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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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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Information is everywhere and its presentation—such as how and when items are presented—can impact our perceptions and decisions surrounding the info. This broad concept umbrellas framing effects—influences that occur due to the way information is framed in its appearance, whether it’s purely the order or the specific wording of a message. Let’s take a look at numerous ways in which two versions of something can objectively say the same thing, yet we respond in...
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Consider a hydraulic hoist supporting a load of 1 kN. Assuming a simplified schematic representation of this frame structure, the force acting on BD and BF members can be determined.
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Consider a jib crane with an external load suspended from the pulley. The dimensions of the crane members are shown in the figure. A systematic analysis of the frame structure is required to determine the reaction forces at the pin joints, assuming that the pulleys are frictionless.
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Related Experiment Video

Updated: Mar 13, 2026

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy
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Pragmatic Frames for Teaching and Learning in Human-Robot Interaction: Review and Challenges.

Anna-Lisa Vollmer1, Britta Wrede2, Katharina J Rohlfing3

  • 1FLOWERS Team , Inria , France ; ENSTA ParisTech, France.

Frontiers in Neurorobotics
|October 19, 2016
PubMed
Summary

Robots can learn more effectively from humans by using pragmatic frames, which are flexible interaction protocols. Enhancing robot learning requires richer, combinable frames and continuous learning, mirroring human social interactions.

Keywords:
action learningcognitive developmental roboticshuman–robot interactionlanguage learningpragmatic framesrobot learningrobot teachingsocial learning

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Area of Science:

  • Robotics and Human-Robot Interaction (HRI)
  • Developmental Psychology
  • Machine Learning

Background:

  • Human-Robot Interaction (HRI) faces challenges in learning from inexperienced users, particularly for teaching skills flexibly.
  • Leveraging human social competences and interactional mechanisms offers a path toward natural and efficient robot learning and teaching.
  • Pragmatic frames, as flexible interaction protocols, provide crucial contextual cues for inferring and conveying skills.

Approach:

  • This article defines and discusses pragmatic frames, drawing on developmental psychology research.
  • It analyzes existing HRI literature on learning-teaching interactions, evaluating mechanisms in light of pragmatic frames.
  • The study identifies limitations in current HRI applications of pragmatic frames compared to human-human interaction.

Key Points:

  • Many HRI studies implicitly use basic elements of pragmatic frames for robot learning and teaching.
  • Current HRI applications of pragmatic frames are restricted, hindering robust multi-task learning.
  • Key absent features include rich, combinable repertoires of frames and continuous frame learning.

Conclusions:

  • The restricted use of pragmatic frames in HRI impedes natural multi-task learning and teaching.
  • Incorporating richer, combinable, and continuously learnable frames is crucial for advancing HRI.
  • Future research should focus on solving challenges to fully leverage pragmatic frames for robot learning and teaching.