73. doi: 10.1016/j.pscychresns.2015.08.007

Hartshorne JK, Germine LT. When does cognitive functioning peak? The asynchronous rise and fall of different cognitive abilities across the life span . Psychol Sci. 2015;26(4):433-43. doi:10.1177/0956797614567339

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Consensus Paper: Current Perspectives on Abstract Concepts and Future Research Directions

Briony banks.

1 Department of Psychology, Lancaster University, UK

Anna M. Borghi

2 Department of Dynamic and Clinical Psychology, and Health Studies, Sapienza University of Rome, Italy

3 Institute of Cognitive Sciences and Technologies, Italian National Research Council, Rome, Italy

Raphaël Fargier

4 Department of Special Needs Education, University of Oslo, Norway

Chiara Fini

Domicele jonauskaite.

5 Faculty of Psychology, University of Vienna, Vienna, Austria

6 Institute of Psychology, University of Lausanne, Lausanne, Switzerland

Claudia Mazzuca

Martina montalti.

7 Department of Clinical and Experimental Sciences, University of Brescia, Italy

8 Department of Medicine and Surgery – Unit of Neuroscience, University of Parma, Italy

Caterina Villani

9 Department of Modern Languages, Literatures, and Cultures, University of Bologna, Italy

Greg Woodin

10 Department of English Language and Linguistics, University of Birmingham, UK

Abstract concepts are relevant to a wide range of disciplines, including cognitive science, linguistics, psychology, cognitive, social, and affective neuroscience, and philosophy. This consensus paper synthesizes the work and views of researchers in the field, discussing current perspectives on theoretical and methodological issues, and recommendations for future research. In this paper, we urge researchers to go beyond the traditional abstract-concrete dichotomy and consider the multiple dimensions that characterize concepts (e.g., sensorimotor experience, social interaction, conceptual metaphor), as well as the mediating influence of linguistic and cultural context on conceptual representations. We also promote the use of interactive methods to investigate both the comprehension and production of abstract concepts, while also focusing on individual differences in conceptual representations. Overall, we argue that abstract concepts should be studied in a more nuanced way that takes into account their complexity and diversity, which should permit us a fuller, more holistic understanding of abstract cognition.

Abstract concepts are currently a topic of great interest in a variety of disciplines, including cognitive science, linguistics, psychology, cognitive, social, and affective neuroscience, and philosophy. Theories are now converging on a broader framework for the study of their nature, representation, and use, with novel methods beginning to appear alongside more traditional ones. This paper brings together the views and latest work of several researchers in the field, providing a consensus on new theoretical and methodological advancements, and recommendations for future research.

In Part 1, we start by discussing the multidimensionality of abstract concepts; particularly that they do not represent a clear dichotomy, but should be considered as points in a multidimensional space, taking evidence from work on sensorimotor experience, social interaction, and conceptual metaphor. We highlight the example of color as a concept which cannot be considered as clearly concrete or abstract, and discuss evidence that the linguistic phenomenon of negation is in fact multidimensional. We then discuss the importance of context, proposing a broader definition comprising three distinct levels (task, individual, and collective), and highlighting the importance of cross-linguistic and cross-cultural context in shaping abstract relations.

In Part 2, we propose some future directions for the field, first considering the advantages and limitations of traditional methods, and discussing the importance of interactive methods. Several new experimental approaches are proposed, along with the need to study language production as well as comprehension, and the need to consider individual differences in conceptual representation, highlighting the example of gender as a concept that differs between individuals. Finally, we discuss the benefits of triangulating these different research methods.

Part 1: Current Perspectives

Multidimensionality.

The classical distinction between concrete and abstract concepts has been clearly overcome. In recent years, we have seen the affirmation and consolidation of multiple representation theories, which have emphasized the role of multiple dimensions, beyond the sensorimotor one, in grounding abstract concepts. These dimensions include interoception, emotions, language, and social interaction. For example, when contrasted with concrete concepts, abstract concepts are typically expressed by words with a later Age of Acquisition, and through linguistic explanations rather than denoting their referents directly (linguistic Modality of Acquisition; Wauters et al., 2003 ). They also tend to be less imageable, have lower Body Object Interaction scores (BOI: Tillotson et al., 2008 ; Pexman et al., 2019 ), and be less easily linked to specific contexts (contextual availability; Schwanenflugel & Stowe, 1989 ). Abstract concepts are also more variable across participants and cultures ( Wang & Bi, 2021 ) and are generally less iconic ( Lupyan & Winter, 2018 ) than concrete concepts.

However, there are strong differences within different kinds of abstract concepts, as recent papers investigating their neural bases and using multiple ratings have revealed (review in Conca et al., 2021 ; see also Desai et al., 2018 ; Harpaintner et al., 2020 ; Mazzuca et al., 2021 ; Muraki et al., 2020 ; Villani et al., 2019 ). For example, interoception characterizes more emotional abstract concepts ( Connell et al., 2018 ), whereas sensorimotor aspects are more pivotal for quantitative and spatiotemporal abstract concepts ( Villani et al., 2021 ). Neuroimaging, TMS and patient studies have also identified specific neural substrates for social (e.g., Zahn et al., 2007 ), mental state (e.g., Baron-Cohen et al., 1994 ) and quantity-related (e.g., Catricala et al., 2021) concepts – whereby specific kinds of abstract concepts are represented in brain systems engaged by their corresponding experiences (see Conca et al., 2021, for a review ). Hence, more than a continuum ranging from concrete to abstract concepts, different kinds of abstract concepts can be conceived as points in a multidimensional space, with the various dimensions assuming different weights for different types of abstract concepts.

This multidimensional view of abstract concepts provides the theoretical framework for the present paper, in which we consider different aspects and implications of multidimensionality (for a similar multidimensional perspective see Dove, 2022 ). Particularly, multidimensionality is linked to the issue of contextuality, and the need for new ways of studying abstract concepts.

Concepts should be studied beyond the concrete-abstract dichotomy

The multidimensional nature of abstract concepts means that defining them purely based on whether they are perceivable or not (i.e., as concrete or abstract) fails to capture their complexity (e.g., Barsalou, Dutriaux & Scheepers, 2018 ; Borghi et al., 2017 ), and indeed can even be misleading. Banks and Connell ( 2022 ) used the Brysbaert et al. ( 2014 ) concreteness ratings to analyze the structure of semantic categories collected in a category production (semantic fluency) task, examining the concreteness of the concepts that comprise ostensibly concrete (e.g., animal, furniture ) and abstract (e.g., science, unit of time) categories. Although members of concrete categories overall were more highly rated on concreteness, many (e.g., metal: silver , hat: beret ) unexpectedly had similarly high concreteness ratings to more abstract category members (e.g., profession: lawyer , social relationship: teammate ). Indeed, certain abstract concepts such as beauty or fitness have been associated with sensory and motor areas of the brain (temporo-occipital visual and fronto-parietal motor areas, respectively; Harpainter et al., 2020). Furthermore, when sensorimotor experience is measured via multiple individual modalities (e.g., Lynott et al., 2020 ; Speed & Brysbaert, 2021 ; Vergallito et al., 2020 ), the concrete-abstract distinction becomes even less clear. When the verbally-produced category members from Banks and Connell ( 2022 ) were analyzed based on their grounding in multiple perceptual modalities (vision, hearing, touch, smell, taste, interoception) and actions involving specific parts of the body (the head, hands/arms, feet/legs, torso and mouth) many abstract category members were in fact found to be strongly grounded in sensorimotor experience (e.g. sport, social gathering, art form ; Banks & Connell, 2021 ) – that is, the concrete-abstract distinction was much less apparent.

So what, then, underlies our intuition that concepts are either concrete or abstract? Concreteness rating tasks instruct participants to rate concepts based on all sensory modalities, but in fact many highly rated concrete concepts (e.g, animal or furniture ) tend to be mostly related to visual and haptic experience and movements with the hands and arms, whereas many abstract concepts are more associated with interoceptive and auditory experience, and actions with other parts of the body such as the mouth and head ( Banks & Connell, 2021 ). Thus, concrete concepts often reflect our priority for visual entities that we can touch and handle, while abstract concepts may reflect experiences with the other less prominent sensorimotor modalities, particularly internal and socially-relevant experiences such as hearing, mouth movements, and interoception ( Connell et al., 2018 ; Mazzuca et al., 2021 ; Reggin et al., 2021 ; Villani et al., 2021 ). Grammaticalization processes ( Heine, Kuteva & Bernd, 2002 ), many of which involve metaphoric mappings, provide another example of the continuity between concrete and abstract concepts. For example, concrete words might become increasingly abstract over time (bleaching). Consider the word “going;” initially, it contained a motion aspect, while now it is often used to refer to actions that will take place in the near future.

Many theories have also argued that our understanding and representation of abstract concepts relies more on language than the sensorimotor dimension, and particularly linguistic distributional relations (e.g., Borghi, 2020 ; Crutch & Warrington, 2005 ; Dove et al., 2020 ; Vigliocco et al., 2009 ). However, this distinction may also not be as clear cut as assumed. In a category production study and corresponding computational model, Banks, Wingfield, and Connell ( 2021 ) examined whether linguistic relations and sensorimotor similarity (based on multidimensional experience ratings) between a category label and its category members are critical for verbally producing category members (e.g., “name as many animals as you can”: cat, rabbit, lion , etc.). Both were equally and independently predictive for producing concrete and abstract category members, implying that the same linguistic and sensorimotor relations can be exploited to access them from long term memory. Similarly, both concrete and abstract words can potentially be produced and understood primarily through linguistic mechanisms; for example, the concreteness advantage for unimodal visual concepts in a lexical decision task is present in both congenitally blind and sighted individuals ( Bottini et al., 2021 ). Thus, studying concepts in terms of the abstract-concrete distinction may not be the most fruitful method, as multiple dimensions likely contribute to all concepts, depending on the context and task demands. Moreover, the distinction between concrete and abstract concepts may vary between languages, cultures and individuals, as we discuss later in this article.

Several accounts of abstract concepts have already argued against studying concepts in terms of a dichotomous distinction between concrete and abstract (e.g., Barsalou, Dutriaux & Scheepers, 2018 ; Borghi et al., 2017 ). Increasingly, research into the multidimensional aspects of abstract concepts is providing support for this argument, revealing the need for a more fine-grained definition, beyond a simple dichotomy. Indeed, examining the contribution of individual perceptual and action modalities (e.g., Banks & Connell, 2021 ), particularly alongside other aspects of conceptual representation such as language and social interaction (e.g., Villani et al., 2019 ), may help to better identify subdomains of concepts and lead to a deeper understanding of their nature and representation.

The concept of color defies the concrete-abstract dichotomy

In the preceding paragraphs we have argued that many concepts do not neatly fit into either the concrete or abstract domain in terms of their nature and mental representation. One such concept is color which, despite being strongly related to visual experience, can also be represented and understood in a purely abstract way.

Imagine an individual blind from birth trying to match the color of their shirt to that of their trousers. While this would be an easy task for the sighted, for the blind, color is only an abstract idea. It is a property of an object that cannot be touched, smelt, or tasted, and yet, it is very important in our everyday lives. Color choices impact on how we judge others’ preferences and personalities ( Pazda & Thorstenson, 2019 ; Yu et al., 2018 ). For the blind, color knowledge can only be learnt through abstract means of communication. Clearly, we are successful at communicating about colors as many blind people have a general understanding of how colors are organized. They can tell that red and orange are closer than red and green ( Saysani et al., 2018 ; Shepard & Cooper, 1992 ). They know that colors are mentally arranged in a circle and can make causal inferences about colors ( Kim et al., 2021 ). These findings point to the idea that color concepts are well established in our shared knowledge as abstract concepts.

To understand the extent to which color is abstracted, one can test for its associations with other entities, like emotions. There is a high degree of systematicity and stability in color-emotion associations in the general population ( Fugate & Franco, 2019 ; Jonauskaite, Abu-Akel, et al., 2020 ; Kaya & Epps, 2004 ). For instance, black was systematically associated with sadness, fear, and other negative emotions, while many bright colors were associated with positive emotions (e.g., yellow with joy, or pink with love). Color-emotion associations have been shown to be stable across cultures, at least when testing up to 30 nations ( Adams & Osgood, 1973 ; Jonauskaite, Abu-Akel, et al., 2020 ; Ou et al., 2018 ), and it mattered little whether one was working with color terms or actual visual experiences of colors ( Jonauskaite, Parraga, et al., 2020 ; Jonauskaite et al., 2021 ).

If visual experience is necessary for such color-emotion associations to arise, then one would conjecture that color concepts are not detached from the visual experience, and vice versa. To this end, researchers have investigated individuals with reduced or non-existent color vision. Color-blind individuals perceive a reduced spectrum of colors and often confuse green with brown ( Linhares et al., 2008 ; Moreira et al., 2021 ). Yet, color-blind individuals perform well on color naming and color identification tasks ( Bonnardel, 2006 ), indicating they can compensate for their visual deficiencies behaviorally, but also on a neural level ( Tregillus et al., 2021 ). When asked to associate color terms with emotion concepts, color-blind men associated them similarly to non-colour-blind men ( Jonauskaite et al., 2021 ). The result held with color patches too, despite limited color perception, suggesting that color-blind individuals rely on abstract knowledge about colors and their relations more than on the immediate visual experience of color. Evidence regarding color associations of the blind is only recently being gathered. A small study with 12 congenitally blind participants showed that the blind judged colors similarly on many affective scales as the sighted, although there was a high degree of individual variability ( Saysani et al., 2021 ).

These diverse research studies highlight the ambiguity in classifying color concepts as either concrete or abstract. Indeed, this dual representation has been identified neurally in a handful of studies revealing that both blind and sighted individuals represent non-sensory (I,e., abstracted) color knowledge in the dorsal ATL, while sighted individuals additionally represent sensory color knowledge in the visual cortex (for a review see Bi, 2021 ). While color concepts have clear perceptual grounding – in the end, we live in a colorful world – they can be mentally represented and understood in an abstract way without referring back to perceptual experience. The high degree of consensus on color associations in the general and visually restricted populations shows just how well the meaning of this concept is ingrained in our language and shared knowledge.

Social interaction has an important role for abstract concepts

As discussed above, multiple representation theories emphasize the role of multiple dimensions beyond sensorimotor experience in the representation of abstract concepts. One dimension that might have an important role is social interaction. Notably, uncertainty on word meaning might characterize more abstract than concrete concepts, also owing to the indeterminate character of the former. This uncertainty might lead people to rely more on others ( Shea, 2018 ; Prinz, 2014 ), since competent others can offer exhaustive explanations of word meanings (social metacognition, Borghi et al., 2018 ). Others can also help us to negotiate word meanings together ( Mazzuca & Santarelli, 2022 ), as it happens when scientists collaboratively define a new term or find a compelling definition for an old one.

According to recent proposals, abstract concepts can be qualified as concepts for which we need others more ( Borghi, 2022 ): we need others to acquire them, collapsing exemplars that might be heterogeneous; to comprehend them, in order to fill our lack of knowledge, and to help us build or reconstruct word meanings. A recent study ( Villani et al., 2022, study described in Part 2 of the present paper ) confirmed that level of uncertainty and interactive exchanges increases with abstractness, leading to generating more questions and requests for clarifications with abstract than concrete sentences during conversation. Importantly, people seem to be aware of the difficulty of abstract concepts and they need others to understand their meaning.s. In a rating study, Villani et al. ( 2019 ) found that abstractness is characterized by high scores of social metacognition (need of others to understand a word’s meaning). Mazzuca et al. ( 2022 ), who collected ratings on various dimensions, also found that higher social metacognition scores are associated with lower confidence in word meanings and lower BOI scores (notably, BOI is negatively correlated with abstractness).

Claiming that the mechanisms of relying on others might characterize all abstract concepts, especially the most difficult ones, does not exclude that the social dimension might be particularly relevant for abstract concepts that directly refer to sociality. These abstract concepts (e.g., “society,” “group,” and “relationship”) might be associated with social contexts, situations, and experiences and engage to a larger extent brain regions generally recruited by social processing.

Overall, results suggest that social interaction, particularly when it accompanies linguistic exchanges, might be more crucial for the acquisition, representation, and use of abstract concepts than concrete ones. Importantly, the stronger need for help from others that characterizes the processing of abstract concepts might lead us to be more synchronous in movement with others ( Fini et al., 2021 ; see Part 2 for an extended discussion). Overall, the role of social interaction might contribute to differently grounding the various kinds of abstract concepts, assuming a stronger weight for those more difficult to acquire without social scaffolding.

The concept of negation is multidimensional

Negation is a universal feature of human communication and reasoning ( Horn, 2001 ) that allows reversing the truth-value of an utterance ( Horn, 1989 ). It has been traditionally considered to be a purely linguistic phenomenon, which would not intuitively fit within the multidimensional framework discussed in this section. The empirical investigation of the embodied grounding of logical operators such as negation is thus a challenging test bed for Multiple Representation Theories but, as we outline below, suggests that even linguistic phenomena can be multidimensional.

Several studies have shown that the processing of sentential negation is associated with cognitive effects, such as a higher cognitive effort and lower accessibility of the negated concept ( Clark & Chase, 1972 ; Carpenter & Just, 1975 ; Kaup, 2001 ; Kaup & Zwaan, 2003 ; MacDonald & Just, 1989 ). These cognitive phenomena are reflected in higher reaction times (RTs) and higher error rates (ERs), which can be explained by a two-step process for the comprehension of negation: initially the affirmative counterpart of the negated sentence is mentally simulated ( Barsalou, 1999 ; Glenberg & Kaschak, 2002 ; Gallese, 2007 ), and only in a second step the negation marker is integrated, leading to the simulation of the actual state of affairs (e.g., Kaup et al., 2006 ; Kaup & Zwaan, 2003 ). Furthermore, several studies using different neuro-behavioural techniques have demonstrated that the presence of negation in a hand-related negative sentence reduces the activation of the corresponding motor areas compared to its affirmative counterparts (functional Magnetic Resonance Imaging: Tettamanti et al., 2008 ; Tomasino et al., 2010 ; electroencephalography (EEG): Alemanno et al., 2012 ; paired-pulses Transcranial Magnetic Stimulation: Liuzza et al., 2011 ; Papeo et al., 2016 ; kinematic measures and grasp force: Aravena et al., 2012 ; Bartoli et al., 2013 ). More recently, a series of studies have been carried out to investigate whether the processing of sentence negation involves motor inhibitory mechanisms ( Beltrán et al., 2018 ; Beltrán et al., 2019 ; Foroni & Semin, 2013 ; García-Marco et al., 2019 ; Liu et al., 2019 / 2020 ; Papeo et al., 2016 ; de Vega et al., 2016 ; Montalti et al., 2021a ; Montalti et al., 2021b ; Vitale et al., 2022 ). The majority of these studies used paradigms such as the Stop Signal Task ( Logan et al., 1984 ) and the Go/NoGo task, which have been developed to measure motor response inhibition, embedded in a sentence comprehension task. Using EEG, these studies demonstrated the involvement of inhibitory mechanisms at a behavioral level (longer Stop Signal Reaction Times, a covert reaction time underlying the inhibitory process, for negative sentences with respect to the affirmative ones; Beltrán et al., 2018 ; Montalti et al., 2021a ) and at a physiological level (reduced power in fronto-central theta rhythms and a modulation of the amplitude in the N2/P3 complex according to the polarity of the sentence: de Vega et al., 2016 ; Beltrán et al., 2018 / 2019 ; Liu et al., 2019 / 2020 ).

Interestingly, in a recent behavioral Go/NoGo study, Montalti and colleagues ( 2021b ) also demonstrated an involvement of motor inhibitory mechanisms during the processing of implicit forms of negation (e.g., “I ignore”). Implicit negation refers to a form of negation that is only present in the intended meaning of a sentence and relies on presuppositions or implicatures ( Clark, 1976 ); in other words, there are no lexicalized elements in the sentence to express this logic operator – negation is implicated but not explicitly asserted. This is a novel perspective in the study of sentence negation processing, since so far, all studies that have dealt with negation have investigated it only in its explicit forms (i.e., through the use of morpho-syntactic expressions such as “not”, “no” or “don’t” which overtly convey a negative meaning; e.g., “I don’t know”). Interestingly, in Montalti et al.’s study ( 2021b ), implicit negation was the condition that most activated the inhibitory system compared to affirmative and explicit negative sentences, as demonstrated by its longer RTs compared to the other two conditions. According to the authors, implicit negation, having an inferential nature, may determine a deeper processing of the negative meaning, leading to a greater activation of the sensorimotor system ( Egorova et al., 2013 ; Kuperberg et al., 2000 ). Together, these novel findings suggest that even a seemingly linguistic phenomenon such as negation can involve multidimensional representation through sensorimotor systems, particularly in certain contexts.

Abstract concepts can be understood via multiple conceptual metaphors

Abstract concepts can also be considered multidimensional in that they can be understood via multiple conceptual metaphors ( Gibbs, 1994 ; Kövecses, 2002 ; Lakoff & Johnson, 1980 ); that is, they can be conceptualized and understood in relation to concepts that are more readily connected with our everyday sensorimotor experience. With a conceptual metaphor, a more abstract target domain (e.g., numerical quantity) is understood in terms of a more concrete source domain (e.g., physical space). Numbers are abstract as they are tools that humans use to measure quantities and do not exist in the external world. In contrast, humans have direct sensorimotor experience of existing in and moving through three-dimensional space. Hence, when numerical quantities are conceptualized in terms of physical space, we call this a conceptual metaphor.

People tend to conceptualize numerical quantities along the vertical axis, with lower space being associated with lesser numerical quantities and upper space with greater numerical quantities (e.g., Hartmann, Grabherr & Mast, 2012 ). As well as the vertical axis, people conceptualize numerical quantity as increasing from left to right across the horizontal axis (e.g., Dehaene, Bossini & Geraud 1993 ). This example shows that there may be multiple options available for the conceptualisation of a target domain, even within a single source domain. Moreover, a single target domain may be understood in terms of multiple source domains. For example, numerical quantities can be conceptualized in terms of color, with increases being associated with green, and decreases associated with red (e.g., Winter & Matlock, 2017 ). The same source domain can also be used to conceptualize multiple target domains. For example, just as vertical space may be used to conceptualize numerical quantities, it may also be used to conceptualize emotional valence – experiments have shown that upper space is usually associated with positive valence (good), whereas lower space is usually associated with negative valence (bad) (e.g., Meier & Robinson, 2004 ). Together, these findings indicate that abstract concepts (e.g., numerical quantity, emotional valence) can be conceptualized flexibly in multiple ways via multiple sensorimotor domains (e.g., physical space, color).

Data visualizations often represent abstract concepts using multiple conceptual metaphors. For instance, the numbers on graphs tend to increase up the y-axis and right across the x-axis, in line with vertical (e.g., Hartmann, Grabherr & Mast, 2012 ) and horizontal (e.g., Dehaene, Bossini & Geraud, 1993 ) metaphors of numerical quantity. Furthermore, news visualizations commonly represent quantity increases with a green, upward-pointing arrow, and decreases with a red, downward-pointing arrow, exploiting associations of quantity with both color and space ( Winter & Matlock, 2017 ). Some scholars have argued that designing graphs to conform with conceptual metaphors in this way can make them easier to understand (e.g., Parsons, 2018 ). In support of this argument, Woodin, Winter, and Padilla ( 2022 ) found that line graphs that conformed with valence metaphors were easier to interpret than graphs that did not. However, it is unknown whether representing abstract concepts via multiple conceptual metaphors at the same time improves graph interpretability more than if just one conceptual metaphor were used. If this were the case, the multidimensionality of abstract concepts in terms of conceptual metaphors could be used to aid the interpretation of data visualizations.

People may habitually conceptualize abstract concepts using multiple spatial dimensions at the same time. Walker and Cooperrider ( 2016 ) found that speakers often gestured by moving their hands both rightward and forward when talking about the future, and leftward and backward when talking about the past. These gestures conformed with both horizontal and sagittal metaphors of time (e.g., Walker, Bergen & Núñez, 2017 ), perhaps showing that both conceptual metaphors were activated in the minds of these gesturers. However, in a task in which participants placed words relating to abstract concepts such as time (e.g., ‘earliest’, ‘earlier’, ‘later’, ‘latest’) on a vertically oriented page, Woodin and Winter ( 2018 ) observed that participants generally preferred horizontal or vertical responses, rather than combining the axes in a diagonal response. More research is needed to determine whether multiple conceptual metaphors can be co-activated, and if so, whether this co-activation is dependent on the abstract concept (e.g., numerical quantity, time, emotional valence) or the context (e.g., task or modality: gesture versus free placement).

While multiple metaphoric dimensions are available to represent abstract concepts, certain of these dimensions may be activated for different people depending on their previous experience. For example, Dutch speakers conceptualize pitch in vertical terms (low and high), whereas Farsi speakers conceptualize it in terms of thickness (thick and thin) ( Dolscheid et al., 2013 ). Despite this difference, research on prelinguistic infants indicates that the vertical and thickness metaphors are co-present across cultures, suggesting that cultural experience (e.g., the use of linguistic metaphors such as the pitch terms high and low ) may strengthen certain conceptual metaphors at the expense of others ( Dolscheid et al., 2012 ). In addition, contrary to typical quantity-color associations, increases in the Chinese stock market are represented with the color red, whereas decreases are represented with green. This association has been shown to influence Chinese stockbrokers’ performance on IQ tests, relative to Chinese college students who did not have the relevant experience to learn this association ( Zhang & Han, 2014 ). Altogether, these findings exemplify the importance of context and cultural experience in regard to the multiple metaphoric dimensions along which abstract concepts may be conceptualized.

Contextuality

Contextual constraints might shape conceptual representation in multiple ways. Research focused on conceptual flexibility has compellingly demonstrated that certain conceptual features might be activated depending on specific goals or tasks ( Yee & Thompson-Schill, 2016 ). Despite this evidence, studies targeting conceptual flexibility have mainly investigated concrete concepts. In this section, our aim is twofold: first, we intend to broaden the definition of “context”, so as to include cultural and linguistic dimensions. Second, we propose an initial analysis of conceptual flexibility showing that abstract concepts vary depending on context too. Thus, we explore the impact of context on abstract concepts starting from a broad definition, and then we focus on more specific contextual factors—such as linguistic and sociocultural contexts.

Operationalizing context

Context, and its interaction with word meaning, remains difficult to conceptualize and to operationalize. It ranges from the environmental conditions surrounding learners when they acquire a word, to task-specific settings when processing language materials, and the social identity of speakers in conversational scenes. Words can have fundamentally different meanings when used in different situations (e.g., bark) but there may be more subtle variations. For instance, linguistic and extra-linguistic context can highlight a particular aspect of meaning (e.g., weight vs. sound in ‘moving the piano’ vs. ‘playing the piano’; motion vs. color in ‘shooting the ball’ vs. ‘seeing the ball’) (see van Dam et al. 2011 ; Rueschemeyer et al. 2010 ; Moody & Gennari 2010 ; Tomasino & Rumiati 2013 ). Such context effects are used to argue for semantic flexibility, which must lie in the distinction between central and peripheral features of lexical concepts, and their respective differential contributions to meaning construction. In line with this, context and content interplay can be seen as dynamic changes in the multidimensional featural semantic space that is operated through semantic cognitive control ( Hoffman, McClelland & Lambon-Ralph, 2018 ). Because abstract words are thought to have more variable meanings than concrete words – that is, that they change more with context, it is assumed that abstract words require greater semantic control effort ( Hoffman, 2016 ). In a way, contextuality and the resulting flexibility occurs at three levels: the task level (microscopic level) where meaning is goal-directed and computed online as a function of task demands, the individual level (mesoscopic level) where semantic processing is influenced by prior idiosyncratic knowledge and updated with lifespan experience, and the collective level (macroscopic level) as language and communication involve individuals in a given social and cultural context and meaning is derived through human interaction. While the task level has been the focus of prior work ( Willems & Casasanto, 2011 ; Kemmerer 2015 ), here we highlight cross-linguistic and cross-cultural variation in abstract concepts that contend to the collective level of contextuality effects. We come back to the individual level of contextuality in Part 2: Future Directions.

Abstract concepts vary across languages and cultures

Our experience of embodied agents is inextricably coupled with the surrounding environment. Among several inputs we are exposed to everyday, from the moment we are born, language and cultural practices permeate our experiences, driving our attention to specific aspects of the world. However, within cognitive sciences, opinions differ as to the impact of language and culture on conceptualization. Traditional, universalist accounts of conceptual knowledge maintain that concepts exist independently of our experience with language ( Pinker 1994 ; Fodor, 1975 ; Tomasello, 2014 ). Accordingly, word meanings map onto pre-existing conceptual distinctions driven by regularities of the environment. For instance, comparative ethnobiological research investigating the classification and naming of animals and plants across non-literate societies showed regularities in the organization of knowledge of these domains—suggesting that the physical environment, rather than specific cultural and linguistic patterns, might be the primary source shaping conceptual boundaries ( Berlin, 1992 ).

However, work on semantic typology undermined these assumptions, underscoring a striking variability in conceptual and lexical patterns across cultures (for an overview see Kemmerer, 2019). To illustrate, across approximately 6,500 languages spoken around the world, common English terms like morning, lunch , or niece do not have corresponding translations in all languages ( Wierzbicka, 2014 , see also Evans & Levinson, 2009 ). These findings suggest that words might not reflect ‘self-evident’ properties of the world ( Malt & Majid, 2013 ), but would instead differentially capture culturally-relevant features ( Majid & Kruspe, 2018 ; Majid, Roberts et al., 2018 ). While some domains like actions and colors may exhibit cross-linguistic constraints driven by biological and physical factors ( Majid, Boster & Bowerman, 2008 ; Huisman, van Hout & Majid, 2021 ; Regier, Kay & Ketharpal, 2007 ), other conceptual categories vary dramatically cross-culturally ( Majid, Jordan & Dunn., 2015 ; Boroditsky, 2018 ; review in Malt & Majid, 2013 ; Malt & Wolff, 2010 ). The heterogeneity of results does not offer a clear-cut answer to the universalist-relativist debate. Instead, some scholars have proposed to look more thoroughly at where instead of whether lexical differences impact thought ( Malt & Majid, 2013 ). For example, Gentner and Boroditsky ( 2001 ) proposed that verb meanings are more variable across languages compared to nouns, because they would be less tied to environment regularities. Moreover, where variation exists, it should increase as a function of concepts’ abstractness ( Borghi, 2019 ). Contrary to this expectation, a recent study targeting semantic alignment of different conceptual domains across 41 languages found intermediate alignment for artifacts, actions, and natural kinds ( Thompson, Roberts & Lupyan, 2020 ). Domains like numbers, temporal terms, and kinship were instead found to be highly aligned across languages, and this alignment was predicted by non-linguistic measures of cultural similarity. Interestingly, these subcategories of abstract concepts were found to be part of a specific cluster (i.e., spatio-temporal and quantitative concepts) in an Italian study targeting 425 abstract concepts ( Villani et al., 2019 ), and this cluster comprised abstract concepts that were judged to be “more concrete” compared to other abstract concepts. This points once again to the importance of not considering abstract concepts as a unitary, homogeneous category.

Further complicating the overall picture, not only do meanings vary across cultures, but there is initial evidence showing that the same abstract-concrete distinction might not be as universal as previously thought. Indeed, if the abstract-concrete distinction is not so clear-cut, it is not surprising that the conceptual structures of different cultures also vary along the abstract-concrete axis, with specific components being more or less salient depending on the culture. For example, Jahai (a Malaysian hunter-gatherer community) and Dutch participants consistently differed in the way they described odors—both in the qualitative terms they used and in their response times in naming odors they were presented with ( Majid, Burenhult et al., 2018 ). While Dutch speakers (similarly to other Western populations) mainly described odors in concrete terms (e.g., by referring to their source of origin: “it smells like lemon”), Jahai speakers employed a refined abstract vocabulary. Not only Jahai speakers in Malaysia, but other communities across the globe also use abstract terms to describe odors ( Majid, Roberts et al., 2018 ). The latter finding can be contrasted with the concept of color. Color is well abstracted in many, especially Western, languages while it is much more concrete in some non-Western languages ( Majid, Roberts et al., 2018 ; Majid & Kruspe, 2018 ).

Initial evidence suggesting specific categories might be more abstract or concrete depending on culture has also been provided with the concept of ‘gender’. For instance, in a free-listing study comparing Italian, Dutch, and English-speaking participants, Mazzuca, Borghi et al. ( 2020 ) found that the three groups differed in their conceptualization of gender. Italian and Dutch participants differed the most across the three groups, with Dutch participants relying more on concrete, biological aspects in their associates to gender, and Italians producing more terms related to the sociocultural interpretation of it. In addition, when asked to rate how much a set of features were related to gender, Dutch and Italian participants consistently differed: Dutch participants rated more concrete features as more related to gender, whereas the opposite pattern was reported for Italian participants. So, categories that are mainly conceptualized in concrete terms by one population, might instead be represented in more abstract terms by a specific cultural and linguistic community. This further supports our proposal of reconsidering, and potentially abandoning, the abstract-concrete dichotomy as an immutable, stable construct.

Part 2: Future Directions

Traditional and interactive methods, traditional methods have both advantages and limitations.

Most studies on abstract concept representation employ tasks like ratings, feature listing, lexical decision, and property verification, and often use single, decontextualized words or very simple sentences. These traditional methods have several advantages; particularly, ratings or feature listing can help us to understand the nature and definition of abstract concepts, such as identifying properties important to their meaning (e.g., Barsalou & Wiemar-Hastings, 2005 ; Recchia & Jones, 2012 ; Vinson & Vigliocco, 2008 ). They also provide a practical way to gain a large amount of data; word ratings in particular allow researchers to examine the properties of thousands of concepts gained from (potentially) thousands of participants, especially via online data collection (e.g., concreteness norms from Brysbaert et al, 2014 ; sensorimotor norms from Lynott et al., 2020 ). Although variations in meaning (e.g., due to polysemy or lack of context) may add noise to this data, such megastudy approaches allow for very large samples of concepts (i.e., words) to be studied, allowing for a high degree of reliability and statistical power. Further, tasks such as lexical or semantic decision offer a standardized way to measure semantic processing in word reading with a high level of experimental control, for example, to test the processes behind concreteness effects in word reading ( Bottini et al., 2021 ; Connell & Lynott, 2012 ), the role of emotional or sensorimotor experience (e.g., Moffat et al., 2015 ; Newcombe et al., 2012 ; Siakaluk et al., 2016 ), or differentiating between conscious and unconscious semantic processing (e.g., Vukovic et al., 2017 ; Ostarek & Huettig, 2017 ). Single-word methods have indeed been used in a wide range of behavioral, imaging and patient studies to identify and study subgroups of abstract concepts such as emotions (e.g., Altarriba, Bauer & Benvenuto, 1999 ; Altarriba & Bauer, 2004 ; Kousta et al., 2011 ; Moseley et al., 2015 ), social concepts (e.g., Binney, Hoffman & Lambon Ralph, 2016 ; Zahn et al., 2007 ; 2009 ) mental states (e.g., Dreyer & Pullvermuller, 2018 ), and mathematical and quantity-related concepts (e.g., Bechtold et al., 2019; Catricalà et al., 2021 ).

Despite the many advantages of traditional methods, they also have some limitations. First, the focus on single, isolated words might lead to misleading findings. Some dimensions that might appear critical while processing isolated words might lose their prominence when words are inserted into a sentence, a discourse, or some other kind of context. As convincingly argued by Lebois et al. ( 2015 ), words do not have conceptual cores that are automatically activated; even salient features in a word’s meaning are flexibly modulated by the context. Studying concepts in isolation risks assuming that ‘gold standards’ exist, and that they are activated independently from the context. Investigating decontextualized concepts and words might thus be risky from a theoretical point of view, since it may lead to formulating theories focused on mechanisms that characterize concepts in isolation. Instead, it is crucial to study concepts while focusing on ‘situated action’, i.e. “not only […] action per se, but all the cognition that supports it, including the comprehension of situations and the production of predictions that make human action possible” ( Barsalou et al., 2018: 1 ).

A second limitation of traditional approaches is that the focus on isolated words and the adoption of tasks to perform individually in front of a computer screen ignores the social dimensions in which words are usually produced and comprehended. We therefore believe that an important step forward in research on abstract concepts will come from the use of methods that first of all investigate concepts in context, and then address how words conveying concepts are employed in online interactive situations. Many scholars in cognitive, social, and affective science and neuroscience are increasingly investigating cognitive and emotional processes in interactive, online situations ( Bolis & Schilbach, 2020 ). More and more, new methods like naturalistic fMRI are employed in interactive contexts (e.g., Rocca et al., 2020 ), allowing the detection of real-time interaction dynamics. We strongly believe that the study of concepts, particularly abstract ones, would benefit considerably from an approach that investigates them during their use in interactions (see the special topic in preparation, Borghi et al., Phil.Trans.B ).

Research can benefit from interactive methods

More so than concrete concepts, abstract concepts are acquired through linguistic experience during social interaction, where negotiation of meanings takes place and allows people to master abstract sophisticated knowledge that cannot be experienced in sensorimotor terms, as in the case of concrete concepts ( Wauters et al., 2003 ; Villani et al., 2019 ). In this sense, social interactions represent the natural environment where abstract concepts develop and serve their communicative function. Thus, since abstract concepts are grounded in social contexts and require more of other people’s contribution to be mastered, studying their features in interactive settings appears to be the most ecological approach. Second, according to the idea that language is a form of joint action ( Clark, 1996 ; Pickering & Garrod, 2004 ), it becomes important to focus on the bodily and psychological synergies which are both causes and effects of linguistic exchanges. While interactive methods might provide many advantages and offer valuable insights, typically, the variables are not controlled as in studies conducted in the lab. Despite this limitation, we believe that they are crucial to advance this area, as they allow taking into account how people use abstract concepts and words in real life rather than in highly constrained situations.

Some recent studies have started to take these novel insights seriously, investigating concepts not only in situated action, but in ‘situated interaction’. An example is a recent study by Zdrazilova et al. ( 2018 ). Pairs of participants were required to perform the so-called ‘taboo task’, i.e. to communicate to a partner the meaning of concrete and abstract words without using the words themselves. The authors then analyzed the speech and gestures associated with the different kinds of concepts, highlighting, for example, that when explaining the meaning of abstract concepts participants used more introspective expressions, referred more to people than to objects, and used more metaphorical and beat gestures, and fewer iconic gestures, than when they explained the meaning of concrete concepts.

In a recent kinematic study, Fini et al. ( 2021 ) used an interactive paradigm ( Moreau et al., 2020 ; Boukarras et al., 2021 ) to investigate whether being helped when guessing abstract concepts from visual images led to improved motor coordination between the participant and the confederate who helped. The results indicate that participants asked for more hints to guess abstract concepts as compared with concrete concepts and were aware that the other’s contribution was more crucial for abstract compared with concrete concepts. Moreover, participants were more synchronous in movement with the experimenter, who could offer them suggestions on abstract words.

Furthermore, the more actors emphasize interlocutors’ contribution to a conversation about abstract topics, the more psychologically close to the interlocutors they feel ( Fini et al., in prep ). As suggested by social metacognition theory ( Borghi et al., 2017 , 2018 , 2019 , Borghi, Fini, & Tummolini, 2021 ), processing complex, shared abstract meanings might require a productive negotiation of intellectual contributions, and here it seems that a successful verbal exchange might also impact self-other processes between the actors. Overall, the novel interactive studies described above demonstrate how such methods can reveal important aspects of abstract concepts, such as their grounding in introspective processes and the importance of social communication to their understanding.

Studying conceptual representation in interactive settings is also a fruitful method to investigate the varieties of abstract and concrete concepts in depth, revealing their differences from their use during linguistic exchanges. In this regard, Villani et al. ( 2019 ) have clearly shown that abstract concepts are not a holistic category but comprise different subclusters, ranging from the most abstract Philosophical and Spiritual concepts (PS, e.g., paradise, value), to more concrete Physical Spatio-Temporal Quantitative concepts (PSTQ, e.g., reflex, sum), to Self-Sociality (SS, e.g., revenge, shame) and Emotive/inner state (EM, e.g., joy, anger) concepts, which rely both on sensorimotor and inner experiences. Villani et al. ( 2022 ) investigated these subclusters further in a novel interactive paradigm in which participants had to simulate a conversation with a familar person by responding to sentences involving sub-kinds of concrete (i.e., tools, food, animals) and abstract concepts (PS, PSQT, EMSS) (e.g., I make a cake/I make a judgment). Conversational dynamics varied considerably between sentences: with the most PS abstract ones, participants often used expressions of uncertainty, they asked for clarification (e.g., ‘What do you mean?’, ‘Explain it to me better’), and produced more ‘why’, ‘how’, and ‘who’ questions.). In contrast, with concrete concepts, participants asked more ‘when’ and ‘where’ questions. Finally, while concrete sentences led to simple, short answers, abstract sentences elicited general opinions and promoted more turn-taking and interactive exchanges with the imaginary interlocutor. Overall, these findings indicate that the conversation of abstract concepts requires an extended monitoring of our own and others’ mental states, which is likely to establish a shared knowledge for successful communication, as in the case of joint actions.

As interactive paradigms provide a new avenue for studying abstract concepts, it might be important to orient our efforts towards the contextualization of conceptual subclusters in pragmatic terms. In this regard, we believe in the importance of characterizing the subclusters by new pragmatic psycholinguistic dimensions, such as how easy it is to start a conversation by using a word, how much a word evokes dominance in an interactive setting, how much a word triggers uncertainties and evokes interactive metacognitive questions, how much a word as a topic of a conversation is pleasant and offers possibilities of expanding the dialogue, or how much it promotes psychological closeness among interlocutors.

The study of abstract concepts can also benefit from the employment of two novel experimental approaches to study communication among humans. The first one is known as the experimental pragmatic approach ( Barr & Keysar, 2007 ; Brennan, 2005 ; Pickering & Garrod, 2004 ) and starts from the assumption that to fully understand language, conceived as “a form of joint action” ( Clark, 1996 ), it is necessary to investigate social interactions ( Pickering & Garrod, 2004 ), which are the background from which different forms of communication emerge and develop.

The second novel experimental approach has developed in the last two decades and pertains to a field of knowledge called experimental semiotics, expanding the focus of research from spoken conversation to human communication in general. This field of research also includes the language of graphics and gestures, and aims to tackle the issue of how new forms of communication evolve through groups (see Galantucci, 2009 ). The methods used within experimental semiotics include vertical transmission paradigms where dyads of people play games, which are solved through the development of efficient communication modalities (social learning), and horizontal paradigms which instead are focused on linguistic exchanges within the same dyad. Communication emerges always within an environment carrying its own features and social-cultural factors, which inevitably mold how different modalities of interactions evolve, according to the Linguistic Niche Hypothesis ( Lupyan & Dale 2010 ). In this regard, both experimental pragmatic and experimental semiotics are promising approaches to investigate within ecological lab settings, exploring how we learn, process, use, and even build new abstract concepts during verbal interaction, i.e., real or virtual conversation.

We believe that bridging insights from experimental pragmatics and semiotics with traditional approaches opens promising research avenues into conceptual representation in general, especially on abstract concepts whose meaning is typically constrained by social and linguistic factors. Further research is needed to capture the use of abstract concepts in real conversations and dialogue.

Both comprehension and production of abstract concepts should be studied

To increase theoretical generalization, assumptions related to representation and processing of abstract concepts must be relevant for both comprehension and production. Abstract words play a central role in communicating our internal states and in conveying cultural meaning. To enable mutual understanding, we need some sort of commonality between speakers and listeners in what those abstract words refer to and how they are processed.

Curiously, there is a large asymmetry in research dedicated to the comprehension and production of abstract words, at the expense of the latter. The result of this asymmetry is twofold: what happens in a speaker’s mind when they talk about abstract concepts is virtually unknown, and the extent of overlap in representations and processes related to abstract concepts in comprehension and production is only assumed. This is mainly due to methodological traditions and constraints: in the language production community, decades of research used referential picture naming to investigate language production (for reviews see Indefrey, 2011 ; Indefrey & Levelt, 2004 ; Nozari & Pinet, 2020 ). Picture naming is confined to imageable concrete objects (e.g. bottle or cat ), and as abstract concepts usually lack straightforward visual descriptions, the use of this paradigm to elicit the production of abstract words (e.g. truth or merit) appears impossible. Consequently, contrasting with hundreds of referential picture naming studies, there are probably fewer than twenty studies that have used inferential naming to study word production (see Race et al., 2013 ; Trebuchon-Da Fonseca et al., 2009 ; Fargier & Laganaro, 2017 ; Marconi et al., 2013 ; Calzavarini, 2017 ; Allen & Hulme, 2006 ; Hanley et al., 2013 ). Inferential naming refers to a process in which the activation of the concept, and subsequent word utterance, is achieved through semantic and/or episodic associations ( Marconi, 1997 ). This task requires a comprehension phase – the definition must be understood, and a production phase – one needs to retrieve a word from memory and overtly produce it, such that it may be used as a proxy for speaking and listening. It also appears suited to investigate differences or commonalities in the production of abstract and concrete words. In this respect, better accuracy and faster responses were found for concrete words relative to abstract words ( Allen & Hulme, 2006 ), while there are also more omissions and more alternatives when producing abstract words compared to concrete words in response to definitions ( Hanley et al., 2013 ). Although these results lead to the conclusion that representations are qualitatively different for abstract words and concrete words, they do not tell us whether word properties are retrieved similarly in comprehension and production.

In an attempt to shed light on this issue, Fargier et al. ( in prep ) used a ‘naming from definition’ task and a set of stimuli that varied in their degree of concreteness, and their sensorimotor and emotional affordances. Similar to what was found in language comprehension tasks, the authors showed that properties like words’ Age of Acquisition and contextual availability predicted the ease of word production, while subjective concreteness of words did not. Sensorimotor and emotional properties associated with concepts also modulated the speed of word production if words were sufficiently available in memory. Note, though, that this research constitutes only indirect evidence that the way speakers activate representations of abstract words is similar to comprehenders.

Other paradigms, such as category production tasks (also called semantic or verbal fluency), can also shed light on the mechanisms involved in producing abstract concepts. In such tasks, participants are asked to name members of categories (e.g. ‘ name as many emotions as you can ’), and these relatively open responses can provide insight into the structure and nature of semantic categories, as well as the process of language production. For example, comparing responses for 67 concrete and 50 abstract categories, Banks and Connell ( 2022 ) found that participants were approximately 800ms slower to verbally name their first concept for abstract categories compared to concrete, suggesting that abstract concepts were more effortful to produce. As in Fargier et al. ( in prep ), category production data can also be used to examine the cognitive and linguistic mechanisms behind the production of abstract concepts, for example comparing the role of sensorimotor grounding and linguistic distributional relations between concepts ( Banks, Wingfield & Connell, 2021 ). Other language production tasks commonly used in cognitive psychology – for example, free association or insight tasks for abstract relations (i.e., finding the word linking several concepts) – could greatly benefit future research into abstract concepts, and help to address the disparity between comprehension and production.

In sum, more work is needed to better understand the complex machinery behind the production of, and conversation about, abstract words. Among the several issues that remain, some of them could be framed in the form of the following questions: to what extent does the definition of an abstract concept for one individual overlap with that of another individual? Are there greater individual differences for abstract concepts than for concrete concepts? If there are individual differences, how is the meaning of words negotiated during conversation? An appropriate way to tackle these issues would thus be to gather two methodological traditions: the study of single words’ multidimensional semantic representations and the study of words in interactive contexts. In the following, we outline how they could both complement each other in the context of individual differences.

Individual differences in conceptual representations should be studied

Single word studies, in particular normative studies, can be used to define words in terms of their semantic properties ( Brysbaert et al. 2014 ; Lynott & Connell, 2013 ; Lynott & all. 2020 ; Vinson & Vigliocco, 2008 ; Cree & McRae, 2003 ) though with different efficiency across concrete and abstract concepts ( Barsalou & Wiemer-Hastings, 2004 ). We now agree that words have multidimensional semantic properties that reflect how words are learned and used. Idiosyncratic differences are assumed to be ‘washed out’ in normative studies, and this also prevents an understanding of social-cultural influences on internal representations. Moreover, even though semantic representations have been seen as fixed for a long time (see Yee, 2017 for a discussion ), semantic properties accumulate with experience and are activated flexibly as a function of context ( Barsalou, 1982 ; Yee & Thompson-Schill, 2016 ). The initial (implicit) assumption that semantic representations are fixed can be appreciated in two observations: average responses of young adults are assumed to generalize to everyone, and individual differences are seen as a problem rather than a solution. We believe that a better understanding of how we convey the meaning of abstract concepts requires tackling the challenge of individual differences.

In the last decade, voices have been raised against the tradition to study human psychology only through individuals in western educated industrialized rich and democratic (WEIRD) societies, as not everyone shares the same cognitive, social and affective processes ( Henrich et al. 2010 ) and thus knowledge cannot generalize to the world population. In fact, research must reflect human diversity ( Ghai, 2021 ). This seems even more important with concrete and abstract words, as the meaning of words is the result of linguistic, socio-cultural, sensorimotor and affective experience. With regard to studies on semantics, there is a prevalence of normative studies in the young adult (student) population, likely because of practical recruitment reasons. Recent studies that have used online platforms were able to reach a more diverse population ( Peer et al. 2017 ), yet demographics may not even be reported, let alone be used. Operationalizing individual differences in word representations is of course a challenge, but recent studies provided evidence for age-group differences in lexical-semantic properties of words ( Wulff et al., 2019 ; Dubossarsky et al., 2017 ; Krethlow et al., 2020 ). In their review, Wulff et al. ( 2019 ) report how lexical networks change as a function of the age of individuals, and putatively attribute these changes to learning experience and cognitive changes. Furthermore, Krethlow et al. ( 2020 ) used a free association task to investigate lexical-semantic networks in different age-groups, ranging from children aged 10 to elderly people aged 70. They computed a measure of lexical-semantic network prototypicality that shows strong variability across age-groups. In a nutshell, when children process the word ‘calendar’ they might think about ‘school’ and ‘homework’, whereas older adults might think about ‘appointment’ and ‘date’, hence illustrating that their semantic understanding, and the semantic properties they rely on, are not fully identical. The authors were even able to show that age-specific measures of lexical-semantic properties predict behavioral performance (language production) of that given age-group, while measures collected only in the classical group of young adults were not able to predict performance of other age-groups. This work highlights a population-based construct that could be extended to other measures of word semantics. As it was restricted to concrete concepts, it remains unknown whether similar or greater differences would be seen for abstract concepts, but there is recent evidence that idiosyncratic semantic representations increased with abstractness ( Wang & Bi, 2021 ).

Research in this area is still in its early stages, but there is other evidence for individual differences in semantic processing: Pexman & Yep ( 2018 ) showed that sensitivity to lexical-semantic predictors of words vary as a function of vocabulary of individuals, with additional differences for concrete and abstract words. Another study that relies on a very different rationale but also highlights population-based construct of variability was conducted by Thompson et al. ( 2020 ). They applied large-scale semantic alignment of words across different languages and showed that words belonging to abstract semantic domains (number, quantity and kinship) aligned better than words referring to natural kinds, actions and artifacts.

These various forms of individual differences matter if we put them into context. If word representations, or even simply the more salient semantic properties, vary from individual to individual, then mechanisms to reach mutual understanding when those individuals socialize will be more costly. In conversation, less negotiation will be required if what is shared in the beginning is greater. This is likely a domain where what is classically labeled as concrete and abstract will differentiate from one another. One future development of the field might thus combine individual differences in single word processing and how this is modulated in situational interactive contexts.

Abstract concepts vary between individuals and cultures

Along these lines, one interesting example is the representation of the concept ‘gender’, and of related gender/sex categories. Within this framework, gender can be considered as a social abstract concept, whose grounding sources can be identified as both perceptual (e.g., physical properties) and sociocultural (e.g., social norms). While the scientific debate on whether gender is to be considered an essential, biological, and perhaps more concrete category or an abstract, sociocultural construct is still ongoing ( Ingalhalikar et al., 2014 ; Fausto-Sterling, 2019 ), recent studies targeting laypeople’s conceptualization of gender suggest there might not be a univocal answer.

In a free-listing study with Italian-speaking participants, Mazzuca, Majid and colleagues ( 2020 ) found that conceptual associations for ‘gender’ varied as a function of participants’ experiences with gender. To illustrate, cisgender, monosexual participants mainly provided associations that relied on the gender-binary paradigm (e.g., female, male ; woman, man ; feminine, masculine ), whereas gender-diverse, non-exclusively monosexual participants evidenced other aspects, often more related to social and cultural factors (e.g., discrimination, construct, fluidity, queer ). There were differences driven by participants’ sexual preferences too. For instance, participants identifying as homosexual were more likely to associate ‘gender’ to words such as freedom, rights and fluidity compared to participants identifying as heterosexual, that in turn highlighted more binary distinctions ( female, male ) and linguistic associations. Similarly, in a study with a US sample varying in their social/sexual positions, Schudson, Beischel and van Anders ( 2019 ) reported interesting differences in the conceptualization of gender/sex categories like female/male; woman/man; feminine/masculine . Specifically, they found that, when asked to provide definitions of these, cisgender sexual majorities (e.g., cisgender heterosexual participants) used more frequently biological contents in their definitions of woman compared to gender/sex minorities (e.g., transgender, genderqueer, homosexual participants). In addition, cisgender sexual majorities incorporated sociocultural aspects less frequently when defining woman and man than gender/sex minorities.

Another promising avenue is to turn to socio-cultural psychology where cultural specificities are assumed to generate differences in how concepts are represented in people’s minds and likely more so for abstract concepts than concrete ones. Recent work on the concept of ‘privacy’ in individuals from Iran and the United States is particularly enlightening ( Zabihzadeh et al. 2019 ). In this study, individuals from Iran and the United States completed a free association task in which they typed at least 10 words related to the word ‘privacy’. The most frequently provided words were used to constitute two lists of word-pairs that were later submitted to a semantic similarity judgment task. The authors finally used correspondence analyses to explore the semantic domains of ‘privacy’ in both cultures. They found similarities in the conceptual representation of privacy in both cultures in line with confidentiality, the idea of secret and being alone, thus pertaining to the idea of informational concerns. However, they also found differences according to a dimension that segregates individualism vs. collectivism. There, ‘privacy’ related more to individual relationships with the government for the American people, but was more centered on the idea of familial privacy for the Iranian people.

These findings constitute preliminary evidence that studying concepts—and particularly abstract concepts—taking into account specific life experiences, and therefore possibly individual differences, might provide a more detailed picture of conceptual representations.

Different experimental approaches should be combined

Interactive methods like those described in this section have multiple advantages. First, they allow researchers to focus on the variable and context-dependent features of concepts, without neglecting the stable ones. Second, they grant researchers the possibility of analyzing the conceptual features that are crucial for online interaction, and focusing on how words are really used in joint action situations ( Pickering & Garrod, 2021 ). Finally, they allow the testing of hypotheses regarding the nature of abstract concepts. For example, if we believe that abstract concepts, due to their difficulty and indeterminate character, might require stronger reliance on other people than concrete concepts, then interactive methods seem to be an excellent instrument with which these predictions can be tested.

The various advantages and limitations of different methodologies point to the utility of triangulation to test the reliability of results obtained via these methodologies and to mitigate their respective limitations. For example, experiments have investigated how people conceptualize numerical magnitudes (e.g., Andres et al., 2004 ; Badets et al., 2007 ; Lindemann et al., 2007 ). However, these experiments constrain participants’ behavior (e.g., requiring participants to respond with button presses) and use a small set of stimuli (e.g., the numbers 1–9), creating task demands that may affect the results obtained. Researching the same topic while accounting for these limitations, Woodin et al. ( 2020 ) investigated the gestures that speakers performed when they used metaphors such as ‘tiny number’ and ‘large number’. Compared with the response medium required by the experiments, gesture is a freer, more spontaneous form of expression, and speakers in the Woodin et al. ( 2020 ) dataset referred to a broad range of quantities, such as millions , one hundred , and forty percent . Moreover, the gestures were observed in the TV News Archive, an online database of over 2.3 million news programmes, which allowed the collection of more data with more people than is usually feasible in an experimental setting. Despite their advantages, more ecologically valid approaches such as this tend to lack the rigorous control that is possible in the lab, which leaves more room for confounding variables to influence the results, such as the possibility that some speakers in the dataset may have received body language training ( Çatak, Açık & Göksun, 2018 ). The more naturalistic format of the data also means that they are substantially messier, as the news broadcasts were not filmed for the purpose of gesture analyses. For example, many videos had to be excluded from the final analysis due to camera angles not featuring speakers’ hands, speakers being offscreen, or the video being an advertisement rather than naturalistic speech. Filtering the dataset to those videos that are appropriate for interrogating one’s research aims is time-consuming. Despite the different benefits and limitations of experimental and more ecologically valid methodologies, Woodin et al. ( 2020 ) obtained converging results with the experiments described above. This example demonstrates the value of triangulation to verify whether results are products of a certain methodology or whether they reflect more general cognitive principles.

Summary of future directions

A key message disseminated throughout this position paper is the need to provide a more ecological approach to the study of concepts in general, but particularly to the study of abstract concepts. We provide several recommendations for future research. First, concepts in general should be studied in a more fine-grained way, taking into account their multiple and varied dimensions – for example, examining multiple individual sensorimotor aspects alongside social interaction, language and other dimensions, and moving away from the concrete-abstract dichotomy. Particularly, a ‘bottom-up’, data-driven approach may be fruitful, and help to identify the different subdomains beyond purely concrete or abstract. The second recommendation is to use more interactive paradigms which will likely shed light on how the meaning of abstract words (more so than concrete words) is negotiated among speakers, and which social factors have a role in the process. The third is to further improve the trade-off between methodological limitation and theoretical generalization, particularly in relation to social interaction where both perception and production must be taken into account. The last recommendation derives from the others and relates to individuality and collectivity: abstract concepts underlie our ability to share ideas about science, religion, politics, and our internal states and emotions, which have both individual and collective social-cultural realizations. Future work needs to find ways to accommodate idiosyncratic experience and social-cultural context in our understanding of abstract concepts.

Acknowledgements

Greg Woodin was supported by the Economic and Social Research Council [grant number ES/P000711/1].

Anna M. Borghi and Claudia Mazzuca were supported by the EU H2020 project TRAINCREASE (grant n.952324); Anna M. Borghi and Chiara Fini by the Sapienza Excellence Project Inner grounding of abstract concepts: the role of interoception and social metacognition - (grant n. RM11816428832AC7).

Domicele Jonauskaite was supported by the Swiss National Science Foundation with the Postdoc.Mobility fellowship grant (P500PS_202956).

Martina Montalti was supported by Mutti’s donation to Prof. Vittorio Gallese (University of Parma, Italy) and by FFABR UNIME 2020 to Prof. Valentina Cuccio (University of Messina, Italy).

Briony Banks was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 682848 awarded to Louise Connell).

Research by Raphaël Fargier was supported by grants ANR-16-CONV-0002 (ILCB) and the Excellence Initiative of Aix-Marseille University (A*MIDEX).

Caterina Villani was supported by the European Research Council - Starting grant under the ABSTRACTION project (Grant agreement No. ERC-2021-STG-101039777 awarded to Marianna Bolognesi).

Funding Statement

Greg Woodin was supported by the Economic and Social Research Council [grant number ES/P000711/1]. Anna M. Borghi and Claudia Mazzuca were supported by the EU H2020 project TRAINCREASE (grant n.952324); Anna M. Borghi and Chiara Fini by the Sapienza Excellence Project Inner grounding of abstract concepts: the role of interoception and social metacognition - (grant n. RM11816428832AC7). Domicele Jonauskaite was supported by the Swiss National Science Foundation with the Postdoc.Mobility fellowship grant (P500PS_202956). Martina Montalti was supported by Mutti’s donation to Prof. Vittorio Gallese (University of Parma, Italy) and by FFABR UNIME 2020 to Prof. Valentina Cuccio (University of Messina, Italy). Briony Banks was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 682848 awarded to Louise Connell). Research by Raphaël Fargier was supported by grants ANR-16-CONV-0002 (ILCB) and the Excellence Initiative of Aix-Marseille University (A*MIDEX). Caterina Villani was supported by the European Research Council - Starting grant under the ABSTRACTION project (Grant agreement No. ERC-2021-STG-101039777 awarded to Marianna Bolognesi).

Ethics and consent

Ethical approval and consent were not required for this article.

Competing Interests

The authors have no competing interests to declare.

Author contributions

All authors have made substantial contributions to the conception, design and drafting of the work, and have revised it critically for important intellectual content. The authors are listed in alphabetical order.

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2.2: Concepts, Constructs, and Variables

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• Anol Bhattacherjee
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We discussed in Chapter 1 that although research can be exploratory, descriptive, or explanatory, most scientific research tend to be of the explanatory type in that they search for potential explanations of observed natural or social phenomena. Explanations require development of concepts or generalizable properties or characteristics associated with objects, events, or people. While objects such as a person, a firm, or a car are not concepts, their specific characteristics or behavior such as a person’s attitude toward immigrants, a firm’s capacity for innovation, and a car’s weight can be viewed as concepts.

Knowingly or unknowingly, we use different kinds of concepts in our everyday conversations. Some of these concepts have been developed over time through our shared language. Sometimes, we borrow concepts from other disciplines or languages to explain a phenomenon of interest. For instance, the idea of gravitation borrowed from physics can be used in business to describe why people tend to “gravitate” to their preferred shopping destinations. Likewise, the concept of distance can be used to explain the degree of social separation between two otherwise collocated individuals. Sometimes, we create our own concepts to describe a unique characteristic not described in prior research. For instance, technostress is a new concept referring to the mental stress one may face when asked to learn a new technology.

Concepts may also have progressive levels of abstraction. Some concepts such as a person’s weight are precise and objective, while other concepts such as a person’s personality may be more abstract and difficult to visualize. A construct is an abstract concept that is specifically chosen (or “created”) to explain a given phenomenon. A construct may be a simple concept, such as a person’s weight , or a combination of a set of related concepts such as a person’s communication skill , which may consist of several underlying concepts such as the person’s vocabulary , syntax , and spelling . The former instance (weight) is a unidimensional construct , while the latter (communication skill) is a multi-dimensional construct (i.e., it consists of multiple underlying concepts). The distinction between constructs and concepts are clearer in multi-dimensional constructs, where the higher order abstraction is called a construct and the lower order abstractions are called concepts. However, this distinction tends to blur in the case of unidimensional constructs.

Constructs used for scientific research must have precise and clear definitions that others can use to understand exactly what it means and what it does not mean. For instance, a seemingly simple construct such as income may refer to monthly or annual income, before-tax or after-tax income, and personal or family income, and is therefore neither precise nor clear. There are two types of definitions: dictionary definitions and operational definitions. In the more familiar dictionary definition, a construct is often defined in terms of a synonym. For instance, attitude may be defined as a disposition, a feeling, or an affect, and affect in turn is defined as an attitude. Such definitions of a circular nature are not particularly useful in scientific research for elaborating the meaning and content of that construct. Scientific research requires operational definitions that define constructs in terms of how they will be empirically measured. For instance, the operational definition of a construct such as temperature must specify whether we plan to measure temperature in Celsius, Fahrenheit, or Kelvin scale. A construct such as income should be defined in terms of whether we are interested in monthly or annual income, before-tax or after-tax income, and personal or family income. One can imagine that constructs such as learning , personality , and intelligence can be quite hard to define operationally.

A term frequently associated with, and sometimes used interchangeably with, a construct is a variable. Etymologically speaking, a variable is a quantity that can vary (e.g., from low to high, negative to positive, etc.), in contrast to constants that do not vary (i.e., remain constant). However, in scientific research, a variable is a measurable representation of an abstract construct. As abstract entities, constructs are not directly measurable, and hence, we look for proxy measures called variables. For instance, a person’s intelligence is often measured as his or her IQ ( intelligence quotient ) score , which is an index generated from an analytical and pattern-matching test administered to people. In this case, intelligence is a construct, and IQ score is a variable that measures the intelligence construct. Whether IQ scores truly measures one’s intelligence is anyone’s guess (though many believe that they do), and depending on whether how well it measures intelligence, the IQ score may be a good or a poor measure of the intelligence construct. As shown in Figure 2.1, scientific research proceeds along two planes: a theoretical plane and an empirical plane. Constructs are conceptualized at the theoretical (abstract) plane, while variables are operationalized and measured at the empirical (observational) plane. Thinking like a researcher implies the ability to move back and forth between these two planes.

Depending on their intended use, variables may be classified as independent, dependent, moderating, mediating, or control variables. Variables that explain other variables are called independent variables , those that are explained by other variables are dependent variables , those that are explained by independent variables while also explaining dependent variables are mediating variables (or intermediate variables), and those that influence the relationship between independent and dependent variables are called moderating variables . As an example, if we state that higher intelligence causes improved learning among students, then intelligence is an independent variable and learning is a dependent variable. There may be other extraneous variables that are not pertinent to explaining a given dependent variable, but may have some impact on the dependent variable. These variables must be controlled for in a scientific study, and are therefore called control variables .

To understand the differences between these different variable types, consider the example shown in Figure 2.2. If we believe that intelligence influences (or explains) students’ academic achievement, then a measure of intelligence such as an IQ score is an independent variable, while a measure of academic success such as grade point average is a dependent variable. If we believe that the effect of intelligence on academic achievement also depends on the effort invested by the student in the learning process (i.e., between two equally intelligent students, the student who puts is more effort achieves higher academic achievement than one who puts in less effort), then effort becomes a moderating variable. Incidentally, one may also view effort as an independent variable and intelligence as a moderating variable. If academic achievement is viewed as an intermediate step to higher earning potential, then earning potential becomes the dependent variable for the independent variable academic achievement , and academic achievement becomes the mediating variable in the relationship between intelligence and earning potential. Hence, variable are defined as an independent, dependent, moderating, or mediating variable based on their nature of association with each other. The overall network of relationships between a set of related constructs is called a nomological network (see Figure 2.2). Thinking like a researcher requires not only being able to abstract constructs from observations, but also being able to mentally visualize a nomological network linking these abstract constructs.

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IResearchNet

Abstract Reasoning

Humans must rely on intrinsic cognitive functions for logical conclusions in a variety of situations. Abstract reasoning is a cognitive mechanism for reaching logical conclusions in the absence of physical data, concrete  phenomena,  or  specific  instances. Abstract reasoning is essentially a generalization about relationships and attributes as opposed to concrete objects. The capacity for abstract reasoning develops from the initial reasoning about physically present, concrete objects and the subsequent formation of categories and schemas, or cognitive structures that organize and generalize information about specific instances.

In the development of abstract reasoning capacity, cognitive  manipulation  of  objects  or  data  is  used to formulate conclusions about relationships. For example, in learning mathematics, one must proceed from understanding the concept of multiple objects in the present visual field to understanding the concept of addition. These new conclusions are successive, just like mathematics themselves. This process is a cognitive transcending of lower-level knowledge to form a new construction, or what Jean Piaget dubbed reflective abstraction.

Piaget concluded that the accumulation of knowledge was based partly on this concept of new construction. His hypothesis of schemas application involves two joint mental activities, which he called assimilation and accommodation. The former involves an integration of new information into previously existing constructs. The latter involves modifying schemas around the new stimulus. Piaget collectively called these operations equilibration, in reference to the laborious attempt to maintain homeostasis in cognitive representation. In essence, Piaget suggested that the accumulation of knowledge is a marriage of experience and adaptation.

Piaget thought that children do not form an internal representation of abstract concepts (such as time) on the basis of experience alone. Rather, they form schemas through constant conduction of assimilation and accommodation. Although his original ideas have been elaborated on, Piaget’s constructionist view has been embraced for defining universal aspects of cognitive development.

Piaget  categorized  cognitive  development  into four  maturational  stages,  and  it  is  in  the  final stage that abstract reasoning is said to develop. The first stage, the sensorimotor stage (birth to 2 years), involves development of goal-oriented interaction and object permanence. The second stage, or preoperational stage (2 to 6 years), is characterized by a child’s response to visual stimuli. That is, internal representations of the environment are shallow and based only on immediate experience. The child is incapable of projecting relationships within the environment to a higher level. The third stage, or concrete operational stage (7 to 12 years), emerges with the development of cognitive reversibility, or the ability to comprehend dynamic states. In the final stage, or formal operational stage, (beginning around 12 years), Piaget proposed that relative abstraction skills have been assembled.

Piaget hypothesized that a child at the formal operational level is capable of forming new constructs and make logical deductions in the absence of firsthand experience; that is, the child is able to reason abstractly. The original theory has been evaluated and elaborated on, yet neo-Piagetian theorists maintain the notion that abstract reasoning requires new construction. It is not believed, however, that abstract reasoning peaks at the formal operational level. Research suggests that the development of abstract skills may continue into late adulthood and is contingent on the amount of experience with abstract reasoning.

References:

• Luszcs, A., & Nettelbeck, T. (Eds.). (1989). Psychological development: Perspectives across the life-span. Amsterdam: Elsevier Science.
• Marini, , & Case, R. (1994). The development of abstract reasoning about the physical and social world. Child Development, 65, 147–159.
• Masami, T., & Overton, W. F. (2002). Wisdom: A culturally inclusive developmental perspectiv International Journal of Behavioral Development, 3 (26), 269–277.
• Medin, T., Ross, H., & Markman, A. B. (2002). Cognitive psychology (3rd ed.). New York: Wiley.
• Messerly,  G.  (1996).   Piaget’s  conception  of  evolution. Lanham: Rowman & Littlefield.

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Article Contents

Understanding frailty: perspectives and experiences of rural older adults in india.

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Sayani Das, Barun Mukhopadhyay, Susmita Mukhopadhyay, Understanding Frailty: Perspectives and Experiences of Rural Older Adults in India, The Journals of Gerontology: Series B , 2024;, gbae096, https://doi.org/10.1093/geronb/gbae096

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In India, frailty has been predominantly studied as a physiological aspect, overlooking the subjective perceptions of community-dwelling older adults, which holds global significance. This study aims to explore frailty perceptions among community-dwelling older adults, comparing those enrolled in a geriatric welfare program facility to those not enrolled.

A cross-sectional design with a qualitative descriptive framework was employed, using focus group methodology. The study took place in rural West Bengal, located in eastern India, with a sample of 27 participants aged 60-87 years. Data collection occurred between October 2018 and January 2020, conducted through a face-to-face, semi-structured discussion guide. Thematic analysis was performed to ensure data saturation and reliability.

Three key themes emerged from the analysis: (1) Perceptions of frailty were associated with aging, functional dependence, and psychosocial health, (2) Exposure to a scientific definition led to an ideological dilemma influenced by personal experiences, (3) Walking speed and grip strength were prominent components of frailty. The findings revealed that there was no difference in perception between program-enrolled and non-enrolled older adults, likely due to the concept of frailty being new to all participants. However, it was noteworthy that participants enrolled in the welfare program exhibited a resilient mindset towards the definition and demonstrated a proactive interest in preserving their overall health.

This novel study underscores the necessity of enhancing community awareness and integrating frailty management into the Indian healthcare system, which is yet to be fully integrated, aiming to promote the well-being of older adults.

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