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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
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Area of Science:

  • Human-Robot Interaction
  • Cognitive Psychology
  • Robotics

Background:

  • The Uncanny Valley hypothesis posits a distinct boundary between human and robot appearances.
  • Consistency of perceived anthropomorphism and robot-human borders across robot occupations is not well understood.

Purpose of the Study:

  • To investigate how different robot occupations affect the perceived robot-human border.
  • To analyze the influence of robot job roles on judgments of anthropomorphism.

Main Methods:

  • Generated morphed images blending robot and human faces at varying proportions.
  • Utilized three image classification tasks under different robot occupational conditions.
  • Determined the point of subjective equality to quantify the robot-human border.

Main Results:

  • Robot occupation significantly influenced perceived anthropomorphism.
  • Participant age and gender also played a role in perception.
  • Findings suggest an implicit link between robot job and appearance, influenced by stereotypes.

Conclusions:

  • Robot occupation can alter the perceived human-likeness of robots.
  • Assigning expected appearances to robots may reinforce stereotypes linking appearance to job roles.
  • Understanding these perceptions is crucial for designing socially acceptable robots.