Leung, E., & Urminsky, O. (2025).
PNAS, 122(13).
Abstract
Information search platforms, from Google to AI-assisted search engines, have transformed information access but may fail to promote a shared factual foundation. We demonstrate that the combination of users’ prior beliefs influencing their search terms and the narrow scope of search algorithms can limit belief updating from search. We test this “narrow search effect” across 21 studies (14 preregistered) using various topics (e.g., health, financial, societal, political) and platforms (e.g., Google, ChatGPT, AI-powered Bing, our custom-designed search engine and AI chatbot interfaces). We then test user-based and algorithm-based interventions to counter the “narrow search effect” and promote belief updating. Studies 1 to 5 show that users’ prior beliefs influence the direction of the search terms, thereby generating narrow search results that limit belief updating. This effect persists across various domains (e.g., beliefs related to coronavirus, nuclear energy, gas prices, crime rates, bitcoin, caffeine, and general food or beverage health concerns; Studies 1a to 1b, 2a to 2g, 3, 4), platforms (e.g., Google—Studies 1a to 1b, 2a to 2g, 4, 5; ChatGPT, Study 3), and extends to consequential choices (Study 5). Studies 6 and 7 demonstrate the limited efficacy of prompting users to correct for the impact of narrow searches on their beliefs themselves. Using our custom-designed search engine and AI chatbot interfaces, Studies 8 and 9 show that modifying algorithms to provide broader results can encourage belief updating. These findings highlight the need for a behaviorally informed approach to the design of search algorithms.
Significance
In a time of societal polarization, the combination of people’s search habits and the search tools they use being optimized for relevance may perpetuate echo chambers. We document this across various diverse studies spanning health, finance, societal, and political topics on platforms like Google, ChatGPT, AI-powered Bing, and our custom-designed search engine and AI chatbot platforms. Users’ biased search behaviors and the narrow optimization of search algorithms can combine to reinforce existing beliefs. We find that algorithm-based interventions are more effective than user-based interventions to mitigate these effects. Our findings demonstrate the potential for behaviorally informed search algorithms to be a better tool for retrieving information, promoting the shared factual understanding necessary for social cohesion.
Here are some thoughts:
For psychologists, this work is a compelling demonstration of how classic cognitive biases operate in modern digital environments and how they can be mitigated not just by changing minds, but by changing the systems that shape information exposure. It calls for greater interdisciplinary collaboration between psychology, human-computer interaction, and AI ethics to design technologies that support, rather than hinder, rational belief updating and informed decision-making.
Clinically, psychologists can now better understand that resistance to change may not stem solely from emotional defenses or entrenched schemas, but also from how people actively seek information in narrow, belief-consistent ways. Crucially, the findings show that structural interventions—like guiding patients to consider broader perspectives or exposing them to balanced evidence—can be more effective than simply urging them to “reflect” on their thinking. This supports the use of active cognitive restructuring techniques in therapy, such as examining multiple viewpoints or generating alternative explanations, to counteract the natural tendency toward narrow search.