Welcome to the Nexus of Ethics, Psychology, Morality, Philosophy and Health Care
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.
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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.