Kanaka Samagna Talanki
Papers
1
Total Citations
3
H-Index
1
About
Kanaka Samagna Talanki is a rising researcher at the intersection of human-robot interaction and cognitive psychology, whose work investigates how cognitive biases shape human perceptions of autonomous systems. Her most-cited paper, "Recency Bias in Task Performance History Affects Perceptions of Robot Competence and Trustworthiness" (2024), demonstrates that humans disproportionately weigh a robot's most recent actions when forming judgments of its competence and trustworthiness—a finding with profound implications for robot design and human-robot teaming. By experimentally isolating the recency effect in human memory, Talanki reveals how subtle ordering of performance information can systematically skew user trust, even when overall performance is constant. This work bridges fundamental cognitive science with applied robotics, offering actionable insights for designing robot behaviors that mitigate biased human evaluations. With 3 citations in its first year, the paper signals growing recognition of her contributions. Talanki’s research is critical for developing transparent, trustworthy AI systems, particularly in high-stakes domains like healthcare and autonomous driving, where accurate human assessment of robot reliability is paramount. Her work positions her as a key voice in understanding the psychological barriers to effective human-robot collaboration.
Research Focus
Key Achievements
Top Papers
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