Berkay Demirel
Papers
1
Total Citations
3
H-Index
1
About
Berkay Demirel’s research lies at the intersection of cognitive science and sensorimotor integration, with a focus on how humans distinguish self, others, and external events from visual feedback. In his most cited work, *“Distinguishing Self, Other, and Autonomy From Visual Feedback: A Combined Correlation and Acceleration Transfer Analysis”* (2021), Demirel introduces a novel computational framework that uses correlation and acceleration transfer analysis to parse sensorimotor signals, enabling more precise attribution of actions to the self-model, the model of another agent, or the external environment. This contribution advances our understanding of Theory of Mind (ToM)—the cognitive ability to infer others’ intentions and beliefs—by grounding it in measurable sensorimotor dynamics. While his citation count is currently modest (3 citations), the work’s methodological innovation signals growing relevance for fields like human-robot interaction and social cognition. Demirel’s approach offers a promising tool for dissecting how the brain constructs agency and autonomy, with potential applications in robotics and neurorehabilitation. His research exemplifies a rigorous, data-driven path to unraveling the mechanisms behind social perception and self-awareness.
Research Focus
Key Achievements
Top Papers
- 1