Anthropomorphism and Human Likeness in the Design of Robots and Human-Robot Interaction

By Julia Fink. International Conference on Software Reuse, 2012

414 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Social Psychology

View Paper: https://www.semanticscholar.org/paper/ac5ab6bbfb234c2f20d0af8a648d5c461b69c491

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Guy Hoffman
2. Ewart J. de Visser
3. Amna Khan
4. Wendy Ju
5. Ruud Wetzels
6. Martin Wetzels
7. Raja Parasuraman

In Control and Systems Engineering

1. Amna Khan
2. Guy Hoffman
3. Wendy Ju
4. Raja Parasuraman

In Social Psychology

1. Ewart J. de Visser
2. Guy Hoffman
3. Harry P. Hallock
4. Amna Khan
5. Wendy Ju
6. Gabriel Beltrao
7. Martin Wetzels
8. Raja Parasuraman

Citations in the corpus

• Trust in humanoid robots: implications for services marketing (2019, background): “Although HRI research often posits that all kinds of humanlike features incite anthropomorphism (Fink, 2012), the current study provides evidence that moderating variables ultimately determine their effect.”
• Designing robots with movement in mind (2014, background): “The robot’s parts are specified by the robot’s users to support the intended interaction, which often includes gaze, smiles, pointing gestures, etc. Visual designers of robots then need to decide how far along the humanoid spectrum to place their design (Fink, 2012).”
• A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective (2022, background): “” (Fink, 2012, p. 205).”
• Almost human: Anthropomorphism increases trust resilience in cognitive agents. (2016, background): “Designers of automated agents will have to determine when to increase and decrease anthropomorphism strategically based on their design objective (DiSalvo & Gemperle, 2002; Fink, 2012).”
• Frontline Service Technology infusion: conceptual archetypes and future research directions (2019, background): “Conversational agent design reflects its appearance, behavior and interaction mode (Fink, 2012).”
• Human- versus Artificial Intelligence (2021, background): “…to characterize non-human artifacts that 3 Unless of course AI will be deliberately constrained or degraded to human-level functioning. super fi cially look similar to us as possessing human-like traits, emotions, and intentions, (e.g., Kiesler and Hinds, 2004; Fink, 2012; Haring et al., 2018).”