Intergroup preference, not dehumanization, explains social biases in emotion attribution

F. Enoch, S. P. Tipper, & H. Over

Cognition

Volume 216, November 2021, 104865

Abstract

Psychological models can only help improve intergroup relations if they accurately characterise the mechanisms underlying social biases. The claim that outgroups suffer dehumanization is near ubiquitous in the social sciences. We challenge the most prominent psychological model of dehumanization - infrahumanization theory - which holds outgroup members are subtly dehumanized by being denied human emotions. We examine the theory across seven intergroup contexts in thirteen pre-registered and highly powered experiments (N = 1690). We find outgroup members are not denied uniquely human emotions relative to ingroup members. Rather, they are ascribed prosocial emotions to a lesser extent but antisocial emotions to a greater extent. Apparent evidence for infrahumanization is better explained by ingroup preference, outgroup derogation and stereotyping. Infrahumanization theory may obscure more than it reveals about intergroup bias.

Highlights

• Infrahumanization theory predicts outgroups are often denied uniquely human emotions.

• However, to date, antisocial uniquely human emotions have not been investigated.

• We test attributions of prosocial and antisocial emotions to social groups.

• Attributions of antisocial human emotions were stronger for outgroups than ingroups.

• We find no support for the predictions of infrahumanization theory.

From the General Discussion

Our results dovetail with recent empirical work that challenges the predictions made by Haslam's (2006) dual model of dehumanization (Enock et al., 2021). This research showed that when undesirable human-specific characteristics (such as ‘corrupt’ and ‘selfish’) are included in overall measures of humanness, there is no evidence for either animalistic or mechanistic dehumanization of outgroups as characterised by the dual model. Rather, desirable human qualities are more strongly attributed to ingroup members and undesirable human qualities to outgroup members. The present work extends these findings by further demonstrating the importance of considering sociality confounds when measuring psychological processes of ‘dehumanization’, this time through another highly prominent framework within the field.

During the review process, it was put to us that because dimensions of valence and sociality correlate highly in our pretest, the two constructs are “indistinguishable”, thus rendering our critique obsolete. We believe this represents a misunderstanding. Height and weight are strongly positively correlated, yet they are distinct constructs. Similarly, even though emotions that are generally perceived as prosocial may also perceived as positive to experience, and emotions that are generally perceived as antisocial may also be perceived as negative to experience, the two constructs are clearly conceptually distinct. While sadness is negative to experience, it is not inherently antisocial in character. Schadenfreude on the other hand is, by definition, positive to experience but antisocial in character.

The research is here.

How Does Cost-Effectiveness Analysis Inform Health Care Decisions?

David D. Kim & Anirban Basu

AMA J Ethics. 2021;23(8):E639-647. 

doi: 10.1001/amajethics.2021.639.

Abstract

Cost-effectiveness analysis (CEA) provides a formal assessment of trade-offs involving benefits, harms, and costs inherent in alternative options. CEA has been increasingly used to inform public and private organizations’ reimbursement decisions, benefit designs, and price negotiations worldwide. Despite the lack of centralized efforts to promote CEA in the United States, the demand for CEA is growing. This article briefly reviews the history of CEA in the United States, highlights advances in practice guidelines, and discusses CEA’s ethical challenges. It also offers a way forward to inform health care decisions.

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Ethical Considerations

There have been a few criticisms on ethical grounds of CEA’s use for decision making. These include (1) controversies associated with the use of QALYs, (2) distributive justice, and (3) incomplete valuation. We discuss each of them in detail here. However, it is worth pointing out that cost-effectiveness evidence is only one of many factors considered in resource allocation decisions. We have found that none of the international HTA bodies bases its decisions solely on cost-effectiveness evidence. Therefore, much of CEA’s criticisms, fair or not, can be addressed through deliberative processes.

QALYs. The lower health utility, or health-related quality of life, assigned to patients with worse health (because of more severe disease, disability, age, and so on) raises distributional issues in using QALYs for resource allocation decisions. For example, because patients with disabilities have a lower overall health utility weight, any extension of their lives by reducing the health burden from one disease “would not generate as many QALYs as a similar extension of life for otherwise healthy people.” This distributional limitation arises because of the multiplicative nature of QALYs, which are a product of life-years and health utility weight. Consequently, the National Council on Disability has strongly denounced the use of QALYs.

Alternatives to QALYs have been proposed. The Institute for Clinical and Economic Review has started using the equal value of life-years gained metric, a modified version of the equal value of life (EVL) metric, to supplement QALYs. In EVL calculations, any life-year gained is valued at a weight of 1 QALY, irrespective of individuals’ health status during the extra year. EVL, however, “has had limited traction among academics and decision-making bodies” because it undervalues interventions that extend life-years by the same amount as other interventions but that substantially improve quality of life. More recently, a health-years-in-total metric was proposed to overcome the limitations of both QALYs and EVL, but more work is needed to fully understand its theoretical foundations.

The info is here.

Fraudulent data raise questions about superstar honesty researcher

Cathleen O'Grady

Sciencemag.com

Originally posted 24 Aug 21

Here is an excerpt:

Some time later, a group of anonymous researchers downloaded those data, according to last week’s post on Data Colada. A simple look at the participants’ mileage distribution revealed something very suspicious. Other data sets of people’s driving distances show a bell curve, with some people driving a lot, a few very little, and most somewhere in the middle. In the 2012 study, there was an unusually equal spread: Roughly the same number of people drove every distance between 0 and 50,000 miles. “I was flabbergasted,” says the researcher who made the discovery. (They spoke to Science on condition of anonymity because of fears for their career.)

Worrying that PNAS would not investigate the issue thoroughly, the whistleblower contacted the Data Colada bloggers instead, who conducted a follow-up review that convinced them the field study results were statistically impossible.

For example, a set of odometer readings provided by customers when they first signed up for insurance, apparently real, was duplicated to suggest the study had twice as many participants, with random numbers between one and 1000 added to the original mileages to disguise the deceit. In the spreadsheet, the original figures appeared in the font Calibri, but each had a close twin in another font, Cambria, with the same number of cars listed on the policy, and odometer readings within 1000 miles of the original. In 1 million simulated versions of the experiment, the same kind of similarity appeared not a single time, Simmons, Nelson, and Simonsohn found. “These data are not just excessively similar,” they write. “They are impossibly similar.”

Ariely calls the analysis “damning” and “clear beyond doubt.” He says he has requested a retraction, as have his co-authors, separately. “We are aware of the situation and are in communication with the authors,” PNAS Editorial Ethics Manager Yael Fitzpatrick said in a statement to Science.

Three of the authors say they were only involved in the two lab studies reported in the paper; a fourth, Boston University behavioral economist Nina Mazar, forwarded the Data Colada investigators a 16 February 2011 email from Ariely with an attached Excel file that contains the problems identified in the blog post. Its metadata suggest Ariely had created the file 3 days earlier.

Ariely tells Science he made a mistake in not checking the data he received from the insurance company, and that he no longer has the company’s original file. He says Duke’s integrity office told him the university’s IT department does not have email records from that long ago. His contacts at the insurance company no longer work there, Ariely adds, but he is seeking someone at the company who could find archived emails or files that could clear his name. His publication of the full data set last year showed he was unaware of any problems with it, he says: “I’m not an idiot. This is a very easy fraud to catch.”

The article is here.

The Case Against Non-Moral Blame

Matheson, B., & Milam, P.E.

Forthcoming in the Oxford Studies 

in Normative Ethics 11

Abstract

Non-moral blame seems to be widespread and widely accepted in everyday life—tolerated at least, but often embraced. We blame athletes for poor performance, artists for bad or boring art, scientists for faulty research, and voters for flawed reasoning. This paper argues that non-moral blame is never justified—i.e. it’s never a morally permissible response to a non-moral failure. Having explained what blame is and how non-moral blame differs from moral blame, the paper presents the argument in four steps. First, it argues that many (perhaps most) apparent cases of non-moral blame are actually cases of moral blame. Second, it argues that even if non-moral blame is pro tanto permissible—because its target is blameworthy for their substandard performance—it often (perhaps usually) fails to meet other permissibility conditions, such as fairness or standing. Third, it goes further and challenges the claim that non-moral blame is ever even pro tanto permissible. Finally, it considers a number of arguments in support of non-moral obligations and argues that none of them succeed.

A pdf is here.

This philosophical piece highlights, in part, the Fundamental Attribution Error in context of moral judgment.

Attorney General James and U.S. Department of Labor Deliver $14 Million to Consumers Who Were Denied Mental Health Care Coverage

Press Release

NY Attorney General

Posted 12 August 21

New York Attorney General Letitia James and the U.S. Department of Labor (USDOL) today announced landmark agreements with UnitedHealthcare (United), the nation’s largest health insurer, to resolve allegations that United unlawfully denied health care coverage for mental health and substance use disorder treatment for thousands of Americans. As a result of these agreements, United will pay approximately $14.3 million in restitution to consumers affected by the policies, including $9 million to more than 20,000 New Yorkers with behavioral health conditions who received denials or reductions in reimbursement. New York and federal law requires health insurance plans to cover mental health and substance use disorder treatment the same way they cover physical health treatment. The agreements — which resolve investigations and litigation — address United’s policies that illegally limited coverage of outpatient psychotherapy, hindering access to these vital services for hundreds of thousands of New Yorkers for whom United administers behavioral health benefits. In addition to the payment to impacted consumers, United will lift the barriers it imposed and pay more than $2 million in penalties, with $1.3 million going to New York state.  

“In the shadow of the most devastating year for overdose deaths and in the face of growing mental health concerns due to the pandemic, access to this care is more critical than ever before,” said Attorney General James. “United’s denial of these vital services was both unlawful and dangerous — putting millions in harm’s way during the darkest of times. There must be no barrier for New Yorkers seeking health care of any kind, which is why I will always fight to protect and expand it. I thank Secretary Walsh for his partnership on this important matter.” 

“Protecting access to mental health and substance use disorder treatment is a priority for the Department of Labor and something I believe in strongly as a person in long-term recovery,” said U.S. Secretary of Labor Marty Walsh. “This settlement provides compensation for many people who were denied full benefits and equitable treatment. We appreciate Attorney General James and her office for their partnership in investigating, identifying, and remedying these violations.” 

New York’s behavioral health parity law — originally enacted as “Timothy’s Law” in 2006 — and the federal Mental Health Parity and Addiction Equity Act of 2008 (MHPAEA) require insurance coverage for mental health and substance use disorder treatment to be no more restrictive than insurance coverage for physical health conditions. The agreements are the product of the first joint state-federal enforcement of these laws.  

The pressor is here.

Why Is It So Hard to Be Rational?

Joshua Rothman

The New Yorker

Originally published 16 Aug 21

Here is an excerpt:

Knowing about what you know is Rationality 101. The advanced coursework has to do with changes in your knowledge. Most of us stay informed straightforwardly—by taking in new information. Rationalists do the same, but self-consciously, with an eye to deliberately redrawing their mental maps. The challenge is that news about distant territories drifts in from many sources; fresh facts and opinions aren’t uniformly significant. In recent decades, rationalists confronting this problem have rallied behind the work of Thomas Bayes, an eighteenth-century mathematician and minister. So-called Bayesian reasoning—a particular thinking technique, with its own distinctive jargon—has become de rigueur.

There are many ways to explain Bayesian reasoning—doctors learn it one way and statisticians another—but the basic idea is simple. When new information comes in, you don’t want it to replace old information wholesale. Instead, you want it to modify what you already know to an appropriate degree. The degree of modification depends both on your confidence in your preexisting knowledge and on the value of the new data. Bayesian reasoners begin with what they call the “prior” probability of something being true, and then find out if they need to adjust it.

Consider the example of a patient who has tested positive for breast cancer—a textbook case used by Pinker and many other rationalists. The stipulated facts are simple. The prevalence of breast cancer in the population of women—the “base rate”—is one per cent. When breast cancer is present, the test detects it ninety per cent of the time. The test also has a false-positive rate of nine per cent: that is, nine per cent of the time it delivers a positive result when it shouldn’t. Now, suppose that a woman tests positive. What are the chances that she has cancer?

When actual doctors answer this question, Pinker reports, many say that the woman has a ninety-per-cent chance of having it. In fact, she has about a nine-per-cent chance. The doctors have the answer wrong because they are putting too much weight on the new information (the test results) and not enough on what they knew before the results came in—the fact that breast cancer is a fairly infrequent occurrence. To see this intuitively, it helps to shuffle the order of your facts, so that the new information doesn’t have pride of place. Start by imagining that we’ve tested a group of a thousand women: ten will have breast cancer, and nine will receive positive test results. Of the nine hundred and ninety women who are cancer-free, eighty-nine will receive false positives. Now you can allow yourself to focus on the one woman who has tested positive. To calculate her chances of getting a true positive, we divide the number of positive tests that actually indicate cancer (nine) by the total number of positive tests (ninety-eight). That gives us about nine per cent.

The info is here.

Reconstructing the Einstellung effect

Binz, M., & Schulz, E. (2021, August 10).

https://doi.org/10.31234/osf.io/yhcf4

Abstract

The Einstellung effect was first described by Abraham Luchins in his doctoral thesis published in 1942. The effect occurs when a repeated solution to old problems is applied to a new problem even though a more appropriate response is available. In Luchins' so-called water jar task, participants had to measure a specific amount of water using three jars of different capacities. Luchins found that subjects kept using methods they had applied in previous trials, even if a more efficient solution for the current trial was available: an Einstellung effect. Moreover, Luchins studied the different conditions that could possibly mediate this effect, including telling participants to pay more attention, changing the number of tasks, alternating between different types of tasks, as well as putting participants under time pressure. In the current work, we reconstruct and reanalyze the data of the various experimental conditions published in Luchins' thesis. We furthermore show that a model of resource-rational decision-making can explain all of the observed effects. This model assumes that people transform prior preferences into a posterior policy to maximize rewards under time constraints. Taken together, our reconstructive and modeling results put the Einstellung effect under the lens of modern-day psychology and show how resource-rational models can explain effects that have historically been seen as deficiencies of human problem-solving.

From the Discussion

It is typically assumed that the best solution for any particular problem is necessarily the shortest, and thus previous research has largely characterized the Einstellung effect as maladaptive behavior.  In the present paper, we have challenged this assumption and provided a resource-rational interpretation of the effect. We did so with the help of an information-theoretic model of decision-making.  The central premise of this  model is to transform prior preferences into posterior policies in a way that trade of expected utility with the time it takes to make a decision. The resulting model incorporates three basic principles: (1) people prefer simple solutions, i.e.,they attempt to spend as little physical effort as possible, (2) they avoid costly computations, i.e., those that require high mental effort, and (3) they adapt to their environment,  i.e., they learn about statistics of the problem they interact with.We found that these simple principles are sufficient to capture the rich characteristics found in Luchins’ data. An additional ablation analysis  confirmed  that  all  of  these  principles  are necessary to reproduce the entire set of phenomena reported in Luchins’ thesis.

The research is here.

What are the obligations of pharmaceutical companies in a global health emergency?

Emanuel, E., et al. 

The Lancet

Originally published 5 August 21

Here is an excerpt:

Principles governing the response to COVID-19

An ethical approach to COVID-19 vaccine production and distribution should satisfy four uncontroversial principles: optimising vaccine production, including development, testing, and manufacturing; fair distribution; sustainability; and accountability.

These four principles should be taken as a coherent whole, for all companies and applied globally. For instance, ensuring accountability should not undermine optimising production. There are multiple ways to balance these principles. Any decision to give greater weight to some principles rather than others is inherently controversial. Optimising production is obviously necessary to end vaccine scarcity. Fair distribution requires that no segment of the world's population should be unvaccinated because of inability to afford vaccination.5 Importantly, any practical approach should ensure sustainability and companies' continued engagement in addressing COVID-19 and their focus on future infectious diseases and health emergencies.

Additionally, all parties' obligations should be coordinated and mutually consistent. For instance, companies should not be obligated to provide host countries with additional booster shots at the expense of fulfilling bilateral contracts with countries in which there are surges.

Finally, any satisfactory approach should include mechanisms for assurance that all parties are honouring their obligations. This assurance enables countries, pharmaceutical companies, global organisations, and others to verify compliance with the chosen approach and protect ethically compliant stakeholders from being unfairly exploited by unethical behaviour of others.

The info is here.

How relationships bias moral reasoning: Neural and self-report evidence

Berg, M. K., Kitayama, S., & Kross, E.

Journal of Experimental Social Psychology

Volume 95, July 2021, 104156

Abstract

Laws govern society, regulating people's behavior to create social harmony. Yet recent research indicates that when laws are broken by people we know and love, we consistently fail to report their crimes. Here we identify an expectancy-based cognitive mechanism that underlies this phenomenon and illustrate how it interacts with people's motivations to predict their intentions to report crimes. Using a combination of self-report and brain (ERP) measures, we demonstrate that although witnessing any crime violates people's expectations, expectancy violations are stronger when close (vs. distant) others commit crimes. We further employ an experimental-causal-chain design to show that people resolve their expectancy violations in diametrically opposed ways depending on their relationship to the transgressor. When close others commit crimes, people focus more on the individual (vs. the crime), which leads them to protect the transgressor. However, the reverse is true for distant others, which leads them to punish the transgressor. These findings highlight the sensitivity of early attentional processes to information about close relationships. They further demonstrate how these processes interact with motivation to shape moral decisions. Together, they help explain why people stubbornly protect close others, even in the face of severe crimes.

Highlights

• We used neural and self-report methods to explain people's reluctance to punish close others who act immorally.

• Close others acting immorally, and severe immoral acts, are highly unexpected.

• Expectancy violations interact with motivation to drive attention.

• For close others, people focus on the transgressor, which yields a more lenient response.

• For distant others, people focus on the immoral act, which yields a more punitive response.

The research is here.