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Related Concept Videos

Observational Learning01:12

Observational Learning

1.2K
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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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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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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Introduction to Learning01:18

Introduction to Learning

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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

Purposive Learning

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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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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Related Experiment Video

Updated: Mar 28, 2026

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
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Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

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Recent Advances of Multimodal Continual Learning: A Comprehensive Survey.

Dianzhi Yu, Xinni Zhang, Yankai Chen

    IEEE Transactions on Neural Networks and Learning Systems
    |March 26, 2026
    PubMed
    Summary

    This survey introduces multimodal continual learning (MMCL), addressing challenges beyond unimodal methods. It categorizes MMCL techniques and highlights future research directions for improved AI learning.

    Related Experiment Videos

    Last Updated: Mar 28, 2026

    Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
    12:55

    Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

    Published on: September 27, 2020

    9.3K

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Vision

    Background:

    • Continual learning (CL) enables models to learn sequentially without forgetting past knowledge.
    • Multimodal continual learning (MMCL) extends CL to handle diverse data types (e.g., text, image, audio).
    • Existing MMCL methods struggle with issues like catastrophic forgetting, modality imbalance, and high computational costs.

    Purpose of the Study:

    • To provide the first comprehensive survey of multimodal continual learning (MMCL) methods.
    • To establish a structured taxonomy for categorizing existing MMCL approaches.
    • To identify and discuss open challenges and future research directions in MMCL.

    Main Methods:

    • Categorization of MMCL methods into four main types: regularization-based, architecture-based, replay-based, and prompt-based.
    • Analysis of the methodologies and innovations within each MMCL category.
    • Compilation of open MMCL datasets and benchmarks for evaluation.

    Main Results:

    • MMCL presents unique challenges beyond unimodal CL, including modality interaction and backbone capability degradation.
    • Straightforward extensions of unimodal CL methods often lead to multimodal catastrophic forgetting.
    • The survey offers a structured overview and taxonomy of current MMCL techniques.

    Conclusions:

    • MMCL is a rapidly evolving field with significant complexities requiring specialized approaches.
    • Further research is needed to address modality imbalance, computational costs, and pretrained capabilities.
    • The survey aims to guide future research and development in effective MMCL.