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Related Concept Videos

Associative Learning01:27

Associative Learning

474
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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Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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Observational Learning01:12

Observational Learning

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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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Cognitive Learning01:21

Cognitive Learning

463
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...
463
Purposive Learning01:22

Purposive Learning

174
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

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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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Learning Good Features to Transfer Across Tasks and Domains.

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    Summary
    This summary is machine-generated.

    This study demonstrates how deep learning models can transfer knowledge across different computer vision tasks and domains, even with limited labeled data. This approach enables effective learning for new tasks and improves performance in challenging scenarios.

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    Area of Science:

    • Computer Vision
    • Deep Learning
    • Machine Learning

    Background:

    • Limited labeled data hinders deep learning deployment in new computer vision domains.
    • Shared architectures across tasks suggest potential for knowledge reuse.

    Purpose of the Study:

    • To develop a method for transferring learned knowledge between different computer vision tasks and domains with minimal supervision.
    • To improve the generalization capability of deep learning models for novel, unseen environments.

    Main Methods:

    • Learning a mapping function between task-specific deep features within a domain.
    • Implementing the mapping function using a neural network.
    • Proposing strategies to constrain learned feature spaces for better generalization.

    Main Results:

    • Demonstrated successful knowledge transfer across tasks and domains.
    • Achieved compelling results in synthetic-to-real adaptation scenarios.
    • Showcased improved performance in monocular depth estimation and semantic segmentation.

    Conclusions:

    • Learned feature mappings can generalize to novel domains.
    • Feature space constraints enhance learning and generalization.
    • The proposed framework effectively addresses data scarcity in computer vision.