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Related Concept Videos

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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Introduction to Learning01:18

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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.
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Cognitive Learning01:21

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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.
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Associative Learning01:27

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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.
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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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Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

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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...
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Related Experiment Video

Updated: Mar 6, 2026

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
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Watch-n-Patch: Unsupervised Learning of Actions and Relations.

Chenxia Wu, Jiemi Zhang, Ozan Sener

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 14, 2017
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    Summary
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    This study introduces an unsupervised model for understanding complex human activities from video. The approach identifies action relationships and enables applications like forgotten action detection for assistive robots.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Robotics

    Background:

    • Human activities are complex and vary widely.
    • Modeling composite activities from basic actions is challenging, especially in unsupervised settings.
    • Existing methods struggle to capture long-range temporal dependencies in activities.

    Purpose of the Study:

    • To develop a novel unsupervised probabilistic model for composite human activity recognition.
    • To effectively learn high-level co-occurrence and temporal relations between actions.
    • To enable applications such as unsupervised action segmentation, clustering, and forgotten action detection (action patching).

    Main Methods:

    • Representing videos as sequences of action and object words.
    • Introducing a probabilistic model that relates words to latent action and object topics.
    • Modeling long-range action dependencies through topic interactions.

    Main Results:

    • Successful application to unsupervised action segmentation and clustering.
    • Demonstration of a novel 'action patching' technique for detecting forgotten actions.
    • Creation and release of a new challenging RGB-D activity video dataset.

    Conclusions:

    • The proposed model effectively captures complex temporal relationships in human activities without supervision.
    • The action patching algorithm has practical applications in assistive robotics.
    • The new dataset facilitates further research in activity recognition and understanding.