Natalie Paul
Papers
1
Total Citations
41
H-Index
1
About
Natalie Paul is a leading researcher in human-robot interaction, with a focus on the cognitive and psychological dimensions of how people perceive and adapt to intelligent machines. Her work explores how existing beliefs about minds and machines shape—and are reshaped by—interactions with robots. In her highly cited 2013 paper, "Cognitive Dissonance as a Measure of Reactions to Human-Robot Interaction" (41 citations), Paul introduced a novel framework for measuring the psychological tension users experience when a robot’s behavior challenges their preconceptions. This contribution has been foundational for understanding user resistance, trust, and attitude change in human-robot teams. Her research bridges social psychology and robotics, offering empirical tools to assess how interaction experiences can transform mental models of agency and intelligence. Paul’s work is widely referenced in studies on social robotics and human-AI collaboration, and she is recognized for bringing rigorous experimental methods to the study of cognitive conflict in human-machine encounters. Her insights continue to inform the design of more intuitive and socially acceptable robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Cognitive Dissonance as a Measure of Reactions to Human-Robot Interaction41 citations · 2013