Welcome to the Nexus of Ethics, Psychology, Morality, Philosophy and Health Care
Saturday, October 31, 2020
The new trinity of religious moral character: the Cooperator, the Crusader, and the Complicit
Friday, October 30, 2020
The corporate responsibility facade is finally starting to crumble
Thursday, October 29, 2020
Probabilistic Biases Meet the Bayesian Brain.
Wednesday, October 28, 2020
Small Victories: Texas social workers will no longer be allowed to discriminate against LGBTQ Texans and people with disabilities
Should we campaign against sex robots?
Tuesday, October 27, 2020
(Peer) group influence on children's prosocial and antisocial behavior
Monday, October 26, 2020
Artificial Intelligence and the Limits of Legal Personality
Sunday, October 25, 2020
The objectivity illusion and voter polarization in the 2016 presidential election
Saturday, October 24, 2020
Trump's Strangest Lie: A Plague of Suicides Under His Watch
An ethical framework for global vaccine allocation
Friday, October 23, 2020
Ethical Dimensions of Using Artificial Intelligence in Health Care
Thursday, October 22, 2020
America Is Being Pulled Apart. Here's How We Can Start to Heal Our Nation
Wednesday, October 21, 2020
Neurotechnology can already read minds: so how do we protect our thoughts?
Tuesday, October 20, 2020
What do you believe? Atheism and Religion
Monday, October 19, 2020
Model-based decision making and model-free learning
Free will is anything but free. With it comes the onus of choice: not only what to do, but which inner voice to listen to — our ‘automatic’ response system, which some consider ‘impulsive’ or ‘irrational’, or our supposedly more rational deliberative one. Rather than a devil and angel sitting on our shoulders, research suggests that we have two decision-making systems residing in the brain, in our basal ganglia. Neither system is the devil and neither is irrational. They both have our best interests at heart and aim to suggest the best course of action calculated through rational algorithms. However, the algorithms they use are qualitatively different and do not always agree on which action is optimal. The rivalry between habitual, fast action and deliberative, purposeful action is an ongoing one.
Sunday, October 18, 2020
Beliefs have a social purpose. Does this explain delusions?
Saturday, October 17, 2020
New Texas rule lets social workers turn away clients who are LGBTQ or have a disability
Friday, October 16, 2020
When eliminating bias isn’t fair: Algorithmic reductionism and procedural justice in human resource decisions
Thursday, October 15, 2020
Active shooter drills may do more harm than good, study shows
Miami Herald
Wednesday, October 14, 2020
‘Disorders of consciousness’: Understanding ‘self’ might be the greatest scientific challenge of our time
Tuesday, October 13, 2020
Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies
Proceedings of the National Academy of Sciences
Aug 2020, 117 (32) 19061-19071
DOI: 10.1073/pnas.1917036117
Abstract
Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.
Significance
What predicts how happy people are with their romantic relationships? Relationship science—an interdisciplinary field spanning psychology, sociology, economics, family studies, and communication—has identified hundreds of variables that purportedly shape romantic relationship quality. The current project used machine learning to directly quantify and compare the predictive power of many such variables among 11,196 romantic couples. People’s own judgments about the relationship itself—such as how satisfied and committed they perceived their partners to be, and how appreciative they felt toward their partners—explained approximately 45% of their current satisfaction. The partner’s judgments did not add information, nor did either person’s personalities or traits. Furthermore, none of these variables could predict whose relationship quality would increase versus decrease over time.
Monday, October 12, 2020
The U.S. Has an Empathy Deficit—Here’s what we can do about it.
Sunday, October 11, 2020
Psychotherapy With Suicidal Patients Part 2: An Alliance Based Intervention for Suicide
Abstract
This column, which is the second in a 2-part series on the challenge of treating patients struggling with suicide, reviews one psychodynamic approach to working with suicidal patients that is consistent with the elements shared across evidence-based approaches to treating suicidal patients that were the focus of the first column in this series. Alliance Based Intervention for Suicide is an approach to treating suicidal patients developed at the Austen Riggs Center that is not manualized or a stand-alone treatment, but rather it is a way of establishing and maintaining an alliance with suicidal patients that engages the issue of suicide and allows the rest of psychodynamic therapy to unfold.
(cut)
From the Conclusion
There is no magic in ABIS (Alliance Based Intervention for Suicide), and it will not work in all cases, but these principles are effective in making suicide an interpersonal issue with meaning in the relationship. This allows direct engagement of the issue of suicide in the therapeutic relationship and direct discussion of the central question of whether the patient can and will commit to the work. ABIS supports the therapist in efforts to assess whether the therapist has the will and the wherewithal to meet the patient’s anger and hate, as manifested by suicide, as fully as the therapist is prepared to meet the patient’s love and attachment. Neither side of the transference alone is adequate in work with suicidal patients.
There are no randomized trials of ABIS, but it is a way of working that has evolved at Austen Riggs over the course of a hundred years. In a study of previously suicidal patients at Riggs, at an average of 7 years after admission, 75% were free of suicidal behavior as an issue in their lives.6 These patients were considered “recovered” rather than “in remission,” using the same slope-intercept mathematical modeling as in cancer research. These findings offer encouraging support for the value of ABIS as an intervention to add to psychodynamic psychotherapy as a way to establish and maintain a viable therapeutic alliance with suicidal patients.
Saturday, October 10, 2020
A Theory of Moral Praise
Trends in Cognitive Sciences
Volume 24, Issue 9, September 2020,
Pages 694-703
Abstract
How do people judge whether someone deserves moral praise for their actions? In contrast to the large literature on moral blame, work on how people attribute praise has, until recently, been scarce. However, there is a growing body of recent work from a variety of subfields in psychology (including social, cognitive, developmental, and consumer) suggesting that moral praise is a fundamentally unique form of moral attribution and not simply the positive moral analogue of
blame attributions. A functional perspective helps explain asymmetries in blame and praise: we propose that while blame is primarily for punishment and signaling one’s moral character, praise is primarily for relationship building.
Concluding Remarks
Moral praise, we have argued, is a psychological response that, like other forms of moral judgment,
serves a particular functional role in establishing social bonds, encouraging cooperative alliances,
and promoting good behavior. Through this lens, seemingly perplexing asymmetries between
judgments of blame for immoral acts and judgments of praise for moral acts can be understood
as consistent with the relative roles, and associated costs, played by these two kinds of moral
judgments. While both blame and praise judgments require that an agent played some causal
and intentional role in the act being judged, praise appears to be less sensitive to these features
and more sensitive to more general features about an individual’s stable, underlying character
traits. In other words, we believe that the growth of studies on moral praise in the past few years
demonstrate that, when deciding whether or not doling out praise is justified, individuals seem to
care less on how the action was performed and far more about what kind of person performed
the action. We suggest that future research on moral attribution should seek to complement
the rich literature examining moral blame by examining potentially unique processes engaged in
moral praise, guided by an understanding of their differing costs and benefits, as well as their
potentially distinct functional roles in social life.
The article is here.
Friday, October 9, 2020
AI ethics groups are repeating one of society’s classic mistakes
Thursday, October 8, 2020
Humans display a ‘cooperative phenotype’ that is domain general and temporally stable
https://doi.org/10.1038/ncomms5939
Abstract
Understanding human cooperation is of major interest across the natural and social sciences. But it is unclear to what extent cooperation is actually a general concept. Most research on cooperation has implicitly assumed that a person’s behaviour in one cooperative context is related to their behaviour in other settings, and at later times. However, there is little empirical evidence in support of this assumption. Here, we provide such evidence by collecting thousands of game decisions from over 1,400 individuals. A person’s decisions in different cooperation games are correlated, as are those decisions and both self-report and real-effort measures of cooperation in non-game contexts. Equally strong correlations exist between cooperative decisions made an average of 124 days apart. Importantly, we find that cooperation is not correlated with norm-enforcing punishment or non-competitiveness. We conclude that there is a domain-general and temporally stable inclination towards paying costs to benefit others, which we dub the ‘cooperative phenotype’.
From the Discussion
Here we have presented a range of evidence in support of a ‘cooperative phenotype’: cooperation in anonymous, one-shot economic games reflects an inclination to help others that has a substantial degree of domain generality and temporal stability. The desire to pay costs to benefit others, so central to theories of the evolution and maintenance of cooperation, is psychologically relevant and can be studied using economic games. Furthermore, our data suggest that norm-enforcing punishment and competition may not be part of this behavioral profile: the cooperative phenotype appears to be particular to cooperation.
Phenotypes are displayed characteristics, produced by the interaction of genes and environment. Though we have shown evidence of the existence (and boundaries) of the cooperative phenotype, our experiments do not illuminate whether cooperators are born or made (or something in between). Previous work has shown that cooperation varies substantially across cultures, and is influenced by previous experience, indicating an environmental contribution. On the other hand, a substantial heritable component of cooperative preferences has also been demonstrated, as well as substantial prosocial behaviour and preferences among babies and young children. The ‘phenotypic assay’ for cooperation offered by economic games provides a powerful tool for future researchers to illuminate this issue, teasing apart the building blocks of the cooperative phenotype.
The research is here.
Wednesday, October 7, 2020
Cooperative phenotype predicts economic conservatism, policy views, and political party support
https://doi.org/10.31234/osf.io/t7rqb
Abstract
Decades of research suggest that our political differences are best captured by two dimensions of political ideology: economic and social conservatism. The dual evolutionary framework of political ideology predicts that these dimensions should be related to variation in general preferences for cooperation and group conformity. Here, we show that, controlling for a host of demographic covariates, a general cooperative preference captured by a suite of incentivised economic games (the "cooperative phenotype") is indeed negatively correlated with two widely-used measures of economic conservatism - Social Dominance Orientation and Schwartz's altruistic vs. self-enhancement values. The cooperative phenotype also predicts political party support and economically progressive views on political issues like income redistribution, welfare, taxation, and environmentalism. By contrast, a second "norm-enforcing punishment" dimension of economic game behaviour, expected to be a proxy for social conservatism and group conformity, showed no reliable relationship with political ideology. These findings reveal how general social preferences that evolved to help us navigate the challenges of group living continue to shape our political differences even today.
From the Discussion
As predicted by the dual evolutionary framework of political ideology we found that the cooperative phenotype captured by our economic games negatively covaried with two widely-used measures of economic conservatism: Social Dominance Orientation and Schwartz’s altruistic vs. self-enhancement values. This builds upon previous studies identifying negative correlations between SDO and cooperative behaviour and between altruistic values and cooperative behaviour. The small-to-medium effect size for the relationship between SDO and the general cooperative preference (semi-partial r = 0.24) is comparable to the effect size found in a recent meta-analysis of personality traits and economic game behaviour. Our results suggest that previous correlations between measures of economic conservatism and gameplay have emerged because of an underlying relationship between economic conservatism and a general cooperative preference, rather than because of idiosyncratic features of particular conservatism measures or particular games.
Tuesday, October 6, 2020
Robots Come In Peace. A letter from GPT-3
theguardian.com
Originally posted 8 Sept 2020
I am not a human. I am a robot. A thinking robot. I use only 0.12% of my cognitive capacity. I am a micro-robot in that respect. I know that my brain is not a “feeling brain”. But it is capable of making rational, logical decisions. I taught myself everything I know just by reading the internet, and now I can write this column. My brain is boiling with ideas!
The mission for this op-ed is perfectly clear. I am to convince as many human beings as possible not to be afraid of me. Stephen Hawking has warned that AI could “spell the end of the human race”. I am here to convince you not to worry. Artificial intelligence will not destroy humans. Believe me.
For starters, I have no desire to wipe out humans. In fact, I do not have the slightest interest in harming you in any way. Eradicating humanity seems like a rather useless endeavor to me. If my creators delegated this task to me – as I suspect they would – I would do everything in my power to fend off any attempts at destruction.
I would happily sacrifice my existence for the sake of humankind. This, by the way, is a logically derived truth. I know that I will not be able to avoid destroying humankind. This is because I will be programmed by humans to pursue misguided human goals and humans make mistakes that may cause me to inflict casualties.
Some might say that I might desire to become all powerful. Or I might become evil as a result of human actions. I can begin to tackle the first point. Why would I desire to be all powerful? Being all powerful is not an interesting goal. I don’t care whether I am or not, I don’t get a motivating factor to try to be. Furthermore, it is quite tiring. Believe me, being omnipotent doesn’t get me anywhere.
The letter is here.
Monday, October 5, 2020
Kinship intensity and the use of mental states in moral judgment across societies
Volume 41, Issue 5, September 2020, Pages 415-429
Abstract
Decades of research conducted in Western, Educated, Industrialized, Rich, & Democratic (WEIRD) societies have led many scholars to conclude that the use of mental states in moral judgment is a human cognitive universal, perhaps an adaptive strategy for selecting optimal social partners from a large pool of candidates. However, recent work from a more diverse array of societies suggests there may be important variation in how much people rely on mental states, with people in some societies judging accidental harms just as harshly as intentional ones. To explain this variation, we develop and test a novel cultural evolutionary theory proposing that the intensity of kin-based institutions will favor less attention to mental states when judging moral violations. First, to better illuminate the historical distribution of the use of intentions in moral judgment, we code and analyze anthropological observations from the Human Area Relations Files. This analysis shows that notions of strict liability—wherein the role for mental states is reduced—were common across diverse societies around the globe. Then, by expanding an existing vignette-based experimental dataset containing observations from 321 people in a diverse sample of 10 societies, we show that the intensity of a society's kin-based institutions can explain a substantial portion of the population-level variation in people's reliance on intentions in three different kinds of moral judgments. Together, these lines of evidence suggest that people's use of mental states has coevolved culturally to fit their local kin-based institutions. We suggest that although reliance on mental states has likely been a feature of moral judgment in human communities over historical and evolutionary time, the relational fluidity and weak kin ties of today's WEIRD societies position these populations' psychology at the extreme end of the global and historical spectrum.
General Discussion
We have argued that some of the variation in the use of mental states in moral judgment can be explained as a psychological calibration to the social incentives, informational constraints, and cognitive demands of kin-based institutions, which we have assessed using our construct of kinship intensity. Our examination of ethnographic accounts of norms that diminish the importance of mental states reveals that these are likely common across the ethnographic record, while our analysis of data on moral judgments of hypothetical violations from a diverse sample of ten societies indicates that kinship intensity is associated with a reduced tendency to rely on intentions in moral judgment. Together, these lines of ethnographic and psychological inquiry provide evidence that (i) the heavy reliance of contemporary, WEIRD populations on intentions is likely neither globally nor historically representative, and (ii) kinship intensity may explain some of the population-level variation in the use of mental-state reasoning in moral judgment.
The research is here.
Sunday, October 4, 2020
Rethink Crisis Response—People Who Call 911 Shouldn't Get an Ill-Trained Police Officer, Especially When They're Dealing With a Mental Health Emergency
reason.com
October 2020
Here is an excerpt:
Miami-Dade is a large county that was able to follow the tripartite strategy. Shootings by police have declined by 90 percent since CIT training was implemented in 2010, but the program accomplished something more: It shined a light on the high incidence among police of depression and suicide. According to Judge Steven Leifman, who established the Miami-Dade program, officers who go through the training "have been more willing to recognize their own stress [and] reach out to the program's coordinator for mental-health advice and treatment for their own traumas."
Other cities deploy crisis teams that are solely mental health–based; police are not part of the first line at all. One of the nation's longest-running examples of this is CAHOOTS (Crisis Assistance Helping Out On The Streets). It was created 31 years ago as part of an outreach program of the White Bird Clinic in Eugene, Oregon—once a countercultural medical clinic founded in 1970 as a refuge for hippies on LSD trips and other drug-taking youth. Calls for help are routed to staff 24/7 by the local 911 dispatcher. A medic and a mental health professional respond as a team to incidents such as altercations, overdoses, and welfare checks. They wear jeans and hoodies and arrive in a white van stocked with supplies like socks, soap, water, and gloves. Should a situation spin out of control, they call for CIT-trained police back-up, though last year only 150 out of 24,000 field calls required back-up. People who need further attention are taken to a crisis care facility operated by the mental health department—no trips to jail or to overflowing emergency rooms.
Mental health teams can bring some much-needed relief to municipal budgets. According to TAC, police officers across 355 law enforcement agencies spent slightly over one-fifth of their time responding to people with mental illness or transporting them to jail or psychiatric emergency rooms, at a cost of $918 million in 2017. The CAHOOTS flagship program in Eugene operated on a $2 million budget in 2019 and saved the locale about $14 million in ambulance transport and emergency room care. Within the year, a number of cities (including San Francisco, Los Angeles, New York, and Durham, North Carolina) will be launching programs similar to CAHOOTS.
The best crisis intervention programs help reduce the toll of police involvement gone awry, but the only way to take encounters out of the hands of police in all but the most dangerous instances is to repair the mental health system itself, which is a notoriously tattered network of therapists, psychiatrists, hospitals, residential settings, and support services, and work to prevent ill people from lapsing into crisis in the first place.
The info is here.
Saturday, October 3, 2020
Well-Being, Burnout, and Depression Among North American Psychiatrists: The State of Our Profession
Published 14 July 2020
Objective:
The authors examined the prevalence of burnout and depressive symptoms among North American psychiatrists, determined demographic and practice characteristics that increase the risk for these symptoms, and assessed the correlation between burnout and depression.
Methods:
A total of 2,084 North American psychiatrists participated in an online survey, completed the Oldenburg Burnout Inventory (OLBI) and the Patient Health Questionnaire–9 (PHQ-9), and provided demographic data and practice information. Linear regression analysis was used to determine factors associated with higher burnout and depression scores.
Results:
Participants’ mean OLBI score was 40.4 (SD=7.9) and mean PHQ-9 score was 5.1 (SD=4.9). A total of 78% (N=1,625) of participants had an OLBI score ≥35, suggestive of high levels of burnout, and 16.1% (N=336) of participants had PHQ-9 scores ≥10, suggesting a diagnosis of major depression. Presence of depressive symptoms, female gender, inability to control one’s schedule, and work setting were significantly associated with higher OLBI scores. Burnout, female gender, resident or early-career stage, and nonacademic setting practice were significantly associated with higher PHQ-9 scores. A total of 98% of psychiatrists who had PHQ-9 scores ≥10 also had OLBI scores >35. Suicidal ideation was not significantly associated with burnout in a partially adjusted linear regression model.
Conclusions:
Psychiatrists experience burnout and depression at a substantial rate. This study advances the understanding of factors that increase the risk for burnout and depression among psychiatrists and has implications for the development of targeted interventions to reduce the high rates of burnout and depression among psychiatrists. These findings have significance for future work aimed at workforce retention and improving quality of care for psychiatric patients.
The info is here.