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Related Concept Videos

Introduction to Learning01:18

Introduction to Learning

364
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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Purposive Learning01:22

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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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Cognitive Learning01:21

Cognitive Learning

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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...
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Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
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Observational Learning01:12

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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...
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Machines: Problem Solving II01:30

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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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Updated: Jun 25, 2025

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Learning to Learn: How to Continuously Teach Humans and Machines.

Parantak Singh1,2, You Li2,3, Ankur Sikarwar1,2

  • 1Nanyang Technological University (NTU), Singapore.

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|May 24, 2024
PubMed
Summary
This summary is machine-generated.

Optimizing the learning sequence, or curriculum, significantly improves knowledge transfer for both humans and machines in continual learning settings. Effective curricula for humans show strong correlation with those beneficial for machine learning algorithms.

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Machine Learning

Background:

  • Curriculum design is crucial for effective knowledge transfer in education.
  • Prior research on curriculum design primarily focused on single, offline tasks.
  • The online class-incremental continual learning setting involves learning tasks sequentially.

Purpose of the Study:

  • To investigate the impact of task order on learning outcomes in online class-incremental continual learning.
  • To compare the effectiveness of curricula for humans and machine learning algorithms.
  • To develop an automated algorithm for designing effective curricula.

Main Methods:

  • Studied the effect of task sequencing in online class-incremental continual learning.
  • Introduced a novel object recognition dataset for human experiments.
  • Developed and evaluated a Curriculum Designer (CD) algorithm based on inter-class feature similarities.

Main Results:

  • Curriculum order significantly influences learning outcomes for both humans and machine learning algorithms.
  • Effective curricula for humans are highly correlated with those effective for machines.
  • The proposed CD algorithm shows significant overlap with empirically effective curricula.

Conclusions:

  • Optimized curricula enhance knowledge transfer and minimize forgetting in continual learning.
  • A unified framework for studying and designing curricula for humans and machines is established.
  • Automated curriculum design for online continual learning is a promising research direction.