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The Neuroscience Power Crisis: What's the fallout?
Last week a paper ($) was published in Nature Reviews Neuroscience that is rocking the world of neuroscience. The crack team of researchers including neuroscientists, psychologists, geneticists and statisticians analysed meta-analyses of neuroscience research to determine the statistical power of the papers contained within.
The group discovered that neuroscience as a field is tremendously underpowered, meaning that most experiments are too small to be likely to find the subtle effects being looked for and the effects that are found are far more likely to be false positives than previously thought. It is likely that many theories that were previously thought to be robust might be far weaker than previously imagined. This topic by its very nature is something that is very difficult to assess on the level of any individual study, but when the field is looked at as a whole, an assessment of the statistical power across a broad spread of the literature becomes possible and this has brought worrying implications.
Something that the research only briefly touches on is that neuroscience may not be alone. Underpowered research could indeed be endemic through other sciences besides neuroscience. This may be a consequence of institutionalised failings resulting in a spread of perverse incentives such as the pressure on scientists to churn out paper after paper rather than genuinely producing quality work. This has big implications on our assumption that science is self correcting; today in certain areas this may not necessarily be the case. I sat down with Katherine Button and Marcus Munafò, a couple of the lead researcherson the project, to discuss the impact of the research. The conversation is below:
I'd like to begin by asking you if any individual low powered studies you might have stumbled upon are particularly striking to you. I'm particularly curious of low powered studies that stand out as having made an impact on the field or perhaps ones that were the most heavily spun upon release or resulted in dubious interpretations.
K: We looked at meta-analyses and didn't look directly at the individual studies which contributed to those meta-analyses. Some of the quality of the meta-analyses stood out because of unclear reporting of results; in some cases we had to work quite hard to extract the data, but because we were working at the meta level we weren't really struck by the individual studies.
M: It's probably worth taking a step back from this paper and thinking about the motivation for doing it in the first place, and the sort of things that gave rise to the motivation to write the paper. My research group is quite broad in its interests, so we do some genetic work, some human psychopharmacology work, I've worked with people on animal studies. Dating back several years, one of the consistent themes that was coming out of my research was that some effects that are apparently robust, if you read the published literature, are actually much harder to replicate than you might think. That's true across a number of different fields; for example if you look at candidate gene studies, it is now quite widely agreed that most of these are just too small to detect an effect that would be plausible, given what we know about genetic effects now. A whole literature has built up around specific associations that captured the scientific imagination, but when you look at the data either through a meta-analysis, or by trying to replicate the finding yourself, you find it's a lot more nebulous than some readings of the literature would have you believe. This was coming out as a really consistent theme. I started by doing meta-analysis as a way of identifying genetic variants robustly associated with outcomes so I could then genotype those outcomes myself, back in the day when genotyping was expensive. It proved that actually none of them was particularly robust, that was the clear finding.
I cut my teeth on meta-analytic techniques in that way and started applying the technique a bit more widely to human behavioural studies and so on, and one of the things that was really striking was that the average power in such diverse fields was really low - about 20%. That was the motivation behind looking at this more systematically and doing it in a way that would allow us to frame the problem, hopefully constructively, to an audience that might not have come across these problems in detail before. I could point at individual papers, but I'd be reluctant to, as that would say more about what I happen to have read rather than particularly problematic papers. It's a broad problem, I don't think it's about a particular field or a particular method.
K: During my PhD I looked at emotional processing in anxiety and whether processing is biased towards a certain type of emotional expressions. In a naive reading of the literature, certain things came out, like there is a strong bias for fearful faces or disgusted faces, for example, but when I tried to replicate these findings, my results didn't seem to fit. When I looked at the literature more critically, I realised that the reported effects were all over the place. I work in a medical department where there is an emphasis of the need for more reliable methods and statistical approaches, and Marcus was one of my PhD supervisors and had investigated the problems of low power in other literatures. Applying the knowledge gained from statistical methods training to critique the emotion processing literature lead me to think that a lot of this literature is probably false-positive. I wouldn't be surprised if that was the same for other fields.
M: We tried to draw in people from a range of fields - John Ioannidis is an epidemiologist, Jonathan Flint is a psychiatric geneticist, Emma Robinson does animal model work and behavioural pharmacology, Brian Nosek is a psychologist, Kate works in a medical department, I work in a psychology department, and one of the points we try to make is that individual fields have learned some specific lessons. Clinical trials have learned about the value of pre-registration of study protocols and power analysis, genetics has learned about the importance of large scale consortial efforts, meta-analysis, stringent statistical criteria and replication. Many of those lessons could be brought together and applied more or less universally.
Can you explain the importance of meta-analyses for assessing the problem of underpowered research?
K: To work out the power that a study has to detect a true effect requires an estimation of the size of that true underlying effect. We can never really know what the true underlying effect is, so the best estimate we have is the effect size indicated by a meta-analysis, because that will be based on several studies’ attempt to measure that effect. We used the meta-analyses as a proxy for the true underlying effect and then went back and looked at the power the individual studies would have had assuming that meta-effect was actually true. That's why you have to do this meta-analytic approach, because just calculating the power an individual study has to detect the effect observed in that study is circular and meaningless in this context.
M: We really are trying to be constructive - we don't want this to be seen as a hatchet job. I think we've all made these kinds of mistakes in the past, certainly I have, and I’m sure I’ll continue to make mistakes without meaning to, but one of the advantages of this kind of project is that it’s made me think about how I can improve my own practices, such as by pre-registering study protocols.
K: And it's not just mistakes, it's also a practicality issue - resources are often limited. Yet even if you know your study is underpowered it's still useful to say that “with this sample size, we can detect an effect of this is the size”. If you are upfront about the limitations of a small sample, then at least you know what size of effects you can and can’t detect, and interpret the results accordingly.
M: And make it clear when your study is confirmatory and when your study is exploratory – that distinction, I think, is blurred at the moment; my big concern is with the incentive structures that scientists have to work within. We are incentivised to crank the handle and run smaller studies that we can get published, rather than take longer to run fewer studies that might be more authoritative but aren't going to make for as weighty a CV in the long run because, however much emphasis there is on quality, there is still an extent to which promotions and grant success are driven just by how heavy your CV is.
I'm also interested in how in your opinion neuroscience compares to psychology and other sciences more broadly in terms of the level of statistical power in published research, do you think neuroscience is an anomaly or is the problem equally prevalent across in other disciplines?
M: My sense is that wherever we've looked we've come up with the same answer. We haven't looked everywhere but there is no field that has particularly stood out as better or worse, with the possible exception of phase three clinical trials that are funded by research councils without vested interests - those tend to be quite authoritative. But again, our motivation was not that neuroscience is particularly problematic - we were trying to raise these issues with a new audience and present some of the potential solutions that have been learned in fields such as genetics and clinical trials. It was more about reaching an audience than saying this field is better or worse than other fields because my sense is this is a universal problem.
Are there any particularly urgent areas you would like to highlight where under-powered research is an issue?
K: The emotional processing and anxiety literature – only because I am familiar with it. But I agree with Marcus’ point that these problems go across research areas and you are only familiar with them within the fields in which you work. I started off thinking that there were genuine effects to be found. There are so many studies with such conflicting evidence that you write a paper and try and say the evidence is conflicting and not very reliable, but then reviewers might say “how about so-and-so’s study?” and you just don’t have the space in papers to give a critique of all the methodological failings of all these studies.
M: I think there is a real distinction to be made between honest error where there are people who are trying to do a good job but they are incentivised to promote their findings and market their findings and it’s all unconscious and not malicious. There may be people who actually think of really gaming the system and don’t actually care whether or not they are right – that’s a really important distinction.
K: Something we do in my department is work with statisticians who are very careful about not overstating the claims of what we’ve found, I’ve done a few things looking at predictors of response to treatment which is effectively subgroup analysis of existing trial data and we try to be really upfront about the fact that these analyses are exploratory and that there are lots of limitations of subgroup analyses. I try to put at the forefront –‘type one and type two errors are possible and these findings need to be replicated before you believe any of them’. But as soon as you find a significant p-value, there are still a lot of reviewers that say ‘oh but this is really important for this, that or the other’ and no one wants to publish a nicely considered paper. There is a real emphasis from people saying ‘but why can’t you speculate on why this is really important and the implications this could have’ and you think that it could be important, but it could also be complete chance, so at every stage you are battling against the hyping up of your research.
M: I’ve had reviewers do this for us. In one case we were fairly transparent about presenting all our data and some of them were messy and some of them less so, and one of the reviewers said ‘just drop this stuff, it makes for a cleaner story and cleaner data if you don’t report all your data’ and we said ‘well actually we’d rather report everything and be transparent!’
K: As soon as you drop the nineteen things that didn’t come out, your one chance finding looks really amazing!
M: This is what I mean about honest error, the reviewer had no vested interest, the reviewer wasn’t trying to hype our results for us because – why would he or she? It’s just the system.
K: I think story telling is a real problem because a good story helps people to understand what your saying – it’s like when you write a blog you have to have a theme so people can follow you but there’s a balance to be struck between making your work accessible to readers but also not missing the point completely and going off on a tangent.
M: But that’s at the design stage; one of the things we are incentivised to do - wrongly in my opinion – is to include loads of measures so you’ve got a chance of finding something and then dropping all the other measures so it’s easier to tell the story. Actually what would be better is from the outset to design a study with relatively few outcomes where they all have their place and then you can write them up with all of them in there even if the results aren’t clear cut.
K: But that would require a lack of publication bias to really incentivise that, throwing all of your eggs into one basket is incentivised against really heavily. What we’ve tried to do recently when we are doing pilot studies, is writing in the protocols ‘we are going to be looking at all these different outcomes but this is our primary analysis and all these others are secondary exploratory analyses’. There are ways to report honestly and include lots of variables.
Q How big do you feel the gap is between bad science and institutionalised problems?
M: It’s not just about statistics; it takes a lot of guts as a PhD student to run the risk of having no publications at the end of your PhD.
K: It’s terrifying. Whether you get a post-doc depends on what your CV looks like.
M: I think of it as a continuum where there are very few people who are fraudulent, but then there are very few people who are perfect scientists, most of us are in the middle, where you become very invested in your ideas, there is confirmation bias, so one of the obvious things is you do an experiment as planned, you get exactly the results you expect and you think – great – and start writing it up, but if that process happens and you don’t get the results you were expecting you go back and check your data. So there can easily be a systematic difference in the amount of error checking that happens from one case to another, but in both cases there is the same likelihood that there will be errors in the data. It takes a lot of courage at the stage where you’ve run the analysis and got the results you were expecting to then go back and test them to destruction. Many scientists do this, but some don’t, not because they’re malicious but because that’s a natural psychological phenomenon – confirmation bias – you see what you are expecting to see.
Q Are there any specific bad practices that you think need to be highlighted?
M: Again, one of my main issues is with current incentive structures, which are hard for people to change from the bottom up – if you change your behaviour you are suddenly disadvantaged, relative to everyone else, in the short term. Then you have the problem that a lot of it is actually unconscious, well meant, non-malicious human instinct. Then you have the problem that when you do identify concerns there is no framework from which you say something without coming across as really hostile and confrontational – and that’s not necessarily constructive.
Button KS, Ioannidis JP, Mokrysz C, Nosek BA, Flint J, Robinson ES, & Munafò MR (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nature reviews Neuroscience, 14 (5), 365-76 PMID: 23571845
Image credit: Shutterstock/Feraru Nicolae
Northwell Health is using insights from website traffic to forecast COVID-19 hospitalizations two weeks in the future.
- The machine-learning algorithm works by analyzing the online behavior of visitors to the Northwell Health website and comparing that data to future COVID-19 hospitalizations.
- The tool, which uses anonymized data, has so far predicted hospitalizations with an accuracy rate of 80 percent.
- Machine-learning tools are helping health-care professionals worldwide better constrain and treat COVID-19.
The value of forecasting<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTA0Njk2OC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYyMzM2NDQzOH0.rid9regiDaKczCCKBsu7wrHkNQ64Vz_XcOEZIzAhzgM/img.jpg?width=980" id="2bb93" class="rm-shortcode" data-rm-shortcode-id="31345afbdf2bd408fd3e9f31520c445a" data-rm-shortcode-name="rebelmouse-image" data-width="1546" data-height="1056" />
Northwell emergency departments use the dashboard to monitor in real time.
Credit: Northwell Health<p>One unique benefit of forecasting COVID-19 hospitalizations is that it allows health systems to better prepare, manage and allocate resources. For example, if the tool forecasted a surge in COVID-19 hospitalizations in two weeks, Northwell Health could begin:</p><ul><li>Making space for an influx of patients</li><li>Moving personal protective equipment to where it's most needed</li><li>Strategically allocating staff during the predicted surge</li><li>Increasing the number of tests offered to asymptomatic patients</li></ul><p>The health-care field is increasingly using machine learning. It's already helping doctors develop <a href="https://care.diabetesjournals.org/content/early/2020/06/09/dc19-1870" target="_blank">personalized care plans for diabetes patients</a>, improving cancer screening techniques, and enabling mental health professionals to better predict which patients are at <a href="https://healthitanalytics.com/news/ehr-data-fuels-accurate-predictive-analytics-for-suicide-risk" target="_blank" rel="noopener noreferrer">elevated risk of suicide</a>, to name a few applications.</p><p>Health systems around the world have already begun exploring how <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315944/" target="_blank" rel="noopener noreferrer">machine learning can help battle the pandemic</a>, including better COVID-19 screening, diagnosis, contact tracing, and drug and vaccine development.</p><p>Cruzen said these kinds of tools represent a shift in how health systems can tackle a wide variety of problems.</p><p>"Health care has always used the past to predict the future, but not in this mathematical way," Cruzen said. "I think [Northwell Health's new predictive tool] really is a great first example of how we should be attacking a lot of things as we go forward."</p>
Making machine-learning tools openly accessible<p>Northwell Health has made its predictive tool <a href="https://github.com/northwell-health/covid-web-data-predictor" target="_blank">available for free</a> to any health system that wishes to utilize it.</p><p>"COVID is everybody's problem, and I think developing tools that can be used to help others is sort of why people go into health care," Dr. Cruzen said. "It was really consistent with our mission."</p><p>Open collaboration is something the world's governments and health systems should be striving for during the pandemic, said Michael Dowling, Northwell Health's president and CEO.</p><p>"Whenever you develop anything and somebody else gets it, they improve it and they continue to make it better," Dowling said. "As a country, we lack data. I believe very, very strongly that we should have been and should be now working with other countries, including China, including the European Union, including England and others to figure out how to develop a health surveillance system so you can anticipate way in advance when these things are going to occur."</p><p>In all, Northwell Health has treated more than 112,000 COVID patients. During the pandemic, Dowling said he's seen an outpouring of goodwill, collaboration, and sacrifice from the community and the tens of thousands of staff who work across Northwell.</p><p>"COVID has changed our perspective on everything—and not just those of us in health care, because it has disrupted everybody's life," Dowling said. "It has demonstrated the value of community, how we help one another."</p>
It's hard to stop looking back and forth between these faces and the busts they came from.
- A quarantine project gone wild produces the possibly realistic faces of ancient Roman rulers.
- A designer worked with a machine learning app to produce the images.
- It's impossible to know if they're accurate, but they sure look plausible.
How the Roman emperors got faced<a href="https://payload.cargocollective.com/1/6/201108/14127595/2K-ENGLISH-24x36-Educational_v8_WATERMARKED_2000.jpg" ><img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDQ2NDk2MS9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYyOTUzMzIxMX0.OwHMrgKu4pzu0eCsmOUjybdkTcSlJpL_uWDCF2djRfc/img.jpg?width=980" id="775ca" class="rm-shortcode" data-rm-shortcode-id="436000b6976931b8320313478c624c82" data-rm-shortcode-name="rebelmouse-image" alt="lineup of emperor faces" data-width="1440" data-height="963" /></a>
Credit: Daniel Voshart<p>Voshart's imaginings began with an AI/neural-net program called <a href="https://www.artbreeder.com" target="_blank">Artbreeder</a>. The freemium online app intelligently generates new images from existing ones and can combine multiple images into…well, who knows. It's addictive — people have so far used it to generate nearly 72.7 million images, says the site — and it's easy to see how Voshart fell down the rabbit hole.</p><p>The Roman emperor project began with Voshart feeding Artbreeder images of 800 busts. Obviously, not all busts have weathered the centuries equally. Voshart told <a href="https://www.livescience.com/ai-roman-emperor-portraits.html" target="_blank" rel="noopener noreferrer">Live Science</a>, "There is a rule of thumb in computer programming called 'garbage in garbage out,' and it applies to Artbreeder. A well-lit, well-sculpted bust with little damage and standard face features is going to be quite easy to get a result." Fortunately, there were multiple busts for some of the emperors, and different angles of busts captured in different photographs.</p><p>For the renderings Artbreeder produced, each face required some 15-16 hours of additional input from Voshart, who was left to deduce/guess such details as hair and skin coloring, though in many cases, an individual's features suggested likely pigmentations. Voshart was also aided by written descriptions of some of the rulers.</p><p>There's no way to know for sure how frequently Voshart's guesses hit their marks. It is obviously the case, though, that his interpretations look incredibly plausible when you compare one of his emperors to the sculpture(s) from which it was derived.</p><p>For an in-depth description of Voshart's process, check out his posts on <a href="https://medium.com/@voshart/photoreal-roman-emperor-project-236be7f06c8f" target="_blank">Medium</a> or on his <a href="https://voshart.com/ROMAN-EMPEROR-PROJECT" target="_blank" rel="noopener noreferrer">website</a>.</p><p>It's fascinating to feel like you're face-to-face with these ancient and sometimes notorious figures. Here are two examples, along with some of what we think we know about the men behind the faces.</p>
Caligula<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDQ2NDk4Mi9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTY3MzQ1NTE5NX0.LiTmhPQlygl9Fa9lxay8PFPCSqShv4ELxbBRFkOW_qM/img.jpg?width=980" id="7bae0" class="rm-shortcode" data-rm-shortcode-id="ce795c554490fe0a36a8714b86f55b16" data-rm-shortcode-name="rebelmouse-image" data-width="992" data-height="558" />
One of numerous sculptures of Caligula, left
Nero<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDQ2NTAwMC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTY1NTQ2ODU0NX0.AgYuQZzRQCanqehSI5UeakpxU8fwLagMc_POH7xB3-M/img.jpg?width=980" id="a8825" class="rm-shortcode" data-rm-shortcode-id="9e0593d79c591c97af4bd70f3423885e" data-rm-shortcode-name="rebelmouse-image" data-width="992" data-height="558" />
One of numerous sculptures of Nero, left
A new study proposes mysterious axions may be found in X-rays coming from a cluster of neutron stars.
Are Axions Dark Matter?<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="5e35ce24a5b17102bfce5ae6aecc7c14"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/e7yXqF32Yvw?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span>
New research suggests you can't fake your emotional state to improve your work life — you have to feel it.
What is deep acting?<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTQ1NDk2OS9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYxNTY5MzA0Nn0._s7aP25Es1CInq51pbzGrUj3GtOIRWBHZxCBFnbyXY8/img.jpg?width=1245&coordinates=333%2C-1%2C333%2C-1&height=700" id="ddf09" class="rm-shortcode" data-rm-shortcode-id="9dc42c4d6a8e372ad7b72907b46ecd3f" data-rm-shortcode-name="rebelmouse-image" data-width="1245" data-height="700" />
Arlie Russell Hochschild (pictured) laid out the concept of emotional labor in her 1983 book, "The Managed Heart."
Credit: Wikimedia Commons<p>Deep and surface acting are the principal components of emotional labor, a buzz phrase you have likely seen flitting about the Twittersphere. Today, "<a href="https://www.bbc.co.uk/bbcthree/article/5ea9f140-f722-4214-bb57-8b84f9418a7e" target="_blank">emotional labor</a>" has been adopted by groups as diverse as family counselors, academic feminists, and corporate CEOs, and each has redefined it with a patented spin. But while the phrase has splintered into a smorgasbord of pop-psychological arguments, its initial usage was more specific.</p><p>First coined by sociologist Arlie Russell Hochschild in her 1983 book, "<a href="https://www.ucpress.edu/book/9780520272941/the-managed-heart" target="_blank">The Managed Heart</a>," emotional labor describes the work we do to regulate our emotions on the job. Hochschild's go-to example is the flight attendant, who is tasked with being "nicer than natural" to enhance the customer experience. While at work, flight attendants are expected to smile and be exceedingly helpful even if they are wrestling with personal issues, the passengers are rude, and that one kid just upchucked down the center aisle. Hochschild's counterpart to the flight attendant is the bill collector, who must instead be "nastier than natural."</p><p>Such personas may serve an organization's mission or commercial interests, but if they cause emotional dissonance, they can potentially lead to high emotional costs for the employee—bringing us back to deep and surface acting.</p><p>Deep acting is the process by which people modify their emotions to match their expected role. Deep actors still encounter the negative emotions, but they devise ways to <a href="http://www.selfinjury.bctr.cornell.edu/perch/resources/what-is-emotion-regulationsinfo-brief.pdf" target="_blank">regulate those emotions</a> and return to the desired state. Flight attendants may modify their internal state by talking through harsh emotions (say, with a coworker), focusing on life's benefits (next stop Paris!), physically expressing their desired emotion (smiling and deep breaths), or recontextualizing an inauspicious situation (not the kid's fault he got sick).</p><p>Conversely, surface acting occurs when employees display ersatz emotions to match those expected by their role. These actors are the waiters who smile despite being crushed by the stress of a dinner rush. They are the CEOs who wear a confident swagger despite feelings of inauthenticity. And they are the bouncers who must maintain a steely edge despite humming show tunes in their heart of hearts.</p><p>As we'll see in the research, surface acting can degrade our mental well-being. This deterioration can be especially true of people who must contend with negative emotions or situations inside while displaying an elated mood outside. Hochschild argues such emotional labor can lead to exhaustion and self-estrangement—that is, surface actors erect a bulwark against anger, fear, and stress, but that disconnect estranges them from the emotions that allow them to connect with others and live fulfilling lives.</p>
Don't fake it till you make it<p>Most studies on emotional labor have focused on customer service for the obvious reason that such jobs prescribe emotional states—service with a smile or, if you're in the bouncing business, a scowl. But <a href="https://eller.arizona.edu/people/allison-s-gabriel" target="_blank">Allison Gabriel</a>, associate professor of management and organizations at the University of Arizona's Eller College of Management, wanted to explore how employees used emotional labor strategies in their intra-office interactions and which strategies proved most beneficial.</p><p>"What we wanted to know is whether people choose to engage in emotion regulation when interacting with their co-workers, why they choose to regulate their emotions if there is no formal rule requiring them to do so, and what benefits, if any, they get out of this effort," Gabriel said in <a href="https://www.sciencedaily.com/releases/2020/01/200117162703.htm" target="_blank">a press release</a>.</p><p>Across three studies, she and her colleagues surveyed more than 2,500 full-time employees on their emotional regulation with coworkers. The survey asked participants to agree or disagree with statements such as "I try to experience the emotions that I show to my coworkers" or "I fake a good mood when interacting with my coworkers." Other statements gauged the outcomes of such strategies—for example, "I feel emotionally drained at work." Participants were drawn from industries as varied as education, engineering, and financial services.</p><p>The results, <a href="https://psycnet.apa.org/doiLanding?doi=10.1037%2Fapl0000473" target="_blank" rel="noopener noreferrer">published in the Journal of Applied Psychology</a>, revealed four different emotional strategies. "Deep actors" engaged in high levels of deep acting; "low actors" leaned more heavily on surface acting. Meanwhile, "non-actors" engaged in negligible amounts of emotional labor, while "regulators" switched between both. The survey also revealed two drivers for such strategies: prosocial and impression management motives. The former aimed to cultivate positive relationships, the latter to present a positive front.</p><p>The researchers found deep actors were driven by prosocial motives and enjoyed advantages from their strategy of choice. These actors reported lower levels of fatigue, fewer feelings of inauthenticity, improved coworker trust, and advanced progress toward career goals. </p><p>As Gabriel told <a href="https://www.psypost.org/2021/01/new-psychology-research-suggests-deep-acting-can-reduce-fatigue-and-improve-your-work-life-59081" target="_blank" rel="noopener noreferrer">PsyPost in an interview</a>: "So, it's a win-win-win in terms of feeling good, performing well, and having positive coworker interactions."</p><p>Non-actors did not report the emotional exhaustion of their low-actor peers, but they also didn't enjoy the social gains of the deep actors. Finally, the regulators showed that the flip-flopping between surface and deep acting drained emotional reserves and strained office relationships.</p><p>"I think the 'fake it until you make it' idea suggests a survival tactic at work," Gabriel noted. "Maybe plastering on a smile to simply get out of an interaction is easier in the short run, but long term, it will undermine efforts to improve your health and the relationships you have at work. </p><p>"It all boils down to, 'Let's be nice to each other.' Not only will people feel better, but people's performance and social relationships can also improve."</p>
You'll be glad ya' decided to smile<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="88a0a6a8d1c1abfcf7b1aca8e71247c6"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/QOSgpq9EGSw?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span><p>But as with any research that relies on self-reported data, there are confounders here to untangle. Even during anonymous studies, participants may select socially acceptable answers over honest ones. They may further interpret their goal progress and coworker interactions more favorably than is accurate. And certain work conditions may not produce the same effects, such as toxic work environments or those that require employees to project negative emotions.</p><p>There also remains the question of the causal mechanism. If surface acting—or switching between surface and deep acting—is more mentally taxing than genuinely feeling an emotion, then what physiological process causes this fatigue? <a href="https://www.frontiersin.org/articles/10.3389/fnhum.2019.00151/full" target="_blank">One study published in the <em>Frontiers in Human Neuroscience</em></a><em> </em>measured hemoglobin density in participants' brains using an fNIRS while they expressed emotions facially. The researchers found no significant difference in energy consumed in the prefrontal cortex by those asked to deep act or surface act (though, this study too is limited by a lack of real-life task).<br></p><p>With that said, Gabriel's studies reinforce much of the current research on emotional labor. <a href="https://journals.sagepub.com/doi/abs/10.1177/2041386611417746" target="_blank">A 2011 meta-analysis</a> found that "discordant emotional labor states" (read: surface acting) were associated with harmful effects on well-being and performance. The analysis found no such consequences for deep acting. <a href="https://doi.apa.org/doiLanding?doi=10.1037%2Fa0022876" target="_blank" rel="noopener noreferrer">Another meta-analysis</a> found an association between surface acting and impaired well-being, job attitudes, and performance outcomes. Conversely, deep acting was associated with improved emotional performance.</p><p>So, although there's still much to learn on the emotional labor front, it seems Van Dyke's advice to a Leigh was half correct. We should put on a happy face, but it will <a href="https://bigthink.com/design-for-good/everything-you-should-know-about-happiness-in-one-infographic" target="_self">only help if we can feel it</a>.</p>
Archaeologists discover a cave painting of a wild pig that is now the world's oldest dated work of representational art.