Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
individual
Campus
Daytona Beach
Authors' Class Standing
Angel Hinojosa, Senior
Lead Presenter's Name
Angel Hinojosa
Lead Presenter's College
DB College of Arts and Sciences
Faculty Mentor Name
Dr. Briana Sobel
Abstract
As technology becomes more intelligent, the relationship between humans and machines is rapidly shifting. Whether a machine is perceived as a capable partner or a source of wariness often depends on the intentions and abilities, we attribute to it. My research in the InTeRACT Lab seeks to empirically assess these perceptions by comparing how we view different nonhuman agents, ranging from animals to robots. This study analyzes qualitative data from a task where participants viewed animations of moving triangles. While the videos remained the same, participants were told the shapes represented either humans, robots, dogs, or inanimate shapes. Previous quantitative results showed significant differences in perception based on these labels; however, the forced-choice scales used in that phase do not fully capture a person’s spontaneous, unbiased understanding of the interaction. My current work involves the systematic coding of approximately 2,500 free-response answers to the question, "What happened in the video?" By converting this abstract, qualitative data into quantitative values, we can perform a direct reliability assessment and compare perceptions across all four groups. The goal is to determine if there are fundamental differences in how we perceive the actions of living versus non-living agents. Understanding these spontaneous attributions is imperative for predicting how people will behave when interacting with advanced technology in the future.
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
Social Attributions of Moving Shapes: Comparing Qualitative Analyses of Humans vs. AI
As technology becomes more intelligent, the relationship between humans and machines is rapidly shifting. Whether a machine is perceived as a capable partner or a source of wariness often depends on the intentions and abilities, we attribute to it. My research in the InTeRACT Lab seeks to empirically assess these perceptions by comparing how we view different nonhuman agents, ranging from animals to robots. This study analyzes qualitative data from a task where participants viewed animations of moving triangles. While the videos remained the same, participants were told the shapes represented either humans, robots, dogs, or inanimate shapes. Previous quantitative results showed significant differences in perception based on these labels; however, the forced-choice scales used in that phase do not fully capture a person’s spontaneous, unbiased understanding of the interaction. My current work involves the systematic coding of approximately 2,500 free-response answers to the question, "What happened in the video?" By converting this abstract, qualitative data into quantitative values, we can perform a direct reliability assessment and compare perceptions across all four groups. The goal is to determine if there are fundamental differences in how we perceive the actions of living versus non-living agents. Understanding these spontaneous attributions is imperative for predicting how people will behave when interacting with advanced technology in the future.