How do chatbots affect our emotional state? What can scientists do to fight fraud on a global scale? How is human conversation evolving?
These queries are just a few of the complex questions psychological scientists tackled this year, and readers of APS journals are taking notice.
Below we list the two most downloaded articles in 2026 from six of APS’s peer-reviewed research journals. They are published in no particular order.
Psychological Science
How Does Turning to AI for Companionship Predict Loneliness and Vice Versa?
Dunigan Folk and Elizabeth Dunn
Advances in AI have enabled chatbots to provide warm, personalized support. Yet little is known about the long-term consequences of AI companionship. Across a 12-month longitudinal study with more than 2,000 adults from four Western countries, we examined the bidirectional relationships between social chatbot use and loneliness. We found evidence that increased social chatbot use predicted increased loneliness, using a single-item measure of emotional isolation. When we used a broader and more stable measure of social connection, we found evidence that feeling less socially connected predicted subsequent increases in social chatbot use; however, chatbot use did not significantly predict decreases in social connection. Taken together, these findings provide initial evidence that being lonely may spur people to seek companionship through chatbots but that such use may, over time, exacerbate feelings of loneliness. We urge caution, however, in drawing strong conclusions given the exploratory nature of our analyses.
Read more on this paper in the Observer: Chatbots Are Like Potato Chips: Understanding Loneliness in the Digital Age
Replication of “Procrastination, Deadlines, and Performance: Self-Control by Precommitment”
Kyle Hyndman and Alberto Bisin
We present the results of a replication of Study 2 from Ariely and Wertenbroch (2002) on the role of deadlines in mitigating procrastination. We show that our results do not replicate those of the original article. In our replication, with adult participants enrolled at a large public university in the United States, changes in the deadlines had a negligible effect on the three performance metrics and several survey metrics used in the original study. Evenly spaced deadlines, externally imposed on participants by the experimenters, did not stand out for their effectiveness in reducing procrastination in participants. The data further indicate consistency with several patterns of participant behavior that should be expected irrespective of our main replication results.
Clinical Psychological Science
The Future of the Biopsychosocial Model: Toward a Transdisciplinary Systems Science of Mental Health
Donald J. Robinaugh, Tessa F. Blanken, Emma K. Bridger, et al.
The biopsychosocial model is perhaps the most widely accepted model of mental health. Yet the model is also surprisingly limited, failing to provide clear guidance for clinical research. In this article, we join recent efforts to chart a path forward for the biopsychosocial model, arguing that the success of this framework will hinge on the ability to engage in transdisciplinary collaboration. We begin by showing that the biopsychosocial model is a systems model, and that collaboration with researchers studying systems in other disciplines will therefore afford insight into the nature, etiology, and treatment of mental disorders. We then illustrate how biological, psychological, social, and environmental systems all contribute to mental health and argue that advancing the understanding of mental health will therefore require collaborative integration of work from across domains of science. We conclude with three concrete steps that can help move the field toward a transdisciplinary systems science of mental health.
Interpretation Biases in Anxiety: A Three-Level Meta-Analysis
Felix Würtz, Marius Kunna, Simon E. Blackwell, et al.
Interpretation biases (IBs) play a central role in cognitive models of anxiety disorders and are considered a transdiagnostic feature across anxiety disorders and severity levels. However, the magnitude of this bias in anxiety and modulating factors are largely unknown. In this three-level meta-analysis, we investigated the associations between IBs and symptoms of anxiety. Database searches (PsycINFO, PubMed, ProQuest Dissertations) led to 295 samples with 1,450 contrasts. An overall medium effect size (g = 0.48, 95% confidence interval = [0.43, 0.52]) was found. Equivalent effect sizes were found for youths and adults and different disorder categories and clinical statuses. Effect sizes were larger for studies using direct measures, verbal stimuli, disorder-specific stimuli, and negative (as opposed to positive) stimuli. Overall, results are consistent with the view that anxiety is associated with IBs across different disorders and severity levels. These findings have implications for cognitive theories of anxiety and clinical interventions.
Check out the most downloaded research from 2025.
Perspectives on Psychological Science
Artificial Intelligence and the Psychology of Human Connection
Ryan L. Boyd and David M. Markowitz
As artificial intelligence (AI) becomes increasingly embedded in social life, understanding its interpersonal and psychological implications is urgent yet undertheorized. This article introduces the machine-integrated relational adaptation (MIRA) model, a transdisciplinary, middle-range theoretical framework that provides a foundational account of when, how, and why AI functions as a relational entity in human ecosystems. MIRA distinguishes two crucial roles of AI: relational partner (direct-interaction companion) and relational mediator (shaping human-to-human communication). Synthesizing psychosocial theories of human relationships, interpersonal communication theory, psycholinguistics, and human–computer interaction, MIRA structures AI’s relational impact within antecedents, processes, moderators, and outcomes. Central to MIRA are four principles describing how AI fosters social adaptation: linguistic reciprocity, psychological proximity, interpersonal trust, and relational substitution versus enhancement. These principles illuminate how adaptive AI language and behavior can elicit emotional investment, simulate mutual understanding, or even supplant human interaction. MIRA integrates established theories—attachment theory, social exchange theory, and epistemic trust frameworks—and proposes a research agenda that bridges foundational psychology with emerging sociotechnical contexts. Rather than offering a deterministic view, MIRA provides a generative, testable structure for investigating the evolving role of AI in relational life and guiding future human–AI-connection research.
Sliding Into Silence? We Are Speaking 300 Daily Words Fewer Every Year
Valeria A. Pfeifer and Matthias R. Mehl
By relating the estimates of participants’ daily spoken words to the year their data were collected, we discovered that for each year between 2005 and 2019 an average of 338 fewer words were spoken per day. At first glance, 300 or so words seems like a small, insignificant loss, as if it would not make much of a difference. And indeed, this finding ultimately was buried in a publication full of seemingly more interesting, larger, and more controversial findings. But, as trivial as 338 words a day may feel, the loss of these daily spoken words unavoidably adds up. It means that, each year, we speak more than 120,000 words fewer than in the previous year.
Read more on this paper in the Observer: Chatbots Are Like Potato Chips: Understanding Loneliness in the Digital Age
Current Directions in Psychological Science
Combining Psychology With Artificial Intelligence: What Could Possibly Go Wrong?
Iris van Rooij and Olivia Guest
The current AI hype cycle combined with psychology’s various crises make for a perfect storm. Psychology, on the one hand, has a history of weak theoretical foundations, a neglect for computational and formal skills, and a hyperempiricist privileging of experimental tasks and testing for effects. Artificial intelligence, on the other hand, has a history of conflating artifacts for theories of cognition, or even minds themselves, and its engineering offspring likes to move fast and break things. Many of our contemporaries now want to combine the worst of these two worlds. What could possibly go wrong? Quite a lot. Does this mean that psychology and artificial intelligence can part ways? Not at all. There are very fruitful ways in which the two disciplines can interact and theoretically contribute to cognitive science, for instance, by studying the scope and limits of computational models of human cognition. But to reap the fruits, one needs to understand how to steer clear of potential traps.
AI-Generated Empathy: Opportunities, Limits, and Future Directions
Desmond C. Ong, Amit Goldenberg, Michael Inzlicht, and Anat Perry
Greater numbers of people are turning to AI for empathy and emotional support. Here, we review and synthesize recent empirical work on how people perceive empathy from AI versus from humans. Growing evidence points to two dueling effects and a paradox: AI produces language that is rated higher in empathy than language written by humans, but when people perceive text as coming from AI versus a human, they rate it as less empathic. However, despite sometimes rating AI more empathic or having to wait for human empathy, people still show a preference for human empathy. This emerging literature carries significant implications for fundamental research on empathy and for public discourse as the use of AI for emotional support continues to grow.
Read more on this paper in the Observer: Seeking Empathy in the Age of AI
Psychological Science in the Public Interest
The Scammers’ Psychological Warfare: A Call to Arms
Yaniv Hanoch, Stacey Wood, Marguerite DeLiema, et al.
Fraud and scams are some of the most common crimes in the world, inflicting staggering emotional, financial, health, and psychological suffering on millions of individuals each year. Despite this widespread and extensive impact, the psychological research is just emerging on what renders some individuals more susceptible to fraudulent solicitations, what mechanisms scammers use to lure potential victims, what therapeutic programs can help victims, what educational/preventive programs can reduce compliance, what can be done to increase reporting, and what policies are needed to safeguard the public. To address this pressing social problem, this article has several goals. First, it is designed to provide readers with state-of-the-art insights into fraud research: What has been done thus far and what is urgently missing. Its second purpose is to highlight the urgency of the problem and encourage researchers to conduct relevant research in their domain of expertise. Our primary goal, however, is to reach beyond the academic sphere. To fight fraud, we must unite forces with private and not-for-profit organizations, education providers, financial institutions, government entities, grassroots organizations, international organizations, law enforcement agencies, and many more. To that end, we hope this article draws the attention of a wide range of stakeholders and encourages collaborations that produce direct action.
Read more on this paper in the Observer: To Fight Fraud, Psychological Scientists Issue a Call to Arms
The Political Psychology of Economic Inequality
Daniel J. Connolly, Nick Chater, and George Loewenstein
The high level of inequality of income and wealth across individuals, groups, and nations is widely regarded as among the most fundamental problems facing humanity. Yet democracies often elect and reelect politicians who deliver policies that exacerbate rather than reduce inequality. We argue that this disconnect arises from three basic features of human social cognition that evolved for interpersonal interactions in small groups rather than for navigating the vast political, economic, and technological systems that shape contemporary life: a focus on local notions of equality and equity between socially connected individuals rather than across society at large; group identification, which directs attention to inequality between groups rather than “pure” inequality across the broader population; and what we call the “i-frame bias”—a tendency to explain social outcomes, including inequality, as the product of individual behaviors, taking the system within which those individuals operate as a given. We extend our analysis to examine how economic elites exploit these features of social cognition to shape the political and policy landscape and examine the role psychology can play in reducing inequality.
Read more on this paper in the Observer: Why Is Economic Inequality the Status Quo?
Advances in Methods and Practices in Psychological Science
Models as Prediction Machines: How to Convert Confusing Coefficients Into Clear Quantities
Julia M. Rohrer and Vincent Arel-Bundock
Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models but is challenging for more complex models with, for example, categorical variables, interactions, nonlinearities, or hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model agnostic; it can be applied in consistent fashion to draw inferences from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports more than 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational studies, randomized experiments) to answer different research questions (e.g., about associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modeling ordinal outcomes, and interpreting nonlinear models).
Effects of Psychological Distance on Mental Abstraction: A Registered Report of Four Tests of Construal-Level Theory
Sofia Calderon, Erik Mac Giolla, Karl Ask, et al.
Construal-level theory (CLT) proposes that psychological distance influences the level of abstraction at which something is mentally construed: Things perceived as less probable (likelihood) or further away from the here (spatial distance), now (temporal distance), or self (social distance) are thought about more abstractly. In this international multilab study, we tested four basic hypotheses derived from core assumptions of CLT and explore potential moderators and boundary conditions of the effects. Participants (N = 11,775) from 27 countries and regions were randomly assigned to one of four experimental protocols focused on different types of psychological distance (temporal, spatial, social, or likelihood), and each experiment manipulated psychological distance (close or distant). The protocols for temporal distance (n = 2,941) and spatial distance (n = 2,973) were direct replications of Liberman and Trope (Study 1) and Fujita et al. (Study 1), respectively. The remaining two protocols were paradigmatic replications, applying to social distance (n = 2,926) and likelihood (n = 2,936). Pretests indicated that valence and abstraction were confounded in response options on the outcome measure. Controlling for this confound eliminated the hypothesis-inconsistent effect of social distance, d = 0.006, 95% CI = [−0.05, 0.07]. These findings provide limited evidence for the predictions of the theory and present a critical challenge for CLT.
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