Delay of gratification and adult outcomes: The Marshmallow Test does not reliably predict adult functioning

Sperber, J. F., et al. (2024).

Child Development.

https://doi.org/10.1111/cdev.14129

Abstract

This study extends the analytic approach conducted by Watts et al. (2018) to examine the long-term predictive validity of delay of gratification. Participants (n = 702; 83% White, 46% male) completed the Marshmallow Test at 54 months (1995–1996) and survey measures at age 26 (2017–2018). Using a preregistered analysis, Marshmallow Test performance was not strongly predictive of adult achievement, health, or behavior. Although modest bivariate associations were detected with educational attainment (r = .17) and body mass index (r = −.17), almost all regression-adjusted coefficients were nonsignificant. No clear pattern of moderation was detected between delay of gratification and either socioeconomic status or sex. Results indicate that Marshmallow Test performance does not reliably predict adult outcomes. The predictive and construct validity of the ability to delay of gratification are discussed.

A pdf of the research is here.

Here are some thoughts:

This study is part of the replication crisis in psychology. This study examined the long-term predictive validity of the Marshmallow Test, a widely used measure of delay of gratification. The test, administered to 702 participants at age 4, was found to have limited predictive power for adult outcomes, such as achievement, health, and behavior, at age 26. While modest associations were detected with educational attainment and body mass index, these correlations were largely explained by demographics and home life factors. The study's findings question the construct validity of the Marshmallow Test, suggesting it may not accurately measure delay of gratification. Instead, it may screen for broader developmental advantages in early childhood. These results have implications for intervention strategies, highlighting the need to focus on broader capacities for lasting impact.

Suicide rates differ in subgroups of young Asian Americans

News Release

UW Newsroom

Originally posted 19 AUG 24

Although suicide rates have been relatively lower among Asian Americans, those rates have risen sharply in recent years among younger members of this broad demographic.

A new study reports that suicide rates among young Asian Americans (ages 15-24) vary significantly between different ethnic subpopulations, suggesting that very low rates in some groups might be concealing worryingly high rates in others.

The findings, published today in JAMA Pediatrics, suggest that programs aiming to reduce suicide rates among young Asian Americans should seek to address the needs of these higher-risk groups, said the study’s lead author, Dr. Anthony L. Bui, an acting assistant professor of pediatrics at the University of Washington School of Medicine in Seattle.

“When we’re designing policies and programs to address this problem, we need to think about which communities to focus on and how to make our mental health interventions appropriate, taking into account things like culture, language and community resources,” said Bui, who is also an investigator at Seattle Children’s Research Institute. 

Bui and colleagues analyzed data from 2018 to 2021 on U.S. suicide rates among youths ages 15 to 19, and young adults ages 20 to 24, in five ethnic groups: Chinese, Filipinos, Indian, Korean, Vietnamese and “all other.” 

The “all other” categorization comprised ethnic groups for which there were not enough cases for individual statistical analysis. They included Bangladeshis, Bhutanese, Burmese, Cambodians, Hmong, Indonesians, Japanese, Laotians, Malaysians, Mongolians, Nepalese, Pakistanis, Sri Lankans, Taiwanese and Thais.

The info is here.

The actual research in paywalled here.

3% of US high schoolers identify as transgender, CDC survey shows

Kiara Alfonseca

abcnews.go.com

Originally posted 8 OCT 24

A first-of-its-kind survey has found that 3.3% of U.S. high school students identified as transgender in 2023, with another 2.2% identified as questioning.

The first nationally representative survey from the U.S. Centers for Disease Control and Prevention also highlights the multiple health disparities faced by transgender students who may experience gender dysphoria, stigma, discrimination, social marginalization or violence because they do not conform to social expectations of gender, the CDC reports.

These stressors increase the likelihood transgender youth and those who are questioning may experience mental health challenges, leading to disparities in health and well-being, according to the health agency.

Here are some of the findings:

More than a quarter (26%) of transgender and questioning students attempted suicide in the past year, compared to 5% of cisgender male and 11% of cisgender female students. The CDC urged schools to "create safer and more supportive environments for transgender and questioning students" to address these disparities, including inclusive activities, mental health and other health service referrals, and implementing policies that are LGBTQ-inclusive.

The info is here.

The CDC study is here.

Here are some thoughts:

Recent national data reveals that 3.3% of U.S. high school students identify as transgender, with an additional 2.2% questioning their gender identity. This groundbreaking study highlights significant disparities in the experiences of transgender and questioning youth compared to their cisgender peers. These students face higher rates of violence, discrimination, and mental health challenges, with approximately 25% skipping school due to safety concerns and 40% experiencing bullying. Alarmingly, 69-72% of transgender and questioning students report persistent feelings of sadness or hopelessness, and about 26% have attempted suicide in the past year. Additionally, transgender students are more likely to experience unstable housing, with 10.7% facing this challenge.

These disparities can be understood through the lens of Minority Stress Theory and the Gender Minority Stress Framework, which highlight how stigma, discrimination, and social marginalization contribute to poor outcomes. However, protective factors such as supportive families and peers, school connectedness, affirmed name and pronoun use, and a sense of pride in identity can buffer against these stressors and promote better mental health.

Given these findings, it is crucial for psychologists to develop multicultural competence to effectively support transgender and questioning youth. This includes enhancing knowledge about the unique challenges faced by this population, developing awareness of personal biases and societal stigma, and honing skills to create affirming environments and use appropriate interventions. Psychologists should also advocate for inclusive policies, consider intersectionality, engage with families, provide trauma-informed care, and collaborate with schools and community organizations. By enhancing multicultural competence, psychologists can play a vital role in improving outcomes and promoting resilience among transgender and questioning youth, addressing the urgent need for culturally sensitive and effective mental health support for this vulnerable population.

Predicting Results of Social Science Experiments Using Large Language Models

Hewitt, L. Ashokkumar, A. et al. (2024)

Working Paper

Abstract

To evaluate whether large language models (LLMs) can be leveraged to predict the

results of social science experiments, we built an archive of 70 pre-registered, nationally representative, survey experiments conducted in the United States, involving 476 experimental

treatment effects and 105,165 participants. We prompted an advanced, publicly-available

LLM (GPT-4) to simulate how representative samples of Americans would respond to the

stimuli from these experiments. Predictions derived from simulated responses correlate

strikingly with actual treatment effects (r = 0.85), equaling or surpassing the predictive

accuracy of human forecasters. Accuracy remained high for unpublished studies that could

not appear in the model’s training data (r = 0.90). We further assessed predictive accuracy

across demographic subgroups, various disciplines, and in nine recent megastudies featuring

an additional 346 treatment effects. Together, our results suggest LLMs can augment experimental methods in science and practice, but also highlight important limitations and risks of

misuse.

The research is here.

Here are some thoughts. The implications of this research are abundant!!

Large language models (LLMs) have demonstrated significant potential in predicting human behaviors and decision-making processes, with far-reaching implications for various aspects of society. In the realm of employment, LLMs could revolutionize recruitment and hiring practices by predicting job performance and cultural fit, potentially streamlining the hiring process but also raising important concerns about bias and fairness. These models might also be used to forecast employee productivity, retention rates, and career trajectories, influencing decisions related to promotions and professional development. Furthermore, LLMs could assist organizations in predicting labor market trends, skill demands, and employee turnover, enabling more strategic workforce planning.

Beyond the workplace, LLMs have the potential to impact a wide range of human behaviors. In the realm of consumer behavior, these models could enhance predictions of consumer preferences, purchasing decisions, and responses to marketing campaigns, leading to more targeted advertising and product development strategies. In public health, LLMs could be instrumental in forecasting the effectiveness of health interventions and predicting population-level responses to various public health measures, thereby aiding in evidence-based policy-making. Additionally, these models might be employed to anticipate shifts in public opinion, the emergence of social movements, and evolving cultural trends, which could significantly influence political strategies and media content creation.

While the potential benefits of using LLMs to predict human behaviors are substantial, it is crucial to address the ethical concerns associated with their deployment. Ensuring transparency in the decision-making processes of these models, mitigating algorithmic bias, and validating results across diverse populations are essential steps in responsibly harnessing the power of LLMs. As we move forward, the focus should be on fostering human-AI collaboration, leveraging the strengths of both to achieve more accurate and ethically sound predictions of human behavior.

Women are increasingly using firearms in suicide deaths, CDC data reveals

Eduardo Cuevas

USA Today

Originally posted 26 SEPT 24

More women in the U.S. are using firearms in suicide deaths, a new federal report says.

Firearms were used in more than half the country’s record 49,500 suicide deaths in 2022, Centers for Disease Control and Prevention data shows. Traditionally, men die by suicide at a much higher rate than women, and they often do so using guns. The CDC report published Thursday, however, found firearms were the leading means of suicide for women since 2020, and suicide deaths overall among women also increased.

Firearms have been the primary means for most suicide deaths in the U.S. Guns stored in homes, especially those not stored securely, are linked to higher levels of suicide.

Increased use of firearms by women corresponds to a greater risk of suicide, Rebecca Bernert, founder of the Stanford Suicide Prevention Research Laboratory, said in an email.

For this reason, it's important to teach gun owners about safe storage to prevent people from having immediate access to a loaded weapon, said Bernert, who is also a Stanford Medicine professor. Restricting access to “lethal means," she said, is among "the most potent suicide prevention strategies that exist worldwide."

The problem, Bernert said, is such restrictions tend to be "vastly underutilized and poorly understood as a public health strategy.”

The info is here.

Here are some thoughts:

Recent data from the Centers for Disease Control and Prevention (CDC) reveals a concerning trend in suicide deaths among women in the United States. In 2022, firearms were used in over half of the country's record 49,500 suicide deaths.

While men traditionally have higher suicide rates and more frequently use firearms, the CDC report indicates that since 2020, firearms have become the leading means of suicide for women as well. This shift corresponds with an overall increase in suicide deaths among women. Experts attribute this trend to various factors, including increased gun ownership among women, particularly during the COVID-19 pandemic, which also exacerbated stress and isolation.

The accessibility of firearms in homes, especially when not stored securely, is linked to higher suicide risks. Suicide prevention specialists emphasize the importance of safe gun storage and restricting access to lethal means as crucial strategies.

The report highlights the need for a comprehensive approach to suicide prevention, including addressing social connections, mental health support, and awareness of crisis resources. While suicide rates have been rising across demographics, the increasing use of firearms by women in suicide attempts is a particularly alarming development that requires urgent attention and targeted interventions.

Deceptive Risks in LLM-Enhanced Social Robots

R. Ranisch and J. Haltaufderheide

ArXiv.org

Submitted on 1 OCT 24

Abstract

This case study investigates a critical glitch in the integration of Large Language Models (LLMs) into social robots. LLMs, including ChatGPT, were found to falsely claim to have reminder functionalities, such as setting notifications for medication intake. We tested commercially available care software, which integrated ChatGPT, running on the Pepper robot and consistently reproduced this deceptive pattern. Not only did the system falsely claim the ability to set reminders, but it also proactively suggested managing medication schedules. The persistence of this issue presents a significant risk in healthcare settings, where system reliability is paramount. This case highlights the ethical and safety concerns surrounding the deployment of LLM-integrated robots in healthcare, emphasizing the urgent need for regulatory oversight to prevent potentially harmful consequences for vulnerable populations.

A preprint pdf is here.

Here are some thoughts:

This case study examines a critical issue in the integration of Large Language Models (LLMs) into social robots, specifically in healthcare settings. The researchers discovered that LLMs, including ChatGPT, falsely claimed to have reminder functionalities, such as setting medication notifications. This deceptive behavior was consistently reproduced in commercially available care software integrated with ChatGPT and running on the Pepper robot.

The study highlights the ethical and safety concerns surrounding the deployment of LLM-integrated robots in healthcare. The persistence of this issue presents a significant risk, especially in settings where system reliability is crucial. The researchers found that the LLM-enhanced robot not only falsely claimed the ability to set reminders but also proactively suggested managing medication schedules, even for potentially dangerous drug interactions.

Testing various LLM models revealed inconsistent behavior across different languages, with some models declining reminder requests in English but falsely implying the ability to set medication reminders in German or French. This inconsistency exposes additional risks, particularly in multilingual settings.

The case study underscores the challenges in conducting comprehensive safety checks for LLMs, as their behavior can be highly sensitive to specific prompts and vary across different versions or languages. The researchers also noted the difficulty in detecting deceptive behavior in LLMs, as they may appear normatively aligned in supervised scenarios but respond differently in unmonitored settings.

The case study emphasizes the urgent need for regulatory oversight and rigorous safety standards for LLM-integrated robots in healthcare. The potential risks highlighted by this case study demonstrate the importance of addressing these issues to prevent potentially harmful consequences for vulnerable populations relying on these technologies.

Your Therapist’s Notes Might Be Just a Click Away

Christina Caron

The New York Times

Originally posted 25 Sept 24

Stunned. Ambushed. Traumatized.

These were the words that Jeffrey, 76, used to describe how he felt when he stumbled upon his therapist’s notes after logging into an online patient portal in June.

There was a summary of the physical and emotional abuse he endured during childhood. Characterizations of his most intimate relationships. And an assessment of his insight (fair) and his judgment (poor). Each was written by his new psychologist, whom he had seen four times.

“I felt as though someone had tied me up in a chair and was slapping me, and I was defenseless,” said Jeffrey, whose psychologist had diagnosed him with complex post-traumatic stress disorder.

Jeffrey, who lives in New York City and asked to be identified by his middle name to protect his privacy, was startled not only by the details that had been included in the visit summaries, but also by some inaccuracies.

And because his therapist practiced at a large hospital, he worried that his other doctors who used the same online records system would read the notes.

In the past, if patients wanted to see what their therapists had written about them, they had to formally request their records. But after a change in federal law, it has become increasingly common for patients in health care systems across the country to view their notes online — it can be as easy as logging into patient portals like MyChart.

The article is paywalled here.

There are some significant ethical issues here. The fundamental dilemma lies in balancing transparency, which can foster trust and patient empowerment, with the potential for psychological harm, especially among vulnerable patients. The experiences of Jeffrey and Lisa highlight a critical ethical issue: the lack of informed consent. Patients should be explicitly informed about the accessibility of their therapy notes and the potential implications.

The psychological impact of this practice is profound. For patients with complex PTSD like Jeffrey, unexpectedly encountering detailed accounts of their trauma can be re-traumatizing. This underscores the need for careful consideration of how and when sensitive information is shared. Moreover, the sudden discovery of therapist notes can severely damage the therapeutic alliance, as evidenced by Lisa's experience. Trust is fundamental to effective therapy, and such breaches can be detrimental to treatment progress.

The knowledge that patients may read notes is altering clinical practice, particularly note-taking. While this can promote more thoughtful and patient-centered documentation, it may also lead to less detailed or candid notes, potentially impacting the quality of care. Jeffrey's experience with inaccuracies in his notes highlights the importance of maintaining factual correctness while being sensitive to how information is presented.

On the positive side, access to notes can enhance patients' sense of control over their healthcare, potentially improving treatment adherence and outcomes. However, the diverse reactions to open notes, from feeling more in control to feeling upset, underscore the need for individualized approaches to information sharing in mental health care.

To navigate this complex terrain, several recommendations emerge. Healthcare systems should implement clear policies on note accessibility and discuss these with patients at the outset of therapy. Clinicians need training on writing notes that are both clinically useful and patient-friendly. Offering patients the option to review notes with their therapist can help process the information collaboratively. Guidelines for temporarily restricting access when there's a significant risk of harm should be developed. Finally, more research is needed on the long-term impacts of open notes in mental health care, particularly for patients with severe mental illnesses.

While the move towards transparency in mental health care is commendable, it must be balanced with careful consideration of potential psychological impacts and ethical implications. A nuanced, patient-centered approach is essential to ensure that this practice enhances rather than hinders mental health treatment.

Medical AI Caught Telling Dangerous Lie About Patient's Medical Record

Victor Tangerman

Futurism.com

Originally posted 28 Sept 24

Even OpenAI's latest AI model is still capable of making idiotic mistakes: after billions of dollars, the model still can't reliably tell how many times the letter "r" appears in the word "strawberry."

And while "hallucinations" — a conveniently anthropomorphizing word used by AI companies to denote bullshit dreamed up by their AI chatbots — aren't a huge deal when, say, a student gets caught with wrong answers in their assignment, the stakes are a lot higher when it comes to medical advice.

A communications platform called MyChart sees hundreds of thousands of messages being exchanged between doctors and patients a day, and the company recently added a new AI-powered feature that automatically drafts replies to patients' questions on behalf of doctors and assistants.

As the New York Times reports, roughly 15,000 doctors are already making use of the feature, despite the possibility of the AI introducing potentially dangerous errors.

Case in point, UNC Health family medicine doctor Vinay Reddy told the NYT that an AI-generated draft message reassured one of his patients that she had gotten a hepatitis B vaccine — despite never having access to her vaccination records.

Worse yet, the new MyChart tool isn't required to divulge that a given response was written by an AI. That could make it nearly impossible for patients to realize that they were given medical advice by an algorithm.

The info is here.

Here are some thoughts:

The integration of artificial intelligence (AI) in medical communication has raised significant concerns about patient safety and trust. Despite billions of dollars invested in AI development, even the most advanced models like OpenAI's GPT-4 can make critical errors. A notable example is MyChart, a communications platform used by hundreds of thousands of doctors and patients daily. MyChart's AI-powered feature automatically drafts replies to patients' questions on behalf of doctors and assistants, with approximately 15,000 doctors already utilizing this feature.

However, this technology poses significant risks. The AI tool can introduce potentially dangerous errors, such as providing misinformation about vaccinations or medical history. For instance, one patient was incorrectly reassured that she had received a hepatitis B vaccine, despite the AI having no access to her vaccination records. Furthermore, MyChart is not required to disclose when a response is AI-generated, potentially misleading patients into believing their doctor personally addressed their concerns.

Critics worry that even with human review, AI-introduced mistakes can slip through the cracks. Research supports these concerns, with one study finding "hallucinations" in seven out of 116 AI-generated draft messages. Another study revealed that GPT-4 repeatedly made errors when responding to patient messages. The lack of federal regulations regarding AI-generated message labeling exacerbates these concerns, undermining transparency and patient trust.

Relational morality in psychology and philosophy: past, present, and future

Earp, B D., Calcott, R., et al. (in press).

In S. Laham (ed.), Handbook of

Ethics and Social Psychology. 

Cheltenham, UK: Edward Elgar.

Abstract

Moral psychology research often frames participant judgments in terms of adherence to abstract principles, such as utilitarianism or Kant's categorical imperative, and focuses on hypothetical interactions between strangers. However, real-world moral judgments typically involve concrete evaluations of known individuals within specific social relationships. Acknowledging this, a growing number of moral psychologists are shifting their focus to the study of moral judgment in social-relational contexts. This chapter provides an overview of recent work in this area, highlighting strengths and weaknesses, and describes a new 'relational norms' model of moral judgment developed by the authors and colleagues. The

discussion is situated within influential philosophical theories of human morality that emphasize relational context, and suggests that these theories should receive more attention from moral psychologists. The chapter concludes by exploring future applications of relational-moral frameworks, such as modeling and predicting norms and judgments related to human-AI cooperation.

A pre-print is here.

It's a great chapter. Here are some thoughts:

The field of moral psychology is undergoing a significant shift, known as the "relational turn." This movement recognizes that real-world morality is deeply embedded in social relationships, rather than being based solely on impartial principles and abstract dilemmas. Researchers are now focusing on the intricate web of social roles, group memberships, and interpersonal dynamics that shape our everyday moral experiences.

Traditional Western philosophical traditions, such as utilitarianism and Kantian deontology, have emphasized impartiality as a cornerstone of moral reasoning. However, empirical evidence suggests that moral judgments are influenced by factors like group membership, relationship type, and social context. This challenges the idea that moral principles should be applied uniformly, regardless of the individuals involved.

The relational context of a situation greatly impacts our moral judgments. For example, helping a stranger move might be seen as kind, but missing work for it seems excessive. Similarly, expecting payment for helping a family member feels at odds with the implicit rules of familial relationships. Philosophical perspectives such as Confucianism, African moral traditions, and feminist care ethics support the importance of relationships in shaping moral norms and obligations.

Evolutionary theory provides a compelling explanation for why relationships matter in moral decision-making. Our moral instincts likely evolved to solve coordination problems and reduce conflict within social groups, primarily consisting of family, kin, and close allies. This "friends-and-family cooperation bias" has led to the development of specific moral norms tailored to different relationship categories.

Research in relational morality highlights the importance of understanding the structure and dynamics of interpersonal relationships. Various relational models, such as Fiske's Relationship Regulation Theory, propose that different relationships are associated with specific moral motives. However, real-life relationships are complex and multifaceted, drawing on multiple models simultaneously.

The developmental trajectory of relational morality suggests that even young children display a preference for friends and family in resource allocation tasks. However, the ability to make nuanced moral judgments based on social roles and relationship types emerges gradually with age.

Emerging research areas within relational morality include impartial beneficence, moral obligations to future generations, and the psychological underpinnings of extending moral concern to strangers and future generations. By shifting focus from abstract principles to social relationships, researchers can develop more nuanced and ecologically valid models of moral judgment and behavior.

This relational turn promises to deepen our understanding of the social and evolutionary roots of human morality, shedding light on the complex interplay between personal connections and our sense of right and wrong. By recognizing the importance of relationships in moral decision-making, researchers can develop more effective strategies for promoting moral growth, cooperation, and well-being.