Since 2019, the Psychology of Technology Institute has given Dissertation Awards annually to recognize exceptional doctoral research that advances our understanding of the relationship between human psychology and technology. These awards facilitate the Institute’s mission of connecting and supporting scholars from multiple scientific disciplines who conduct research on the psychological determinants and consequences of adopting new technologies (e.g., AI assistants, LLMs, social media, algorithmic decision making, robots, smartphones, AR/VR, etc.), including how the adoption and use of these technologies are transforming how people live, work, play, and interact.
The eight outstanding scholars we acknowledge below have produced work with significant implications for how we understand technology’s impact on human cognition, mental health, and decision-making. Please join us in congratulating them!
Award Winners
Dunigan Folk, Ph.D., Psychology, University of British Columbia
“When, Why, and for Whom Do Social Chatbots Provide Feelings of Social Connection?”
At a time when AI companions are widely promoted as a remedy for loneliness, Folk suggests chatbots may function more like social junk food, providing short-term satiation at the cost of long-term fulfillment. Drawing on experimental, longitudinal, and cross-cultural methods, Folk investigates when, why, and for whom AI companions fall short of human connection. He shows that chatbots disappoint when they perform prototypically human behaviors, that their appeal depends on individual and cultural tendencies to anthropomorphize technology, and that, over the course of a year, chatbot use and loneliness reinforce one another. Folk’s work offers guidance for how we design, deploy, and think about AI companionship in an era of growing social disconnection.
Rafael M. Batista, Ph.D., Behavioral Science, Chicago Booth School of Business
“Words that Work: Using AI to Accelerate the Discovery of How Language Affects Human Decisions”
Machine learning can detect patterns in data that humans reliably miss, but its predictions emerge from processes opaque to human understanding, limiting its usefulness for scientific discovery. Batista’s dissertation bridges this gap with a three-step framework — Hypothesize, Intervene, Predict — that converts the pattern-detection power of ML into human-interpretable hypotheses. Applied to a corpus of nearly 65,000 headlines tested across 32,487 randomized field experiments, the framework surfaced novel hypotheses about what drives online engagement, five of which were confirmed in held-out experiments and two of which represent genuine discoveries beyond fifty established psychological constructs. Batista’s work offers researchers a way to convert unstructured text into ranked, testable ideas.
Georgia Turner, Ph.D., Neuroscience, University of Cambridge
“Computational approaches to understanding personal autonomy over digital technology use”
Concerns that social media and smartphones compromise users’ autonomy are widespread, but the mechanisms behind this loss of control remain poorly understood. Turner’s dissertation investigates how people come to feel out of control over their technology use, working across theoretical, epidemiological, behavioural, and cognitive levels. She develops a unifying framework for digital autonomy as a hierarchical control system, and finds that 47% of UK adolescents report feeling addicted to social media, with three distinct behavioural-emotional profiles underlying that experience. Turner also introduces a novel “App-journey” network analysis that predicts mental health more reliably than screen time, and applies reinforcement learning to real-world Twitter data to show that a hybrid goal-directed and habitual process drives posting. She closes by arguing that the field’s dependence on industry-controlled data is itself a barrier to progress, pointing to external regulation of research transparency as an underexplored path forward.
Pat Pataranutaporn, Ph.D., Media Arts & Sciences, Massachusetts Institute of Technology
“Cyborg Psychology: The Art & Science of Designing Human-AI Systems that Support Human Flourishing”
As humans and AI become increasingly integrated, every design choice in an AI system quietly shapes how people think, feel, and decide, yet the psychological consequences of these choices remain understudied. Pataranutaporn’s dissertation introduces “Cyborg Psychology,” an interdisciplinary framework for understanding how AI systems influence human cognition and for designing AI that supports human flourishing. Across a series of systems and studies, Pataranutaporn shows that a wearable AI improves users’ ability to distinguish evidence-based from unsupported claims, that Socratic-style AI feedback scaffolds reasoning more effectively than direct answers, that a brief conversation with an AI-generated future self reduces anxiety, and that users’ preexisting beliefs about AI co-construct their experience of it. Taken together, Pataranutaporn’s work argues that the psychological impact of AI is shaped not by the system alone but by how it is designed, framed, and introduced, and offers Cyborg Psychology as a foundation for building AI that augments rather than diminishes human capability.
Honorable Mentions
Trevor Spelman, Management & Organizations, Kellogg School of Management
“The Suppression–Misperception Cycle: How Miscalibrated Expectations Drive Self-Censorship in Online Group Discussions and How Platform Design Can Disrupt It”
Spelman examines how online discussion platforms create a self-reinforcing “suppression–misperception cycle,” in which individuals silence themselves out of inflated fears of social rejection, group opinion appears more extreme than it is, and the resulting distortion drives further silence. His research shows that this cycle is sustained less by misperceived norms than by miscalibrated expectations of social cost, and identifies platform design interventions that correct those expectations and meaningfully reduce self-censorship.
Tara Srirangarajan, Ph.D., Psychology, Stanford University
“From Immersive Virtual Reality to Social Media Analytics: A Multimodal Approach to Understanding Affect Dynamics”
Srirangarajan uses immersive virtual reality and large-scale social media analytics as complementary tools for studying emotion, treating technology as both a mirror that reflects affective experience and a lens that measures it with new precision. Her dissertation captures the second-to-second dynamics of affect inside controlled VR environments and shows, in collaboration with National Geographic, that individual neural responses to wildlife imagery can forecast which images generate the most engagement on Instagram, linking individual affect to collective online behavior.
Yuning Liu, Ph.D., Population Health Science, Harvard University
“Understanding Digital Well-being in Social Media Use: App-Specific Behavior, Goal Pursuit, and Platform Design”
Liu argues that digital well-being cannot be understood through screen time alone and reframes social media use along three dimensions: app-specific behaviors, momentary goal pursuit, and platform-level design choices. Combining ecological momentary assessment, user-donated platform data, and large-scale analysis of nearly 13,500 platform announcements, her dissertation shows that the effects of social media on well-being are small, mixed, and highly context-dependent, while exposing transparency gaps in how platforms themselves report what they do to protect users.
Eric Park, Ph.D., Marketing, Columbia University
“Immersive Technologies in Marketing: A Spatial Framework”
Park’s dissertation proposes spatial perception as a unifying lens for understanding how immersive technologies shape social experience. In a series of eight experiments and a large-scale dataset of over 340,000 Twitch chats, Park shows that faster-scrolling livestream chat acts as a “socio-spatial cue,” making virtual spaces feel more crowded, streamers seem more popular, and tipping more generous. A second essay develops a conceptual framework for augmented reality, introducing “egocentric spatial repositioning” to explain how AR’s translocation of virtual content into physical space can uniquely amplify or diminish empathy and social connection.
Congratulations to all our award and honorable mention recipients! Their research exemplifies academic rigor while addressing some of the most pressing challenges at the intersection of technology and human behavior. We look forward to seeing how their research continues to shape understanding and practice related to the psychology of technology for years to come.