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Theta oscillations in reward processing during social robot interactions
Ukoha, James
Ukoha, James
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2026
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Ukoha, J., & Abubshait, Abdulaziz. Theta oscillations in reward processing during social robot interactions. -- FYRE in STEM Showcase, 2026.
Abstract
Today, we are interacting more with robots in schools, elderly homes, and even hospitals. Since robots are being embedded in our social environments, researchers must understand how human decision making is impacted in the presence of social robots, especially since human decision making is impacted by interactions with other humans. This study builds on prior work that investigated how individuals who interacted or who did not interact with a social robot made decisions during a learnable decision-making task while collecting electroencephalogram (EEG) data. Prior work showed that when participants became familiar with a social robot, they exhibit exhibited larger event related potentials (ERP) that measured how rewarding an outcome experienced is. The work also indicated that their decisions were less accurate when they interacted with the robot. To extend this study, we used Time-Frequency techniques to determine how the brain’s oscillatory behavior underlying rewarding processing changed during the learnable decision-making task. Time-Frequency analysis enabled us to observe brief moments of brain activity that a standard EEG misses, especially in the 3-7 Hz (Theta) band, a band where feedback and reward processing occurs. We hypothesized that participants who interacted with the social robot Cozmo will show greater theta-band activity compared to participants who did not interact with the robot. Analyses concluded that theta activity was greater for losses (i.e., incorrect decisions) than for wins (i.e., correct decisions), regardless of who the outcome affected (i.e., the participant or the robot). Additionally, the level of familiarity with the social robot did not impact our decision making. This suggests that future robots and brain-computer interfaces can be optimized to give superior and ethical feedback, especially since this feedback can influence our decision making. Furthermore, humans can benefit from these optimizations without individuals needing to be familiar with the system.
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Description
Poster and abstract presented at the FYRE in STEM Showcase, 2026.
Research project completed at the Department of Psychology, Wichita State University.
Research project completed at the Department of Psychology, Wichita State University.
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Wichita State University
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FYRE in STEM 2026