Lyle Ungar

In 73% of the world’s countries, adults under 30 years of age report the highest life evaluation (i.e., happiness). A notable exception has emerged in North America and Northern Europe, where this pattern has reversed, with older adults reporting greater life evaluation than the young. We analyzed Gallup World Poll data from 2006–2024, covering 2.6 million respondents worldwide and 16,009 in Norway, to examine shifts in the age gap in life evaluation and their determinants. Norway is a particularly valuable case for illustrating this reversal: since ranking as the world’s happiest country in 2017, it has declined in both absolute terms and relative rankings, with national statistics revealing a pronounced well-being gap between young and old. We found that the decline in life evaluation scores in Norway largely reflects decreasing scores among young adults (18-30 years), whose scores have dropped significantly since 2017 (p < .001; d = 0.37) and are now markedly lower than those of older adults (p < .001; d = 0.41). We further confirmed that this trend is limited to a small set of countries; of all 2,508 linear correlations between age and life evaluation in different countries, 83% were negative, and only 2% were r > .10, which was the case in Norway 2022–2024. We examined potential explanations of the observed change in Norway. Only a few of the 70 variables in the Gallup World Poll seem potentially relevant for the increased age difference in life evaluations in Norway. Across multiple analyses we find the most important variables are i) stress-related factors (e.g., feeling well-rested and stress), and ii) economic standards (e.g., standard of living satisfaction and household income satisfaction). The study emphasizes the imperative for highly developed countries to prioritise the well-being of their young despite overall prosperity and high living standards.

Self-reported rating scales have been central to social science for decades, even though language is our primary form of communication. We used language analysis with machine learning to compare self-reported ratings with language-based responses of experienced well-being (i.e., daily emotions) linked with human traits, states, and behaviors. In a sample of 764 U.S. service workers who reported their feelings daily for up to 12 weeks, responses were provided in 1) numerical ratings, 2) short essays, and 3) descriptive words. When predicting nine self-rated affective traits, essays were slightly less accurate than the ratings (r = .49 v .55, p < .001), while the word-based responses slightly outperformed the ratings (r = .53 vs .51, p < .001). For external predictive validity across 24 traits, demographics, and behaviors, both language formats doubled the ratings’ accuracy, with words reaching an average of r = .18 and essays r = .24 (p < .001). Beyond their greater accuracy, both language formats offered rich psychological insights, with words focusing on emotions and essays encompassing broader life domains. While both language-based formats showed advantages and disadvantages, each demonstrated greater information-richness than traditional rating scales.

Despite growing interest in studying human-AI interaction and conducting conversational interventions at scale, technical barriers in deploying and managing large language model (LLM) systems limit their use in behavioral research. In this tutorial, we present ChatbotLab, an open-source software package that provides an end-to-end framework for conducting chatbot-based research without requiring programming expertise. ChatbotLab embeds LLM-powered conversations directly into standard crowdsourcing services (Prolific, MTurk) and survey platforms (Qualtrics, REDCap, LimeSurvey), handling the technical infrastructure that custom solutions often overlook: secure data linkage between surveys and conversations, reproducible study records that capture each conversation's full bot configuration, content moderation, standardized data export for downstream analysis, and server load testing for managing thousands of simultaneous conversations. We guide researchers through the complete workflow, from deploying a chatbot server and configuring naturalistic conversation features (typing delays, message chunking, and personas) to integrating conversations into existing study pipelines and exporting data for analysis. By lowering the technical barriers to chatbot-based research, ChatbotLab allows researchers to focus on their research questions rather than infrastructure.

Previous AI alignment efforts have focused primarily on first-order social norms -- teaching models what is socially acceptable or unacceptable (e.g., "do not steal"). However, social intelligence depends not only on norm recognition, but also on anticipating who will enforce it and how (e.g., public shame or even imprisonment). These second-order expectations, known as metanorms, govern how people respond when social rules are broken. We introduce a novel framework for evaluating metanorm reasoning in Large Language Models (LLMs) along two dimensions: emotional appraisal and behavioral response, and propose new classification tasks, namely, predicting self-regulation in violators, and other-regulation in observers. We release a multi-perspective dataset, NormReact, of 450 norm violation scenarios, hand-annotated for emotions and behavioral responses across norm violators' gender and observers' social closeness. Current LLMs portray a harsher social world: across six models, they overpredict negative sanctions where humans would expect inaction, and alignment with human judgments deteriorates as social distance increases. These findings suggest that AI systems in norm-sensitive domains, from conflict mediation to policy simulation, may risk producing a distorted picture of social regulation: one that over-represents punishment and under-represents the tolerance, restraint, and relational calibration that characterize actual norm enforcement in the real world.

Gray-market retatrutide use is increasing, but patient safety experiences remain poorly characterized. This cross-sectional analysis examined Reddit posts and comments from retatrutide-specific and broader peptide or weight-management communities through December 2025. A validated large language model classified self-reported retatrutide use and extracted author-attributed symptoms mapped to MedDRA Preferred Terms. Among 13,589 users reporting current use, 7,823 had at least one mapped symptom after exclusions. Unlike phase 2 trial findings dominated by gastrointestinal events, Reddit reports most often described appetite increase, fatigue, increased energy, nausea, food craving, insomnia, and elevated heart rate. Findings are hypothesis-generating and warrant pharmacovigilance attention.

The advancement of science depends on rigorous tests of competing hypotheses, yet many disputes are left unresolved. Adversarial collaboration—where opposing scientists jointly design decisive tests—is one proposed solution. We examine whether large language models (LLMs) can play a role by organizing information, structuring the debate and generating candidate experimental designs. This article reports an AI-assisted adversarial collaboration designed to resolve a debate in PNAS on minority salience—an overestimation of the percentage of minority faces in a visual display. The debate focused on whether there would be further overestimation when minorities in the displays were the same minorities in participants’ communities (or social environments). Using LLMs to extract and organize competing propositions, we identified central disagreements and generated initial experimental designs to test claims. Human collaborators refined the designs and created two preregistered experiments that factorially manipulated the ethnicity of minority faces and the ethnicity of participants’ communities. Data showed that people exaggerated the percentage of minorities in facial displays. Furthermore, overestimation was even greater when minorities in facial displays were also minorities in participants’ communities. When the two camps of researchers saw the results, their confidence in key hypotheses converged. We do not experimentally test AI-assisted adversarial collaboration relative to traditional adversarial collaboration or other forms of dispute resolution. Rather, our study illustrates how an AI tool can be used with adversarial collaboration to formalize claims, structure disagreements, lower barriers to collaboration, and serve as an impartial observer to strengthen perceptions of fairness.

AI systems for mental health are developed predominantly using data from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) populations, raising concerns about their validity, fairness, and generalizability across diverse cultural and geographic contexts. This limitation is especially consequential in mental health, where linguistic expression, symptom presentation, help-seeking behavior, and access to care vary substantially across populations. We argue that culture must be treated as a first-class design requirement throughout the AI development lifecycle, not an afterthought applied post-hoc, a principle we term culture by design. Drawing on evidence from NLP, clinical psychology, HCI, and global mental health, we present ten practical recommendations spanning data collection, modeling, evaluation, deployment, and governance, providing researchers and practitioners with a concrete roadmap for building culturally grounded, equitable, and contextually appropriate mental health AI systems.

1. Producing or endorsing statements reshapes subsequent beliefs, as demonstrated across diverse paradigms such as induced compliance (Festinger & Carlsmith, 1959), saying-is-believing studies (Tor...