Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Automatic Processing and Automatic Social Behavior

Automatic Processing and Automatic Social Behavior

Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Low-cost social robot designs for education: a review.

Frontiers in robotics and AI·2026

Same author

Multi-party open-ended conversation with a social robot.

Frontiers in robotics and AI·2026

Same author

Efficacy and effectiveness of robot-assisted therapy for autism spectrum disorder: From lab to reality.

Science robotics·2025

Same author

"Robots for good": Ten defining questions.

Science robotics·2023

Same author

Behavioural Models of Risk-Taking in Human-Robot Tactile Interactions.

Sensors (Basel, Switzerland)·2023

Same author

Do different robot appearances change emotion recognition in children with ASD?

Frontiers in neurorobotics·2023

Search research articles

Related Experiment Video

Updated: Apr 1, 2026

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

Published on: September 6, 2024

Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction.

Joachim de Greeff¹, Tony Belpaeme²

¹Centre for Robotics and Neural Systems, Plymouth University, Plymouth, United Kingdom; Interactive Intelligence Group, Delft University of Technology, Delft, the Netherlands.

|October 1, 2015

Summary

Robots learning from humans improve when they signal learning needs. Social cues from robots enhance human teaching, leading to faster and better artificial intelligence (AI) learning, with notable gender differences in responsiveness.

More Related Videos

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Published on: August 8, 2011

Related Experiment Videos

Last Updated: Apr 1, 2026

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

Published on: September 6, 2024

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Published on: August 8, 2011

Area of Science:

Human-Robot Interaction
Artificial Intelligence
Cognitive Science

Background:

Social learning is crucial for knowledge and skill propagation in humans.
Implementing social learning in artificial systems (social machine learning) is complex.
Understanding how artificial systems can elicit effective human tutoring is key.

Purpose of the Study:

To investigate if humans respond to social cues from a learning robot.
To determine if social cues improve robot learning efficiency.
To explore the impact of robot social cues on human teaching strategies.

Main Methods:

A child-like social robot learned word meanings through language games.
Two conditions: robot expressed learning preference via social cues vs. no cues.
Human participants tutored the robot, with their interactions analyzed.

Main Results:

Robots using social cues to express learning preferences learned faster and better.
Humans adapted their teaching based on a perceived 'mental model' of the robot.
Female participants were more responsive to the robot's social bids than males.

Conclusions:

Incorporating social cues into social machine learning enhances learning input quality.
Social cues facilitate more effective human-to-robot knowledge transfer.
This research highlights the potential of social cues for improving AI learning performance.