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Related Concept Videos

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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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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Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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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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Machines: Problem Solving II01:30

Machines: Problem Solving II

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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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Machines: Problem Solving I01:22

Machines: Problem Solving I

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
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Related Experiment Video

Updated: Nov 11, 2025

Working Memory Training for Older Participants: A Control Group Training Regimen and Initial Intellectual Functioning Assessment
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Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Conditional Learning.

Jianwen Xie, Zilong Zheng, Xiaolin Fang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 26, 2021
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    This study introduces a novel cooperative training method for learning conditional distributions between domains. It uses a fast initializer and a slow solver to refine outputs, outperforming GANs in tasks like image generation.

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    Area of Science:

    • Machine Learning
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Learning conditional distributions between high-dimensional data domains is challenging.
    • Existing methods like Generative Adversarial Networks (GANs) have limitations.

    Purpose of the Study:

    • To develop a novel method for learning conditional distributions across different data domains.
    • To improve generative tasks by incorporating a refinement process guided by an objective function.

    Main Methods:

    • Cooperative training of a fast initializer and a slow solver.
    • Initializer generates initial outputs with noise for variability.
    • Solver learns a conditional energy function for iterative refinement.

    Main Results:

    • Demonstrated effectiveness on class-to-image generation, image-to-image translation, and image recovery.
    • The proposed method shows advantages over GAN-based approaches.

    Conclusions:

    • The cooperative training approach with a slow-thinking solver refines solutions effectively.
    • This method offers a more robust approach to conditional generative tasks.