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• Beauty sleep:...

Beauty sleep: experimental study on the perceived health and attractiveness of sleep deprived people

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• Peer review
• John Axelsson , researcher 1 2 ,
• Tina Sundelin , research assistant and MSc student 2 ,
• Michael Ingre , statistician and PhD student 3 ,
• Eus J W Van Someren , researcher 4 ,
• Andreas Olsson , researcher 2 ,
• Mats Lekander , researcher 1 3
• 1 Osher Center for Integrative Medicine, Department of Clinical Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden
• 2 Division for Psychology, Department of Clinical Neuroscience, Karolinska Institutet
• 3 Stress Research Institute, Stockholm University, Stockholm
• 4 Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and Sciences, and VU Medical Center, Amsterdam, Netherlands
• Correspondence to: J Axelsson john.axelsson{at}ki.se
• Accepted 22 October 2010

Objective To investigate whether sleep deprived people are perceived as less healthy, less attractive, and more tired than after a normal night’s sleep.

Design Experimental study.

Setting Sleep laboratory in Stockholm, Sweden.

Participants 23 healthy, sleep deprived adults (age 18-31) who were photographed and 65 untrained observers (age 18-61) who rated the photographs.

Intervention Participants were photographed after a normal night’s sleep (eight hours) and after sleep deprivation (31 hours of wakefulness after a night of reduced sleep). The photographs were presented in a randomised order and rated by untrained observers.

Main outcome measure Difference in observer ratings of perceived health, attractiveness, and tiredness between sleep deprived and well rested participants using a visual analogue scale (100 mm).

Results Sleep deprived people were rated as less healthy (visual analogue scale scores, mean 63 (SE 2) v 68 (SE 2), P<0.001), more tired (53 (SE 3) v 44 (SE 3), P<0.001), and less attractive (38 (SE 2) v 40 (SE 2), P<0.001) than after a normal night’s sleep. The decrease in rated health was associated with ratings of increased tiredness and decreased attractiveness.

Conclusion Our findings show that sleep deprived people appear less healthy, less attractive, and more tired compared with when they are well rested. This suggests that humans are sensitive to sleep related facial cues, with potential implications for social and clinical judgments and behaviour. Studies are warranted for understanding how these effects may affect clinical decision making and can add knowledge with direct implications in a medical context.

Introduction

The recognition [of the case] depends in great measure on the accurate and rapid appreciation of small points in which the diseased differs from the healthy state Joseph Bell (1837-1911)

Good clinical judgment is an important skill in medical practice. This is well illustrated in the quote by Joseph Bell, 1 who demonstrated impressive observational and deductive skills. Bell was one of Sir Arthur Conan Doyle’s teachers and served as a model for the fictitious detective Sherlock Holmes. 2 Generally, human judgment involves complex processes, whereby ingrained, often less consciously deliberated responses from perceptual cues are mixed with semantic calculations to affect decision making. 3 Thus all social interactions, including diagnosis in clinical practice, are influenced by reflexive as well as reflective processes in human cognition and communication.

Sleep is an essential homeostatic process with well established effects on an individual’s physiological, cognitive, and behavioural functionality 4 5 6 7 and long term health, 8 but with only anecdotal support of a role in social perception, such as that underlying judgments of attractiveness and health. As illustrated by the common expression “beauty sleep,” an individual’s sleep history may play an integral part in the perception and judgments of his or her attractiveness and health. To date, the concept of beauty sleep has lacked scientific support, but the biological importance of sleep may have favoured a sensitivity to perceive sleep related cues in others. It seems warranted to explore such sensitivity, as sleep disorders and disturbed sleep are increasingly common in today’s 24 hour society and often coexist with some of the most common health problems, such as hypertension 9 10 and inflammatory conditions. 11

To describe the relation between sleep deprivation and perceived health and attractiveness we asked untrained observers to rate the faces of people who had been photographed after a normal night’s sleep and after a night of sleep deprivation. We chose facial photographs as the human face is the primary source of information in social communication. 12 A perceiver’s response to facial cues, signalling the bearer’s emotional state, intentions, and potential mate value, serves to guide actions in social contexts and may ultimately promote survival. 13 14 15 We hypothesised that untrained observers would perceive sleep deprived people as more tired, less healthy, and less attractive compared with after a normal night’s sleep.

Using an experimental design we photographed the faces of 23 adults (mean age 23, range 18-31 years, 11 women) between 14.00 and 15.00 under two conditions in a balanced design: after a normal night’s sleep (at least eight hours of sleep between 23.00-07.00 and seven hours of wakefulness) and after sleep deprivation (sleep 02.00-07.00 and 31 hours of wakefulness). We advertised for participants at four universities in the Stockholm area. Twenty of 44 potentially eligible people were excluded. Reasons for exclusion were reported sleep disturbances, abnormal sleep requirements (for example, sleep need out of the 7-9 hour range), health problems, or availability on study days (the main reason). We also excluded smokers and those who had consumed alcohol within two days of the protocol. One woman failed to participate in both conditions. Overall, we enrolled 12 women and 12 men.

The participants slept in their own homes. Sleep times were confirmed with sleep diaries and text messages. The sleep diaries (Karolinska sleep diary) included information on sleep latency, quality, duration, and sleepiness. Participants sent a text message to the research assistant by mobile phone (SMS) at bedtime and when they got up on the night before sleep deprivation. They had been instructed not to nap. During the normal sleep condition the participants’ mean duration of sleep, estimated from sleep diaries, was 8.45 (SE 0.20) hours. The sleep deprivation condition started with a restriction of sleep to five hours in bed; the participants sent text messages (SMS) when they went to sleep and when they woke up. The mean duration of sleep during this night, estimated from sleep diaries and text messages, was 5.06 (SE 0.04) hours. For the following night of total sleep deprivation, the participants were monitored in the sleep laboratory at all times. Thus, for the sleep deprivation condition, participants came to the laboratory at 22.00 (after 15 hours of wakefulness) to be monitored, and stayed awake for a further 16 hours. We therefore did not observe the participants during the first 15 hours of wakefulness, when they had had a slightly restricted sleep, but had good control over the last 16 hours of wakefulness when sleepiness increased in magnitude. For the sleep condition, participants came to the laboratory at 12.00 (after five hours of wakefulness). They were kept indoors two hours before being photographed to avoid the effects of exposure to sunlight and the weather. We had a series of five or six photographs (resolution 3872×2592 pixels) taken in a well lit room, with a constant white balance (×900l; colour temperature 4200 K, Nikon D80; Nikon, Tokyo). The white balance was differently set during the two days of the study and affected seven photographs (four taken during sleep deprivation and three during a normal night’s sleep). Removing these participants from the analyses did not affect the results. The distance from camera to head was fixed, as was the focal length, within 14 mm (between 44 and 58 mm). To ensure a fixed surface area of each face on the photograph, the focal length was adapted to the head size of each participant.

For the photo shoot, participants wore no makeup, had their hair loose (combed backwards if long), underwent similar cleaning or shaving procedures for both conditions, and were instructed to “sit with a straight back and look straight into the camera with a neutral, relaxed facial expression.” Although the photographer was not blinded to the sleep conditions, she followed a highly standardised procedure during each photo shoot, including minimal interaction with the participants. A blinded rater chose the most typical photograph from each series of photographs. This process resulted in 46 photographs; two (one from each sleep condition) of each of the 23 participants. This part of the study took place between June and September 2007.

In October 2007 the photographs were presented at a fixed interval of six seconds in a randomised order to 65 observers (mainly students at the Karolinska Institute, mean age 30 (range 18-61) years, 40 women), who were unaware of the conditions of the study. They rated the faces for attractiveness (very unattractive to very attractive), health (very sick to very healthy), and tiredness (not at all tired to very tired) on a 100 mm visual analogue scale. After every 23 photographs a brief intermission was allowed, including a working memory task lasting 23 seconds to prevent the faces being memorised. To ensure that the observers were not primed to tiredness when rating health and attractiveness they rated the photographs for attractiveness and health in the first two sessions and tiredness in the last. To avoid the influence of possible order effects we presented the photographs in a balanced order between conditions for each session.

Statistical analyses

Data were analysed using multilevel mixed effects linear regression, with two crossed independent random effects accounting for random variation between observers and participants using the xtmixed procedure in Stata 9.2. We present the effect of condition as a percentage of change from the baseline condition as the reference using the absolute value in millimetres (rated on the visual analogue scale). No data were missing in the analyses.

Sixty five observers rated each of the 46 photographs for attractiveness, health, and tiredness: 138 ratings by each observer and 2990 ratings for each of the three factors rated. When sleep deprived, people were rated as less healthy (visual analogue scale scores, mean 63 (SE 2) v 68 (SE 2)), more tired (53 (SE 3) v 44 (SE 3)), and less attractive (38 (SE 2) v 40 (SE 2); P<0.001 for all) than after a normal night’s sleep (table 1 ⇓ ). Compared with the normal sleep condition, perceptions of health and attractiveness in the sleep deprived condition decreased on average by 6% and 4% and tiredness increased by 19%.

 Multilevel mixed effects regression on effect of how sleep deprived people are perceived with respect to attractiveness, health, and tiredness

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A 10 mm increase in tiredness was associated with a −3.0 mm change in health, a 10 mm increase in health increased attractiveness by 2.4 mm, and a 10 mm increase in tiredness reduced attractiveness by 1.2 mm (table 2 ⇓ ). These findings were also presented as correlation, suggesting that faces with perceived attractiveness are positively associated with perceived health (r=0.42, fig 1 ⇓ ) and negatively with perceived tiredness (r=−0.28, fig 1). In addition, the average decrease (for each face) in attractiveness as a result of deprived sleep was associated with changes in tiredness (−0.53, n=23, P=0.03) and in health (0.50, n=23, P=0.01). Moreover, a strong negative association was found between the respective perceptions of tiredness and health (r=−0.54, fig 1). Figure 2 ⇓ shows an example of observer rated faces.

 Associations between health, tiredness, and attractiveness

Fig 1  Relations between health, tiredness, and attractiveness of 46 photographs (two each of 23 participants) rated by 65 observers on 100 mm visual analogue scales, with variation between observers removed using empirical Bayes’ estimates

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Fig 2  Participant after a normal night’s sleep (left) and after sleep deprivation (right). Faces were presented in a counterbalanced order

To evaluate the mediation effects of sleep loss on attractiveness and health, tiredness was added to the models presented in table 1 following recommendations. 16 The effect of sleep loss was significantly mediated by tiredness on both health (P<0.001) and attractiveness (P<0.001). When tiredness was added to the model (table 1) with an estimated coefficient of −2.9 (SE 0.1; P<0.001) the independent effect of sleep loss on health decreased from −4.2 to −1.8 (SE 0.5; P<0.001). The effect of sleep loss on attractiveness decreased from −1.6 (table 1) to −0.62 (SE 0.4; P=0.133), with tiredness estimated at −1.1 (SE 0.1; P<0.001). The same approach applied to the model of attractiveness and health (table 2), with a decrease in the association from 2.4 to 2.1 (SE 0.1; P<0.001) with tiredness estimated at −0.56 (SE 0.1; P<0.001).

Sleep deprived people are perceived as less attractive, less healthy, and more tired compared with when they are well rested. Apparent tiredness was strongly related to looking less healthy and less attractive, which was also supported by the mediating analyses, indicating that a large part of the found effects and relations on appearing healthy and attractive were mediated by looking tired. The fact that untrained observers detected the effects of sleep loss in others not only provides evidence for a perceptual ability not previously subjected to experimental control, but also supports the notion that sleep history gives rise to socially relevant signals that provide information about the bearer. The adaptiveness of an ability to detect sleep related facial cues resonates well with other research, showing that small deviations from the average sleep duration in the long term are associated with an increased risk of health problems and with a decreased longevity. 8 17 Indeed, even a few hours of sleep deprivation inflict an array of physiological changes, including neural, endocrinological, immunological, and cellular functioning, that if sustained are relevant for long term health. 7 18 19 20 Here, we show that such physiological changes are paralleled by detectable facial changes.

These results are related to photographs taken in an artificial setting and presented to the observers for only six seconds. It is likely that the effects reported here would be larger in real life person to person situations, when overt behaviour and interactions add further information. Blink interval and blink duration are known to be indicators of sleepiness, 21 and trained observers are able to evaluate reliably the drowsiness of drivers by watching their videotaped faces. 22 In addition, a few of the people were perceived as healthier, less tired, and more attractive during the sleep deprived condition. It remains to be evaluated in follow-up research whether this is due to random error noise in judgments, or associated with specific characteristics of observers or the sleep deprived people they judge. Nevertheless, we believe that the present findings can be generalised to a wide variety of settings, but further studies will have to investigate the impact on clinical studies and other social situations.

Importantly, our findings suggest a prominent role of sleep history in several domains of interpersonal perception and judgment, in which sleep history has previously not been considered of importance, such as in clinical judgment. In addition, because attractiveness motivates sexual behaviour, collaboration, and superior treatment, 13 sleep loss may have consequences in other social contexts. For example, it has been proposed that facial cues perceived as attractive are signals of good health and that this recognition has been selected evolutionarily to guide choice of mate and successful transmission of genes. 13 The fact that good sleep supports a healthy look and poor sleep the reverse may be of particular relevance in the medical setting, where health estimates are an essential part. It is possible that people with sleep disturbances, clinical or otherwise, would be judged as more unhealthy, whereas those who have had an unusually good night’s sleep may be perceived as rather healthy. Compared with the sleep deprivation used in the present investigation, further studies are needed to investigate the effects of less drastic acute reductions of sleep as well as long term clinical effects.

Conclusions

People are capable of detecting sleep loss related facial cues, and these cues modify judgments of another’s health and attractiveness. These conclusions agree well with existing models describing a link between sleep and good health, 18 23 as well as a link between attractiveness and health. 13 Future studies should focus on the relevance of these facial cues in clinical settings. These could investigate whether clinicians are better than the average population at detecting sleep or health related facial cues, and whether patients with a clinical diagnosis exhibit more tiredness and are less healthy looking than healthy people. Perhaps the more successful doctors are those who pick up on these details and act accordingly.

Taken together, our results provide important insights into judgments about health and attractiveness that are reminiscent of the anecdotal wisdom harboured in Bell’s words, and in the colloquial notion of “beauty sleep.”

What is already known on this topic

Short or disturbed sleep and fatigue constitute major risk factors for health and safety

Complaints of short or disturbed sleep are common among patients seeking healthcare

The human face is the main source of information for social signalling

What this study adds

The facial cues of sleep deprived people are sufficient for others to judge them as more tired, less healthy, and less attractive, lending the first scientific support to the concept of “beauty sleep”

By affecting doctors’ general perception of health, the sleep history of a patient may affect clinical decisions and diagnostic precision

Cite this as: BMJ 2010;341:c6614

We thank B Karshikoff for support with data acquisition and M Ingvar for comments on an earlier draft of the manuscript, both without compensation and working at the Department for Clinical Neuroscience, Karolinska Institutet, Sweden.

Contributors: JA designed the data collection, supervised and monitored data collection, wrote the statistical analysis plan, carried out the statistical analyses, obtained funding, drafted and revised the manuscript, and is guarantor. TS designed and carried out the data collection, cleaned the data, drafted, revised the manuscript, and had final approval of the manuscript. JA and TS contributed equally to the work. MI wrote the statistical analysis plan, carried out the statistical analyses, drafted the manuscript, and critically revised the manuscript. EJWVS provided statistical advice, advised on data handling, and critically revised the manuscript. AO provided advice on the methods and critically revised the manuscript. ML provided administrative support, drafted the manuscript, and critically revised the manuscript. All authors approved the final version of the manuscript.

Funding: This study was funded by the Swedish Society for Medical Research, Rut and Arvid Wolff’s Memory Fund, and the Osher Center for Integrative Medicine.

Competing interests: All authors have completed the Unified Competing Interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any company for the submitted work; no financial relationships with any companies that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: This study was approved by the Karolinska Institutet’s ethical committee. Participants were compensated for their participation.

Participant consent: Participant’s consent obtained.

Data sharing: Statistical code and dataset of ratings are available from the corresponding author at john.axelsson{at}ki.se .

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode .

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Academic writing and English language editing services

Authors who feel that their manuscript may benefit from additional academic writing or language editing support prior to submission are encouraged to seek out such services at their host institutions, engage with colleagues and subject matter experts, and/or consider several vendors that offer discounts to APA authors .

Please note that APA does not endorse or take responsibility for the service providers listed. It is strictly a referral service.

Use of such service is not mandatory for publication in an APA journal. Use of one or more of these services does not guarantee selection for peer review, manuscript acceptance, or preference for publication in any APA journal.

Submitting supplemental materials

APA can place supplemental materials online, available via the published article in the PsycArticles ® database. Please see Supplementing Your Article With Online Material for more details.

Author contribution statements using CRediT

The APA Publication Manual (7th ed.) stipulates that “authorship encompasses…not only persons who do the writing but also those who have made substantial scientific contributions to a study.” In the spirit of transparency and openness, Behavioral Neuroscience has adopted the Contributor Roles Taxonomy (CRediT) to describe each author's individual contributions to the work. CRediT offers authors the opportunity to share an accurate and detailed description of their diverse contributions to a manuscript.

Submitting authors will be asked to identify the contributions of all authors at initial submission according to this taxonomy. If the manuscript is accepted for publication, the CRediT designations will be published as an Author Contributions Statement in the author note of the final article. All authors should have reviewed and agreed to their individual contribution(s) before submission.

CRediT includes 14 contributor roles, as described below:

• Conceptualization: Ideas; formulation or evolution of overarching research goals and aims.
• Data curation: Management activities to annotate (produce metadata), scrub data and maintain research data (including software code, where it is necessary for interpreting the data itself) for initial use and later reuse.
• Formal analysis: Application of statistical, mathematical, computational, or other formal techniques to analyze or synthesize study data.
• Funding acquisition: Acquisition of the financial support for the project leading to this publication.
• Investigation: Conducting a research and investigation process, specifically performing the experiments, or data/evidence collection.
• Methodology: Development or design of methodology; creation of models.
• Project administration: Management and coordination responsibility for the research activity planning and execution.
• Resources: Provision of study materials, reagents, materials, patients, laboratory samples, animals, instrumentation, computing resources, or other analysis tools.
• Software: Programming, software development; designing computer programs; implementation of the computer code and supporting algorithms; testing of existing code components.
• Supervision: Oversight and leadership responsibility for the research activity planning and execution, including mentorship external to the core team.
• Validation: Verification, whether as a part of the activity or separate, of the overall replication/reproducibility of results/experiments and other research outputs.
• Visualization: Preparation, creation and/or presentation of the published work, specifically visualization/data presentation.
• Writing—original draft: Preparation, creation and/or presentation of the published work, specifically writing the initial draft (including substantive translation).
• Writing—review and editing: Preparation, creation and/or presentation of the published work by those from the original research group, specifically critical review, commentary or revision—including pre- or post-publication stages.

Authors can claim credit for more than one contributor role, and the same role can be attributed to more than one author.

Abstract and keywords

All manuscripts must include an abstract containing a maximum of 250 words typed on a separate page. After the abstract, please supply up to five keywords or brief phrases.

List references in alphabetical order. Each listed reference should be cited in text, and each text citation should be listed in the references section.

Examples of basic reference formats:

Journal article

McCauley, S. M., & Christiansen, M. H. (2019). Language learning as language use: A cross-linguistic model of child language development. Psychological Review , 126 (1), 1–51. https://doi.org/10.1037/rev0000126

Authored book

Brown, L. S. (2018). Feminist therapy (2nd ed.). American Psychological Association. https://doi.org/10.1037/0000092-000

Chapter in an edited book

Balsam, K. F., Martell, C. R., Jones. K. P., & Safren, S. A. (2019). Affirmative cognitive behavior therapy with sexual and gender minority people. In G. Y. Iwamasa & P. A. Hays (Eds.), Culturally responsive cognitive behavior therapy: Practice and supervision (2nd ed., pp. 287–314). American Psychological Association. https://doi.org/10.1037/0000119-012

Data set citation

Alegria, M., Jackson, J. S., Kessler, R. C., & Takeuchi, D. (2016). Collaborative Psychiatric Epidemiology Surveys (CPES), 2001–2003 [Data set]. Inter-university Consortium for Political and Social Research. https://doi.org/10.3886/ICPSR20240.v8

Software/Code citation

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package.  Journal of Statistical Software , 36(3), 1–48. https://www.jstatsoft.org/v36/i03/

Wickham, H. et al., (2019). Welcome to the tidyverse. Journal of Open Source Software, 4 (43), 1686, https://doi.org/10.21105/joss.01686

All data, program code, and other methods must be cited in the text and listed in the references section.

Preferred formats for graphics files are TIFF and JPG, and preferred format for vector-based files is EPS. Graphics downloaded or saved from web pages are not acceptable for publication. Multipanel figures (i.e., figures with parts labeled a, b, c, d, etc.) should be assembled into one file. When possible, please place symbol legends below the figure instead of to the side.

• All color line art and halftones: 300 DPI
• Black and white line tone and gray halftone images: 600 DPI

Line weights

• Color (RGB, CMYK) images: 2 pixels
• Grayscale images: 4 pixels
• Stroke weight: 0.5 points

APA offers authors the option to publish their figures online in color without the costs associated with print publication of color figures.

The same caption will appear on both the online (color) and print (black and white) versions. To ensure that the figure can be understood in both formats, authors should add alternative wording (e.g., “the red (dark gray) bars represent”) as needed.

For authors who prefer their figures to be published in color both in print and online, original color figures can be printed in color at the editor's and publisher's discretion provided the author agrees to pay:

• $900 for one figure
• An additional $600 for the second figure
• An additional $450 for each subsequent figure

Permissions

Authors of accepted papers must obtain and provide to the editor on final acceptance all necessary permissions to reproduce in print and electronic form any copyrighted work, including test materials (or portions thereof), photographs, and other graphic images (including those used as stimuli in experiments).

On advice of counsel, APA may decline to publish any image whose copyright status is unknown.

• Download Permissions Alert Form (PDF, 13KB)

Reporting standards

Journal article reporting standards.

Authors should consider the APA Style Journal Article Reporting Standards (JARS) for a helpful resource for reporting data and the outcomes of inferential statistical tests. The standards offer ways to improve transparency in reporting to ensure that readers have the information necessary to evaluate the quality of the research and to facilitate collaboration and replication.

• recommend the division of hypotheses, analyses, and conclusions into primary, secondary, and exploratory groupings to allow for a full understanding of quantitative analyses presented in a manuscript and to enhance reproducibility;
• offer modules for authors reporting on replications, clinical trials, longitudinal studies, and observational studies, as well as the analytic methods of structural equation modeling and Bayesian analysis; and
• include guidelines on reporting of study preregistration (including making protocols public); participant characteristics (including demographic characteristics); inclusion and exclusion criteria; psychometric characteristics of outcome measures and other variables; and planned data diagnostics and analytic strategy.

The guidelines focus on transparency in methods reporting, recommending descriptions of how the researcher's own perspective affected the study, as well as the contexts in which the research and analysis took place.

Transparency and openness

Empirical research, including meta-analyses, submitted to the Journal of Experimental Psychology: Learning, Memory, and Cognition must meet Level 1 (Disclosure) for all eight aspects of research planning and reporting as well as Level 2 (Requirement) for citation; data, materials, and code transparency; and study and analysis plan preregistration. Authors should include a subsection in the method section titled “Transparency and openness.” This subsection should detail the efforts the authors have made to comply with the TOP guidelines.

For example:

• We report how we determined our sample size, all data exclusions (if any), all manipulations, and all measures in the study, and the study follows JARS (Appelbaum, et al., 2018). All data, analysis code, and research materials are available at [stable link to repository]. Data were analyzed using R, version 4.0.0 (R Core Team, 2020) and the package ggplot , version 3.2.1 (Wickham, 2016). This study’s design and its analysis were not pre-registered.

Data, materials, and code

Authors must state whether data, code, and study materials are posted to a trusted repository and, if so, how to access them, including their location and any limitations on use. If they cannot be made available, authors must state the legal or ethical reasons why they are not available. Trusted repositories adhere to policies that make data discoverable, accessible, usable, and preserved for the long term. Trusted repositories also assign unique and persistent identifiers.. Recommended repositories include APA’s repository on the Open Science Framework (OSF), or authors can access a full list of other recommended repositories .

In a subsection titled "Transparency and Openness" at the end of the method section, specify whether and where the data and material will be available or note the legal or ethical reasons for not doing so. For submissions with quantitative or simulation analytic methods, state whether the study analysis code (e.g., scripts for generating stimuli, conducting simulations, or performing data analyses) is posted to a trusted repository, and, if so, where to access it or the legal or ethical reason why it is not available.

• All data have been made publicly available at the [trusted repository name] and can be accessed at [persistent URL or DOI].
• Materials and analysis code for this study are not available.
• The code behind this analysis/simulation has been made publicly available at the [trusted repository name] and can be accessed at [persistent URL or DOI].

Preregistration of studies and analysis plans

Preregistration of studies and specific hypotheses can be a useful tool for making strong theoretical claims. Likewise, preregistration of analysis plans can be useful for distinguishing confirmatory and exploratory analyses. Investigators are encouraged to preregister their studies and analysis plans prior to conducting the research via a publicly accessible registry system (e.g., OSF , ClinicalTrials.gov, or other trial registries in the WHO Registry Network).

There are many available templates; for example, APA, the British Psychological Society, and the German Psychological Society partnered with the Leibniz Institute for Psychology and Center for Open Science to create Preregistration Standards for Quantitative Research in Psychology (Bosnjak et al., 2022).

Articles must state whether or not any work was preregistered and, if so, how to access the preregistration. Preregistrations must be available to reviewers; authors may submit a masked copy via stable link or supplemental material. Links in the method section should be replaced with an identifiable copy on acceptance.

• This study’s design was preregistered; see [STABLE LINK OR DOI].
• This study’s design and hypotheses were preregistered; see [STABLE LINK OR DOI].
• This study’s analysis plan was preregistered; see [STABLE LINK OR DOI].
• This study was not preregistered.

Open science badges

Articles are eligible for open science badges recognizing publicly available data, materials, and/or preregistration plans and analyses. These badges are awarded on a self-disclosure basis.

At submission, authors must confirm that criteria have been fulfilled in a signed badge disclosure form (PDF, 33KB) that must be submitted as supplemental material. If all criteria are met as confirmed by the editor, the form will then be published with the article as supplemental material.

Authors should also note their eligibility for the badge(s) in the cover letter.

For all badges, items must be made available on an open-access repository with a persistent identifier in a format that is time-stamped, immutable, and permanent. For the preregistered badge, this is an institutional registration system.

Data and materials must be made available under an open license allowing others to copy, share, and use the data, with attribution and copyright as applicable.

Available badges are:

Note that it may not be possible to preregister a study or to share data and materials. Applying for open science badges is optional.

Publication policies

For full details on publication policies, including use of Artificial Intelligence tools, please see APA Publishing Policies .

APA policy prohibits an author from submitting the same manuscript for concurrent consideration by two or more publications.

See also APA Journals ® Internet Posting Guidelines .

APA requires authors to reveal any possible conflict of interest in the conduct and reporting of research (e.g., financial interests in a test or procedure, funding by pharmaceutical companies for drug research).

• Download Full Disclosure of Interests Form (PDF, 41KB)

In light of changing patterns of scientific knowledge dissemination, APA requires authors to provide information on prior dissemination of the data and narrative interpretations of the data/research appearing in the manuscript (e.g., if some or all were presented at a conference or meeting, posted on a listserv, shared on a website, including academic social networks like ResearchGate, etc.). This information (2–4 sentences) must be provided as part of the Author Note.

Ethical Principles

It is a violation of APA Ethical Principles to publish "as original data, data that have been previously published" (Standard 8.13).

In addition, APA Ethical Principles specify that "after research results are published, psychologists do not withhold the data on which their conclusions are based from other competent professionals who seek to verify the substantive claims through reanalysis and who intend to use such data only for that purpose, provided that the confidentiality of the participants can be protected and unless legal rights concerning proprietary data preclude their release" (Standard 8.14).

APA expects authors to adhere to these standards. Specifically, APA expects authors to have their data available throughout the editorial review process and for at least 5 years after the date of publication.

Authors are required to state in writing that they have complied with APA ethical standards in the treatment of their sample, human or animal, or to describe the details of treatment.

• Download Certification of Compliance With APA Ethical Principles Form (PDF, 26KB)

The APA Ethics Office provides the full Ethical Principles of Psychologists and Code of Conduct electronically on its website in HTML, PDF, and Word format. You may also request a copy by emailing or calling the APA Ethics Office (202-336-5930). You may also read "Ethical Principles," December 1992, American Psychologist , Vol. 47, pp. 1597–1611.

Other information

See APA’s Publishing Policies page for more information on publication policies, including information on author contributorship and responsibilities of authors, author name changes after publication, the use of generative artificial intelligence, funder information and conflict-of-interest disclosures, duplicate publication, data publication and reuse, and preprints.

Visit the Journals Publishing Resource Center for more resources for writing, reviewing, and editing articles for publishing in APA journals.

Incoming editor

Jeffrey Starns, PhD University of Massachusetts Amherst

Outgoing editor

Aaron S. Benjamin, PhD University of Illinois at Urbana–Champaign, United States

Associate editors

Erik M. Altmann, PhD Michigan State University, United States

Tanjeem Azad, PhD Kwantlen Polytechnic University, Canada

Julie M. Bugg, PhD Washington University in St. Louis, United States

Heather J. Ferguson, PhD University of Kent, United Kingdom

Andrew Hollingworth, PhD University of Iowa, United States

Keith Hutchison, PhD Montana State University, United States

Klaus Oberauer, PhD University of Zürich, Switzerland

Matthew G. Rhodes, PhD Colorado State University, United States

Jörg Rieskamp, PhD University of Basel, Switzerland

Evan Risko, PhD University of Waterloo, Canada

Adam Sanborn, PhD University of Warwick, United Kingdom

L. Robert Slevc, PhD University of Maryland, College Park, United States

Jonathan Garrett Tullis, PhD University of Arizona, United States

Nash Unsworth, PhD University of Oregon, United States

Ronaldo Vigo, PhD Ohio University, United States

Tessa C. Warren, PhD University of Pittsburgh, United States

Duane G. Watson, PhD Vanderbilt University, United States

Jennifer Wiley, PhD University of Illinois at Chicago, United States

Incoming consulting editors

Laurel Brehm, PhD University of California, Santa Barbara, United States

Robert Davies, PhD Lancaster University, United Kingdom

Lena Jäger, PhD University of Zurich, Switzerland

Lynn Lohnas, PhD Syracuse University, United States

Adam Osth, PhD University of Melbourne, Australia

Andrea Patalano, PhD Wesleyan University, United States

Kimele Persaud, PhD Rutgers University Newark, United States

Pooja Sydney, PhD University of Kentucky, United States

Aaron Veldre, PhD University of Technology Sydney, Australia

Outgoing consulting editors

Sally Andrews, PhD University of Sydney, Australia

Julie E. Boland, PhD University of Michigan, United States

Laura Carlson, PhD University of Notre Dame, United States

Sven Mattys, PhD The University of York, United Kingdom

Antje S. Meyer, PhD Max Planck Institute for Psycholinguistics & Radboud University Nijmegen, Netherlands

Adrian Staub, PhD University of Massachusetts Amherst, United States

Anna M. Woollams, PhD University of Manchester, United Kingdom

Consulting editors

Jeanette Altarriba, PhD University at Albany & State University of New York, United States

Blair C. Armstrong, PhD University of Toronto, Canada

Jason Arndt, PhD Middlebury College, United States

Kate Arrington, PhD Lehigh University, United States

Hunter Ball, PhD University of Texas at Arlington, United States

Karl-Heinz Thomas Bäuml, PhD Regensburg University, Germany

C. J. Brainerd, PhD Cornell University, United States

David W. Braithwaite, PhD Florida State University, United States

Gene A. Brewer, PhD Arizona State University, United States

Thomas Busey, PhD Indiana University, Bloomington, United States

Andrew C. Butler, PhD Washington University in St. Louis, United States

Valérie Camos, PhD Université de Fribourg, Switzerland

Shana Carpenter, PhD Iowa State University, United States

Anne M. Cleary, PhD Colorado State University, United States

Gabriel I. Cook, PhD Claremont McKenna College, United States

Sarah Creel, PhD University of California, San Diego, United States

Matthew J. C. Crump, PhD Brooklyn College of CUNY, United States

Peter F. Delaney, PhD University of North Carolina at Greensboro, United States

Gary S. Dell, PhD University of Illinois at Urbana–Champaign, United States

Gesine Dreisbach, PhD Universität Regensburg, Germany

Nicolas Dumay, PhD University of Exeter, United Kingdom

John Dunlosky, PhD Kent State University, United States

Lisa K. Fazio, PhD Vanderbilt University, United States

Laurie Beth Feldman, PhD State University of New York at Albany, United States

Myra A. Fernandes, PhD University of Waterloo, Canada

Ruth Filik, PhD University of Nottingham, United Kingdom

Bridgid Finn, PhD Educational Testing Service, United States

Rico Fischer, PhD University of Greifswald, Germany

Wendy S. Francis, PhD University of Texas at El Paso, United States

David A. Gallo, PhD University of Chicago, United States

Tamar H. Gollan, PhD University of California, San Diego, United States

Corentin Gonthier, PhD University of Rennes 2, France

James A. Hampton, PhD City University of London, United Kingdom

Deborah Hannula, PhD University of Wisconsin-Milwaukee

Alice F. Healy, PhD University of Colorado, Boulder, United States

William S. Horton, PhD Northwestern University, United States

Kathleen L. Hourihan, PhD Memorial University of Newfoundland, Canada

Yi Ting Huang, PhD University of Maryland College Park, United States

R. Reed Hunt, PhD University of Mississippi, United States

Edward Matthew Husband, PhD University of Oxford, United Kingdom

Helene Intraub, PhD University of Delaware, United States

Andrew F. Jarosz, PhD Mississippi State University, United States

Luis Jimenez, PhD Universidad de Santiago de Compostela, Spain

Michael J. Kane, PhD University of North Carolina at Greensboro, United States

Jeffrey D. Karpicke, PhD Purdue University, United States

David Kellen, PhD Syracuse University, United States

Jonathan W. Kelly, PhD Iowa State University, United States

Annette Kinder, PhD Freie Universitaet Berlin, Germany

Sachiko Kinoshita, PhD Macquarie University, Australia

Karl Christoph Klauer, PhD Albert-Ludwigs-Universität Freiburg, Germany

Iring Koch, PhD RWTH Aachen University, Germany

Agnieszka E. Konopka, PhD University of Aberdeen, United Kingdom

Mike E. Le Pelley, PhD UNSW Sydney, Australia

Robyn A. LeBoeuf, PhD Washington University in St. Louis, United States

Vanessa M. Loaiza, PhD University of Essex, United Kingdom

Stephen J. Lupker, PhD University of Western Ontario, Canada

Randi Martin, PhD Rice University, United States

John Paul Minda, PhD University of Western Ontario, Canada

Laura Morett University of Alabama, United States

Weimin Mou, PhD University of Alberta, Canada

Moshe Naveh-Benjamin, PhD University of Missouri, United States

James H. Neely, PhD University at Albany & State University of New York, United States

David E. Over, PhD Durham University, United Kingdom

Thorsten Pachur, PhD Max Planck Institute for Human Development, Germany

Manuel Perea, PhD Universitat de València, Spain

John Philbeck, PhD George Washington University, United States

Valerie F. Reyna, PhD Cornell University, United States

Henry L. Roediger III, PhD Washington University in St. Louis, United States

Nathan S. Rose, PhD University of Notre Dame, United States

Caren M. Rotello, PhD University of Massachusetts, United States

Jan Rummel, PhD Heidelberg University, Germany

Lili Sahakyan, PhD University of Illinois at Urbana–Champaign, United States

Elizabeth Roye Schotter, PhD University of South Florida, United States

Colleen M. Seifert, PhD University of Michigan, Ann Arbor, United States

Rebekah Smith, PhD University of Mississippi, United States

Benjamin C. Storm, PhD University of California, Santa Cruz, United States

Aimee M. Surprenant, PhD Memorial University of Newfoundland, Canada

Holly A. Taylor, PhD Tufts University, United States

Anjali Thapar, PhD Bryn Mawr College, United States

Catherine Thevenot, PhD University of Lausanne, Switzerland

Ayanna K. Thomas, PhD Tufts University, United States

Monika Undorf, PhD University of Mannheim, Germany

Mieke Verfaellie, PhD Boston University & VA Boston Healthcare System, United States

Timothy J. Vickery, PhD University of Delaware, United States

Michael S. Vitevitch, PhD University of Kansas, United States

Chris Wahlheim, PhD University of North Carolina at Greensboro, United States

Deanne Westerman, PhD Binghamton University & State University of New York, United States

Holger Wiese, PhD Durham University, United Kingdom

Naohide Yamamoto, PhD Queensland University of Technology, Australia

Andrew Yonelinas, PhD University of California Davis, United States

Editorial fellows

Abhilasha Kumar, PhD Bowdoin College, United States

Kimele Persaud, PhD Rutgers University, United States

Peer review coordinator

Kris Guin American Psychological Association

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Transparency and Openness Promotion

APA endorses the Transparency and Openness Promotion (TOP) Guidelines by a community working group in conjunction with the Center for Open Science ( Nosek et al. 2015 ). The TOP Guidelines cover eight fundamental aspects of research planning and reporting that can be followed by journals and authors at three levels of compliance.

• Level 1: Disclosure—The article must disclose whether or not the materials are available.
• Level 2: Requirement—The article must share materials when legally and ethically permitted (or disclose the legal and/or ethical restriction when not permitted).
• Level 3: Verification—A third party must verify that the standard is met.

Empirical research, including meta-analyses, submitted to the Journal of Experimental Psychology: Learning, Memory, and Cognition must, at a minimum, meet Level 1 (Disclosure) for all eight aspects of research planning and reporting as well as Level 2 (Requirement) for citation; data, code, and materials transparency; and study and analysis plan preregistration. Authors should include a subsection in their methods description titled “Transparency and openness.” This subsection should detail the efforts the authors have made to comply with the TOP guidelines.

The list below summarizes the minimal TOP requirements of the journal. Please refer to the Center for Open Science TOP guidelines for details, and contact the editor (Aaron S. Benjamin, PhD) with any further questions. APA recommends sharing data, materials, and code via  trusted repositories (e.g.,  APA’s repository  on the Open Science Framework (OSF)). Trusted repositories adhere to policies that make data discoverable, accessible, usable, and preserved for the long term. Trusted repositories also assign unique and persistent identifiers.

We encourage investigators to preregister their studies and to share protocols and analysis plans prior to conducting the research. There are many available preregistration forms (e.g., the APA Preregistration for Quantitative Research in Psychology template, ClinicalTrials.gov , or other preregistration templates available via OSF ). Completed preregistration forms should be posted on a publicly accessible registry system (e.g., OSF , ClinicalTrials.gov, or other trial registries in the WHO Registry Network).

A list of participating journals is also available from APA.

The following list presents the eight fundamental aspects of research planning and reporting, the TOP level required by the Journal of Experimental Psychology: Learning, Memory, and Cognition , and a brief description of the journal's policy.

• Citation: Level 2, Requirement—All data, program code, and other methods developed by others must be appropriately cited in the text and listed in the references section.
• Data Transparency: Level 2, Requirement—Article states whether the raw and/or processed data on which study conclusions are based are posted to a trusted repository and either how to access them or the legal or ethical reasons why they are not available.
• Analytic Methods (Code) Transparency: Level 2, Requirement—Article states whether computer code or syntax needed to reproduce analyses in an article is posted to a trusted repository and either how to access it or the legal or ethical reasons why it is not available.
• Research Materials Transparency: Level 2, Requirement—Article states whether materials described in the method section are posted to a trusted repository and either how to access them or the legal or ethical reasons why they are not available.
• Design and Analysis Transparency (Reporting Standards): Level 1, Disclosure—Authors should consider APA Style Journal Article Reporting Standards (JARS-Quant, JARS-Qual, and/or MARS) as a helpful resource for reporting data and the outcomes of inferential statistical tests.
• Study Preregistration: Level 2, Requirement—Article states whether the study design and (if applicable) hypotheses of any of the work reported was preregistered and, if so, how to access it. Access to the preregistration must be available at submission. Authors requesting masked review should submit a masked copy via stable link or supplemental material.
• Analysis Plan Preregistration: Level 2, Requirement—Article states whether any of the work reported preregistered an analysis plan and, if so, how to access it. Access to the preregistration must be available at submission. Authors requesting masked review should submit a masked copy via stable link or supplemental material.
• Replication: Level 3, Verification—The journal publishes replications and Registered Reports.

Other open science initiatives

• Open Science badges: Offered
• Public significance statements: Not offered
• Author contribution statements using CRediT: Required
• Registered Reports: Published
• Replications: Published

Explore open science at APA .

Inclusive study designs

Definitions and further details on inclusive study designs are available on the Journals EDI homepage .

Inclusive reporting standards

• Bias-free language and community-driven language guidelines (required)
• Data sharing and data availability statements (required)

More information on this journal’s reporting standards is listed under the submission guidelines tab .

Pathways to authorship and editorship

Editorial fellowships.

Editorial fellowships help early-career psychologists gain firsthand experience in scholarly publishing and editorial leadership roles. This journal offers an editorial fellowship program for early-career psychologists from historically excluded communities.

Other EDI offerings

Orcid reviewer recognition.

Open Research and Contributor ID (ORCID) Reviewer Recognition provides a visible and verifiable way for journals to publicly credit reviewers without compromising the confidentiality of the peer-review process. This journal has implemented the ORCID Reviewer Recognition feature in Editorial Manager, meaning that reviewers can be recognized for their contributions to the peer-review process.

Masked peer review

This journal offers masked peer review (where both the authors’ and reviewers’ identities are not known to the other). Research has shown that masked peer review can help reduce implicit bias against traditionally female names or early-career scientists with smaller publication records (Budden et al., 2008; Darling, 2015).

Announcement

• Call for editorial fellowships nominations

Editor Spotlight

• Read an interview with Editor Aaron S. Benjamin, PhD
• Editorial by Aaron S. Benjamin, PhD February 2019

From APA Journals Article Spotlight ®

• Qualitative changes in the processes supporting math performance across learning
• General valence asymmetry in similarity

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• Knowledge Base

Methodology

• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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Exploring Experimental Research: Methodologies, Designs, and Applications Across Disciplines

9 Pages Posted: 23 Apr 2024

Sereyrath Em

National University of Cheasim Kamchaymear, Kampong Cham, Cambodia; Suranaree University of Technology Nakhon Ratchasima, Thailand

Date Written: 2024

Experimental research serves as a fundamental scientific method aimed at unraveling cause-and-effect relationships between variables across various disciplines. This paper delineates the key features of experimental research, including the manipulation of variables, controlled conditions, random assignment, and meticulous measurement techniques to facilitate causal inferences. It elucidates different experimental designs such as randomized controlled trials, true experimental designs, quasi-experimental designs, and single-case designs, each tailored to specific research contexts. Moreover, the paper expounds on the procedural steps in conducting experimental research, emphasizing the importance of methodological rigor from study design to result interpretation. Additionally, it delineates the potential threats to internal and external validity, highlighting the significance of mitigating confounding factors for robust experimental outcomes. Furthermore, the paper discusses the timing of pre-tests and post-tests, the intricacies of experiment design, and emerging trends such as internet-based experiments and ex post facto research. Through a comprehensive examination of experimental research methodologies, designs, and applications, this paper aims to provide researchers with a nuanced understanding of experimental inquiry across diverse academic domains.

Keywords: Experimental Research, Causal Inference, Research Designs, Internal Validity, External Validity

Suggested Citation: Suggested Citation

Sereyrath Em (Contact Author)

National university of cheasim kamchaymear, kampong cham, cambodia ( email ).

Kampong Cham, Cambodia Phnom Penh, 855 Cambodia

Suranaree University of Technology Nakhon Ratchasima, Thailand ( email )

Nakhon, Ratchasima, 3000, Thailand Thailand

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Experimental Research on Dreaming: State of the Art and Neuropsychoanalytic Perspectives

Perrine m. ruby.

1 INSERM U1028, Lyon Neuroscience Research Center, Brain Dynamics and Cognition Team, Lyon, France

2 CNRS UMR5292, Lyon Neuroscience Research Center, Brain Dynamics and Cognition Team, Lyon, France

3 University Lyon 1, Lyon, France

Dreaming is still a mystery of human cognition, although it has been studied experimentally for more than a century. Experimental psychology first investigated dream content and frequency. The neuroscientific approach to dreaming arose at the end of the 1950s and soon proposed a physiological substrate of dreaming: rapid eye movement sleep. Fifty years later, this hypothesis was challenged because it could not explain all of the characteristics of dream reports. Therefore, the neurophysiological correlates of dreaming are still unclear, and many questions remain unresolved. Do the representations that constitute the dream emerge randomly from the brain, or do they surface according to certain parameters? Is the organization of the dream’s representations chaotic or is it determined by rules? Does dreaming have a meaning? What is/are the function(s) of dreaming? Psychoanalysis provides hypotheses to address these questions. Until now, these hypotheses have received minimal attention in cognitive neuroscience, but the recent development of neuropsychoanalysis brings new hopes of interaction between the two fields. Considering the psychoanalytical perspective in cognitive neuroscience would provide new directions and leads for dream research and would help to achieve a comprehensive understanding of dreaming. Notably, several subjective issues at the core of the psychoanalytic approach, such as the concept of personal meaning, the concept of unconscious episodic memory and the subject’s history, are not addressed or considered in cognitive neuroscience. This paper argues that the focus on singularity and personal meaning in psychoanalysis is needed to successfully address these issues in cognitive neuroscience and to progress in the understanding of dreaming and the psyche.

The word “dream” is commonly used to express an unattainable ideal or a very deep and strong desire:

I have a dream that my four little children will one day live in a nation where they will not be judged by the color of their skin, but by the content of their character. Martin Luther King

In dream reports, however, one often notices banal situations, strange scenes, or even frightening events. Why is there such a contrast between the popular meaning of the word “dream” and the content of dream reports? Why are some dream scenes so bizarre? Are dreams built from images that arise randomly from the sleeping brain? Or is the emergence and organization of dream images controlled by currently unknown parameters? Does dreaming have a function?

Answering these questions is not easy because dreaming is elusive. We still do not know when it happens during the night, how long it lasts, whether we can recall its entire content, or how to control it. For more than a century, such limited understanding of dreaming has seriously hampered experimental investigations. Nonetheless, scientific research has managed to produce considerable information about the phenomenology and physiology of dreaming and has improved our understanding of this fascinating phenomenon.

Experimental Research on Dreaming

Dreaming and experimental psychology, dream content.

Dreaming was first investigated on an experimental level in the nineteenth century. Calkins ( 1893 ) published the first statistical results about dreaming and argued that some aspects of dream content could be quantified. Later, questionnaires and automatic analysis of the lexical content of dream reports allowed psychologists to show that dream content has some precise phenomenological characteristics. According to psychological studies (Hall and Van de Castle, 1966 ; Schwartz, 1999 ), visual imagery occurs more frequently in dreams than imagery of other senses (audition, olfaction, touch, and taste); the dream drama is mostly lived by the dreamer from a first-person perspective; some elements of real-life events previously experienced by the dreamer often contribute to the scene of the dream; most often, the dream sequence is not within the dreamer’s voluntary control (i.e., the dreamer may be convinced during the dream that the dream’s story is really happening); temporal and spatial incoherencies can occur in the dream story; the dream report is often full of people interacting with each other (e.g., discussions, fights, pursuit, sexuality); and finally, the dream report often contains strong emotions.

Substantial variability of content exists, however, among the same individual’s dreams and among the dreams of different individuals. Further, psychological studies have shown that many internal and external parameters can influence dream content. For example, males report more aggression and violence in their dreams than do females (Nielsen et al., 2003 ; Schredl et al., 2004 ). External stimulation perceived by the dreamer can be incorporated into dreams (Koulack, 1969 ; Saint-Denys, 1867; Hoelscher et al., 1981 ), as illustrated by the famous Dali painting Dream Caused by the Flight of a Bee around a Pomegranate a Second before Awakening . The current concerns of the subject may also be found in the content of his/her dreams (Schwartz, 1999 ; Domhoff and Schneider, 2008 ), and many aspects of the subject’s daily life were found to influence dream content, including news events (Bulkeley and Kahan, 2008 ), musical practice (Uga et al., 2006 ), religious beliefs (Domhoff and Schneider, 2008 ), chronic pain (Raymond et al., 2002 ), mood (Cartwright et al., 1998a ), or a violent living environment (Valli et al., 2005 ). By contrast, congenital or acquired malformations do not seem to significantly influence dream content (Voss et al., 2010 ; Saurat et al., 2011 ).

Based on these results, two opposing hypotheses were formulated: the continuity hypothesis (Schredl and Hofmann, 2003 ) and the discontinuity hypothesis (Rechtschaffen, 1978 ; Kahn et al., 1997 ; Stickgold et al., 2001 ). The former relies on results showing that the themes of an individual’s thoughts during waking life and dreaming are similar; the latter focuses on the fundamentally different structures of thoughts during waking life and dreaming. Voss et al. ( 2010 ) stressed in their recent paper that these hypotheses represent oversimplified approaches to dream analysis and argued that waking and dreaming thoughts were related but structurally independent; in other words, she argued in favor of merging the continuity and discontinuity hypotheses.

Dream report frequency

Dream report frequency (DRF) can vary within subjects and varies substantially among subjects. In a study of 900 German subjects with a large age range from various socioprofessional categories, the mean DRF was approximately 1 dream report per week (Schredl, 2008 ). This result shows that the dream experience is common and familiar to everyone. Psychological studies have demonstrated that many parameters covary with DRF and may thus influence it.

Sleep parameters

First, DRF varies according to the sleep stage preceding awakening (e.g., Dement and Kleitman, 1957b ; Nielsen, 2000 , for a review). More dream reports are obtained after an awakening during rapid eye movement (REM) sleep than after an awakening during non-REM (NREM) sleep. These results inspired the REM sleep hypothesis of dreaming (see the section Dreaming and Neuroscience). Second, DRF increases with the number of awakenings during sleep, according to retrospective self-evaluations of awakenings (Cory and Ormiston, 1975 ; Schredl et al., 2003 ). Such studies showed that the more the subjects tended to awaken during sleep, the higher their DRF. These results support the hypothesis of Koulack and Goodenough ( 1976 ), which proposes that nocturnal awakenings facilitate the encoding of the dream in memory and thus facilitate dream recall upon awakening. However, this hypothesis has not been tested by measuring awakenings with polysomnographic recordings in healthy subjects with various DRFs. Finally, DRF varies according to the method of awakening. Abrupt awakenings lead to more dream reports than gradual awakenings (Shapiro et al., 1963 , 1965 ; Goodenough et al., 1965 ).

Physiological and environmental parameters

Dream report frequency deceases with age (e.g., Schredl, 2008 ) and tends to be slightly higher among females than males (e.g., Schredl, 2008 ; Schredl and Reinhard, 2008 ). Remarkably, Schredl’s ( 2008 ) results revealed that DRF also varied according to the size of the subject’s place of residence.

Psychological parameters

First, increased professional stress or interpersonal stress resulted in an increase in DRF (for a review, see Schredl, 1999 ). Second, an interest in dreams or a positive attitude toward dreams clearly covaries with DRF (Hill et al., 1997 ; Schredl, 1999 ; Schredl et al., 2003 ). The greater an individual’s interest in dreams, the higher his/her DRF. Third, several cognitive abilities have been found to covary with DRF. Contradictory results have been reported for the correlation between DRF and memory abilities (short-term, long-term, visual, verbal, implicit, and explicit; significant positive correlation: Cory and Ormiston, 1975 ; Belicki et al., 1978 ; Butler and Watson, 1985 ; Schredl et al., 1995 ; Solms, 1997 ; no significant correlation: Cohen, 1971 ; Belicki et al., 1978 ; Schredl et al., 1995 , 1997 , 2003 ; Solms, 1997 ) and the correlation between DRF and visual imagery ( significant positive correlation : Hiscock and Cohen, 1973 ; Richardson, 1979 ; Okada et al., 2000 ; no significant correlation : Hill et al., 1997 ; Okada et al., 2000 ). However, several studies have consistently shown that DRF is positively correlated with creativity (Fitch and Armitage, 1989 ; Schredl, 1999 ; Schredl et al., 2003 ) and intelligence scales (multiple-choice vocabulary test, Schonbar, 1959 ; Shipley Intelligence Scale, Connor and Boblitt, 1970 ). Finally, many authors have reported a correlation between DRF and personality traits. Subjects with a high DRF are more likely to have a personality with thinner boundaries (Hartmann described people with thin boundaries as being open, trustworthy, vulnerable, and sensitive; Hartmann, 1989 ; Hartmann et al., 1991 ; Schredl et al., 2003 ), to be more anxious (Schonbar, 1959 ; Tart, 1962 ), to have a higher level of absorption (the absorption scale measures the capacity to become absorptively involved in imaginative and esthetic experiences; Hill et al., 1997 ; Schredl, 1999 ; Schredl et al., 2003 ), to be more open to experience (Hill et al., 1997 ; Schredl et al., 2003 ), and to be less alexithymic (alexithymia is a personality variable that incorporates difficulty identifying and describing feelings, difficulty distinguishing between feelings and the physical sensation of emotional arousal, limited imaginative processes, and an externally oriented cognitive style; De Gennaro et al., 2003 ; Nielsen et al., 2011 ) compared to subjects with a low dream recall frequency. However, those results have not always been reproducible (e.g., Schredl, 2002 for openness to experience; Cory and Ormiston, 1975 ; Hill et al., 1997 for anxiety; Nielsen et al., 1997 for alexithymia) and, according to the recent review by Blagrove and Pace-Schott ( 2010 ), it is difficult to draw conclusions about a possible link between personality traits and DRF.

In conclusion, numerous parameters have been identified that covary with DRF. Schredl stressed in many of his papers that the studied parameters usually explain only a small percentage of the total variance (e.g., Schredl, 2008 ). Thus, the DRF variation profile suggests that the production, encoding and recall of dreams are influenced by numerous parameters that probably interact with each other.

Dreaming and neuroscience

The neuroscientific approach to dreaming arose at the end of the 1950s with the discovery of REM during human sleep by the American physiologist Nathaniel Kleitman and his team (Aserinsky and Kleitman, 1953 ; Dement and Kleitman, 1957a ). During these sleep episodes with saccades, the researchers noticed a decrease in voltage and an increase in frequency in the EEG, accompanied by an increase in cardiac frequency variability and a decrease in body movements. They concluded that these physiological modifications indicate a particular sleep stage, which they called REM sleep. A few years later, the French team led by neurobiologist Michel Jouvet discovered that the lack of movement during REM sleep in cats was due to a general muscular atonia, controlled notably by the locus coeruleus α in the brainstem (Jouvet and Michel, 1959 ; Berger, 1961 later showed that muscular atonia during REM sleep also occurs in humans). Interestingly, the inability to move during REM sleep indicates deep sleep and paradoxically, the fast EEG activity of REM sleep resembles EEG activity in wakefulness. Jouvet concluded that this particular physiological state is associated with a “third state” of the brain (in addition to the brain states associated with wakefulness and NREM sleep) which he called “paradoxical sleep” instead of “REM sleep” (Jouvet et al., 1959 ; Jouvet, 1992 ). Several years later, Fisher et al. ( 1965 ) discovered another physiological characteristic of REM sleep: the penile erection.

During the same period, the American team noticed that a subject awakened during REM sleep very often reported a dream (80% of awakenings in REM sleep vs. 6% of awakenings in NREM sleep are followed by a dream report, according to Dement and Kleitman, 1957b ). Researchers concluded that dreaming occurs during REM sleep. The eye movements of REM sleep would allow the dreamer to scan the imaginary scene of the dream (the scanning hypothesis); the cerebral cortex activation revealed by the rapid EEG would allow intense cognitive activity, creating the complex stories of a dream; and the lack of muscle tone would prevent the dreamer from acting out his dreams. From that time on, researchers investigated REM sleep to obtain answers about dreaming.

In the 1990s, researchers used functional neuroimaging techniques such as positron emission tomography (PET) to investigate brain activity during REM sleep in humans. This new approach enabled researchers to demonstrate that the functional organization of the brain during REM sleep is different from the functional organization of the brain during wakefulness (Maquet et al., 1996 ; Braun et al., 1998 ). In comparison to wakefulness, brain activity during REM sleep is decreased in some brain regions (e.g., in the dorsolateral prefrontal cortex; Braun et al., 1998 ) and increased in other regions (e.g., in the occipital and temporal cortex, the hippocampus and parahippocampus, the anterior cingulate, the precentral and postcentral gyri, the superior parietal cortex, and the pons; Braun et al., 1998 ; Maquet et al., 2000 ). Looking more generally for brain activity correlating with REM sleep (the vigilance states considered included wakefulness, slow-wave sleep, and REM sleep), Maquet et al. ( 1996 ) found negative correlations in the precuneus, posterior cingulate cortex, temporoparietal junction, and dorsolateral prefrontal cortex and positive correlations in the amygdala, anterior cingulate, postcentral gyrus, thalamus, and pons (see Schwartz and Maquet, 2002 ; Maquet et al., 2005 ; Nir and Tononi, 2010 for reviews). Based on these results, researchers argued that the particular functional organization of the brain during REM sleep could explain the phenomenological characteristics of dream reports (Hobson and Pace-Schott, 2002 ; Schwartz and Maquet, 2002 ; Maquet et al., 2005 ; Nir and Tononi, 2010 ). They considered that brain activity increases and decreases during REM sleep could be interpreted on the basis of what we know about brain activity during wakefulness. In this context, the increased occipital cortex activity during REM sleep could explain the visual component of dream reports because neuroimaging results during wakefulness showed that visual imagery with the eyes closed activates the occipital cortex (Kosslyn and Thompson, 2003 ). The decreased activity in the temporoparietal junction during REM sleep may explain why dreams are mainly experienced in the egocentric coordinates of the first-person; indeed, during wakefulness, activity in the temporoparietal junction was reported to be greater for allocentric vs. egocentric representation (e.g., Ruby and Decety, 2001 ; Zacks et al., 2003 ) and for third- vs. first-person perspective (e.g., Ruby and Decety, 2003 , 2004 ). The increased activity in the hippocampus during REM sleep could explain why dreams are often composed of known images or characters, as the hippocampus is known to be associated with the encoding and retrieval of lived events during wakefulness (e.g., Piolino et al., 2009 ). The decreased activity in the lateral prefrontal cortex during REM sleep could explain why dream stories lack consistency, why the dreamer’s perception of time is altered, why the dream story is beyond the control of the dreamer and why the dreamer is convinced that the dream story is really happening. Indeed, during wakefulness, the lateral prefrontal cortex is involved in executive function, cognitive control, and working memory (Petrides, 2005 ; Koechlin and Hyafil, 2007 ). The increased activity in the medial prefrontal cortex during REM sleep could explain the attribution of thoughts, beliefs, and emotions to the characters in the dream because, during wakefulness, the medial prefrontal cortex is known to participate in mind reading (Ruby et al., 2007 , 2009 ; Legrand and Ruby, 2009 ). The increased activity in the motor cortex (precentral gyrus) during REM sleep could explain the movements of the characters’ bodies in the dream because, during wakefulness, motor imagery, and the imagination of someone’s action from the third-person perspective involve the precentral gyrus (Decety et al., 1994 ; Ruby and Decety, 2001 ). Finally, the amygdala’s activity during REM sleep could explain why emotions, especially fear, are often mentioned in dream reports; indeed, the amygdala is involved in the processing of emotional stimuli during wakefulness (Adolphs, 2008 ).

In conclusion, results from experimental psychology and neuroscience allow us to better understand the phenomenology of dreaming and the cerebral correlates of some characteristics of dream reports. Still, what do they tell us about the role of dreaming? What are the current hypotheses about dream function(s)?

Hypotheses about dream function(s)

No function.

At the end of the twentieth century, the neurologist Alan Hobson, who was profoundly anti-psychoanalysis, proposed a theory that deprived dreaming of any function. Hobson argued that dreaming is an epiphenomenon of REM sleep: “Because dreams are so difficult to remember, it seems unlikely that attention to their content could afford much in the way of high-priority survival value. Indeed, it might instead be assumed that dreaming is an epiphenomenon of REM sleep whose cognitive content is so ambiguous as to invite misleading or even erroneous interpretation” (Hobson et al., 1998 ).

Psychological individualism

In contrast, other teams, like Michel Jouvet’s, believed that dreaming serves a vital function. In 1979, Jouvet’s team blocked muscular atonia during REM sleep in a cat by damaging the locus coeruleus α in its brainstem. This lesion resulted in the appearance of movements during REM sleep. Movies from the Jouvet lab show sleeping cats performing complex motor actions (with altered control and coordination) resembling those of wakefulness, such as fur licking, growling, chasing prey, mastication, and fighting. From these videos, the authors concluded that the cat was acting out its dream, and they called this non-physiological state “oneiric behavior” (Sastre and Jouvet, 1979 ). These results led Jouvet to propose that dreaming plays a role in reinforcing a species’ typical behavior. Later in his career, Jouvet moved toward a hypothesis focusing on the role of dreaming in the individual dimension. He speculated that dreams (note that, for Jouvet, dreams and paradoxical sleep were equivalent) could be involved in psychological individualism and in the stability of the dreamer’s personality (Jouvet, 1991 , 1992 , 1998 ). According to Jouvet, “the brain is the sole organ of homeotherms that do not undergo cell division. We thus have to explain how certain aspects of psychological heredity (found in homozygote twins raised in different surroundings) may persist for a whole life (psychological individuation). A definitive genetic programming during development (by neurogenesis) is unlikely due to the plasticity of the nervous system. That is why we have to consider the possibility of an iterative genetic programming. The internal mechanisms (synchronous) of paradoxical sleep (SP) are particularly adapted to such programming. This would activate an endogenous system of stimulation that would stimulate and stabilize receptors genetically programmed by DNA in some neuronal circuits. The excitation of these neurons during SP leads to oniric behaviors that could be experimentally revealed – the lists of these behaviors are specific to each individual and indirect data suggest a genetic component of this programming. Amongst the mechanisms allowing the iterative programming of SP, sleep is particularly important. Security – and hence the inhibition of the arousal system – is a sine qua non-condition for genetic programming to take place. In that sense, sleep could very well be the guardian of dreaming” (Jouvet, 1991 ). In other words, Jouvet’s hypothesis is that paradoxical sleep restores neuronal circuitry that was modified during the day to preserve the expression of the genetic program that codes for psychological characteristics. This process would ensure the stability of personality across time.

The threat simulation theory

The Finnish psychologist Antti Revonsuo recently proposed a hypothesis called threat simulation theory, which explains the fearful characteristics of dream content (Revonsuo, 2000 ; Valli and Revonsuo, 2009 ). According to this theory, dreams serve as virtual training places to improve threat avoidance or threat fighting ability. The theory postulates that such nocturnal training makes the dreamer more efficient at resolving threatening situations during wakefulness.

Emotional regulation

Cartwright et al. ( 1998a , b ) defended the idea that dreaming is involved in emotional regulation. Her team showed that, in healthy subjects, the depression level before sleep was significantly correlated with affect in the first REM report. Her team also observed that low scorers on the depression scale displayed a flat distribution of positive and negative affect in dreams, whereas those with a depressed mood before sleep showed a pattern of decreasing negative and increasing positive affect in dreams reported from successive REM periods (Cartwright et al., 1998a ). These results led Cartwright’s team to suggest that dreaming may actively moderate mood overnight in normal subjects. The team strengthened this hypothesis by showing that among subjects who were depressed because of a divorce, those who reported more negative dreams at the beginning of sleep and fewer at the night’s end were more likely to be in remission 1 year later than subjects who had fewer negative dreams at the beginning of sleep and more at the end of the night (Cartwright et al., 1998b ). The researchers concluded that negative dreams early in the night may reflect a within-sleep mood regulation process, whereas those that occur later may indicate a failure in the completion of this process.

Memory consolidation

Finally, a current mainstream hypothesis in cognitive neuroscience credits sleep and dreaming with a role in memory consolidation (for a recent review, see Diekelmann and Born, 2010 ). Numerous studies have shown that brain activity during training is replayed during post-training sleep (e.g., using a serial reaction time task Maquet et al., 2000 , demonstrated replay during REM sleep; using a maze exploration task Peigneux et al., 2004 , demonstrated replay during slow-wave sleep). Decreased performance during the post-training day in sleep-deprived subjects further suggested that the replay of brain activity at night contributes to memory consolidation (e.g., Maquet et al., 2003 ). Only recently, however, have experimental results in humans argued in favor of a role of dreaming per se in memory consolidation. In one study, subjects were trained on a virtual navigation task before taking a nap. Post-nap tests showed that subjects who dreamed about the task performed better than subjects who did not dream (note that only 4 out of 50 subjects dreamed about the task in this study; Wamsley et al., 2010 ). Using a different approach, Nielsen and colleagues provided additional arguments supporting a link between dreams and memory (Nielsen et al., 2004 ; Nielsen and Stenstrom, 2005 ). This team demonstrated that dreams preferably incorporate events that the dreamer lived the day before and events that the dreamer lived 7 days before the dream (U shaped curve). Animal studies have shown that after associative learning, the excitability of hippocampal cells increases (which leads to an increase in neuronal plasticity) and then returns to baseline 7 days after training (Thompson et al., 1996 ). The similarity between the delay of episodic event incorporation into dreams and the delay of post-training cellular plasticity in the hippocampus led the Canadian team to suggest a link between dreaming and episodic memory consolidation.

In summary, the preceding section describes the current state of the art on dreaming, its phenomenology and cerebral correlates and hypotheses about its functions. Some substantial advances have been made, but much remains to be understood.

Unresolved Issues

The link between oneiric behaviors and dream reports.

A piece of evidence in favor of a strong link between REM sleep and dreaming is the oneiric behavior (the appearance of complex motor behaviors when motor inhibition is suppressed during REM sleep) discovered by Sastre and Jouvet ( 1979 ) in cats and reproduced by Sanford et al. ( 2001 ) in rats. Researchers interpreted these results as the animal acting out its dream. However, as animals do not talk, the link between oneiric behavior and dream recall cannot be tested experimentally. This limitation seriously hampers our understanding of dreaming. In humans, complex motor behaviors (e.g., talking, grabbing, and manipulating imaginary objects, walking, and running) can also occur during REM sleep in a pathological context. This syndrome is called REM sleep behavior disorder (RBD). It can be caused by substance withdrawal (e.g., alcohol, Nitrazepam) or intoxication (e.g., caffeine, tricyclic antidepressants) or by various diseases (e.g., Parkinson’s and Alzheimer’s diseases, pontine neoplasms). According to physicians experts on this syndrome, some patients report dreams that are consistent with their behaviors in REM sleep (Mahowald and Schenck, 2000 ). According to the literature, however, such matches seem to be loose and not systematic. Only one study has tested whether observers can link dream content to sleep behaviors in RBD (Valli et al., 2011 ). In this study, each video recording of motor manifestations was combined with four dream reports, and seven judges had to match the video clip with the correctly reported dream content. The authors found that reported dream content can be linked to motor behaviors at a level better than chance. However, only 39.5% of video-dream pairs were correctly identified. Note, however, that because the authors obtained only movements and not behavioral episodes for many RBD patients, the link between videos and dream reports was unfairly difficult to make.

It is important to note that motor behavior during sleep can happen outside of REM sleep. Sleepwalking and sleep terrors, which occur during NREM sleep, are usually not considered dream enactments. However, we know that dreams can happen during NREM sleep, and many patients report dreamlike mentation after awakening from sleepwalking or sleep terrors (71%, according to Oudiette et al., 2009 ). In addition, Oudiette et al. ( 2009 ) reported that the dreamlike mentation can correspond with the sleep behavior in NREM sleep. Consequently, the authors concluded that sleepwalking may represent an acting out of corresponding dreamlike mentation.

Recent research suggests that any kind of motor behavior during sleep can be considered an oneiric behavior. One of the challenges for future research is to test the strength of the link between these oneiric behaviors and dream reports in a controlled and systematic way.

Neurophysiological correlates of dreaming

Despite the numerous neuroimaging studies of sleep in humans, the neurophysiological correlates of dreaming remain unclear.

Indeed, dreaming can happen during NREM sleep, and although NREM brain activity differs substantially from REM sleep brain activity (Maquet et al., 2000 ; Buchsbaum et al., 2001 ), some NREM dreams are phenomenologically indistinguishable from REM dreams (Hobson, 1988 ; Cavallero et al., 1992 ; Cicogna et al., 1998 ; Wittmann et al., 2004 ). This phenomenon is difficult to understand given what we currently know about the sleeping brain and about dreaming. One explanation may rely on the possibility that brain activity during sleep is not as stable as we think.

Brain activity during REM sleep in humans is considered to be well understood (Hobson and Pace-Schott, 2002 ; Schwartz and Maquet, 2002 ; Nir and Tononi, 2010 ), but several results question this notion. First, contrary to the common belief that dorsolateral prefrontal cortex activity decreases during REM sleep, several studies have reported increased activity in the dorsolateral prefrontal cortex during REM sleep (Hong et al., 1995 , 2009 ; Nofzinger et al., 1997 ; Kubota et al., 2011 ). Second, brain activity during REM sleep is heterogeneous. The mean regional cerebral blood flow during 1 min of REM sleep (e.g., as reported in Maquet et al., 1996 ) and the regional cerebral blood flow associated with the rapid eye movements of REM sleep (Hong et al., 2009 ; Miyauchi et al., 2009 ) highlight different brain regions. Finally, few congruencies have been noted in the results of studies investigating brain activity during REM sleep (Hong et al., 1995 , 2009 ; Maquet et al., 1996 , 2000 ; Braun et al., 1997 , 1998 ; Nofzinger et al., 1997 ; Peigneux et al., 2001 ; Wehrle et al., 2005 ; Miyauchi et al., 2009 ; Kubota et al., 2011 ), even between studies using the same technique and the same contrasts (e.g., Braun et al., 1998 ; Maquet et al., 2000 ), or between studies investigating the same REM event (e.g., brain activity associated with rapid eyes movements, as in Peigneux et al., 2001 ; Wehrle et al., 2005 ; Hong et al., 2009 ; Miyauchi et al., 2009 ). Furthermore, few brain regions are consistently reported across the majority of the studies. This inconsistency suggests great intra- and intersubject variability in brain activity during REM sleep in humans. A challenge for future research will be to find out whether the variability in brain activity during REM sleep can be explained by the variability in dream content.

Because dream reports can be collected after awakenings from any sleep stage, one may hypothesize that the brain activity that subserves dreaming (if such brain activity is reproducible across dreams) is quite constant throughout the night and can be observed during all sleep stages. Some results have supported this hypothesis and encouraged further attention in this direction. Buchsbaum et al. ( 2001 ), for example, reported that metabolism in the primary visual areas and certain parts of the lateral temporal cortex does not fluctuate much across REM and slow-wave sleep. Similarly, Nielsen’s team found that dream recall (vs. no dream recall) was associated with decreased alpha (8–12 Hz) power in the EEG preceding awakening, regardless of the sleep stage (Stage 2 or REM sleep; Esposito et al., 2004 ). Interestingly, some authors have suggested that decreased power in the alpha band during wakefulness reflects search and retrieval processes in long-term memory (for a review, see Klimesch, 1999 ).

Processes of selection and organization of dream representations

Nielsen’s team found that episodic events from the 1, 7, and 8 days before a dream were more often incorporated into the dream than were events from 2 or 6 days before the dream (Nielsen et al., 2004 ; results reproduced by Blagrove et al., 2011 ). This result tells us that internal processes control and shape dream content and thus help us to constrain and shape hypotheses about the function and biological basis of dreaming.

At the end of the nineteenth century, Saint-Denys (1867) showed that a sensory stimulus (e.g., the scent of lavender) presented to a sleeping subject without his or her knowledge could induce the incorporation of an event associated with the stimulus (e.g., holidays spent near a lavender field) into the dream, regardless of the delay between the dream and the association stimulus/events (lavender scent/holidays). The author demonstrated that the external world can influence dream content in a direct or indirect way.

Finally, it appears that both external and internal parameters can shape or govern dream content. Nonetheless, few of these parameters are known, and some regularities in the phenomenology of dreams suggest that more influencing parameters remain to be discovered. For example, some individuals experience recurring themes, characters, or places in their dreams. In line with this observation, Michael Schredl’s team showed that the content and style of a person’s life strongly influence dream content (Schredl and Hofmann, 2003 ). However, the rule(s) governing which lived events are incorporated into dreams remain unknown. Do the representations constituting the dream emerge randomly from the brain, or do they surface according to certain parameters? Similarly, is the organization of the dream’s representations chaotic, or is it determined by rules? Does dreaming have a meaning? What is/are the function(s) of dreaming?

Dreaming, Psychoanalysis, and Neuropsychoanalysis

Psychoanalysis, which was developed by the neurologist Sigmund Freud in the beginning of the twentieth century, proposes answers to the questions raised above. Indeed, his theory of the human mind comprises hypotheses about the rules of selection and organization of the representations that constitute dreams.

At the beginning of the twentieth century, Freud presented the concept of the unconscious. He proposed that a part of our mind is made up of thoughts, desires, emotions, and knowledge that we are not aware of, but that nevertheless profoundly influence and guide our behaviors. In his books (e.g., Freud, 1900, 1920 ), Freud proposes that the unconscious mind comes out in slips and dreams. Its expression, however, is coded within dreams (the work of dream), and unconscious thoughts are distorted before they emerge in the conscious mind of the sleeping subject (manifest content of the dream). As a consequence, the dreamer is not disturbed by repressed and unacceptable thoughts (latent content of the dream) and can continue sleeping (this is the reason why Freud considered dreams the guardians of sleep). Hence, according to Freud, decoding dreams’ latent content provides an access to the unconscious mind.

In Freud’s theory of the mind, unconscious thoughts and feelings may cause the patient to experience life difficulties and/or maladjustment, and free unconscious thoughts can help the patient gain insight into his/her situation. As a consequence, Freud developed techniques to decode dreams and provide a way for an analyst to look inside the words and unconscious images of the patient, and to free them through patient insight. One of these techniques is called free association, and is regarded as an essential part of the psychoanalytic therapy process. In order for an analyst to get to the latent content of a dream, he requires the patient to discuss the dream’s manifest content and encourage free association about the dream. Free association is the principle that the patient is to say anything and everything that comes to mind. This includes decensoring his/her own speech so that he/she truly expresses everything. Over time, the therapist or analyst will draw associations between the many trains of uncensored speech the patient shares during each session. This can lead to patient insight into their unconscious thoughts or repressed memories, and the accomplishment of their ultimate goal of “freedom from the oppression of the unconscious” (Trull, 2005 ).

Hence, Freud considered that dreams, as well as slips, have a meaning and can be interpreted, so that one is justified in inferring from them the presence of restrained or repressed intentions (Freud, 1900, 1920 ). Note that, in Freud’s theory of the mind, the words “meaning” and “intention” are closely linked: “Let us agree once more on what we understand by the ‘meaning’ of a psychic process. A psychic process is nothing more than the purpose which it serves and the position which it holds in a psychic sequence. We can also substitute the word ‘purpose’ or ‘intention’ for ‘meaning’ in most of our investigations” (Freud, 1920 ).

In other words, according to Freud, decoding dreams with the free association method provides an access to what makes each of us so special, uncorvering the forces that guide one’s behavior. It gives access to an unknown dimension of ourselves that is fundamental in understanding who we are. It provides access to personal meaning.

This hypothesis, attributing significant importance and meaning to dreams, has rarely been considered by neuroscientists who often consider Freud’s work and theory unscientific.

However, this situation may change as the relationship between psychoanalysis and neuroscience evolves. The starting point was the creation of the International Society for Neuropsychoanalysis in 2000. It was founded by neuropsychologist and psychoanalyst Mark Solms with the intention to promote interactions and collaborations between psychoanalysis and neuroscience. The challenge was serious, as illustrated by neuroscientist Alan Hobson’s aggressiveness in the famous dream debate (Alan Hobson vs. Mark Solms) entitled “Should Freud’s dream theory be abandoned?” held in Tucson, Arizona, in 2006 during the Towards a Science of Consciousness meeting (scientific arguments can be found in Solms, 2000 and Hobson et al., 2000 ). Alan Hobson tried to convince the assembly that Freud was 100% wrong and that Freud’s dream theory was misguided and misleading and should be abandoned. He aimed to demonstrate that Freud’s dream theory is incompatible with what we know about how the brain works. He added that Freud’s dream theory was not scientific because it was not testable or falsifiable. Finally, he presented his model of dreaming, the activation-synthesis hypothesis (Hobson and McCarley, 1977 ; Hobson et al., 2000 ): “The Activation-Synthesis model of dream construction proposed that the phasic signals arising in the pontine brainstem during REM sleep and impinging upon the cortex and limbic forebrain led directly to the visual and motor hallucinations, emotion, and distinctively bizarre cognition that characterize dream mentation. In doing so, these chaotically generated signals arising from the brain stem acted as a physiological Rorschach test, initiating a process of image and narrative synthesis involving associative and language regions of the brain and resulting in the construction of the dream scenarios.” In contrast, Mark Solms demonstrated that what is currently known about the dreaming brain is at least broadly consistent with Freud’s dream theory. He argued that it is generally accepted that brain stem activation is necessary, but not sufficient, to explain the particular characteristics of dream consciousness. What does explain the particular characteristics of dream consciousness, according to Solms, are the following features of brain activity during REM sleep (Braun et al., 1997 ): the activation of core forebrain emotion and instinctual drive mechanisms, i.e., the limbic and paralimbic brain areas (the anterior cingulate, insula, hippocampus, parahippocampal gyrus, and temporal pole), and of the posterior perceptual system (the fusiform gyrus, superior, inferior and middle temporal gyrus, and angular gyrus) and the deactivation of executive dorsolateral frontal control mechanisms (the dorsolateral prefrontal cortex). He further argued that his lesion studies (Solms, 1997 ) are congruent with neuroimaging results because they showed that a total cessation of dreaming results from lesions in the medial part of the frontal lobe and in the temporoparietal junction (whereas no cessation of dreaming was observed for core brainstem lesions or for dorsolateral prefrontal lesions). Finally he emphasized that the activation of motivational mechanisms (such as drives and basic emotions) and of posterior perceptual system associated with deactivation of the executive control (i.e., reality oriented regulatory mechanism) during REM sleep, is broadly consistent with Freud’s dream theory which claims that our instinctual drive states (notably appetitive and libidinal drive system) are relatively disinhibited during sleep. Note that experimental results demonstrating the existence of unconscious representations that guide behavior (e.g., Shevrin and Fritzler, 1968 ; Bunce et al., 1999 ; Arminjon, 2011 , for a review) could also have been cited in support of Freud’s dream theory. This debate was a success for Mark Solms and neuropsychoanalysis. Indeed, at the end of the debate, approximately 100 people voted “no” (i.e., “Freud’s dream theory should not be abandoned”), approximately 50 people voted “yes” and 50 voted “I don’t know”.

Solms’ ( 1997 , 2000 ) approach to dreaming and his experimental results fundamentally challenged our current understanding of dreaming. He proposes that dreaming and REM sleep are controlled by different brain mechanisms. According to Solms, REM sleep is controlled by cholinergic brain stem mechanisms, whereas dreaming is mediated by forebrain mechanisms that are probably dopaminergic. This implies that dreaming can be activated by a variety of NREM triggers. Several experimental results support this hypothesis.

First, behavioral studies have demonstrated that the link between REM sleep and dream reports is lax. Subjects awakened during NREM sleep can recall dreams at a high rate (Foulkes, 1962 : 74% of awakenings in NREM sleep were followed by dream reports; Cavallero et al., 1992 : 64%; Wittmann et al., 2004 : 60%); dreams can be recalled after a nap consisting only of NREM sleep (Salzarulo, 1971 ; Palagini et al., 2004 ); and some individuals never recall dreams, even when awakened from REM sleep (Pagel, 2003 ). In addition, in healthy subjects with a normal dream recall frequency (around 1 dream recall per week, Schredl, 2008 ), dream recall after an awakening during REM sleep is not systematic: 5–30% of awakenings in REM sleep are not followed by a dream recall, according to the literature (e.g., Dement and Kleitman, 1957a , b ; Foulkes, 1962 ; Hobson, 1988 ). Finally, 5–10% of NREM dreams cannot be distinguished from REM dreams based on their content (Hobson, 1988 ; Cavallero et al., 1992 ; Cicogna et al., 1998 ; Wittmann et al., 2004 ).

Second, as Solms ( 2000 ) argued, the amount of dream recall can be modulated by dopamine agonists (Scharf et al., 1978 ; Nausieda et al., 1982 ) without concomitant modification of the duration and frequency of REM sleep (Hartmann et al., 1980 ). Dream recall can be suppressed by focal brain lesions (at the temporo-parieto-occipital junction and ventromedial prefrontal cortex; Solms, 1997 , 2000 ). These lesions do not have any appreciable effects on REM frequency, duration, or density (Kerr et al., 1978 ; Michel and Sieroff, 1981 ). Finally, some clinical studies suggest that a dream can be triggered by nocturnal seizures in NREM sleep, i.e., by focal brain stimulation. Some cases of recurring nightmares caused by epileptiform activity in the temporal lobe have indeed been reported (Solms, 2000 ).

Conclusion: Collaboration between Neuroscience and Psychoanalysis Would Benefit Dream Research

Considering the issues that remain unresolved (e.g., neurophysiologic variability, parameter(s) influencing the emergence of representations in dreams, the meaning of dreams), a psychoanalytic perspective would certainly benefit dream research by providing new directions/leads and helping to reach a comprehensive understanding of dreaming.

On the one hand, psychological research has demonstrated that dream content is influenced by one’s personal life, especially personal concerns (Schwartz, 1999 ; Schwartz and Maquet, 2002 ; Schredl and Hofmann, 2003 ), and some neuroscientists have hypothesized that dreaming is involved in psychological individualism. Thus, both psychology and neuroscience have provided results and hypotheses that validate the possibility that dreaming has something to do with personal and meaningful issues. On the other hand, Freud argued that the unconscious, which guides behaviors and desires, express itself during dreams. The two disciplines’ (cognitive neuroscience and psychoanalysis) convergence on dreaming thus seems obvious; however, very little collaboration has occurred to date.

Note that some experimental studies in psychology have considered the psychoanalytic perspective. For example, Greenberg et al. ( 1992 ) attempted “a research-based reconsideration of the psychoanalytical theory of dreaming.” They evaluated the presence of problems (defined as an expression of negative feeling or any situation evoking such feeling or requiring some change or adaptation) during dreaming and pre- and post-sleep wakefulness in two subjects. They showed that problems occurred very frequently in the manifest dream content and that these problems were nearly systematically related to the problems noted during pre-sleep wakefulness. In addition, they observed that effective dreams (i.e., dreams that presented some solution to the individuals’ problems) were followed by a waking state in which the impact of the problems was diminished, whereas ineffective dreams were followed by the persistence of the problems. This study thus confirmed that personal concerns influence dream content. In addition it provided new results suggesting that dreaming may have some psychological problem-solving function (this result recalls the neuroscientific findings that sleep has a cognitive problem-solving function associated with brain reorganization; e.g., Wagner et al., 2004 ; Darsaud et al., 2011 ). Greenberg et al.’s ( 1992 ) study managed to quantify personal issues and clearly broadened the cognitive neuroscience perspective on dreaming. To proceed further, approaches integrating psychoanalysis and neuroscience must now be developed. Several subjective issues at the core of the psychoanalytic approach, such as the concept of personal meaning, the concept of unconscious episodic memory and the subject’s history, are not addressed or considered in cognitive neuroscience. This limitation hampers the understanding of psychological and neurophysiological functioning in humans. These issues must be addressed, and the expertise of psychoanalysts in singularity and personal meaning is needed to do so in neuroscience and to further the understanding of dreaming and of the psyche.

Conflict of Interest Statement

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

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Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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APA Sample Paper: Experimental Psychology

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Writing Center: Experimental Research Papers

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FAQs About Experimental Research Papers (APA)

What is a research paper? 

A researcher uses a research paper to explain how they conducted a research study to answer a question or test a hypothesis. They explain why they conducted the study, the research question or hypothesis they tested, how they conducted the study, the results of their study, and the implications of these results. 

What is the purpose of an experimental research paper? 

A research paper is intended to inform others about advancement in a particular field of study. The researcher who wrote the paper identified a gap in the research in a field of study and used their research to help fill this gap. The researcher uses their paper to inform others about the knowledge that the results of their study contribute. 

What sections are included in an experimental research paper?

A typical research paper contains a Title Page, Abstract, Introduction, Methods, Results, Discussion, and References section. Some also contain a Table and Figures section and Appendix section. 

What citation style is used for experimental research papers? 

APA (American Psychological Association) style is most commonly used for research papers. 

Structure Of Experimental Research Papers (APA)

• Answers the question of “What is this paper about and who wrote it?”
• Located on the first page of the paper 
• The author’s note acknowledges any support that the authors received from others
• A student paper also includes the course number and name, instructor’s name, and assignment due date
• Contains a title that summarizes the purpose and content of the research study and engages the audience 
• No longer than 250 words
• Summarizes important background information, the research questions and/or hypothesis, methods, key findings, and implications of the findings
• Explains what the topic of the research is and why the topic is worth studying
• Summarizes and discusses prior research conducted on the topic 
• Identifies unresolved issues and gaps in past research that the current research will address
• Ends with an overview of the current research study, including how the independent and dependent variables, the research questions or hypotheses, and the objective of the research 
• Explains how the research study was conducted 
• Typically includes 3 sections: Participants, Materials, and Procedure
• Includes characteristics of the subjects, how the subjects were selected and recruited, how their anonymity was protected, and what feedback was provided to the participants
• Describes any equipment, surveys, tests, questionnaires, informed consent forms, and observational techniques 
• Describes the independent and dependent variables, the type of research design, and how the data was collected
• Explains what results were found in the research study 
• Describes the data that was collected and the results of statistical tests 
• Explains the significance of the results 
• Accepts or denies the hypotheses 
• Details the implications of these findings 
• Addresses the limitations of the study and areas for future research 
• Includes all sources that were mentioned in the research study 
• Adheres to APA citation styles
• Includes all tables and/or figures that were used in the research study 
• Each table and figure is placed on a separate page 
• Tables are included before figures
• Begins with a bolded, centered header such as “ Table 1 ”
• Appends all forms, surveys, tests, etc. that were used in the study 
• Only includes documents that were referenced in the Methods section 
• Each entry is placed on a separate page 
• Begins with a bolded, centered header such as “ Appendix A ”

Tips For Experimental Research Papers (APA)

• Initial interest will motivate you to complete your study 
• Your entire study will be centered around this question or statement 
• Use only verifiable sources that provide accurate information about your topic 
• You need to thoroughly understand the field of study your topic is on to help you recognize the gap your research will fill and the significance of your results
• This will help you identify what you should study and what the significance of your study will be 
• Create an outline before you begin writing to help organize your thoughts and direct you in your writing 
• This will prevent you from losing the source or forgetting to cite the source 
• Work on one section at a time, rather than trying to complete multiple sections at once
• This information can be easily referred to as your write your various sections 
• When conducting your research, working general to specific will help you narrow your topic and fully understand the field your topic is in 
• When writing your literature review, writing from general to specific will help the audience understand your overall topic and the narrow focus of your research 
• This will prevent you from losing sources you may need later 
• Incorporate correct APA formatting as you write, rather than changing the formatting at the end of the writing process 

Checklist For Experimental Research Papers (APA)

• If the paper is a student paper, it contains the title of the project, the author’s name(s), the instructor's name, course number and name, and assignment due date
• If the paper is a professional paper, it includes the title of the paper, the author’s name(s), the institutional affiliation, and the author note
• Begins on the first page of the paper
• The title is typed in upper and lowercase letters, four spaces below the top of the paper, and written in boldface 
• Other information is separated by a space from the title

Title (found on title page)

• Informs the audience about the purpose of the paper 
• Captures the attention of the audience 
• Accurately reflects the purpose and content of the research paper 

Abstract 

• Labeled as “ Abstract ”
• Begins on the second page 
• Provides a short, concise summary of the content of the research paper 
• Includes background information necessary to understand the topic 
• Background information demonstrates the purpose of the paper
• Contains the hypothesis and/or research questions addressed in the paper
• Has a brief description of the methods used 
• Details the key findings and significance of the results
• Illustrates the implications of the research study 
• Contains less than 250 words

Introduction 

• Starts on the third page 
• Includes the title of the paper in bold at the top of the page
• Contains a clear statement of the problem that the paper sets out to address 
• Places the research paper within the context of previous research on the topic 
• Explains the purpose of the research study and what you hope to find
• Describes the significance of the study 
• Details what new insights the research will contribute
• Concludes with a brief description of what information will be mentioned in the literature review

Literature Review

• Labeled as “ Literature Review”
• Presents a general description of the problem area 
• Defines any necessary terms 
• Discusses and summarizes prior research on the selected topic 
• Identifies any unresolved issues or gaps in research that the current research plans to address
• Concludes with a summary of the current research study, including the independent and dependent variables, the research questions or hypotheses, and the objective of the research  
• Labeled as “ Methods ”
• Efficiently explains how the research study was conducted 
• Appropriately divided into sections
• Describes the characteristics of the participants 
• Explains how the participants were selected 
• Details how the anonymity of the participants was protected 
• Notes what feedback the participants will be provided 
• Describes all materials and instruments that were used 
• Mentions how the procedure was conducted and data collected
• Notes the independent and dependent variables 
• Includes enough information that another researcher could duplicate the research 

Results 

• Labeled as “ Results ”
• Describes the data was collected
• Explains the results of statistical tests that were performed
• Omits any analysis or discussion of the implications of the study 

Discussion 

• Labeled as “ Discussion ”
• Describes the significance of the results 
• Relates the results to the research questions and/or hypotheses
• States whether the hypotheses should be rejected or accepted 
• Addresses limitations of the study, including potential bias, confounds, imprecision of measures, and limits to generalizability
• Explains how the study adds to the knowledge base and expands upon past research
• Labeled as “ References ”
• Correctly cites sources according to APA formatting 
• Orders sources alphabetically
• All sources included in the study are cited in the reference section 

Table and Figures (optional)

•  Each table and each figure is placed on a separate page 
• Tables and figures are included after the reference page
• Tables and figures are correctly labeled
• Each table and figure begins with a bolded, centered header such as “ Table 1 ,” “ Table 2 ,”

Appendix (optional) 

• Any forms, surveys, tests, etc. are placed in the Appendix
• All appendix entries are mentioned in the Methods section 
• Each appendix begins on a new page
• Each appendix begins with a bolded, centered header such as “ Appendix A, ” “ Appendix B ”

Additional Resources For Experimental Research Papers (APA)

• https://www.mcwritingcenterblog.org/single-post/how-to-conduct-research-using-the-library-s-resources
• https://www.mcwritingcenterblog.org/single-post/how-to-read-academic-articles
• https://researchguides.ben.edu/source-evaluation   
• https://researchguides.library.brocku.ca/external-analysis/evaluating-sources
• https://writing.wisc.edu/handbook/assignments/planresearchpaper/
• https://nmu.edu/writingcenter/tips-writing-research-paper
• https://writingcenter.gmu.edu/guides/how-to-write-a-research-question
• https://www.unr.edu/writing-speaking-center/student-resources/writing-speaking-resources/guide-to-writing-research-papers
• https://drive.google.com/drive/folders/1F4DFWf85zEH4aZvm10i8Ahm_3xnAekal?usp=sharing
• https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/general_format.html
• https://libguides.elmira.edu/research
• https://www.nhcc.edu/academics/library/doing-library-research/basic-steps-research-process
• https://libguides.wustl.edu/research
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ORIGINAL RESEARCH article

Impact of weekly physical activity on stress response: an experimental study.

• 1 Department of Physical Education, Sport and Human Movement, Autonomous University of Madrid, Madrid, Spain
• 2 Didactic and Behavioral Analysis in Sport Research Group, Faculty of Sport Sciences, University of Extremadura, Cáceres, Spain
• 3 Sport of Studies Center, Rey Juan Carlos University, Madrid, Spain

The aim of this research is focused on analyzing the alteration of the psychophysiological and cognitive response to an objective computerized stress test (Determination Test - DT-, Vienna test System ® ), when the behavioral response is controlled. The sample used was sports science students (N = 22), with a mean age of 22.82 (M age = 22.82; SD years = 3.67; M PhysicalActivity hours/Week = 7.77; SD hours / week = 3.32) A quasi-experimental design was used in which the response of each participant to the DT test was evaluated. The variable “number of hours of physical activity per week” and the variable “level of behavioral response to stress” were controlled. Before and after this test, the following parameters were measured: activation and central fatigue (Critical Flicker Fusion Threshold (CFF Critical flicker fusion ascending and Critical flicker fusion descending; DC potential), and perceived exertion (Central Rating of Perceived Exertion and Peripheral Rating of Perceived Exertion). Significant differences were found in all of the measures indicated. The usefulness of this protocol and the measures used to analyze the stress response capacity of the study subjects are discussed.

Introduction

The analysis of psychophysiological fatigue is considered very important in different contexts ( Lohani et al., 2019 ). In this sense, the consideration of the study of humans’s response to external and internal loads ( Wijesuriya et al., 2007 ; Wilson et al., 2007 ) has become one of the most important research topics. The external loads exerted on the individual are added to their skills and coping strategies, resulting in a level of tolerance and adaptation to each situation ( Folkman and Lazarus, 1988 ). Along the last decades, distinctions are often made between physical and mental fatigue role, indicating clear methodologies for the analysis of physiological fatigue, but with clear limitations in the study of central fatigue, because this is measurable only indirectly, which emphasizes the importance of developing new central fatigue analysis procedures ( Bittner et al., 2000 ).

Throughout the decades of research on this topic, different strategies have been used to evaluate the adaptation to these external and internal loads ( Lazarus, 1990 ; Amann, 2011 ). Thus, for example, a multitude of self-reports and standardized tests have been used ( Britner et al., 2003 ), to which physiological and biological measures have been added ( Arza et al., 2019 ). However, relatively low attention is usually given to the Central Nervous System (CNS)-related mechanisms, which play a major role on the development of fatigue ( Tarvainen et al., 2014 ), but are rarely monitored in the sport and physical activity field ( Valenzuela et al., 2020 ). Most of the studies related to central fatigue to date have focused on the effect it has on performing strenuous physical tasks ( Amann and Dempsey, 2008 ), although over the last few years there has been a notable increase in interest in studying the role of central fatigue in explaining human performance ( Inzlicht and Marcora, 2016 ). In this sense, the psychobiological model based on motivational intensity theory has gained special strength ( Gendolla and Richter, 2010 ). This model emphasizes that perception of effort and potential motivation are the central determinants of task engagement. Both variables are taken into consideration in our research, controlling the involvement in the task (motivation), by applying a computerized test, and analyzing the perception of both central and peripheral effort as detailed in the methodological section.

Two of these measures, which focus the methodological attention of this research due to its great potential in the study of this topic, are the Critical Flicker Fusion Threshold (CFFT), evaluated using one Flicker Fusion instrument ( Vicente-Rodríguez et al., 2020 ), and the DC Potential, evaluated using the OmegaWave technology. The neuro-physiological basis of flicker perception is complex but well established ( Görtelmeyer and Zimmermann, 1982 ). In particular, flickering light directly influences cortical activity. The CFFT was measured using two red light- emitting diodes in binocular foveal fixation. The continuous psychophysical method of limits was employed to determine CFFT ( Woodworth and Schlosberg, 1954 ). The utility of CFFT in sport has been focused on the relationship of arousal level with CNS ( Görtelmeyer and Zimmermann, 1982 ). Increase in CFFT suggests an increase in cortical arousal and sensory sensitivity. By contrast, a decrease of CFFT suggests a reduction in the efficiency of the system to process information ( Li et al., 2004 ; Clemente and Díaz, 2019 ). On the other hand, for the evaluation of the brain’s direct current (DC) potentials -slow potentials that reflect alterations in brain excitability- OmegaWave technology has gained strength in recent years ( Naranjo-Orellana et al., 2020 ; Valenzuela et al., 2020 ). This device not only measures the Heart Rate Variability (HRV) but it also simultaneously a brainwave signal (DC potential) in order to complement the information obtained from HRV to assess the athlete’s functional state ( Naranjo-Orellana et al., 2020 ). DC potentials—frequency ranges between 0 and 0.5 Hz, are correlated with different brain processes, such as take consciousness during decision making ( Guggisberg and Mottaz, 2013 ) high alertness states ( Bachmann, 1984 ), arousal state ( Haider et al., 1981 ), or attention ( Rösler et al., 1997 ).

To date, most studies conducted in the evaluation of central fatigue have shown that the greatest disturbances are produced by tasks that require efforts at maximum speed that involve a large amount of force ( Davranche and Pichon, 2005 ; Clemente and Díaz, 2019 ). However, there are very few studies that have analyzed central fatigue through controlled analysis of a task that primarily involves central fatigue ( Fuentes et al., 2019 ). In this sense, the aim is to apply a computerized test (DT, Vienna Test System), that allows evaluating people’s tolerance to stress and central fatigue by applying a standardized protocol, in physical activity practitioners. The knowledge in this field is really limited, for this reason we developed the present research with the aim of studying the modifications in CFFT and DC potentials in a sample group of regular physical activity. The first hypothesis establishes that the computerized stress task increases the participants’ perception of central fatigue, while keeping the perception of peripheral fatigue stable. As a consequence, the second hypothesis establishes that differences will be found in the “post” situation in the CFFT measures and in the central physiological indicators, which would indicate a relationship between the subjective and objective measures of central fatigue.

Materials and Methods

This study followed a quasi-experimental design ( Montero and León, 2007 ) and it received the approval of the University Ethical Commission in compliance with the Helsinki Declaration. All subjects were informed about the procedure and gave their written consent to participate. This study was carried out complying with the Standards for Ethics in Sport and Exercise Science Research ( Harriss et al., 2019 ).

Participants

The participants included 22 individuals from Madrid (Spain), 18 of whom were male and 4 females. These participants were aged between 18 and 36 years ( M years = 22.82, SD years = 3.67). All of the participants regularly engaged in physical activity, between 4 and 14 h per week ( M hours / week = 7.77, SD hours / week = 3.32). The inclusion criteria was that they performed physical activity at least 3 times a week and 150 min of moderate/vigorous physical activity. The exclusion criteria was not correctly performing the proposed measurements. Four participants were excluded from the study for not completing the measurements correctly. Intentional sampling methods were used ( Montero and León, 2007 ). Due to the impossibility of continuing with the data collection due to the Alert State decreed by the Spanish Government as a result of COVID-19, the sample had to be closed with the participants who had passed all the tests before March 2020.

Instrumentation and Study Variables

The number of hours of physical activity per week and the scores obtained on the DT test were used as controlled variables. This allows us to know that the differences found are not due to the ability to respond to stress, or to the weekly amount of physical exercise performed. Therefore, only the subjects in which there were no statistically significant differences in their weekly level of physical exercise, nor in the scores obtained in the DT test, were used.

To carry out this research, three measurement systems have been used: OmegaWave device, Flicker Fusion Unit (Vienna Test System), and the Determination Test (Vienna Test System). OmegaWave is a device that assesses the physiological readiness of athletes by examining the autonomic balance through HRV and brain‘s energy balance via DC potential ( Gómez-Oliva et al., 2019 ), Elastic chest band MEDITRACE (dominant hand and forehead). Coach + application (OmegaWave Ltd, Espoo, Finland) was used on Ipad mini 2 32GB. The Vienna Test System is an instrument for computerized psychological assessments that allows the objective evaluation of different psychological parameters. The Determination Test (DT Vienna test system) ( Whiteside, 2002 ; Whiteside et al., 2003 ) was used to determine neuropsychological fatigue. The test studied the attentional capacity, reactive stress tolerance, reaction speed among continuously, and quickly changing acoustic and visual stimuli. The test is simple, the difficulty of the task lies in the different modality of the arriving stimuli and their speed. This way we measure those cognitive abilities of the people involved that are needed for the distinction of colors and sounds, the perception of the characteristics of stimuli, their memorization, and finally, the selection of the adequate answer. The stimuli coming during the test are not predictable. Instead, the subjects need to react to them randomly ( Schuhfried, 2013 ). We study four key variables: the average value of reaction speed (sec), the number of correct answers (raw score), which reflects the ability of the respondent to precisely and quickly select the adequate answer even under pressure. Furthermore, we also examine the number of incorrect answers (raw score) which can show us how likely the respondent is to get confused under stress and pressure; finally, the high number of missed answers (raw score) reveals that the respondent is not capable of maintaining his/her attention under stress and is prone to giving up these situations ( Neuwirth and Benesch, 2012 ). The duration of this test was 6 min.

Before and after the stress test the following parameters were analyzed in this order:

Parameters analyzed through OmegaWave Coach + device ® (OmegaWave Ltd, Espoo, Finland):

– Hear Rate Variability (HRV). Square root of the mean of the squares of successive RR interval differences (RMSSD), Standard deviation of all normal to normal RR intervals (SDNN), and Standard deviation of successive squares of intervals RR (SDSD). OmegaWave is a device that assesses the physiological readiness of athletes by examining autonomic balance through HRV and brain‘s metabolic state via DC potential ( Ilyukhina and Zabolotskikh, 2020 ). Elastic chest band MEDITRACE (dominant hand and forehead). Coach + application (Omegawave Ltd., Espoo, Finland) was used on Ipad mini 2 32GB. For calculating HRV it be used the Root Mean Square of the Successive Differences score (RMSSD) ( Ilyukhina et al., 1982 ). It was used before and after the stress test.

– DC potential dynamics. DC Potential represent changes in the brain’s metabolic balance in response to increased exercise intensity or psychological challenges and are linked to cognitive and mental load ( Wagshul et al., 2011 ; Ilyukhina, 2015 ).

– CNS System Readiness ( Ilyukhina, 1986 ). It’s indicated by a floating grade from 1.0 to 7.0, where 7.0 is the optimal state. This index represents the state of the brain’s energy level and is composed of three factors (in order of significance): stabilization point of DC potential (mV), stabilization time (reduces system readiness state of 1.0–7.0, if not optimal), and curve shape (reduces system readiness state of 1.0–7.0, if not optimal).

– Stabilization point of DC Potential (mV) ( Ilyukhina et al., 1982 ; Ilyukhina, 2013 ): The first priority in DC analysis is the stabilization point of DC Potential. In the literature, especially by Ilyukhina, this point is defined as Level of Operational Rest. In 1982, the combined work of Ilyukhina and Sychev was published which outlined quantitative parameters of LOR for the assessment of the healthy human’s adaptation and compensatory−adaptive abilities to physical and mental loads in sports.

– Stabilization time ( Ilyukhina and Zabolotskikh, 1997 ). The second priority of analysis is to look at the stabilization time. measured in minutes. The spontaneous relaxation speed represents neuroreflex reactivity (neural control of baroreflex arch) of cardiovascular and respiratory systems. This measure associated with psycho-emotional dynamic and stability. Normal stabilization time occurs within 2 min and represents optimal balance within stress-regulation systems.

– Curve Shape: The curve shape is composed of two elements: Difference between measurement start mV and end mV values ( Table 1 ). The optimal shape of the curve should show a smooth transition from a higher initial value (active wakefulness) to a lower stabilization value (operational rest DC potential form represents the dynamic interaction within stress-regulation systems). DC potential form can indicate the level of CNS activation balance.

Parameters analyzed though Flicker Fusion unit (Vienna Test System ® ):

– Critical flicker fusion ascending (Hz) (CFFA) and Critical flicker fusion descending (Hz) (CFFD). Cortical arousal was measured using the critical flicker fusion threshold (Hz) (CFFT) in a viewing chamber (Vienna Test System ® ), following the procedure of previous studies ( Clemente et al., 2016 ). An increase in CFFT suggests an increase in cortical arousal and information processing; a decrease in CFFT values below the baseline reflects a reduction in the efficiency of information processing and central nervous system fatigue ( Whiteside, 2002 ). It was used before and after the stress test.

Parameters analyzed though DT test (Vienna Test System ® ):

– We study four key variables: the average value of reaction speed (msec), the number of correct answers (raw score), which reflects the ability of the respondent to precisely and quickly select the adequate answer even under pressure. Furthermore, we also examine the number of incorrect answers (raw score) which can show us how likely the athlete is to get confused under stress and pressure; finally, the high number of missed answers (raw score) reveals that the respondent is not capable of maintaining his/her attention under stress and is prone to giving up these situations ( Neuwirth and Benesch, 2012 ). The duration of this test was 6 min without instructions.

Parameters analyzed by self-report instruments:

– Central Rating of Perceived Exertion (RPEC) and Peripheral Rating of Perceived Exertion (RPEP). The Rating of Perceived Exertion ( Borg, 1998 ), was used as a measure of central (cardiorespiratory) and peripheral (local-muscular, metabolic) exertion before and after the stress test ( Bolgar et al., 2010 ; Cárdenas et al., 2017 ). The RPE is a 15 point category-ratio; the odd numbered categories have verbal anchors. Beginning at 6, “no exertion at all,” and goes up to 20, “maximal exertion.” Before testing, subjects were instructed on the use of the RPE scale ( Noble and Robertson, 1996 ). We use the scale with the clear differentiation between central as peripheral perceived exertion following the recommendations of the medical staff and under the guideline of Borg ( Borg, 1982 ), for applied studies.

Table 1. Simplified curve change mV reduction algorithm.

The participants were contacted and informed about the measurement protocol and of the date and time of the data collection. All of the measurements were collected during the same day. The total data collection time per participant was approximately 45 min. The order of measurements was the following: CFFT, DC Potential, RPE, DT test, RPE, CFFT, and DC Potential.

Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 21 (SPSS Inc., Chicago, Ill., United States). Means and SDs were calculated using traditional statistical techniques. Normality was tested with the Shapiro-Wilk test. As the distributions were not adjusted to the normal, non-parametric tests were used. A Wilcoxon sign ranges test for intragroup comparisons were conducted to analyze differences between pre and post-test. A Rho Spearman coefficient was used to know the correlations between variables. The Effect Size was tested using the formula = Z/ N for non-parametric tests ( Tomczak and Tomcak, 2014 ). Following the considerations of Cohen (1988) , the effect size is considered small when the value is inferior to 0.10, medium when it varies between 0.10 and 0.30 and high when it is superior to 0.50. The significance level was set at p < 0.05.

Descriptive Analysis, Normality Test According N, Wilcoxon Test, and Effect Sizes

Firstly, the normality tests were realized with the Shapiro-Wilk test. It was determined that most of the variables were not normal, due to which non-parametric statistical tests were applied. In relation to the descriptive analyzes of the study variables, shown in Table 2 , after applying the stressor via the DT test, worse values were obtained in all the variables measured. This reflects the alterations in the central response evaluated. Regarding the Wilcoxon rank test that was used to analyze whether there were differences between the scores obtained before and after applying the stressor (DT test), significant differences were found in the variables OverallDc ( p < 0.05), Flicker ascending ( p < 0.01), Flicker descending ( p < 0.01), Central RPE ( p < 0.01) and Physical RPE ( p < 0.01), while not finding significant differences in the rest of the variables ( Table 2 ).

Table 2. Descriptive analysis of the measured variables.

Correlation Analysis

A Spearman bivariate correlation analysis was performed. Spearman’s Rho coefficient was used, since the distribution was non-parametric. Note that significant correlations were found ( Table 3 ) entre OverallDC con DCSSatabilizationLevel ( p = 0.000; r = 0.791 ∗∗ ); OWCNS ( p = 0.005; r = 0.581 ∗∗ ); OWDCC ( p = 0.013; r = 0.522 ∗ ); Flicker Descending ( p = 0.044; r = 0.432 ∗ ). DCSStabilizationLevel con OWCNS ( p = 0.000; r = 0.766 ∗∗ ); Flicker Descending ( p = 0.049; r = 0.424 ∗ ). DCSStabilizationTime con OWCNS ( p = 0.005; r = 0.572 ∗ ); OWDCC ( p = 0.046; r = 0.430 ∗ ); Flicker Ascending ( p = 0.006; r = 0.563 ∗∗ ). OWCNS correlated with Flicker Ascending ( p = 0.018; r = 0.499 ∗ ), and SDSD with Flicker Descending score ( p = 0.046; r = −0.430 ∗ ).

Table 3. Rho Spearman coefficient.

The objective of the present research was to study the modification of DC potentials and the CFFT scores after the computerized stress test (DT). The analysis of the subjective cognitive responses about fatigue after DT test reveals significant differences in the participants, both at a physical and central level. As regards the first hypothesis, it is partially fulfilled. There are significant differences in central perceived fatigue, with a very high effect size, which supports the hypothesis and emphasizes the usefulness of the established research protocol. However, significant differences also appear in peripheral perceived fatigue, which is beyond the initial approaches. This result is of special interest because it allows to consider the relationship between both types of perceived fatigue ( Bittner et al., 2000 ; Clemente et al., 2016 ). These results, taking into account that the participants did the test sitting down, emphasize the effect achieved through the protocol used to generate stress in them, without significant differences in the performance achieved in the task. Previous research carried out with the DT test already points in this same direction ( Ong, 2015 ). The differences found in the perception of physical fatigue even without previous movement are interesting. Similar results are found in studies carried out in contexts such as chess ( Fuentes et al., 2019 ), where central fatigue due to the demands of each game also leads to physical fatigue of the players. This fact seems relevant insofar as the studies should incorporate measures of both dimensions to be able to explain a higher percentage of variance of the results found.

As regards the second hypothesis, the decrease of CFFD values indicates that it has a negative effect generating central fatigue and an alteration in cortical activation ( Li et al., 2004 ; Clemente, 2016 ). These results confirm the alterations in cortical activation found in physiological efforts of high intensity and of short duration, such as sprints at maximum speed ( Clemente et al., 2011 ). This same trend is also observed in research focused on generating a high level of stress in soldiers, which emphasizes the usefulness of using the DT test to create stress in the participants ( Clemente et al., 2016 ). In line with the ideas defended by Clemente (2016) , decreased in CFFD scores seem to be linked to high sympathetic autonomous nervous system activation, which could also affect higher cognitive functions, such as executive processes (i.e., making complex decisions, memory, and attention processes) ( Shields et al., 2016 ). These same considerations can also be made with respect to the significant differences found in CFFA scores. Higher scores are found after the stress test, which implies that the participants have needed more time to respond to the flicker task as consequence of central fatigue ( Fuentes et al., 2019 ; Lohani et al., 2019 ).

Regarding the results obtained in the Overall DC scores, the significant differences show a pattern of alteration as a consequence of the stress test. As Naranjo-Orellana et al. (2020) point out, the OW test obtains good reliability and validity values using the heart rate variability as a measure in conjunction with the DC Potential (stabilitation DC, stabilitation time, and curve shape). Changes in the DC potentials have been reported to be reflective of performance in different brain processes ( Haider et al., 1981 ; Valenzuela et al., 2020 ). The lower scores obtained after the stress test could indicate, as with the CFF scores, an increase in central fatigue detected by the OmegaWave system ( Valenzuela et al., 2020 ). This result, in any case, needs to be analyzed in detail in future research.

Therefore, monitoring the DC potentials and the CFF scores could be useful to control the cognitive load of the different tasks that having a high mental demand.

Due to the exceptional circumstances of data collection in the present study, some of the study limitations were the sample size and the small number of women who participated in it. Future research works should expand the sample power, as well as determine its effect in a sedentary sample.

To conclude, this is the first study that has jointly analyzed the scores obtained in the analysis of low-frequency brain waves (DC potentials), together with those obtained in the Flicker test. In this sense, although the performance in a specific task seems similar, the demand it has for the person must be evaluated, being useful the use of research protocols similar to the ones we have used. The results open a new field where both measurements could be interesting and useful to assess the cognitive demands of persons.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.

Ethics Statement

The studies involving human participants were reviewed and approved by the University Ethical Commission in compliance with the Helsinki Declaration. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

RV: conceptualization, investigation, resources, writing—review and editing, and project administration. RV, ML-R, and RJ-C: methodology, data curation, writing—original draft preparation, visualization, supervision, and formal analysis. ML-R and RJ-C: software and validation.

Conflict of Interest

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

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Keywords : central fatigue, omega wave, cognitive response, psychophysiology, stress

Citation: de la Vega R, Jiménez-Castuera R and Leyton-Román M (2021) Impact of Weekly Physical Activity on Stress Response: An Experimental Study. Front. Psychol. 11:608217. doi: 10.3389/fpsyg.2020.608217

Received: 19 September 2020; Accepted: 04 December 2020; Published: 12 January 2021.

Reviewed by:

Copyright © 2021 de la Vega, Jiménez-Castuera and Leyton-Román. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Marta Leyton-Román, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Mastering the Art of Writing Experimental Research Papers

By charlesworth author services.

• 21 November, 2023

Writing an experimental research paper is a challenging yet rewarding task that requires careful planning, attention to detail, and adherence to a structured format. Understanding the intricacies of composing a well-organised experimental research paper can help you communicate your research findings effectively.

Experimental research design is a framework for conducting research, utilising two sets of variables. The first set remains constant, facilitating the measurement of differences in the second set. The importance of a well-crafted experimental research design is underscored, as it forms the foundation for publishing significant results , aids in decision-making, structures research for easier analysis, and addresses the main research question. 

Structure of an Experimental Research Paper

The structure of an experimental research paper plays a pivotal role in presenting your study in a logical and organised manner. The sections included in an experimental research paper typically follows the IMRAD formula: Introduction, Methods, Results, and Discussion. Additionally, there is the Title Page, Abstract, References, and, optionally, a Table and Figures section, as well as an Appendix section. Each section serves a specific purpose, contributing to the overall structure and coherence of the research paper.

The key components of this structure include:

1. Title and Abstract:

- The title should succinctly convey the essence of your research.

- The abstract provides a brief overview of your study, including the research question, methods, results, and conclusion.

2. Methods:

- The methods section must clearly define your research question or objective.

- One must conduct a thorough literature review and describe the experimental design in sufficient detail for reproducibility.

3. Results:

- The results must have a logical organisation of the data, using tables, figures, or graphs to present data effectively.

4. Discussion:

- The discussion must include the objective interpretation of your results by comparing them to existing literature.

- Acknowledge any limitations or potential sources of bias in your study.

5. Conclusion:

- Provide a concise conclusion by summarising the key findings and implications for the broader field.

6. References:

- Cite all sources accurately and in the appropriate format.

7. Appendices (Supplementary Materials):

- Include additional materials that support and complement the main text, such as raw data or detailed experimental protocols.

The purpose of an experimental research paper is to inform others about advancements in a particular field of study. It serves as a vehicle for researchers to share their findings, filling gaps in existing research and contributing to the collective knowledge of a specific subject. The researcher identifies a void in the literature and utilises their study to address and provide insights into this gap. The ultimate goal of these papers is to disseminate valuable information, fostering the progression of knowledge in the field.

Practical Tips for Writing an Experimental Research Paper

Writing an experimental research paper include selecting a topic of interest, creating a clear research question or hypothesis, conducting thorough research, identifying gaps in existing literature , and organizing thoughts through an outline. Generally, the "trial and error" approach is recommended for learning basic skills in constructing a research paper. 

To ensure the success of your experimental research paper, consider the following practical tips:

1. Define clearly your research question or objective

2. Conduct a thorough review of relevant literature

3. Provide a detailed description of the experimental design and methodology

4. Organise your results logically, using tables, figures, or graphs

5. Interpret and discuss your results objectively, comparing them to existing literature

6. Acknowledge any limitations or potential sources of bias in your study

7. Proofread and edit your paper for grammar, spelling, and clarity.

Mastering the art of writing experimental research papers requires a combination of careful planning, attention to detail, and adherence to a structured format. By understanding the essential components of the paper, implementing effective writing strategies, and following appropriate formatting guidelines, researchers can ensure that their work is presented in a compelling and accessible manner. A well-crafted experimental research paper not only contributes to the scientific discourse but also showcases the researcher's commitment to excellence in communication and research methodology.

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Academic Referencing: How to Cite a Research Paper

Learning how to conduct accurate, discipline-specific academic research can feel daunting at first. But, with a solid understanding of the reasoning behind why we use academic citations coupled with knowledge of the basics, you’ll learn how to cite sources with accuracy and confidence.

When it comes to academic research, citing sources correctly is arguably as important as the research itself. "Your instructors are expecting your work to adhere to these professional standards," said Amanda Girard , research support manager of Shapiro Library at Southern New Hampshire University (SNHU).

With Shapiro Library for the past three years, Girard manages the library’s research support services, which includes SNHU’s 24/7 library chat and email support. She holds an undergraduate degree in professional writing and a graduate degree in library and information science. She said that accurate citations show that you have done your research on a topic and are knowledgeable about current ideas from those actively working in the field.

In other words, when you cite sources according to the academic style of your discipline, you’re giving credit where credit is due.

Why Cite Sources?

Citing sources properly ensures you’re following high academic and professional standards for integrity and ethics.

“When you cite a source, you can ethically use others’ research. If you are not adequately citing the information you claim in your work, it would be considered plagiarism ,” said Shannon Geary '16 , peer tutor at SNHU.

Geary has an undergraduate degree in communication  from SNHU and has served on the academic support team for close to 2 years. Her job includes helping students learn how to conduct research  and write academically.

“In academic writing, it is crucial to state where you are receiving your information from,” she said. “Citing your sources ensures that you are following academic integrity standards.”

According to Geary and Girard, several key reasons for citing sources are:

• Access. Citing sources points readers to original sources. If anyone wants to read more on your topic, they can use your citations as a roadmap to access the original sources.
• Attribution. Crediting the original authors, researchers and experts  shows that you’re knowledgeable about current ideas from those actively working in the field and adhering to high ethical standards, said Girard.
• Clarity. “By citing your sources correctly, your reader can follow along with your research,” Girard said.
• Consistency. Adhering to a citation style provides a framework for presenting ideas within similar academic fields. “Consistent formatting makes accessing, understanding and evaluating an author's findings easier for others in related fields of study,” Geary said.
• Credibility. Proper citation not only builds a writer's authority but also ensures the reliability of the work, according to Geary.

Ultimately, citing sources is a formalized way for you to share ideas as part of a bigger conversation among others in your field. It’s a way to build off of and reference one another’s ideas, Girard said.

How Do You Cite an Academic Research Paper?

Any time you use an original quote or paraphrase someone else’s ideas, you need to cite that material, according to Geary.

“The only time we do not need to cite is when presenting an original thought or general knowledge,” she said.

While the specific format for citing sources can vary based on the style used, several key elements are always included, according to Girard. Those are:

• Title of source
• Type of source, such as a journal, book, website or periodical

By giving credit to the authors, researchers and experts you cite, you’re building credibility. You’re showing that your argument is built on solid research.

“Proper citation not only builds a writer's authority but also ensures the reliability of the work,” Geary said. “Properly formatted citations are a roadmap for instructors and other readers to verify the information we present in our work.”

Common Citation Styles in Academic Research

Certain disciplines adhere to specific citation standards because different disciplines prioritize certain information and research styles . The most common citation styles used in academic research, according to Geary, are:

• American Psychological Association, known as APA . This style is standard in the social sciences such as psychology, education and communication. “In these fields, research happens rapidly, which makes it exceptionally important to use current research,” Geary said.
• Modern Language Association, known as MLA . This style is typically used in literature and humanities because of the emphasis on literature analysis. “When citing in MLA, there is an emphasis on the author and page number, allowing the audience to locate the original text that is being analyzed easily,” Geary said.
• Chicago Manual of Style, known as Chicago . This style is typically used in history, business and sometimes humanities. “(Chicago) offers flexibility because of the use of footnotes, which can be seen as less distracting than an in-text citation,” Geary said.

The benefit of using the same format as other researchers within a discipline is that the framework of presenting ideas allows you to “speak the same language,” according to Girard.

How to Ensure Proper Citations

Keeping track of your research as you go is one of the best ways to ensure you’re citing appropriately and correctly based on the style that your academic discipline uses.

“Through careful citation, authors ensure their audience can distinguish between borrowed material and original thoughts, safeguarding their academic reputation and following academic honesty policies,” Geary said.

Some tips that she and Girard shared to ensure you’re citing sources correctly include:

• Keep track of sources as you work. Writers should keep track of their sources every time an idea is not theirs, according to Geary. “You don’t want to find the perfect research study and misplace its source information, meaning you’d have to omit it from your paper,” she said.
• Practice. Even experienced writers need to check their citations before submitting their work. “Citing requires us to pay close attention to detail, so always start your citation process early and go slow to ensure you don’t make mistakes,” said Geary. In time, citing sources properly becomes faster and easier.
• Use an Online Tool . Geary recommends the Shapiro Library citation guide . You can find sample papers, examples of how to cite in the different academic styles and up-to-date citation requirements, along with information and examples for APA, MLA and Chicago style citations.
• Work with a Tutor. A tutor can offer support along with tips to help you learn the process of academic research. Students at SNHU can connect with free peer tutoring through the Academic Support tab in their online courses, though many colleges and universities offer peer tutoring.

Find Your Program

How to cite a reference in academic writing.

A citation consists of two pieces: an in-text citation that is typically short and a longer list of references or works cited (depending on the style used) at the end of the paper.

“In-text citations immediately acknowledge the use of external source information and its exact location,” Geary said. While each style uses a slightly different format for in-text citations that reference the research, you may expect to need the page number, author’s name and possibly date of publication in parentheses at the end of a sentence or passage, according to Geary.

A longer entry listing the complete details of the resource you referenced should also be included on the references or works cited page at the end of the paper. The full citation is provided with complete details of the source, such as author, title, publication date and more, Geary said.

The two-part aspect of citations is because of readability. “You can imagine how putting the full citation would break up the flow of a paper,” Girard said. “So, a shortened version is used (in the text).”

“For example, if an in-text citation reads (Jones, 2024), the reader immediately knows that the ideas presented are coming from Jones’s work, and they can explore the comprehensive citation on the final page,” she said.

The in-text citation and full citation together provide a transparent trail of the author's process of engaging with research.

“Their combined use also facilitates further research by following a standardized style (APA, MLA, Chicago), guaranteeing that other scholars can easily connect and build upon their work in the future,” Geary said.

Developing and demonstrating your research skills, enhancing your work’s credibility and engaging ethically with the intellectual contributions of others are at the core of the citation process no matter which style you use.

A degree can change your life. Choose your program  from 200+ SNHU degrees that can take you where you want to go.

A former higher education administrator, Dr. Marie Morganelli is a career educator and writer. She has taught and tutored composition, literature, and writing at all levels from middle school through graduate school. With two graduate degrees in English language and literature, her focus — whether teaching or writing — is in helping to raise the voices of others through the power of storytelling. Connect with her on LinkedIn .

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Title: coderefine: a pipeline for enhancing llm-generated code implementations of research papers.

Abstract: This paper presents CodeRefine, a novel framework for automatically transforming research paper methodologies into functional code using Large Language Models (LLMs). Our multi-step approach first extracts and summarizes key text chunks from papers, analyzes their code relevance, and creates a knowledge graph using a predefined ontology. Code is then generated from this structured representation and enhanced through a proposed retrospective retrieval-augmented generation approach. CodeRefine addresses the challenge of bridging theoretical research and practical implementation, offering a more accurate alternative to LLM zero-shot prompting. Evaluations on diverse scientific papers demonstrate CodeRefine's ability to improve code implementation from the paper, potentially accelerating the adoption of cutting-edge algorithms in real-world applications.

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How to Write an Experimental Research Paper

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• M. N. Pamir M.D. 2 , 3  

Part of the book series: Acta Neurochirurgica Supplements ((NEUROCHIRURGICA,volume 83))

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The art and practice of academic neurosurgery are mastered by defining and learning the pertinent basic principles and skills. This article aims to present general guidelines to one of the many roles of a neurosurgeon: Writing an experimental research paper.

Every research report must use the “IMRAD formula: introduction, methods, results and discussion”. After the IMRAD is finished, abstract should be written and the title should be “created”. Your abstract should answer these questions: “Why did you start?, what did you do?, what answer did you get?, and what does it mean?”. Title of the research paper should be short enough to catch glance and memory of the reader and be long enough to give the essential information of what the paper is about.

Writing about the results of the experiment is no easier than the research itself. As surgery, writing a scientific paper is also an improvisation, but general principles should be learned and used in practice. The most effective style of learning basic skills to construct a research paper is the “trial and error” type.

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How to build a successful academic innovation program and why innovation should be a core neurosurgical competency.

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Institute of Neurological Sciences, Marmara University, Istanbul, Turkey

M. N. Pamir M.D.

Norolojik Bilimler Enstitusu, Marmara University, PK 53 Maltepe, Istanbul, Turkey

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Pamir, M.N. (2002). How to Write an Experimental Research Paper. In: Kanpolat, Y. (eds) Research and Publishing in Neurosurgery. Acta Neurochirurgica Supplements, vol 83. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6743-4_18

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Conducting Research in the New Abortion Care Policy Landscape

• 1 University of Michigan Ford School of Public Policy, Ann Arbor
• Research Letter A Cautionary Note on Trends in Maternal Death Post- Dobbs v Jackson Women’s Health Amanda Jean Stevenson, PhD; Leslie Root, PhD JAMA Network Open

The public policy chaos fueled by the June 2022 Dobbs v Jackson Women’s Health Organization Supreme Court decision has created a critical need for objective and high-quality abortion policy evaluation research. Stevenson and Root 1 rose to this challenge by conducting a convincing analysis of recent trends in maternal mortality, motivated in part by pro-life advocate claims that the recent observed decline in pregnancy-related death is the counterintuitive result of more restrictive abortion policies post– Dobbs v Jackson Women’s Health . Using 5.75 years of monthly national maternal mortality data from January 2018 through September 2023, their decomposition approach considered spikes in mortality from COVID-19 and potential seasonal fluctuations in maternal deaths. The findings clearly demonstrate that the observed decline in maternal mortality after the 2022 Dobbs v Jackson Women’s Health ruling is the result of a resolution of the COVID-19 mortality shock, not because of new restrictive abortion laws being passed by state legislatures.

This work is among the first published analyses of the potential effects of restrictive state abortion policies in the post– Dobbs v Jackson Women’s Health aftermath. Additional research on a wide array of potential outcomes needs to be conducted to fully understand the range and magnitude of the outcomes of state-level abortion policies. Based on a plethora of prior research regarding unintended pregnancy and abortion, reduced access to abortion care is thought to decrease the incidence of abortion but also increase the risk and incidence of myriad adverse maternal and infant health outcomes. 2 Restrictive abortion policies are also expected to increase child poverty, increase the number of families that experience serious financial instability or hardship, and put additional pressures on underresourced social welfare systems. 2 In addition, there is great concern that restrictive abortion laws will negatively impact physician choice regarding where to train or practice and the availability of obstetric care across states. 3

After major public policy reforms, it is common for stakeholders—proponents and opponents alike—to look for positive and negative policy effects immediately. While easy to conduct, armchair policy impact pontification is fraught with error and false conclusions. Identifying the impact of public policy change is not a simple task. Besides the obvious cautionary chant that correlation is not causation, evaluating the degree to which public policy shifts are having both intended and unintended consequences requires sound approaches to methods, timely access to quality data, and sophisticated statistical analyses that control for underlying trends and confounding factors. 4

Furthermore, there is often discussion and debate among researchers themselves regarding how to investigate the effects of important public health, medical, and health care interventions and policies. The convening of expert panels or working groups to provide guidance regarding research priorities and best approaches to research design, data or measurement, and analyses on specific topics is long-standing. It can provide essential scientific direction to emerging issues, including ones fraught with political controversy. For example, the National Academy of Medicine has convened many methods-focused committees in challenging areas of research, including reports such as Priorities for Research to Reduce the Threat of Firearm-Related Violence (2013) and A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters (2020).

There is a crucial need for investment in scientific discourse regarding how best to investigate the wide range of potential medical, public health, and social welfare effects of public policy related to abortion care. 5 An organization with authority and resources, such as the Department of Health and Human Services, the National Academy of Science, Engineering, and Medicine, or a foundation with a health-related mission, should quickly convene an ad hoc panel of experts to produce a nonpartisan, nonideological consensus document that provides guidance for objective public policy evaluation research associated with state-level restrictive abortion care policies. Such a panel could help create a priority research agenda for funders and provide guidance regarding data or measurement, research designs, statistical analyses approaches, and other methodology issues. This includes guidance for taking advantage of state differences in policy design, timing, and state contextual factors (eg, state Medicaid policy) and for subpopulation analyses since restrictive abortion laws are likely to have differential effects based on race or ethnicity, age, socioeconomic status, and geography.

In summary, the analysis by Stevenson and Root 1 provides a convincing assessment of an issue regarding abortion policy: the recent decline in maternal mortality after the Dobbs v Jackson Women’s Health ruling may be the result of a decrease in COVID-19–related maternal death rather than state legislatures’ new power to ban or severely restrict abortion care. However, there are many other important research questions regarding the health and social effects of the new abortion policy regime in the US. Investments in public policy evaluation research must be made quickly, including guidance for a priority research agenda and associated objective research designs, methods, and analysis approaches. Otherwise, the erroneous musings of amateur analysts of all ideological perspectives are likely to obscure a deep understanding of the actual effects of restrictive abortion policies on individuals and on population health outcomes, including medical care access, maternal and infant health, and the social welfare of families and children.

Published: August 27, 2024. doi:10.1001/jamanetworkopen.2024.30000

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Lantz PM. JAMA Network Open .

Corresponding Author: Paula M. Lantz, PhD, MS, University of Michigan Ford School of Public Policy, 735 S State St, Ann Arbor, MI 48109-3091 ( [email protected] ).

Conflict of Interest Disclosures: None reported.

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Lantz PM. Conducting Research in the New Abortion Care Policy Landscape. JAMA Netw Open. 2024;7(8):e2430000. doi:10.1001/jamanetworkopen.2024.30000

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