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Related Concept Videos

Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Purposive Learning01:22

Purposive Learning

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 bonus...
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Machines: Problem Solving II01:30

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.

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Related Experiment Video

Updated: Jun 4, 2026

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

Exploring child engagement in a multi-robot tutor-peer learning scenario.

Luca Raggioli1,2, Rahul Singh Maharjan2, Joanna Kolak3

  • 1Department of Neuroscience, and Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy.

Frontiers in Robotics and AI
|June 3, 2026
PubMed
Summary
This summary is machine-generated.

This study shows that young children can learn effectively with two robots, one as a tutor and one as a peer. This multi-robot setup supports sustained engagement and structured attention in early word learning.

Keywords:
child engagementchild-robot interactionearly language learningmulti-robot systemssocial robots

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

Published on: November 24, 2015

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Last Updated: Jun 4, 2026

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
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SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

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

Published on: November 24, 2015

Area of Science:

  • Robotics in Education
  • Child Development
  • Human-Robot Interaction

Background:

  • Social robots in education often use a single robot with manipulated roles (tutor, peer).
  • The impact of multiple, distinct robots with complementary roles on children's learning is less understood.

Purpose of the Study:

  • To explore the feasibility of a multi-robot learning environment with complementary roles (tutor and novice peer).
  • To investigate children's attention, affect, and engagement during an early word-learning task with two robots.
  • To examine the relationship between individual characteristics and task performance.

Main Methods:

  • An exploratory study with 16 children aged 4-5 years using a blue multi-robot learning environment.
  • Children learned novel object names from tutor and novice peer robots, followed by recall tasks.
  • Recorded children's attention distribution, affect, and engagement, alongside individual characteristics (language ability, age, media exposure).

Main Results:

  • Children consistently attended to task-relevant objects and robots in structured patterns.
  • Attentional complexity correlated with sustained engagement and positive affect.
  • Task performance was not significantly affected by age or media exposure, but baseline language ability showed a negative association with recall.

Conclusions:

  • A multi-robot learning configuration is feasible and supports sustained engagement and structured attention in young children.
  • Complementary robot roles can be integrated into early learning activities.
  • Further systematic investigation is warranted given the exploratory nature and small sample size.