Article Critique Instructions (60 points possible)
Ryan J. Winter
Florida International University
Purpose of The Article Critique Paper
1). Psychological Purpose
This paper serves several purposes, the first of which is helping you gain insight into research papers in psychology. As this may be your first time reading and writing papers in psychology, one goal of Paper I is to give you insight into what goes into such papers. This article critique paper will help you learn about the various sections of an empirical research report by reading at least one peer-reviewed articles (articles that have a Title Page, Abstract*, Literature Review, Methods Section, Results Section, and References Page—I have already selected some articles for you to critique, so make sure you only critique one in the folder provided on Canvas) This paper will also give you some insights into how the results sections are written in APA formatted research articles. Pay close attention to those sections, as throughout this course you’ll be writing up some results of your own!
In this relatively short paper, you will read one of five articles posted on Canvas and summarize what the authors did and what they found. The first part of the paper should focus on summarizing the design the authors used for their project. That is, you will identify the independent and dependent variables, talk about how the authors carried out their study, and then summarize the results (you don’t need to fully understand the statistics in the results, but try to get a sense of what the authors did in their analyses). In the second part of the paper, you will critique the article for its methodological strengths and weaknesses. Finally, in part three, you will provide your references for the Article Critique Paper in APA format.
2). APA Formatting Purpose
The second purpose of the Article Critique paper is to teach you proper American Psychological Association (APA) formatting. In the instructions below, I tell you how to format your paper using APA style. There are a lot of very specific requirements in APA papers, so pay attention to the instructions below as well as the APA style powerpoint on Canvas. We are using the 7th edition of the APA style manual.
3). Writing Purpose
Finally, this paper is intended to help you grow as a writer. Few psychology classes give you the chance to write papers and receive feedback on your work. This class will! We will give you feedback on this paper in terms of content, spelling, and grammar.
Article Critique Paper (60 points possible)
Each student is required to write an article critique paper based on one of the research articles present on Canvas only those articles listed on Canvas can be critiqued – if you critique a different article, it will not be graded). If you are unclear about any of this information, please ask.
What is an article critique paper?
An article critique is a written communication that conveys your understanding of a research article and how it relates to the conceptual issues of interest to this course.
This article critique paper will include 5 things:
1.Title page: 1 page- 4 points
· Use APA style to present the appropriate information:
· A Running head must be included and formatted APA style
· The running head is a short title of your creation (no more than 50 characters) that is in ALL CAPS. This running head is left-justified (flush left on the page). Look at the first page of these instructions, and you will see how to set up your running head.
· There must be a page number on the title page that is right justified. It is in the header on the title page and all subsequent pages.
· Your paper title appears on the title page. This is usually 12 words or less, and the first letter of each word is capitalized. It should be descriptive of the paper (For this paper, you should use the title of the article you are critiquing. The paper title can be the same title as in the Running head or it can differ – your choice). The title should be bolded.
· Your name will appear on the title page, include 2 double spaced lines between the title and your name (see the title page here). Your name and institutional affiliation (the name of your university) should not be bold.
· Your institution will appear on the title page as well
· For all papers, make sure to double-space EVERYTHING and use Times New Roman font. This includes everything from the title page through the references.
· This is standard APA format. ALL of your future papers will include a similar title page
2.Summary of the Article: 1 ½ page minimum, 3 pages maximum – 14 points
An article critique should briefly summarize, in your own words, the article research question and how it was addressed in the article. Below are some things to include in your summary.
· The summary itself will include the following:
1. Type of study (Was it experimental or correlational? How do you know?)
2. Variables (What were the independent and dependent variables? How did they manipulate the IV? How did they operationally define the DV? Be specific with these. Define the terms independent and dependent variable and make sure to identify how they are operationally defined in the article)
3. Method (What did the participants do in the study? How was it set up? Was there a random sample of participants? Was there random assignment to groups?). How was data collected (online, in person, in a laboratory?).
4. Summary of findings (What were their findings?)
3.Critique of the study: 1 ½ pages minimum – 3 pages maximum – 16 points
1. This portion of the article critique assignment focuses on your own thoughts about the content of the article (i.e. your own ideas in your own words). For this section, please use the word “Critique” below the last sentence in your summary, and have the word “Critique” flush left.
1. This section is a bit harder, but there are a number of ways to demonstrate critical thinking in your writing. Address at least four of the following elements. You can address more than four, but four is the minimum.
· 1). In your opinion, are there any confounding variables in the study (these could be extraneous variables or nuisance variables)? If so, explain what the confound is and specifically how it is impacting the results of the study. A sufficient explanation of this will include at least one paragraph of writing.
· 2). Is the sample used in the study an appropriate sample? Is the sample representative of the population? Could the study be replicated if it were done again? Why or why not?
· 3). Did they measure the dependent variable in a way that is valid? Be sure to explain what validity is, and why you believe the dependent variable was or was not measured in a way that was valid.
· 4). Did the study authors correctly interpret their findings, or are there any alternative interpretations you can think of?
· 5). Did the authors of the study employ appropriate ethical safeguards?
· 6). Briefly describe a follow-up study you might design that builds on the findings of the study you read how the research presented in the article relates to research, articles or material covered in other sections of the course
· 7). Describe whether you feel the results presented in the article are weaker or stronger than the authors claim (and why); or discuss alternative interpretations of the results (i.e. something not mentioned by the authors) and/or what research might provide a test between the proposed and alternate interpretations
· 8). Mention additional implications of the findings not mentioned in the article (either theoretical or practical/applied)
· 9). Identify specific problems in the theory, discussion or empirical research presented in the article and how these problems could be corrected. If the problems you discuss are methodological in nature, then they must be issues that are substantial enough to affect the interpretations of the findings or arguments presented in the article. Furthermore, for methodological problems, you must justify not only why something is problematic but also how it could be resolved and why your proposed solution would be preferable.
· 10). Describe how/why the method used in the article is either better or worse for addressing a particular issue than other methods
4. Brief summary of the article: One or Two paragraphs-6 points
· Write the words “Brief Summary”, and then begin the brief summary below this
· In ONE or TWO paragraphs maximum, summarize the article again, but this time I want it to be very short. In other words, take all of the information that you talked about in the summary portion of this assignment and write it again, but this time in only a few sentences.
· The reason for this section is that I want to make sure you can understand the whole study but that you can also write about it in a shorter paragraph that still emphasizes the main points of the article. Pretend that you are writing your own literature review for a research study, and you need to get the gist of an article that you read that helps support your own research across to your reader. Make sure to cite the original study (the article you are critiquing).
5. References – 1 page-4 points
· Provide the reference for this article in proper APA format (see the book Chapter 14 for appropriate referencing guidelines or the Chapter 14 powerpoint).
· If you cited other sources during either your critique or summary, reference them as well (though you do not need to cite other sources in this assignment – this is merely optional IF you happen to bring in other sources). Formatting counts here, so make sure to italicize where appropriate and watch which words you are capitalizing!
6. Grammar and Writing Quality-6 points
· Few psychology courses are as writing intensive as Research Methods (especially Research Methods Two next semester!). As such, I want to make sure that you develop writing skills early. This is something that needs special attention, so make sure to proofread your papers carefully.
· Avoid run-on sentences, sentence fragments, spelling errors, and grammar errors. Writing quality will become more important in future papers, but this is where you should start to hone your writing skills.
· We will give you feedback on your papers, but I recommend seeking some help from the FIU writing center to make sure your paper is clear, precise, and covers all needed material. I also recommend asking a few of your group members to read over your paper and make suggestions. You can do the same for them!
· If your paper lacks originality and contains too much overlap with the paper you are summarizing (i.e. you do not paraphrase appropriately or cite your sources properly), you will lose some or all of the points from writing quality, depending on the extent of the overlap with the paper. For example, if sentences contain only one or two words changed from a sentence in the original paper, you will lose points from writing quality.
Please note that you do not need to refer to any other sources other than the article on which you have chosen to write your paper. However, you are welcome to refer to additional sources if you choose.
7. Self-Rating Rubric (10 points). On canvas, you will find a self-rating rubric. This rubric contains a summary of all the points available to you in this paper. You must submit your ratings for your own paper, using this rubric (essentially, you’ll grade your own paper before you hand it in). You will upload your completed rubric to the “article critique rubric” assignment on Canvas.
· Please put effort into your ratings. Do not simply give yourself a 50/50. Really reflect on the quality of your paper and whether you meet all the criteria listed.
1. If it is clear that you have not reflected sufficiently on your paper (e.g., you give a rating of 2/2 for something that is not included in your paper), you will lose points.
· This does not mean that you are guaranteed whatever grade you give to yourself. Instead, this will help you to 1) make sure that you have included everything you need to include, and 2) help you to reflect on your own writing.
· In fact, we will use this very same rubric when we grade your paper, so you should know exactly what to expect for your grade!
Other guidelines for the article critique papers
1. 1). Pay attention to the page length requirements – 1 page for the title page, 1.5 pages to 3 pages for the summary, 1.5 pages to 3 pages for the critique, one or two paragraphs for the brief summary, and 1 page for the references page. If you are under the minimum, we will deduct points. If you go over the maximum, we are a little more flexible (you can go over by half page or so), but we want you to try to keep it to the maximum page.
1. 2). Page size is 8 1/2 X 11” with all 4 margins set one inch on all sides. You
use 12-point Times New Roman font.
1. 3). As a general rule, ALL paragraphs and sentences are double spaced in APA papers. It even includes the references, so make sure to double space EVERYTHING
1. 4). When summarizing the article in your own words, you need not continually cite the article throughout the rest of your critique. Nonetheless, you should follow proper referencing procedures, which means that:
3. If you are inserting a direct quote from any source, it must be enclosed in quotations and followed by a parenthetical reference to the source. “Let’s say I am directly quoting this current sentence and the next. I would then cite it with the author name, date of publication, and the page number for the direct quote” (Winter, 2013, p . 4).
0. Note: We will deduct points if you quote more than once per page, so keep quotes to a minimum. Paraphrase instead, but make sure you still give the original author credit for the material by citing him or using the author’s name (“In this article, Smith noted that …” or “In this article, the authors noted that…”)
3. If you choose to reference any source other than your chosen article, it must be listed in a reference list.
1. 5). Proofread everything you write. I actually recommend reading some sentences aloud to see if they flow well, or getting family or friends to read your work. Writing quality will become more important in future papers, so you should start working on that now!
If you have any questions about the articles, your ideas, or your writing, please ask. Although we won’t be able to review entire drafts of papers before they are handed in, we are very willing to discuss problems, concerns or issues that you might have.
Comment by Ryan: There are a few things to keep in mind as you write your article critique paper. First, use appropriate APA formatting. For example, note the Running head above. A maximum of 50 characters, counting letters, punctuation, and spaces between words as characters. Write the running head in the page header, flush left, in all-capital letters. Use the same running head on every page, including the title page; do not include the label “Running head” to identify the running head on any page. If the title is already 50 characters or fewer, the full title can be used as the running head, but it can also differ. Just make sure that it is descriptive.
Also notice the page number. The title page starts on page one
Are you unfamiliar with headers?To insert them, go into the “Insert” menu, and click on “Headers”. It will open a box at the top of the page. Just type in the box for the header and use spacing and rulers as usual.
Weapons as Aggression-Eliciting Stimuli Comment by Lu: Paper title centered, bold, and positioned in the upper half of the title page. Comment by Ryan: This title is from the article that Jane Doe critiqued. You can use the original article title as well or come up with one similar that is descriptive. This title can also be the same one used in the running head, though Jane Doe has different titles
Jane Doe Comment by Ryan: Your name goes here, first name first, middle name initial, and surname last
Florida International University Comment by Ryan: Your institution. Spell out the university, do not use abbreviation.
Weapons as Aggression-Eliciting Stimuli Comment by Ryan:The title “Weapons as Aggression-Eliciting Stimuli” is EXACTLY the same as on the title page, and it is centered and bold
: Comment by Ryan: Please include this word “Summary” so we know which section we are grading. This isn’t a true aspect of APA style, but for this article critique paper I want to make sure we know when each section starts and ends
Berkowitz and Lepage (1967) designed a study to test the hypothesis that individuals who are in a state of anger are more likely to act out their aggression if cues associated with violence and aggression are present.The sample consisted of 100 male students from the University of Wisconsin who were all enrolled in an introductory level psychology course. Comment by Lu Liang: Cite at the beginning of the summary, then no need to cite unless you used other sources. Comment by Ryan: This is correct APA citation for the article that Jane Doe critiqued. The paper was by Berkowitz and Lepage. . Notice that the author names are in the order they are found on the original study, and that the date is present in parentheses. Jane Doe used the word “and” and not “&”, which is correct here since the word “and” appears outside of the parentheses.
Note that Jane Doe could have also said “Two researchers (Berkowitz & Lapage. 1967) designed a study …” This is correct APA format as well. When the “&” appears within parentheses, make sure to use that & symbol
This study used an experimental research method because it manipulated the independent variable and presumably involved random assignment (although this was not stated in the text). There were two main independent variables.The first one was the subject’s level of anger and this was determined by whether the subject was shocked once or seven times.The second independent variable was the kind of cue present near the shock button when it was the subject’s turn to evaluate the confederate.For one group there was no object, in the control group there was a neutral object (a badminton racquet), and for the last group there was a gun that was supposedly part of a different study.This last group was further separated into 2 subgroups with some being told that the gun belonged to the confederate while others were told that it was left behind by someone else.These independent variables were then combined to see how they affected the dependent variable, which was the level of aggression the subject displayed.The dependent variable was measured by how many shocks the subject delivered to the confederate. Comment by Ryan: The summary does just that – summarizes the design without commenting too much on other matters. I want to make sure you can follow the design, noting whether it is experimental or correlational, and highlighting the independent and dependent variables as well as the procedure and hypotheses.
The procedure ran as follows: volunteers were told that they were participating in a study to test the physiological effects of stress.To do this the subject and the other participant (who was actually a confederate) were both given a social problem and they had to think up ways to solve it.After they completed this task (in separate rooms) their problem solving ideas were then exchanged so they could evaluate each other.The evaluation was done by pushing a button that was supposed to shock the person in the other room (although they still could not see each other); 1 shock represented the best rating while a lesser evaluation was communicated through a higher number of shocks.The confederate was the first to do the evaluation.The number of shocks given to the actual volunteer was already determined as 1 or 7 though (depending on the random assignment) and was not based on a real rating.After this came the volunteer’s turn to do the same evaluation, but the number of shocks was not predetermined.Next to the shock button was one of the previously stated objects, and the gun was the only cue hypothesized to elicit increased aggression. Comment by Ryan: A good description here of exactly what the original authors did
The results of this study confirmed the hypothesis.Those participants who were more angered (given 7 shocks) and were cued by the violent object (a gun) and told that it belonged to the person they were rating, outwardly expressed their aggression the most by giving the confederate a higher number of shocks.The next highest number of shocks was by the group in the presence of a gun, but had been told the gun was left behind by someone else.Those who did not see any objects gave on average one less shock and the least number of shocks were given by those in the presence of the badminton racquets.On the other hand, when the volunteer was not as angered (only shocked once by the confederate), outward expression of aggression was relatively low and stable regardless of what type of cue was present.The researchers used these results to theorize that a person who is already aroused and is then cued by a violent object is more likely to have an impulsive reaction to act more aggressively. Comment by Ryan: The results summary here once again focuses on the original article without editorializing too much.
: Comment by Ryan: Insert the word “Critique” so we know you have moved onto the next section. In the critique, you finally get to express your own opinion. There are eight things listed in the instructions that you can cover, and you must cover at least four of them. You can do more than four, but four is the minimum.
Overall this study was well designed in order to test the given hypothesis that weapons are aggression-eliciting stimuli.The method of using different objects to induce a given response is very similar to the phenomenon of priming.Priming is where certain information is more attended to when related cues are presented.Therefore the results of Berkowitz and Lepage (1967) make sense because weapons are connected to aggression, which increases the person’s awareness of his or her aggressive feelings, and consequently makes the outward act of aggression more likely. Comment by Lu: Comment by Lu: One critique element one paragraph will make your paper organized and fully described. Comment by Lu Liang: No citation in critique section unless your critique is about results/findings/methodology issues, like this.
Based on the results, chances are high that these men would always act in this way when in a similar situation, so this study can be considered reliable (that is, it is repeatable).Validity is not as strong, though.Validity refers to whether the study is measuring what it purports to measure. When the participant was already aroused (given 7 shocks) there was a significant difference in the amount of retaliation depending on which cue was present.However, this retaliation did not depend on the cues if the participant was not as initially aroused (only given 1 shock).So how can they be measuring the impact of a priming mechanism like the gun in the room if they need participants to already be aroused? I am not sure they are measuring their variables correctly. That being said, it did show that although the cues do have an effect on aggressive behavior, initial aggression level plays a much larger role in the causal relationship.The ethicalness of this study is also questionable.Receiving and delivering shocks could potentially cause physical pain and also have a negative effect on one’s emotional well-being.Nonetheless, most participants probably did not suffer any serious consequences.Also, due to the nature of this specific research question it does not seem like there is another way to measure aggression that would be anymore ethical. Comment by Lu Liang: It is ok if you want address definitions of reliability and validity, but do not only write definitions. More importantly, you should explain if the study is valid or reliable, and why. Comment by Ryan: Good analysis of validity and reliability here Comment by Ryan: Also gets into the ethical nature, covering some of those requirements once again
One major methodological problem that should have been addressed is the sample that was obtained.The sample used in this experiment is not a good representation of humans in general, because it only involved college-aged men.It is possible that women or people of different ages may respond differently to the cues.Women are often thought of as less violent, so their reaction to a negative stimulus might cause them to deliver fewer shocks.A weapon makes the seriousness of the situation salient and may cause some people to think rationally about their behavior in the near future.Clearly this proposal requires actual testing before making further assumptions, but it does show the need for a more diverse sample of participants.
Along the same line as the previous issue, a follow-up study could more carefully look at the relationship between peoples’ attitudes towards guns (or other weapons) and their corresponding level of aggressive behavior when given the chance to retaliate.This would be more of a quasi-experiment because in order to test the independent variable of attitudes towards weapons the groups could not be randomly assigned.Three existing groups would be used; those who support weapons, those who are against them, and those who feel neutral (the control group).The hypothesis would predict that if prior arousal level was high, participants who support weapons would show increased aggression when cued by the gun, but the group of participants with negative attitudes towards guns would not be as aggressive. If the subjects did not receive prior arousal (if they were only shocked once by their “evaluator”), then neither group would be significantly affected by the cues. Comment by Lu: Notice that when commenting each critique element, do not simply say “the study is well-designed”, or “the study met certain ethical principles”. More importantly, you should address why or what makes the study well-designed. Give sufficient reasons to support your opinions.
Even if initial aggression is a greater cause in inducing violent behavior than the existence of weapon-related cues, this study has serious implications for social policies related to gun control.It is apparent from the results that if someone is angry and is near a gun, then that person will likely act more aggressively than he or she typically would.Since the guns in the experiment were not loaded and the situation was controlled, the heightened aggression was not transferred over to actually using the guns.In a private home though, arguments occurring with a gun nearby might make it more likely that a gun will be used.Knowing that the mere presence of a weapon increases violence should urge lawmakers to consider adopting stricter gun laws. Comment by Ryan: See, reaching your minimum page number isn’t that tough for the summary and critique! Jane Doe did these two section in 5 pages when only 3 pages minimum are required (1.5 for each section). If you write both sections in a total of three pages, though, it better be very, very good. I would expect at least 4 pages from most students (2 pages for each section)
Brief Summary Comment by Ryan: Add in this brief summary phrase so we know you are moving onto the next section.
The reason for this section is twofold: First, I want to make sure you have a good grasp on the study, and second, I want to make sure that you can summarize more briefly what you wrote above.
In later papers, you will write a literature review of your own, where you summarize prior research as you work toward your hypotheses. You won’t always spend a page and a half writing about each study in your literature review. Sometimes a short paragraph (like this brief summary) is better, and I want to make sure you can write both ways!
Berkowitz and Lepage (1967) conducted a study in which they hypothesized that priming people with an aggressive object (a gun) would lead them to act aggressively. The authors gave electrical shocks (from 1 to 7 of them) to 100 male undergraduates. They told them that one of their peers had delivered the shock. The participant could then retaliate, but they did so in the presence of either a gun or a tennis racket (which was supposedly left in the researcher room from a different study). Participants given the highest number of shocks (7) gave higher retaliation shocks to the peer, but this was more likely when they were in the presence of a gun (compared to a tennis racket). The authors concluded that the guns increased aggressive responses from male participants who were highly aroused.
References Comment by Ryan: Make sure the word References is centered, bold, and starts on its own page. Try to use “Page breaks” in Word so that the references section always starts on its own page, even when you open it later or use earlier versions of Word. That is, go up to the “Insert” menu in word and click on “Page Break”
Berkowitz, L., & Lepage, A. (1967). Weapons as aggression-eliciting stimuli.Journal of Personality and Social Psychology, 7(3), 202-207. Comment by Lu: Make sure you follow APA format for references section. Italicize periodical’s title and volume number; Capitalize article title using sentence case, end references with DOI, etc. Comment by Lu Liang: Make sure you have the correct doi format. DOI: present DOI as hyperlink, i.e., beginning with “http://” or “https://”; it is not necessary to include the words “Retrieved from” or “Accessed from” before a DOI. xxxx refers to DOI number
PSY 3211 Article Critique Self-Rating Rubric
You will get 10 points for filling out and submitting this along with your article critique paper. The purpose of this is for you to reflect on the quality of your paper. This is due when your article critique is due.
Place your rating for each category below, then add your ratings for each category at the bottom where it says “total”. For example, if you believe that your description of the kind of study deserves 3 out of 4 points (because you know your explanation is not sufficient), it should look like this: “your rating: 3” Please write your ratings in a different color font so that they are easier to see.
Please put effort into your ratings. Do not simply give yourself a 50/50. Really reflect on the quality of your paper and whether you meet all the criteria listed. If it is clear that you have not reflected sufficiently on your paper (e.g., you give a rating of 2/2 for something that is actually missing form your paper), you will lose points.
We will use this very same rubric when we grade your paper! This does not mean that you are guaranteed to get the grade you give yourself, but it should help to minimize any surprises when you get your grade.

Title page (4 points total)

Your Rating:

Header Format

a) Is your Running head title in ALL CAPS? 1 point

b) Is everything in your paper in 12 point Times New Roman font?—1 points

c) Do you have a page number that is flush right (also in 12 point Times New Roman font)? —-0.5 points

Title / Name / Institution

d) Do all title words with four letters or more start with a capital letter?— 0.5 points

e) Are your name and institution correct? Are your title, name, and institution elements centered and in 12 point Times New Roman font? See regulations for spacing and bolding of the title.—1 points

Total for Title Page (add up your ratings for a-e):

Summary of the Article (14 points total)

Your Rating:

General Format and Header

f) Is your paper title present and identical to your title on the title page? —1 points

g) Does your summary have min 1 and half page and max 3 pages? —2 points

Summary of the Article

h) Does your summary note the type of design (experimental vs. correlational)? —2 points

i) Does your summary note the independent variables? —2 points

j) Does your summary note dependent variables? —2 points

k) Does your summary describe the methods for the article, including participants, measurement, methodology, and procedure? —3 points

l) Does your summary describe the findings? —2 points

Total for Summary (add up your ratings for f-l):

Your Rating:

Critique of the Article (16 points total)

Does your critique identify at least four of the needed elements (see a list on the assignment instructions, including confounding variables, appropriate sample, valid/reliable dv, interpreting findings, ethics, follow-up study, weak vs strong results, implications not mentioned in the article, theory problems, why the methods used are better or worse than alternatives —Each element is worth 4 points, and must include sufficient detail and explanation.

Total for Critique (add up your ratings for each of the 4 critiques)

Your Rating:

Brief Summary (6 points total)

m) Do you summarize the article in one to two paragraphs? —1 points

n) Does your brief summary highlight the main article points (hypotheses, method, and subjects)? —3 points

o) Does your brief summary highlight the conclusions drawn by authors? —2 points

Total for Brief Summary (add up your ratings for m-o):

In-text Citations and References Page (4 points total)

Your Rating:

In-text Citation

p) Are in-text citations (including direct quotations) in APA format? —2 points


q) Are your references in APA format? —2 points

Total for Brief Summary (add up your ratings for p & q):

Grammar and Writing Quality (6 points total)

Check your grammar and writing for the entire paper. Make sure to proof read, and cite & paraphrase properly. Avoid personal pronouns like “us”, “we”, “you”, and “I”. For a scientific paper like this, go with more objective words like “people”, “participants”, “users”, or “viewers” etc.
If your paper lacks originality and contains too much overlap with the paper you are summarizing (i.e. you do not paraphrase appropriately or cite your sources properly), you will lose some or all of the points from writing quality, depending on the extent of the overlap with the paper. For example, if sentences contain only one or two words changed from a sentence in the original paper, you will lose points from writing quality.

Your Rating for Writing Quality:
Your Total Grade:/50 points (add up all “total ratings” above from each of the 6 categories to get your total self-grade).
Psychological Science
21(10) 1363 –1368
© The Author(s) 2010
Reprints and permission:
DOI: 10.1177/0956797610383437
The proud peacock fans his tail feathers in pursuit of a mate.
By galloping sideways, the cat manipulates an intruder’s per-
ception of her size. The chimpanzee, asserting his hierarchical
rank, holds his breath until his chest bulges. The executive in
the boardroom crests the table with his feet, fingers interlaced
behind his neck, elbows pointing outward. Humans and other
animals display power and dominance through expansive non-
verbal displays, and these power poses are deeply intertwined
with the evolutionary selection of what is “alpha” (Darwin,
1872/2009; de Waal, 1998).
But is power embodied? What happens when displays of
power are posed? Can posed displays cause a person to feel
more powerful? Do people’s mental and physiological sys-
tems prepare them to be more powerful? The goal of our
research was to test whether high-power poses (as opposed to
low-power poses) actually produce power. To perform this
test, we looked at the effects of high-power and low-power
poses on some fundamental features of having power: feelings
of power, elevation of the dominance hormone testosterone,
lowering of the stress hormone cortisol, and an increased tol-
erance for risk.
Power determines greater access to resources (de Waal,
1998; Keltner, Gruenfeld, & Anderson, 2003); higher levels of
agency and control over a person’s own body, mind, and
positive feelings (Keltner et al., 2003); and enhanced cogni-
tive function (Smith, Jostmann, Galinsky, & van Dijk, 2008).
Powerful individuals (compared with powerless individuals)
demonstrate greater willingness to engage in action (Galinsky,
Gruenfeld, & Magee, 2003; Keltner et al., 2003) and often show
increased risk-taking behavior1 (e.g., Anderson & Galinsky,
The neuroendocrine profiles of the powerful differentiate
them from the powerless, on two key hormones—testosterone
and cortisol. In humans and other animals, testosterone levels
both reflect and reinforce dispositional and situational status
and dominance; internal and external cues cause testosterone
to rise, increasing dominant behaviors, and these behaviors
can elevate testosterone even further (Archer, 2006; Mazur &
Corresponding Authors:
Dana R. Carney, Columbia University, Graduate School of Business,
717 Uris Hall, 3022 Broadway, New York, NY 10027-6902
Amy J.C. Cuddy, Harvard Business School, Baker Library 449, Boston,
MA 02163
Power Posing: Brief Nonverbal
Displays Affect Neuroendocrine
Levels and Risk Tolerance
Dana R. Carney1, Amy J.C. Cuddy2, and Andy J.Yap1
1Columbia University and 2Harvard University
Humans and other animals express power through open, expansive postures, and they express powerlessness through closed,
contractive postures. But can these postures actually cause power? The results of this study confirmed our prediction that
posing in high-power nonverbal displays (as opposed to low-power nonverbal displays) would cause neuroendocrine and
behavioral changes for both male and female participants: High-power posers experienced elevations in testosterone, decreases
in cortisol, and increased feelings of power and tolerance for risk; low-power posers exhibited the opposite pattern. In short,
posing in displays of power caused advantaged and adaptive psychological, physiological, and behavioral changes, and these
findings suggest that embodiment extends beyond mere thinking and feeling, to physiology and subsequent behavioral choices.
That a person can, by assuming two simple 1-min poses, embody power and instantly become more powerful has real-world,
actionable implications.
cortisol, embodiment, hormones, neuroendocrinology, nonverbal behavior, power, risk taking, testosterone
Received 1/20/10; Revision accepted 4/8/10
Research Report
1364Carney et al.
Booth, 1998). For example, testosterone rises in anticipation
of a competition and as a result of a win, but drops following
a defeat (e.g., Booth, Shelley, Mazur, Tharp, & Kittok, 1989),
and these changes predict the desire to compete again (Mehta
& Josephs, 2006). In short, testosterone levels, by reflecting
and reinforcing dominance, are closely linked to adaptive
responses to challenges.
Power is also linked to the stress hormone cortisol: Power
holders show lower basal cortisol levels and lower cortisol
reactivity to stressors than powerless people do, and cortisol
drops as power is achieved (Abbott et al., 2003; Coe, Mendoza,
& Levine, 1979; Sapolsky, Alberts, & Altmann, 1997).Although
short-term and acute cortisol elevation is part of an adaptive
response to challenges large (e.g., a predator) and small (e.g.,
waking up), the chronically elevated cortisol levels seen in
low-power individuals are associated with negative health
consequences, such as impaired immune functioning, hyper-
tension, and memory loss (Sapolsky et al., 1997; Segerstrom
& Miller, 2004). Low-power social groups have a higher inci-
dence of stress-related illnesses than high-power social groups
do, and this is partially attributable to chronically elevated cor-
tisol (Cohen et al., 2006). Thus, the power holder’s typical
neuroendocrine profile of high testosterone coupled with low
cortisol—a profile linked to such outcomes as disease resis-
tance (Sapolsky, 2005) and leadership abilities (Mehta & Josephs,
2010)—appears to be optimally adaptive.
It is unequivocal that power is expressed through highly
specific, evolved nonverbal displays. Expansive, open pos-
tures (widespread limbs and enlargement of occupied space
by spreading out) project high power, whereas contractive,
closed postures (limbs touching the torso and minimization
of occupied space by collapsing the body inward) project
low power. All of these patterns have been identified in
research on actual and attributed power and its nonverbal
correlates (Carney, Hall, & Smith LeBeau, 2005; Darwin,
1872/2009; de Waal, 1998; Hall, Coats, & Smith LeBeau,
2005). Although researchers know that power generates
these displays, no research has investigated whether these
displays generate power. Will posing these displays of power
actually cause individuals to feel more powerful, focus on
reward as opposed to risk, and experience increases in testos-
terone and decreases in cortisol?
In research on embodied cognition, some evidence sug-
gests that bodily movements, such as facial displays, can affect
emotional states. For example, unobtrusive contraction of the
“smile muscle” (i.e., the zygomaticus major) increases enjoy-
ment (Strack, Martin, Stepper, 1988), the head tilting upward
induces pride (Stepper & Strack, 1993), and hunched postures
(as opposed to upright postures) elicit more depressed feelings
(Riskind & Gotay, 1982). Approach-oriented behaviors, such
as touching, pulling, or nodding “yes,” increase preference for
objects, people, and persuasive messages (e.g., Briñol & Petty,
2003; Chen & Bargh, 1999; Wegner, Lane, & Dimitri, 1994),
and fist clenching increases men’s self-ratings on power-related
traits (Schubert & Koole, 2009). However, no research has
tested whether expansive power poses, in comparison with
contractive power poses, cause mental, physiological, and
behavioral change in a manner consistent with the effects of
power. We hypothesized that high-power poses (compared
with low-power poses) would cause individuals to experience
elevated testosterone, decreased cortisol, increased feelings of
power, and higher risk tolerance. Such findings would suggest
that embodiment goes beyond cognition and emotion and
could have immediate and actionable effects on physiology
and behavior.
Participants and overview of procedure
Forty-two participants (26 females and 16 males) were ran-
domly assigned to the high-power-pose or low-power-pose
condition. Participants believed that the study was about the
science of physiological recordings and was focused on how
placement of electrocardiography electrodes above and below
the heart could influence data collection. Participants’ bodies
were posed by an experimenter into high-power or low-power
poses. Each participant held two poses for 1 min each. Partici-
pants’ risk taking was measured with a gambling task; feelings
of power were measured with self-reports. Saliva samples,
which were used to test cortisol and testosterone levels, were
taken before and approximately 17 min after the power-pose
Power poses
Poses were harvested from the nonverbal literature (e.g., Car-
ney et al., 2005; Hall et al., 2005) and varied on the two non-
verbal dimensions universally linked to power: expansiveness
(i.e., taking up more space or less space) and openness (i.e.,
keeping limbs open or closed). The two high-power poses into
which participants were configured are depicted in Figure 1,
and the two low-power poses are depicted in Figure 2. To be
sure that the poses chosen conveyed power appropriately, we
asked 95 pretest participants to rate each pose from 1 (very low
power) to 7 (very high power). High-power poses (M = 5.39,
SD = 0.99) were indeed rated significantly higher on power
than were low-power poses (M = 2.41, SD = 0.93), t(94) =
21.03, p < .001; r = .99. To be sure that changes in neuroendocrine levels, powerful feelings, or behavior could be attributed only to the high- power or low-power attributes of the poses, we had 19 pretest participants rate the comfort, difficulty, and pain of the poses. Participants made all four poses (while wearing electrocardi- ography leads) and completed questionnaires after each pose. There were no differences between high-power and low-power poses on comfort, t(16) = 0.24, p > .80; difficulty, t(16) = 0.77,
p > .45; or painfulness, t(16) = –0.82, p > .42.
Power Posing 1365
To configure the test participants into the poses, the experi-
menter placed an electrocardiography lead on the back of each
participant’s calf and underbelly of the left arm and explained,
“To test accuracy of physiological responses as a function of
sensor placement relative to your heart, you are being put into
a certain physical position.” The experimenter then manually
configured participants’ bodies by lightly touching their arms
and legs. As needed, the experimenter provided verbal
Fig. 1. The two high-power poses used in the study. Participants in the high-power-pose condition
were posed in expansive positions with open limbs.
Fig. 2. The two low-power poses used in the study. Participants in the low-power-pose condition were
posed in contractive positions with closed limbs.
1366Carney et al.
instructions (e.g., “Keep your feet above heart level by putting
them on the desk in front of you”). After manually configuring
participants’ bodies into the two poses, the experimenter left
the room. Participants were videotaped; all participants cor-
rectly made and held either two high-power or two low-power
poses for 1 min each. While making and holding the poses,
participants completed a filler task that consisted of viewing
and forming impressions of nine faces.
Measure of risk taking and powerful feelings
After they finished posing, participants were presented with the
gambling task. They were endowed with $2 and told they could
keep the money—the safe bet—or roll a die and risk losing the
$2 for a payoff of $4 (a risky but rational bet; odds of winning
were 50/50). Participants indicated how “powerful” and “in
charge” they felt on a scale from 1 (not at all) to 4 (a lot).
Saliva collection and analysis
Testing was scheduled in the afternoon (12:00 p.m.–6:00
p.m.) to control for diurnal rhythms in hormones. Saliva
samples were taken before the power-pose manipulation
(approximately 10 min after arrival; Time 1) and again 17
min after the power-pose manipulation (M = 17.28 min,
SD = 4.31; Time 2).
Standard salivary-hormone collection procedures were
used (Dickerson & Kemeny, 2004; Schultheiss & Stanton,
2009). Before providing saliva samples, participants did not
eat, drink, or brush their teeth for at least 1 hr. Participants
rinsed their mouths with water and chewed a piece of sugar-
free Trident Original Flavor gum for 3 min to stimulate saliva-
tion (this procedure yields the least bias compared with passive
drool procedures; Dabbs, 1991). Participants provided approx-
imately 1.5 ml of saliva through a straw into a sterile polypro-
pylene microtubule. Samples were immediately frozen to
avoid hormone degradation and to precipitate mucins. Within
2 weeks, samples were packed in dry ice and shipped for anal-
ysis to Salimetrics (State College, PA), where they were
assayed in duplicate for salivary cortisol and salivary testos-
terone using a highly sensitive enzyme immunoassay.
For cortisol, the intra-assay coefficient of variation (CV)
was 5.40% for Time 1 and 4.40% for Time 2. The average
interassay CV across high and low controls for both time
points was 2.74%. Cortisol levels were in the normal range
at both Time 1 (M = 0.16 μg/dl, SD = 0.19) and Time 2 (M =
0.12 μg/dl, SD = 0.08). For testosterone, the intra-assay CV
was 4.30% for Time 1 and 3.80% for Time 2. The average
interassay CV across high and low controls for both time
points was 3.80%. Testosterone levels were in the normal
range at both Time 1 (M = 60.30 pg/ml, SD = 49.58) and Time
2 (M = 57.40 pg/ml, SD = 43.25). As would be suggested by
appropriately taken and assayed samples (Schultheiss & Stanton,
2009), men were higher than women on testosterone at both
Time 1, F(1, 41) = 17.40, p < .001, r = .55, and Time 2, F(1, 41) = 22.55, p < .001, r = .60. To control for sex differences in tes- tosterone, we used participant’s sex as a covariate in all analy- ses. All hormone analyses examined changes in hormones observed at Time 2, controlling for Time 1. Analyses with cor- tisol controlled for testosterone, and vice versa.2 Results One-way analyses of variance examined the effect of power pose on postmanipulation hormones (Time 2), controlling for baseline hormones (Time 1). As hypothesized, high-power poses caused an increase in testosterone compared with low-power poses, which caused a decrease in testosterone, F(1, 39) = 4.29, p < .05; r = .34 (Fig. 3). Also as hypothesized, high-power poses caused a decrease in cortisol compared with low-power poses, which caused an increase in cortisol, F(1, 38) = 7.45, p < .02; r = .43 (Fig. 4). Also consistent with predictions, high-power posers were more likely than low-power posers to focus on rewards— 86.36% took the gambling risk (only 13.63% were risk averse). In contrast, only 60% of the low-power posers took the risk (and 40% were risk averse), χ2(1, N = 42) = 3.86, p < .05; Φ = .30. Finally, high-power posers reported feeling significantly more “powerful” and “in charge” (M = 2.57, SD = 0.81) than low-power posers did (M = 1.83, SD = 0.81), F(1, 41) = 9.53, p < .01; r = .44. Thus, a simple 2-min power-pose manipulation was enough to significantly alter the physiological, mental, and feeling states of our participants. The implications of these results for everyday life are substantial. Discussion Our results show that posing in high-power displays (as opposed to low-power displays) causes physiological, psycho- logical, and behavioral changes consistent with the literature on the effects of power on power holders—elevation of the dominance hormone testosterone, reduction of the stress hor- mone cortisol, and increases in behaviorally demonstrated risk tolerance and feelings of power. These findings advance current understanding of embod- ied cognition in two important ways. First, they suggest that the effects of embodiment extend beyond emotion and cogni- tion, to physiology and subsequent behavioral choice. For example, as described earlier, nodding the head “yes” leads a person to be more easily persuaded when listening to a per- suasive appeal, and smiling increases humor responses. We suggest that these simple behaviors, a head nod or a smile, might also cause physiological changes that activate an entire trajectory of psychological, physiological, and behavioral shifts—essentially altering the course of a person’s day. Sec- ond, these results suggest that any psychological construct, such as power, with a signature pattern of nonverbal corre- lates may be embodied. Power Posing 1367 These results also offer a methodological advance in research on power. Many reported effects of power are limited by the methodological necessity of manipulating power in a laboratory setting (e.g., complex role assignments). The sim- ple, elegant power-pose manipulation we employed can be taken directly into the field and used to investigate ordinary people in everyday contexts. Is it possible that our findings are limited to the specific poses utilized in this experiment? Although the power-infusing attri- bute of expansiveness and the poses that capture it require fur- ther investigation, findings from an additional study (N = 49) suggest that the effects reported here are not idiosyncratic to these specific poses. In addition to the poses used in the cur- rent report, an additional three high-power poses and an addi- tional three low-power poses produced the same effects on feelings of power, F(1, 48) = 4.38, p < .05, r = .30, and risk taking, χ2(1, N = 49) = 4.84, p < .03, Φ = .31. By simply changing physical posture, an individual pre- pares his or her mental and physiological systems to endure difficult and stressful situations, and perhaps to actually improve confidence and performance in situations such as interviewing for jobs, speaking in public, disagreeing with a boss, or taking potentially profitable risks. These findings suggest that, in some situations requiring power, people have the ability to “fake it ’til they make it.” Over time and in aggregate, these minimal postural changes and their out- comes potentially could improve a person’s general health and well-being. This potential benefit is particularly impor- tant when considering people who are or who feel chroni- cally powerless because of lack of resources, low hierarchical rank in an organization, or membership in a low-power social group. Acknowledgments We are gratefully indebted to the following individuals for their insight, support, and assistance with this research: Daniel Ames, Max Bazerman, Joe Ferrero, Alan Fiske and lab, Adam Galinsky, Deborah Gruenfeld, Lucia Guillory, Brian Hall, Bob Josephs, Brian Lucas, Malia Mason, Pranj Mehta, Michael Morris, Joe Navarro, Michael Norton, Thomas Schubert, Steve Stroessner, and Bill von Hippel. Declaration of Conflicting Interests The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article. 16.00 12.00 8.00 4.00 0.00 High-Power Poses Low-Power Te st os te ro ne C ha ng e (p g/ m l) –4.00 –8.00 –12.00 –16.00 Fig. 3. Mean changes in the dominance hormone testosterone following high-power and low-power poses. Changes are depicted as difference scores (Time 2 – Time 1). Error bars represent standard errors of the mean. 0.06 0.04 0.02 High-Power Low-Power C or tis ol C ha ng e (μ g/ dl ) 0.00 –0.02 –0.04 –0.06 Poses Fig. 4. Mean changes in the stress hormone cortisol following high-power and low-power poses. Changes are depicted as difference scores (Time 2 – Time 1). Error bars represent standard errors of the mean. 1368Carney et al. Notes 1. The effect of power on risk taking is moderated by factors such as prenatal exposure to testosterone (Ronay & von Hippel, in press). 2. 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Inhibiting and facili- tating conditions of the human smile: A nonobtrusive test of the facial feedback hypothesis. Journal of Personality and Social Psychology, 54, 768–777. Wegner, D.M., Lane, J.D., & Dimitri, S. (1994). The allure of secret relationships. Journal of Personality and Social Psychology, 66, 287–300. Psychological Science 21(12) 1770 –1776 © The Author(s) 2010 Reprints and permission: DOI: 10.1177/0956797610387441 A well-established finding is that mood interacts with cogni- tive processing (for a review, see Isen, 1999), with executive functioning implicated as a possible source of the effects of this interaction (Mitchell & Phillips, 2007). Positive mood leads to enhanced cognitive flexibility,1 whereas negative mood may reduce (or may not affect) cognitive flexibility (for a review, see Ashby, Isen, & Turken, 1999). Category learning has also been associated with cognitive flexibility (Ashby et al., 1999; Maddox, Baldwin, & Markman, 2006), making cat- egory learning well suited to the study of the effects of mood on cognition. For example, Ashby, Alfonso-Reese, Turken, and Waldron (1998) predicted that depressed subjects should be impaired in learning rule-described (RD) category sets. Smith, Tracy, and Murray (1993) supported this prediction and also found that depressed subjects were not impaired when learning non-RD categories. However, the more general ques- tion of how induced positive and negative mood states influ- ence category learning remains unanswered. We addressed this question by using two kinds of categories, one in which learning is thought to be enhanced by cognitive flexibility and one in which learning is not thought to be enhanced by cogni- tive flexibility (Maddox et al., 2006). Our starting point was the competition between verbal and implicit systems (COVIS) theory, which posits the existence of separate explicit and implicit category-learning systems (Ashby et al., 1998). The explicit system enables people to learn RD categories and is associated with the prefrontal cor- tex (PFC) and the anterior cingulate cortex (ACC). RD cate- gory learning uses hypothesis testing, rule selection, and inhibition to find and apply rules that can be verbalized, and it is influenced by cognitive flexibility. The implicit system enables people to learn non-RD categories, relies on connec- tions between visual cortical areas and the basal ganglia, and is not affected by cognitive flexibility. This system is likely procedural in nature and dependent on a dopamine-mediated reward signal (Maddox, Ashby, Ing, & Pickering, 2004). RD and non-RD category sets have been dissociated behaviorally (for a review, see Maddox & Ashby, 2004) and neurobiologi- cally (Nomura et al., 2007), making them appropriate for the study of mood effects. We argue that positive mood increases cognitive flexibility, and this effect enhances the explicit category-learning system Corresponding Author: Ruby T. Nadler, The University of Western Ontario, Department of Psychology, Social Science Centre, Room 7418, 1151 Richmond St., London, Ontario, Canada N6A 5C2 E-mail: Better Mood and Better Performance: Learning Rule-Described Categories Is Enhanced by Positive Mood Ruby T. Nadler, Rahel Rabi, and John Paul Minda The University of Western Ontario Abstract Theories of mood and its effect on cognitive processing suggest that positive mood may allow for increased cognitive flexibility. This increased flexibility is associated with the prefrontal cortex and the anterior cingulate cortex, both of which play crucial roles in hypothesis testing and rule selection. Thus, cognitive tasks that rely on behaviors such as hypothesis testing and rule selection may benefit from positive mood, whereas tasks that do not rely on such behaviors should not be affected by positive mood. We explored this idea within a category-learning framework. Positive, neutral, and negative moods were induced in our subjects, and they learned either a rule-described or a non-rule-described category set. Subjects in the positive-mood condition performed better than subjects in the neutral- or negative-mood conditions in classifying stimuli from rule-described categories. Positive mood also affected the strategy of subjects who classified stimuli from non-rule-described categories. Keywords frontal lobe, emotions, hypothesis testing, selective attention, response inhibition Received 4/7/10; Revision accepted 6/28/10 Research Report Better Mood and Better Performance 1771 mediated by the PFC (Ashby et al., 1999; Ashby & Ell, 2001; Minda & Miles, 2010). We base our predictions on two lines of research. First, Ashby et al. (1999) hypothesized that posi- tive affect is associated with enhanced cognitive flexibility as a result of increased dopamine in the frontal cortical areas of the brain. Second, the COVIS theory predicts that increased dopamine in the PFC and ACC should enhance learning on RD tasks, and reduced dopamine should impair learning on RD tasks (Ashby et al., 1998). Thus, positive mood should be associated with enhanced RD category learning, an important prediction that has not to our knowledge been tested directly. We induced a positive, neutral, or negative mood in sub- jects and presented them with one of two kinds of category sets that have been widely used in the category-learning litera- ture (Ashby & Maddox, 2005). These sets consisted of sine- wave gratings (Gabor patches) that varied in spatial frequency and orientation. The RD set of Gabor patches required learners to find a single-dimensional rule in order to correctly classify the stimuli on the basis of frequency but not orientation, and the non-RD, information-integration (II) set of Gabor patches required learners to assess both orientation and frequency. Subjects in the RD condition were able to formulate a verbal rule to ensure optimal performance, but subjects in the II con- dition were not able to form a rule that could be easily verbalized. We predicted that subjects in a positive mood, compared with those in a neutral or negative mood, would perform better when learning RD categories. It was unclear whether a nega- tive mood would impair RD learning relative to a neutral mood, as the effects of negative mood on cognitive processing are variable and difficult to predict (for a review, see Isen, 1990). Because the PFC and the ACC do not mediate the implicit system, we did not expect mood to affect II category learning. Method Subjects Subjects were 87 university students (61 females and 26 males), who received $10.00 or course credit for participation. Subjects were randomly assigned to one of the three mood- induction conditions and one of the two category sets. Six sub- jects who scored below 50% on the categorization task were excluded from data analysis. Materials We used a series of music clips and video clips from YouTube2 to establish affective states. We verified that these clips evoked the intended emotions by conducting a pilot study. After each viewing or listening, subjects in the pilot study (7 graduate students, who did not participate in the main experiment) rated how the clip made them feel on a 7-point scale, which ranged from 1 (very sad) to 4 (neutral) to 7 (very happy). Table 1 shows the complete list of clip selections and the average rat- ings by pilot subjects; it also denotes the clips selected for the main experiment. As a manipulation check during the main experiment, we queried subjects with the Positive and Nega- tive Affect Schedule (PANAS) after using the selected clips to induce moods. The PANAS assesses positive and negative affective dimensions (Watson, Clark, & Tellegen, 1988). The Gabor patches used in the main experiment were gen- erated according to established methodologies (see Ashby & Gott, 1988; Zeithamova & Maddox, 2006). For each category (RD and II), we randomly sampled 40 values from a multivari- ate normal distribution described by that category’s parame- ters (shown in Table 2). The resulting structures for the RD and II category sets are illustrated in Figure 1.3 We used the PsychoPy software package (Pierce, 2007) to generate a Gabor patch corresponding to each coordinate sampled from the mul- tivariate distributions. Procedure In the main experiment, subjects were assigned randomly to one of three mood-induction conditions (positive, neutral, or negative), as well as to one of two category sets (RD or II). Table 1. Music and Video Clips Used in the Pilot Study Selection Average subject rating Positive music Mozart: “Eine Kleine Nachtmusik—Allegro”* 6.57 Handel: “The Arrival of the Queen of Sheba” 5.00 Vivaldi: “Spring” 6.14 Neutral music Mark Salona: “One Angel’s Hands”* 3.86 Linkin Park: “In the End (Instrumental)” 4.14 Stephen Rhodes: “Voice of Compassion” 3.29 Negative music Schindler’s List Soundtrack: “Main Theme”* 2.00 I Am Legend Movie Theme Song 2.71 Distant Everyday Memories 2.57 Positive video Laughing Baby* 6.57 Whose Line Is It Anyway: Sound Effects 6.43 Where the Hell Is Matt? 6.00 Neutral video Antiques Roadshow Television Show* 4.14 Facebook on 60 Minutes 3.71 Report About the Importance of Sleep 4.29 Negative video Chinese Earthquake News Report* 1.43 Madison’s Story (About Child With Cancer) 1.71 Death Scene From the Film The Champ 1.86 Note: Clips were taken from the YouTube Web site. Asterisks denote clips that were used in the main experiment. 1772Nadler et al. Subjects were presented with the clips (music first, then video) from their assigned mood condition and then completed the PANAS so we could ensure that the mood induction was successful. After receiving instructions, subjects performed a category- learning task on a computer. On each trial, a Gabor patch appeared in the center of the screen, and subjects pressed the “A” or the “B” key to classify the stimulus. Subjects who viewed the RD category set (Fig. 1a) had to find a single- dimensional rule to correctly classify the stimuli on the basis of the frequency of the grating, while ignoring the more salient dimension of orientation. The optimal verbal rule for such classification could be phrased as follows: “Press ‘A’ if the stimulus has three or more stripes; otherwise, press ‘B.’” The non-RD, II category set (Fig. 1b) required learners to assess both orientation and frequency. There was no rule for this set that could be easily verbalized to allow for optimal perfor- mance. In both conditions, feedback (“CORRECT” or “INCORRECT”) was presented after each response. Subjects completed four unbroken blocks of 80 trials each (320 total). The presentation order of the 80 stimuli was randomly gener- ated within each block for each subject. Results PANAS Scores on the Positive Affect scale were as follows—positive- mood condition: 2.89; neutral-mood condition: 2.45; and neg- ative-mood condition: 2.42. A significant effect of mood on positive affect was found, F(2, 78) = 3.98, p < .05, η2 = .093. Positive-mood subjects showed only marginally more positive Table 2. Distribution Parameters for the Rule-Described and Non-Rule- Described Category Sets Category set and category µ f µ o σ f 2 σ o 2 cov f,o Rule-described Category A 280 125 75 9,000 0 Category B 320 125 75 9,000 0 Non-rule-described Category A 268 157 4,538 4,538 435 Category B 33293 4,538 4,538 4,351 Note: Dimensions are in arbitrary units; see Figure 1 for scaling factors. The sub- scripted letters o and f refer to orientation and frequency, respectively. –200 –100 0 100 200 300 400 500 –100 0 100 200 300 400 500 600 O rie nt at io n Frequency Rule-Described –200 –100 0 100 200 300 400 500 –100 0 100 200 300 400 500 600 O rie nt at io n Frequency Non-Rule-Described ba Fig. 1. Structures used in the (a) rule-described category set and (b) non-rule-described, information-integration category set. Category A stimuli are represented by light circles, and Category B stimuli are represented by dark circles. The solid lines show the optimal decision boundaries between the stimuli. The values of the stimulus dimensions are arbitrary units. Each stimulus was created by converting the value of these arbitrary units into a frequency value (cycles per stimulus) and an orientation value (degree of tilt). For both category sets, the grating frequency (f) was calculated as 0.25 + (x f /50) cycles per stimulus, and the grating orientation (o) was calculated as xo × (π/20)°. The Gabor patches are examples of the actual stimuli seen by subjects. Better Mood and Better Performance 1773 affect than neutral-mood subjects did (p < .06), but they showed significantly more positive affect than negative-mood subjects did (p < .05). These scores indicate that the mood- induction procedures were effective. Scores on the Negative Affect scale were as follows—positive-mood condition: 1.15; neutral-mood condition: 1.18; and negative-mood condition: 2.13. A significant effect of mood on negative affect was found, F(2, 78) = 30.36, p < .001, η2 = .438, with negative- mood subjects showing significantly more negative affect than positive- and neutral-mood subjects did (p < .0001 in both cases). These results again indicate that the mood-induction procedures were effective. Category learning Figure 2 shows the learning curve (average proportion of cor- rect responses in Blocks 1–4) for each condition and each cat- egory set. A mixed analysis of variance revealed main effects of category set, F(1, 75) = 31.94, p < .001, η2 = .257; mood, F(2, 75) = 4.40, p < .05, η2 = .071; and block, F(3, 225) = 41.33, p < .001, η2 = .322. It also revealed a significant interac- tion between mood and category set, F(2, 75) = 4.17, p < .05, η2 = .067. We conducted two separate analyses of variance (one for the RD category and one for the II category) to exam- ine this interaction. A main effect of mood on overall performance was found for the RD category set, F(2, 35) = 6.28, p < .001, η2 = .264. A Tukey’s honestly significant difference test showed that over- all performance by subjects in the positive-mood condition (M = .85) was higher than performance by subjects in the neg- ative-mood condition (M = .73, p < .0001) and subjects in the neutral-mood condition (M = .73, p < .0001). Performance did not differ between subjects in the neutral- and negative-mood conditions (p = .69). No effect of mood on overall perfor- mance was found for the II category set (p = .71). Overall pro- portions correct were as follows—positive-mood condition: .64; negative-mood condition: .66; and neutral-mood condi- tion: .64. Computational modeling For insight into the response strategies used by our subjects, we fit decision-bound models to the first block of each sub- ject’s data (for details, see Ashby, 1992a; Maddox & Ashby, 1993). We analyzed the first block of trials because that is when mood-induction effects are likely to be strongest, and it is also when cognitive flexibility is most needed. One class of models assumed that each subject’s performance was based on a single-dimensional rule (we used an optimal version with a fixed intercept and a version with the intercept as a free param- eter). Another class of models assumed that each subject’s per- formance was based on the two-dimensional II boundary (we used an optimal version with a fixed intercept and slope, a ver- sion with a fixed slope, and a version with a freely varying P ro po rt io n C or re ct RD Category Set II Category Set .0 .2 .4 .6 .8 1.0 P ro po rt io n C or re ct .0 .2 .4 .6 .8 1.0 Block Block Positive Mood Neutral Mood Negative Mood Positive Mood Neutral Mood Negative Mood 1 2 3 4 1 2 3 4 Fig. 2. Average proportion of correct responses to stimuli in the three mood conditions as a function of trial block. Subjects were tested on either the rule-described (RD) category set (left graph) or the non-RD, information-integration (II) category set (right graph). Error bars denote standard errors of the mean. 1774Nadler et al. slope and intercept). We fit these models to each subject’s data by maximizing the log likelihood. Model comparisons were carried out using Akaike’s information criterion, which penal- izes a model for the number of free parameters (Ashby, 1992b). The proportion of subjects whose responses were best fit by their respective optimal model is shown in Figure 3. For the RD categories, .83 of positive-mood subjects, .62 of neutral- mood subjects, and .54 of negative-mood subjects were fit best by a model that assumed a single-dimensional rule. For the II categories, .71 of positive-mood subjects, .40 of neutral-mood subjects, and .43 of negative-mood subjects were fit best by one of the II models. Discussion In this experiment, positive, neutral, and negative moods were induced before subjects learned either an RD or a non-RD, II category set. The RD set required subjects to use hypothesis testing, rule selection, and response inhibition to achieve opti- mal performance, and the II set was best learned by associat- ing regions of perceptual space with responses (Ashby & Gott, 1988). We found that positive mood enhanced RD learning com- pared with neutral and negative moods. Mood did not seem to affect II learning. However, a comparison of decision-bound models suggested that positive-mood subjects displayed a greater degree of cognitive flexibility compared with neutral- and negative-mood subjects by adopting an optimal strategy early in both RD and II learning. The COVIS theory suggests that people learn categories using an explicit, rule-based system or an implicit, similarity- based system (Ashby et al., 1998; Ashby & Maddox, 2005; Minda & Miles, 2010). The brain areas that mediate these sys- tems have been well studied, linking the PFC, ACC, and medial temporal lobes to the explicit system but not to the implicit system. Our experiment highlights a variable that facilitates the learning of RD categories using the explicit system. The finding that positive mood enhances performance of the explicit system posited by the COVIS theory corresponds with the dopamine hypothesis of positive affect (Ashby et al., 1999). Our results connect this research with existing work on category learning, and we view this connection as a substan- tial step forward in the study of cognition and mood. We sus- pect that our positive-mood subjects experienced increased cognitive flexibility, which allowed them to find the optimal verbal rule faster than negative-mood subjects and neutral- mood subjects did. Performance on the II category set did not differ strongly across the different mood conditions. This result is also in line with the dopamine hypothesis, as positive mood is not theorized to affect the same brain regions P ro po rt io n F it by O pt im al M od el .0 .2 .4 .6 .8 1.0 P ro po rt io n F it by O pt im al M od el .0 .2 .4 .6 .8 1.0 Positive Neutral Negative Mood-Induction Condition Positive Neutral Negative Mood-Induction Condition RD Category Set II Category Set Fig. 3. Proportion of subjects in each mood-induction condition whose responses best fit the optimal model for the category set to which they were assigned. Subjects learned either the rule-described (RD) category set (left graph) or the non-RD, information-integration (II) category set (right graph). Better Mood and Better Performance 1775 hypothesized by the COVIS theory to be involved with the learning of non-RD category sets. However, our modeling results suggest that the cognitive flexibility associated with positive mood may affect the strategies used in II category learning. This cognitive flexibility could allow the explicit system to exhaust rule searches more effectively, even though performance levels may remain unchanged between the conditions. We failed to find an effect of negative mood in RD learn- ing. This is in line with previous research that reported no dif- ferences between negative- and neutral-mood subjects on measures of cognitive flexibility (Isen, Daubman, & Nowicki, 1987). It may be that negative mood does not affect RD cate- gory learning, although we think it could, given the right cir- cumstances. One possible explanation of why we did not find such an effect is that the induced negative mood may not have been sustained long enough to interfere with performance. We suspect that subjects in certain negative states will be impaired in RD category learning. Future work should examine ways of sustaining mood states and should explore a wider range of negative mood states. An intriguing possibility that was not observed is that nega- tive mood could enhance II category learning. Recent research suggests that affective states low in motivational intensity (e.g., amusement, sadness) are associated with broadened attention, and affective states high in motivational intensity (e.g., desire, disgust) are associated with narrowed attention (Gable & Harmon-Jones, 2008, 2010). Thus, for example, sad- ness may facilitate performance when broadened attention is beneficial for category learning. We did not find this effect, either because learning of the II category set used did not ben- efit from broadened attention or because the induced negative mood was high in motivational intensity. These interesting ideas require further research. Smith et al. (1993) showed that clinically depressed sub- jects were impaired in RD category learning and unimpaired in II category learning, but our research is the first to investi- gate how experimentally induced mood states influence cate- gory learning. We have shown that positive mood enhanced the learning of an RD category set, an advantage that was strong and sustained throughout the task. Positive mood did not improve the learning of II categories, though there was evidence that positive mood enhanced selection of the optimal strategy. By connecting theories of multiple-system category learning and positive affect, our research suggests that positive affect enhances performance when category learning benefits from cognitive flexibility. Future work should examine the interaction between mood states (motivationally weak com- pared with intense), valence (positive compared with nega- tive), and category type (explicit compared with implicit) in category learning. Acknowledgments We thank E. Hayden for many valuable insights on this project. Declaration of Conflicting Interests The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article. Funding This research was supported by Natural Sciences and Engineering Research Council (NSERC) Grant R3507A03 to J.P.M., an Ontario Graduate Scholarship award to R.T.N., and an NSERC fellowship to R.R. Notes 1. We define cognitive flexibility as the ability to seek out and apply alternate strategies to problems (Maddox, Baldwin, & Markman, 2006) and to find unusual relationships between items (Isen, Johnson, Mertz, & Robinson, 1985). 2. The clips can be found by searching for their titles on YouTube (, or URLs can be obtained from the first author. 3. Stimulus parameters and generation were the same as those used by Zeithamova and Maddox (2006). References Ashby, F.G. (1992a). Multidimensional models of categorization. In F.G. 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Zeithamova, D., & Maddox, W. (2006). Dual-task interference in per- ceptual category learning. Memory & Cognition, 34, 387–398. Psychological Science 2021, Vol. 32(6) 934 –943 © The Author(s) 2021 Article reuse guidelines: DOI: 10.1177/0956797620977518 ASSOCIATION FOR PSYCHOLOGICAL SCIENCEShort Report Research has demonstrated that voters are influenced by split-second judgments on the basis of candidates’ faces (Todorov et al., 2005). However, even though gender and age are automatically inferred from faces (Brewer & Lui, 1989; North & Fiske, 2015), to our knowledge no research to date has systematically exam- ined the role of candidates’ perceived age and gender on voting intention. A recent study of 28 national legislatures in the Euro- pean Union (Stockemer & Sundström, 2018) found that although female legislators were generally underrepre- sented, they were most underrepresented among older legislators. Could it be because people are more inclined to vote for female candidates only when the candidates are younger? From Susan Sontag’s classic essay “The Double Standard of Aging” (Sontag, 1972) to reports of gender-based age discrimination ( Dockterman, 2016), anecdotal evidence suggests that age has a greater influ- ence on how women are perceived than on how men are perceived. In the studies reported below, we sought to isolate the roles of candidates’ age and gender (sur- mised only from their photos) and examine how those two factors interact to predict voting intention in hypo- thetical elections. The present research also examined how basic char- acteristics of candidates inferred from their photos, such as perceived competence (Olivola & Todorov, 2010; Todorov etal., 2005), warmth (Sutherland etal., 2013), and attractiveness (Berggren etal., 2010; Chiao etal., 2008; McLellan & McKelvie, 1993), account for the rela- tions between candidates’ age and people’s intentions to vote for them. Study 1 Study 1 was preregistered ( ng5vg.pdf ) and examined how voting intentions were related to candidates’ apparent age and gender using a hypothetical election paradigm. 977518PSSXXX10.1177/0956797620977518Shen, ShodaCandidates’ Age and Gender Predict Voter Preference research-article2021 Corresponding Author: Yiqin Alicia Shen, University of Washington, Department of Psychology E-mail: How Candidates’ Age and Gender Predict Voter Preference in a Hypothetical Election Yiqin Alicia Shenand Yuichi Shoda Department of Psychology, University of Washington Abstract Are preferences for political candidates influenced by how old they appear to be? Amazon Mechanical Turk workers and undergraduate students were shown photos of 93 state legislators as candidates in hypothetical elections. Other information about the candidates (e.g., party affiliation) was held constant, randomized, or not presented. For very young candidates (< 35 years old), participants favored women over men. However, participants’ intention to vote for male candidates increased with age until candidates were about 45 years old and then slightly decreased. In contrast, participants’ intention to vote for female candidates consistently decreased with candidates’ age. Perceived attractiveness and warmth accounted for some of the gender differences in the effect of candidates’ perceived age. Keywords age, gender, voting, preregistered Received 9/1/17; Revision accepted 9/29/20 Candidates’ Age and Gender Predict Voter Preference 935 Method Stimulus selection and preparation. Color images of 365 state legislators from the United States were obtained in 2014 from 14 state government websites (California, Nevada, Idaho, Wyoming, New Mexico, Texas, North Dakota, Iowa, Ohio, Michigan, Florida, Virginia, Pennsylvania, and New York). Those states were selected because in the 2012 U.S. presidential elec- tion, roughly half of them voted for Barack Obama and the other half voted for Mitt Romney. Stimuli were sam- pled to cover a wide age range for both genders and were selected without regard to their perceived attrac- tiveness, competence, or electability (see the Supple- mental Material available online for more information). All images were cropped so that only the head, neck, and shoulders of the person were visible. Backgrounds, often of patriotic themes (e.g., national flag, state capi- tol), were digitally removed and replaced with a uniform color background. Example stimuli are shown in Figure S18 in the Supplemental Material. Fifteen Amazon Mechanical Turk (MTurk) workers rated the perceived age of these 365 legislators. Those legislators were grouped into five age groups (30–39 years, 40–49 years, 50–59 years, 60–69 years, 70 years and above) on the basis of the average of workers’ responses. Images of approximately 10 female and 10 male legislators were randomly selected from the first four age groups. Because of the small number of legisla- tors in the highest age group (70 years and above), all images in that group were selected. On their web pages, approximately half of the selected legislators self- identified as Republican, and the other half self- identified as Democrat. The resulting stimulus set contained a total of 93 legislators (51 males, 42 females). Participants. Participants were 300 MTurk workers. The sample size was based on the preregistered plan, which in turn was based on samples sizes used in previ- ous studies using hypothetical election paradigms (see Studies S1–S6 in the Supplemental Material). In accor- dance with the preregistration plan, we excluded four participants because they took an excessively long time (> 60 min) to complete the task, and seven participants
were excluded because of an extremely low variation in
their responses throughout the study (i.e., SD < 3 on a 100-point slider). This resulted in a final sample size of 289 participants (158 women, 131 men; age: M = 49.1 years, SD = 14.5 years; 237 self-reported as White, 14 as Hispanic or Latinx, 17 as Black or African American, 10 as East Asian, four as “other,” two as American Indian/ Alaska Native, two as South Asian, and three as more than one race). Prior to the experiment, MTurk collected “premium- qualification” information from the potential partici- pants, including participants’ self-reported gender and age. Using that data, we were able to recruit a sample with a gender and age distribution that approximated that of voters in the 2016 presidential election (for the sampling scheme and sample size in each age and gender bin, see Section III.10 in the Supplemental Mate- rial). Data were collected in early 2019. All participants had Internet protocol (IP) addresses within the United States. No analyses were conducted before data collec- tion was completed. Procedure. Study 1 employed a highly repeated within- person design (Whitsett & Shoda, 2014; for a more recent implementation of this paradigm, see Zayas et al., 2019), in which each participant responds to a large number of stimuli, and the effect of the stimulus characteristics of interest (e.g., the age and gender of candidates) on behavior (e.g., voting intention) is assessed separately for each participant. Participants were invited to an experiment on “poli- tics and impression formation.” Using the Inquisit Web platform (Millisecond, 2015), we presented the 93 pho- tos, described above, one at a time in random order, and participants were prompted to estimate each Statement of Relevance People make split-second judgments about others only on the basis of their faces. Does this ten- dency extend even to highly consequential deci- sions, such as whom to vote for in an election? To understand how such judgments are influenced by apparent gender and age, we showed partici- pants photographs of a number of state legislators and asked them to indicate the likelihood of vot- ing for each candidate, while controlling other information. For candidates judged to be under 35 years old, participants favored women over men. However, as the perceived age of the can- didate increased, the likelihood of voting for female candidates steadily decreased. In contrast, the likelihood of voting for male candidates first increased until the candidates were about 45 years old and then slightly decreased. Although we can- not directly extrapolate beyond the present studies, these findings suggest that candidates’ gender and age, surmised only from photos, can influence vot- ing intentions, and they underscore the importance of raising voter’s awareness of such influences. 936 Shen, Shoda candidate’s age in years by entering a two-digit number in a text box below each image. After estimating the age of all candidates, participants were given the fol- lowing instructions and then shown each of the 93 candidates again in randomized order: In the following task, you will view pictures of the same 93 candidates. Imagine they are all candidates for the next United States of America Senate or House of Representatives. Please indicate how likely you are to vote for each person. After rating all the candidates, participants reported their age, gender, and race/ethnicity. Results To observe the relationship between perceived age and voting intention without imposing assumptions about the shape (e.g., linear, quadratic) of the relationship, we first used locally weighted scatterplot smoothing (LOWESS) with 95% bootstrapped confidence intervals (CIs) to plot participants’ voting intentions for each candidate as a function of the perceived age of that candidate. This process was done in R (Version 3.4.0; R Core Team, 2017). Means were plotted separately for younger and older participants (Figs. 1a and 1b) and for male and female participants (Figs. 1c and 1d). Figure 1 also shows the 95% CI at each age point (shaded areas), which were based on bootstrapping with 5,000 resamples. As shown in the figure, younger female candidates were preferred to younger male can- didates, but voting intentions toward female candidates steadily decreased with candidates’ age. In contrast, for male candidates, the relationship between voting intention and candidates’ age appeared to follow an inverted-U pattern: Voting intentions increased with age up to approximately the perceived age of 45 years and then decreased. This pattern of relationship remained qualita- tively the same regardless of participants’ age or gender. Using the HLM package (Version 7; Bryk etal., 2010), we applied a two-level random-slope-and-intercept model to formally model the relationship between the perceived age of each candidate and participants’ vot- ing intention toward them. Specifically, the voting intention of participant j for candidate i was modeled as a function of candidate i’s age as perceived by par- ticipant j (ageij), its quadratic term (ageij 2), candidate i’s apparent gender (genderi), and the interaction of candidate gender and candidate age (Ageij × Genderi, Ageij 2 × Genderi). This model is formally expressed as follows: Voting intention Age Gender 1 1 2 2ij j ij j i g g u g u= + + × + + × + 00 0 0( ) ( ) (gg u g u g u j i ij j ij ij j 3 3 4 4 5 5 Gender Age Age Age Ge 0 0 0 + × × + + × × + + × ) ( ) ( ) nnder Age Age i ij ij ijr × × + Variables starting with g are estimates of global slopes and intercept, variables starting with u are estimates of participant-to-participant variations, and variables start- ing with r are model residuals. Consistent with the trends visible in Figure 1, the quadratic effects of age were qualified by an interaction with candidate gender, β = −1.85, t(288) = −6.04, p< .001 (see Table S4 in the Supplemental Material for other coefficients). A random-effects test indicated that the standard devi- ations for the regression coefficients were significantly greater than zero (see Table S4 in the Supplemental Material), indicating reliable participant-to-participant variation in the effects of candidate gender and age. Participants’ gender accounted for some of that vari- ability. Participant gender interacted with the effect of candidate age (p < .005), the effect of candidate gen- der (p < .001), and their interaction terms (p < .001) to predict voting intention: Intentions to vote for female candidates decreased with age faster for male participants than for female participants (see Figs. 1c–1d; for statistical details, see Table S6 in the Sup- plemental Material). Comparing younger with older participants, we found that voting intention toward female candidates decreased faster with perceived age than for male candidates, but this two-way interaction was stronger for younger, compared with older, par- ticipants (Figs. 1a and 1b). The Participant Age × Can- didate Age × Candidate Gender interaction was statistically significant (p= .04) in predicting voting intention (for statistical details, see Table S10 in the Supplemental Material). Nevertheless, even after participant gender and participant age were taken into account, there were still statistically significant individual differences in slopes, suggesting that individual differences in the effects of perceived candidate age and gender could not be accounted for solely by participants’ gender and age. We conducted six additional studies with a combined sample size of 555 participants. As shown in Section I in the Supplemental Material, the Candidate Gender × Perceived Age interactions were statistically significant in all six studies. Candidates’ Age and Gender Predict Voter Preference 937 Study 2 Study 2 was preregistered ( t2yc2.pdf ) and examined the role of candidate gender and age on voting intentions using an expanded stimu- lus set and a forced-choice paradigm that more closely mimics actual voting. Method Stimulus selection and preparation. We selected pairs of gender-matched candidates who clearly differed in age. From the initial set of 365 stimuli, we first identified younger candidates by selecting those whose mean esti- mated age was below 40. Then we selected candidates who were perceived as being clearly older than them (operationally defined as being rated as older than the oldest of the younger candidates by at least 14 of the 15 raters in a pilot test). Participants. The preregistration called for collecting data from 300 MTurk workers. We wanted to let all partici- pants who started the experiment finish, so we collected data from 303 participants. Five participants were excluded in accordance with our preregistration plan. This left a final sample of 298 participants (171 women, 126 men, 1 who did not report gender; age: M = 37.66 years, SD = 12.74 years; 241 self-reported as White, 2 as South Asian, 27 as Black or African American, one as American Indian/ Native, 16 as East Asian, and seven as mixed race; one did not report race/ethnicity). All participants had IP addresses within the United States. No analyses were conducted before data collection was completed. Procedure. Each participant saw 16 pairs of candidates presented in random sequence: six pairs of younger female versus older female candidates, six pairs of younger male versus older male candidates, and four same-age different-gender pairs featuring middle-age Female Candidates Male Candidates Female Candidates Male Candidates Female Candidates Male Candidates Female Candidates Male Candidates Study 1 (Female Participants)Study 1 (Male Participants) Study 1 (Older Participants)Study 1 (Younger Participants) 100 80 60 40 20 0 100 80 60 40 20 0 30 7060504030 70605040 30 70605040 100 80 60 40 Vo tin g In te nt io n Vo tin g In te nt io n Vo tin g In te nt io n Perceived Age of Candidate Perceived Age of Candidate Perceived Age of Candidate 100 80 60 40 20 0 30 70605040 Perceived Age of Candidate Vo tin g In te nt io n 20 0 ba dc Fig. 1. (continued on next page) 938 Shen, Shoda female versus middle-age male candidates, which were included to make sure that not all trials showed candi- dates of the same gender. Following the preregistration plan, we did not present any pairs containing younger female versus younger male candidates or older female versus older male candidates. The four trials featuring middle-age female versus middle-age male candidates were not included in the analysis. On any given trial, participants viewed pairs of can- didates presented side by side on the screen on the Inquisit Web platform (Millisecond, 2015). One candi- date was presented on the left and another candidate on the right. In addition to each candidate’s photo, the following information was given: (a) a name (sampled from a pool of 100 common female and male names), (b) a randomly generated post office box number, (c) a randomly generated phone number, (d) an education attainment (sampled from four different pairs of edu- cational attainments), (e) an occupation (sampled from four different pairs of occupations), and (f) a statement adapted from actual voter pamphlets (sampled from 16 different pairs of statements). Pairs of statements were pilot tested to be about equally likely to receive votes. From the set of matched pairs of statements, one pair Female Candidates Male Candidates Female Candidates Male Candidates Study 3 (Female Participants)Study 3 (Male Participants) Study 2 (Female Participants)Study 2 (Male Participants) Younger Candidate Older Candidate Younger Candidate Older Candidate 100 80 60 40 20 0 30 70605040 Perceived Age of Candidate Vo tin g In te nt io n 100 80 60 40 20 0 30 70605040 Perceived Age of Candidate Vo tin g In te nt io n Vo tin g In te nt io n Vo tin g In te nt io n fe hg Candidate Gender Female Candidates Male Candidates Candidate Gender 1.00 0.75 0.50 0.25 0.00 1.00 0.75 0.50 0.25 0.00 Female Candidates Male Candidates Fig. 1. Voting intention in Studies 1 through 3. For Study 1 (a–d) and Study 3 (g, h), voting intention on a 100-point scale is shown as a function of participants’ estimates of candidates’ ages. Results are broken down in Study 1 by par- ticipant age (a, b) and gender (c, d) and in Study 3 by participant gender. Shaded areas represent bootstrapped 95% confidence intervals (CIs). For Study 2, the graphs show the proportion of (e) male and (f) female participants who voted for candidates in each of four age–gender categories (i.e., younger female, older female, younger male, or older male). Error bars represent 95% CIs. Candidates’ Age and Gender Predict Voter Preference 939 was randomly selected for each trial. Within each selected pair, one was randomly assigned to be pre- sented on the left and the other on the right (for stimuli, see Section III in the Supplemental Material). After see- ing the candidate photos and reading their profiles, participants were asked to vote for one of the two candidates by checking the box below that candidate’s profile. Results Multilevel logistic regression was used to examine whether participants were more likely to vote for younger candidates than for older candidates and whether those intentions were moderated by candidate gender. For candidate pair i and participant j, the dependent variable ln(Pij /(1 − Pij)) represents the log odds of the younger candidate receiving the vote. The dependent variable was regressed on the gender of candidate pairs (either both females or both males), with a random intercept and random slope that were fitted using the following formula: ln 1PairGender1 1( /( )) ( ) ( )P P g u g uij ij j j i− = +00 0 0+ + × The parameters were estimated using the generalized linear mixed model (glmer) module in lme4 (Version 1.1-14; Bates, 2005) and lmerTest (Version 2.0-33; Kuznetsova etal., 2014) in R. The results indicated that participants were on average more likely to vote for the younger candidate over the older candidate (g00= 0.46, SE = 0.07, p < .001). Most importantly, the negative effect of age was stronger for female than for male candidates (g10 = −0.23, SE = 0.08, p = .004). The effect of candidate gender and age on voting intention differed significantly across participants, χ2(2, N = 298) = 11.99, p = .002. Through exploratory analy- sis, we found that this effect was moderated by partici- pant gender: Male participants were significantly more likely to exhibit a preference for younger candidate when both candidates were female than when both candidates were male (p < .001; see Figs. 1e–1f; for statistical details, see Section II.3 in the Supplemental Material). The Participant Age × Candidate Age × Can- didate Gender interaction was not statistically signifi- cant (p > .1) in predicting voting intention.
Study 3
Study 3 replicated the findings of Study 1 and examined
the extent to which the results could be accounted for
by the influence of perceived competence, warmth, and
Participants. One hundred eighty undergraduate stu-
dents at the University of Washington participated in the
study in exchange for course credit. Five participants
were excluded because they failed to complete the study.
This resulted in a final sample of 175 participants (49%
male, 51% female; age: M = 19.38 years, SD = 2.86 years;
76 participants self-identified as White, 33 as East Asian,
28 as South Asian, 11 as Black or African American, 10 as
mixed race, five as Native Hawaiian, two as American
Indian/Alaska Native, and 10 as “other/unspecified”).
Data were collected in early 2016. No analyses were con-
ducted before data collection was completed.
Procedure. Participants came to the lab to participate in
a study called “Impression Formation and Politics.” They
were first asked to estimate the age of the same 93 can-
didates used in Study 1. Next, they were asked to rate the
same 93 candidates on competence, warmth, and attrac-
tiveness. Participants rated all 93 candidates on one dimen-
sion (e.g., competence) before moving to the next dimen –
sion. The order of the rating tasks and the sequence of
candidates were randomized. Finally, participants indi-
cated their voting intention toward the same 93 candi-
dates on a 100-point slider.
Consistent with the findings from Studies 1 and 2, results
showed that participants favored the very young female
candidates over the very young male candidates. How-
ever, voting intentions toward female candidates
decreased with candidate age, whereas voting intentions
for male candidates first increased and then decreased
with age (see Figs. 1g–1h; for statistical details, see Table
S12 in the Supplemental Material). Also consistent with
the findings from Study 1, results showed a significant
Candidate Age × Candidate Gender interaction, but the
interaction was stronger for male participants (Fig. 1g)
than for female participants (Fig. 1h; p < .01 for the Candidate Age × Candidate Gender × Participant Gen- der interaction; for other statistical details, see Table S8 in the Supplemental Material). Next, we examined the role of perceived warmth, competence, and attractiveness in accounting for the relationship between candidate age and voting inten- tion. Specifically, we conducted separate mediation analyses for female and male candidates that assumed a causal sequence in which perceived age influenced perceived competence, attractiveness, and warmth of candidates, which in turn influenced participants’ vot- ing intention. Although we believe this sequence is reasonable, the results should be interpreted as 940 Shen, Shoda conditional on this specific causal-sequence assumption (Baron & Kenny, 1986, p. 1177). In this within-subject mediation analysis, first the outcome variable was simultaneously regressed on all the mediators and the independent variable (i.e., can- didate age). Then each mediator was regressed on the independent variable. The analysis was done separately for younger candidates (age < 45 years) and older can- didates (age ≥ 45 years) because the assumption of linear relations made by the mediation analysis would not hold when the entire age range was included (see Fig. 1), whereas the assumption was met reasonably well when the data for younger candidates and older candidates were analyzed separately. The results are shown in Figure 2. The within-subject regression coef- ficients for female candidates (averaged across all par- ticipants) are shown in red, and the coefficients for male candidates are shown in blue. Most notably, among younger candidates, candidate age had a greater negative effect on perceived attrac- tiveness for female candidates compared with male candidates. In addition, again among younger candi- dates, candidate age had a negative effect on perceived warmth for female candidates but a positive effect for male candidates. Candidate age had virtually no effect on perceived competence for female candidates but a positive effect for male candidates. For candidates per- ceived to be older than 45, gender differences in the relations between candidate age and the mediators were much smaller in magnitude compared with those for candidates younger than 45. Following the method for assessing the indirect effects through mediators (Preacher & Hayes, 2008), we computed the product of the regression coefficient pre- dicting a given mediator (e.g., perceived attractiveness) from the independent variable (i.e., candidate age) and the coefficient predicting the outcome variable (e.g., voting intention) from the mediator. For example, the indirect effect of perceived age on voting intention through perceived attractiveness was 5.59 (−1.03 × 5.43) for younger female candidates (see Fig. 2). The standard errors of the product of these coefficients were esti- mated by bootstrapping, providing the basis for statisti- cal significance testing. There were significant differences (p < .001) between younger female candidates and younger male candi- dates in all three indirect effects. Specifically, the effect of candidate age on voting intention toward younger female candidates was substantially mediated by per- ceived attractiveness (mean indirect effect = −5.60, 95% CI= [−6.74, −4.91], p < .001), but for younger male candi- dates, the same indirect path was notably weaker, although still statistically significant (mean indirect effect = −1.72, 95% CI= [−2.28, −1.35], p < .001). Similarly, for younger female candidates, perceived warmth significantly medi- ated the relationship between perceived age and voting intentions (mean indirect effect = −2.71, 95% CI = [−3.39, −2.22], p< .001), whereas the same indirect path was much weaker for younger male candidates, although still statistically significant (mean indirect effect = 0.54, 95% CI = [0.33, 0.92], p < .001). The indirect effect through perceived competence was not statistically sig- nificant (mean indirect effect = −0.25, 95% CI = [−1.10, 0.57]) for younger female candidates but highly significant for younger male candidates (mean indirect effect = 1.69, 95% CI = [1.06, 2.32], p < .001). Voting intentions toward older candidates were also mediated by perceived attractiveness, warmth, and competence (all ps< .01), but the magnitude of indirect effects did not differ between older female and older male candidates as much as they did for younger candidates (for more statistical details on the candidate gender differences in indirect effects, see Table S5 in the Supplemental Material). We also applied the approach proposed by Hayes and Preacher (2010) to estimate the indirect effects in the presence of quadratic relationships (see Section II.7 and Fig. S15 in the Supplemental Material). The results from these analyses were entirely consistent with those from the Preacher and Hayes (2008) method. General Discussion When averaged across participants, preferences for the candidates in the present studies were systematically related to the candidates’ age and gender, gleaned from their photos even when all factors beyond appearance were held constant or randomized. Specifically, for very young candidates (< 35 years old), participants favored women over men. However, their intention to vote for female candidates steadily decreased with candidates’ age, whereas their intention to vote for male candidates increased until the candidates were around 45 years old and then slightly decreased. When the effects were exam- ined separately for each participant, which was possible because we used the highly repeated within-person para- digm, we found that the effects of candidate age and gender on voting intention varied reliably across partici- pants. Even after participant gender and participant age were taken into account, there were still reliable indi- vidual differences in these effects. Our findings were highly consistent across the study paradigms, stimulus samples, and participant groups represented in the present studies. We also believe our findings likely generalize to our initial set of 365 candi- dates (see Study S4 in the Supplemental Material). Candidates’ Age and Gender Predict Voter Preference 941 However, generalizability beyond that set is unknown. Our stimuli came from individuals who had already been elected, and thus the stimulus set may already reflect biases present in the U.S. electorate. The partici- pants of the present studies were undergraduate stu- dents and MTurk workers, who are often younger and more liberal than the general population (Berinsky etal., 2012). Thus unlike opinion polls, lab studies such as this do not provide direct predictions of election outcomes. A surprising finding from the current studies was that MTurk workers and undergraduate students consistently Perceived Age Perceived Warmth Voting Intention Perceived Competence Perceived Attractiveness −0.55 0.12 5.43 6.02 −1.03 −0.28 8.52 7.12 −0.02 0.23 ∗ Perceived Age Perceived Warmth Voting Intention Perceived Competence Perceived Attractiveness −0.10 −0.14 3.95 4.03 6.85 6.47 −0.38 −0.31 6.92 6.45 −0.20 −0.06 ∗ 4.42 4.86∗ ∗ Younger Candidates (< 45 Years Old) Older Candidates (≥ 45 Years Old) b a Fig. 2. Within-subject mediation models showing the effects of perceived age on vot- ing intention, as mediated by perceived attractiveness, perceived warmth, and perceived competence (Study 3). Models are shown separately for (a) candidates perceived to be younger than 45 years old and (b) candidates perceived to be 45 years or older. All variables except for voting intention were standardized. Values in red are within-subjects regression coefficients for female candidates (averaged across all participants), and values in blue are within-subjects regression coefficients for male candidates. The solid lines represent indirect effects, and dashed lines represent the remaining effect not accounted for by the mediators examined in this study. Asterisks indicate paths for which the coefficients for female and male candidates differ significantly (p < .05). The p values were obtained on the basis of 5,000 bootstrapped samples. 942 Shen, Shoda favored the younger female candidates in our stimulus set. One potential mechanism for this finding is that participants in Studies 2 and 3, who were generally young, preferred younger and presumably more liberal (e.g., female) candidates. However, Study 1 had par- ticipants who were older. They also preferred younger female candidates, albeit less strongly than younger participants. This suggests that there were other mecha- nisms at play. For example, female and male candidates may differ in how their perceived age is related to their perceived attractiveness, warmth, and competence, which in turn predict voting intentions toward them. Study 3 supports such a possibility, but more research is needed in which candidates’ perceived age and gen- der are experimentally manipulated in order to address the causal role of these factors. Transparency Action Editor: John Jonides Editor: D. Stephen Lindsay Author Contributions Y. A. Shen and Y. Shoda developed the hypothesis and designed the studies. Data were collected by Y. A. Shen. Y. A. Shen analyzed the data under the guidance of Y. Shoda. Y. A. Shen and Y. Shoda drafted and revised the manuscript. Both authors approved the final manuscript for submission. Declaration of Conflicting Interests The author(s) declared that there were no conflicts of interest with respect to the authorship or the publication of this article. Funding This research was partly supported by the Bolles Fund from the University of Washington Department of Psychology. Open Practices The design and analysis plans for Studies 1 and 2 were preregistered at AsPredicted ( ng5vg.pdf and, respec- tively). Data and materials for Studies 1 through 3 have not been made publicly available. This article has received the badge for Preregistration. More information about the Open Practices badges can be found at http://www.psy ORCID iD Yiqin Alicia Shen Acknowledgments We thank Jason Webster for his help through every phase of preparing this manuscript. Supplemental Material Additional supporting information can be found at http:// References Baron, R. M., & Kenny, D. A. (1986). The moderator–media- tor variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173– 1182. Bates, D. (2005). Fitting linear mixed models in R: Using the lme4 package. R News, 5(1), 27–30. https://www Berggren, N., Jordahl, H., & Poutvaara, P. (2010). The looks of a winner: Beauty and electoral success. Journal of Public Economics, 94(1–2), 8–15. .jpubeco.2009.11.002 Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012). Evaluating online labor markets for experimental research: Amazon. com’s Mechanical Turk. Political Analysis, 20, 351–368. Brewer, M. B., & Lui, L. N. (1989). The primacy of age and sex in the structure of person categories. Social Cognition, 7(3), 262–274. Bryk, A. S., Raudenbush, S. W., & Congdon, R. (2010). HLM 7 for Windows [Computer software]. Scientific Software International. Chiao, J. Y., Bowman, N. E., & Gill, H. (2008). The political gender gap: Gender bias in facial inferences that predict voting behavior. PLOS ONE, 3(10), Article e3666. https:// Dockterman, E. (2016, May 11). ACLU: The federal government is investigating sexism against directors in Hollywood. 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Journal of Nonverbal Behavior, 34, 83–110. https://doi .org/10.1007/s10919-009-0082-1 Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resa- mpling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879–891. .3.879 Candidates’ Age and Gender Predict Voter Preference 943 R Core Team. (2017). R: A language and environment for statistical computing (Version 3.4.0) [Computer software]. Retrieved from Sontag, S. (1972, September 23). The double standard of aging. The Saturday Review, pp. 29–38. http://www.unz .com/print/SaturdayRev-1972sep23-00029 Stockemer, D., & Sundström, A. (2018). Age representation in parliaments: Can institutions pave the way for the young? European Political Science Review, 10(3), 467–490. Sutherland, C. A. M., Oldmeadow, J. A., Santos, I. M., Towler, J., Burt, D. M., & Young, A. W. (2013). Social inferences from faces: Ambient images generate a three-dimensional model. Cognition, 127(1), 105–118. .cognition.2012.12.001 Todorov, A., Mandisodza, A. N., Goren, A., & Hall, C. C. (2005). Inferences of competence from faces predict elec- tion outcomes. Science, 308(5728), 1623–1626. https:// Whitsett, D. D., & Shoda, Y. (2014). An approach to test for individual differences in the effects of situations with- out using moderator variables. Journal of Experimental Social Psychology, 50, 94–104. .jesp.2013.08.008 Zayas, V., Sridharan, V., Lee, R. T., & Shoda, Y. (2019). Addressing two blind spots of commonly used experi- mental designs: The highly-repeated within-person approach. Social and Personality Psychology Com pass, 13(9), Article e12487. 12487 Materials and Instructions: 1. Article Critique Instructions  Article Critique Self-Rating Rubric 1. (note that you must submit this rubric with your paper when you hand it in! There is a separate assignment on canvas where you can submit this). Example Papers: Article Critique Example Paper_7th Ed APA Style_updated.docx Articles (choose one): Power Posing.pdf How Candidates’ Age and Gender Predict Voter Preference in a Hypothetical Election.pdf Better Mood and Better Performance- Learning Rule-Described Categories Is Enhanced by Positive Mood.pdf

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