Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Kavya Dipen Shah, Freshman Caroline Deck, Graduate Student
Lead Presenter's Name
Kavya Dipen Shah
Lead Presenter's College
DB College of Engineering
Faculty Mentor Name
Dr. Briana Sobel
Abstract
As technology becomes more advanced, it is important to understand how people perceive the intentions and abilities of machines. This project, conducted in the InTeRACT Lab, explores how we attribute humanlike qualities to different types of agents, ranging from animals to robots. The study analyzes data from an experiment where participants watched animations of moving triangles. Although the videos were identical, participants were told the shapes represented either humans, robots, dogs, or inanimate objects. While previous math-based data showed that these labels changed how people felt, those structured scales didn't allow for a natural, unbiased explanation of what people actually saw. To address this, my current work focuses on coding about 2,500 written responses to the question, "What happened in the video?" I am converting these open-ended descriptions into data that can be measured and compared across the four groups. The objective is to see if there are basic differences in how we view the actions of living things versus machines. By understanding these natural reactions, we can better predict how people will behave around advanced technology in the future. This research provides a clearer look at the human tendency to project mental states onto nonhuman entities.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Included in
Artificial Intelligence and Robotics Commons, Cognition and Perception Commons, Human Factors Psychology Commons
A Qualitative Analysis of Human-AI Interaction Through Animated Shapes
As technology becomes more advanced, it is important to understand how people perceive the intentions and abilities of machines. This project, conducted in the InTeRACT Lab, explores how we attribute humanlike qualities to different types of agents, ranging from animals to robots. The study analyzes data from an experiment where participants watched animations of moving triangles. Although the videos were identical, participants were told the shapes represented either humans, robots, dogs, or inanimate objects. While previous math-based data showed that these labels changed how people felt, those structured scales didn't allow for a natural, unbiased explanation of what people actually saw. To address this, my current work focuses on coding about 2,500 written responses to the question, "What happened in the video?" I am converting these open-ended descriptions into data that can be measured and compared across the four groups. The objective is to see if there are basic differences in how we view the actions of living things versus machines. By understanding these natural reactions, we can better predict how people will behave around advanced technology in the future. This research provides a clearer look at the human tendency to project mental states onto nonhuman entities.