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Related Concept Videos

Stereotype Content Model02:16

Stereotype Content Model

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 categorization, a person will feel...
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Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...

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Related Experiment Videos

Politeness cannot make up for robots' errors.

Shikhar Kumar1, Eliran Itzhak1, Noam Tractinsky2

  • 1Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Be'er Sheva, Israel.

Frontiers in Robotics and AI
|July 2, 2026
PubMed
Summary
This summary is machine-generated.

Robot politeness and errors were studied in user perception. Correct, strict robots were preferred over polite, error-prone ones, showing performance outweighs politeness for technical users.

Keywords:
assistive robothuman-robot interaction (HRI)politenessrobot errorstypes of robots

Related Experiment Videos

Area of Science:

  • Human-Robot Interaction (HRI)
  • Robotics
  • User Perception Studies

Background:

  • Understanding user perceptions of robot behavior is crucial for effective HRI.
  • Robot politeness and error correction are key factors influencing user experience.
  • Previous research has explored politeness strategies but less on their interaction with errors.

Purpose of the Study:

  • To investigate the impact of robot politeness and error-prone behavior on user perceptions.
  • To determine if politeness can mitigate negative user responses to robot errors.
  • To compare user preferences for polite-but-erroneous robots versus correct-but-strict robots.

Main Methods:

  • Two user studies were conducted with non-humanoid robots (mobile and manipulator).
  • Politeness was manipulated based on Lakoff's politeness rules (high vs. low politeness).
  • Robot correctness was manipulated (error-free vs. intentional errors) across two tasks with 59 engineering students.

Main Results:

  • Participants consistently rated correct and polite robots most favorably.
  • Politeness did not offset the negative impact of erroneous robot behavior.
  • Correct but strict robots were rated more positively than polite but erroneous robots.

Conclusions:

  • Robot performance accuracy is more critical than politeness in utilitarian settings for technically proficient users.
  • Polite robots making errors can cause greater user frustration than straightforward, accurate robots.
  • HRI performance evaluation must consider robot type and task specifics to understand user perceptions.