Autonomy in the Attention Economy
Awarded NIAA grant to investigate misalignments between user preferences regarding their online behaviour and the reality, and between users’ self-perception vs how algorithms perceive them. I am co-supervising two student academic assistants, one from STEM and one from the Humanities.
Timeline
Oct 2025 - Apr 2026
Collaborators
- Dr. Marcos Oliveira (co-supervisor), AI & Behaviour, Dept. Computer Science, VU Amsterdam
- Dr. Tim Groot Kormelink (co-supervisor), Dept. Journalism Studies, VU Amsterdam
- Aalhad Patwardhan (student assistant)
- Roos van der Molen (student assistant)
Project Summary
Digital platforms have reshaped how we spend our time and attention. Although technologies increasingly mediate all aspects of human life, there are often subtle but significant misalignments between what users want, what algorithms can infer about users and their needs, and what platforms are designed to promote. These misalignments undermine users’ autonomy over their behaviour, and may profoundly affect their wellbeing and sense of identity over time.
How do such misalignments emerge? To what extent do algorithms accurately represent who users are—or who they want to be? Despite growing awareness of the psychological and social impacts of digital media, we still do not understand how these mismatches arise or how to quantify them.
To tackle these questions, our project aims to uncover mechanisms driving three fundamental misalignments: (i) how users intend to spend their time versus how they actually spend it, (ii) how users believe they spend their time versus their actual usage patterns, and (iii) how users perceive their interests and identity versus how algorithms construct them.
We will recruit adult smartphone users for a three-month study. Participants will log their intentions and reflect weekly on their long-term goals, and write self-reflections on their daily device usage in a diary study. In parallel, participants will donate their digital trace data (app usage, video viewing patterns, etc.). By combining subjective reflection and individual-level digital behavioral data, we will make quantitative as well as qualitative comparisons between users’ stated goals and preferences, and their actual digital behaviour.
