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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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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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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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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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Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
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Related Experiment Video

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Learning to Deblur Images with Exemplars.

Jinshan Pan, Wenqi Ren, Zhe Hu

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    |July 12, 2018
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    Summary

    This study introduces a new method for deblurring face images by leveraging facial structures. The approach effectively restores sharp facial details, outperforming existing deblurring techniques.

    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • Generic image deblurring methods struggle with face images due to limited edge information.
    • Existing algorithms rely on edge restoration for kernel estimation, which is insufficient for faces.

    Purpose of the Study:

    • To develop an effective algorithm for deblurring face images.
    • To exploit unique facial structures for improved deblurring performance.

    Main Methods:

    • A novel deblurring algorithm utilizing an exemplar dataset and facial structures.
    • Development of a convolutional neural network (CNN) for sharp edge restoration in blurry images.

    Main Results:

    • The proposed algorithm significantly outperforms state-of-the-art methods in face deblurring.

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  • Demonstrated effectiveness in restoring sharp edges and facial details.

    • Conclusions:

    • The facial structure-based deblurring algorithm is highly effective for face images.
    • The developed CNN approach shows potential for general image deblurring applications beyond faces.