Paul J. Beatty
Papers
1
Total Citations
20
H-Index
1
About
Paul J. Beatty is a cognitive neuroscientist whose research sits at the intersection of social robotics, feedback processing, and human learning. His most-cited work, "A win-win situation: Does familiarity with a social robot modulate feedback monitoring and learning?" (2021, 20 citations), explores how humans interact with and learn from social robots, specifically examining how familiarity with a robotic partner influences the brain's feedback-monitoring systems and subsequent learning outcomes. This line of inquiry is pivotal for understanding human-robot collaboration and the design of more effective educational and assistive technologies. Beatty's contributions extend beyond this flagship study, as he investigates the neural mechanisms—such as the error-related negativity (ERN) and feedback-related negativity (FRN)—that underpin how we process social and non-social feedback. His work bridges cognitive psychology, neuroscience, and human-robot interaction, offering insights into how artificial agents can be integrated into learning environments. With a growing citation record and a focus on the dynamic interplay between humans and machines, Beatty is shaping our understanding of the cognitive and neural foundations of social learning in an increasingly automated world.
Research Focus
Key Achievements
Top Papers
- 1