Algorithmic personalization of information can cause inaccurate generalization and overconfidence

Personalization algorithms are widely used online to deliver recommendations fine-tuned to individual users. This specificity comes at the cost of the diversity of information presented to users, limiting exposure to alternative perspectives and potentially reinforcing existing beliefs. We investigated the degree to which personalization can hinder the acquisition of new knowledge of categories. We asked participants to learn about alien categories under different levels of personalization and tested their knowledge using a postlearning categorization task. Our results show that learners in personalized environments sample feature information more selectively during the learning phase and develop inaccurate representations about the categories. Critically, they also report inflated confidence about their inaccurate decisions for categories for which they had little exposure. Our results suggest that personalization can distort learners' understanding of the environment, bias information sampling, and induce incorrect generalization of knowledge. (PsycInfo Database Record (c) 2025 APA, all rights reserved).