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Related Concept Videos

Prosopagnosia01:24

Prosopagnosia

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Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
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Visual Agnosia01:12

Visual Agnosia

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Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
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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

761
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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Observational Learning01:12

Observational Learning

713
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.
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Related Experiment Video

Updated: Dec 12, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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SensitiveNets: Learning Agnostic Representations with Application to Face Images.

Aythami Morales, Julian Fierrez, Ruben Vera-Rodriguez

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |August 11, 2020
    PubMed
    Summary

    This study introduces a new privacy-preserving neural network method that removes sensitive data, ensuring user privacy and equality of opportunity without sacrificing performance. This approach enhances data protection for sensitive information.

    Related Experiment Videos

    Last Updated: Dec 12, 2025

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.8K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • New international regulations mandate robust data protection and avoidance of discrimination.
    • Existing methods often focus on fairness directly, not as a consequence of privacy.

    Purpose of the Study:

    • To propose a novel privacy-preserving neural network feature representation.
    • To suppress sensitive information while maintaining data utility.
    • To ensure privacy and equality of opportunity as a result of privacy preservation.

    Main Methods:

    • Developed a privacy-preserving feature representation for neural networks.
    • Utilized an adversarial regularizer with a sensitive information removal function.
    • Introduced a new, balanced face annotation dataset.

    Main Results:

    • Demonstrated improved privacy and equality of opportunity.
    • Maintained competitive performance across various tasks (identity, attractiveness, smiling).
    • Validated on three public benchmarks and the new dataset.

    Conclusions:

    • Privacy preservation can lead to fairness and equality of opportunity.
    • The proposed method effectively protects sensitive attributes in learned representations.
    • This approach offers a viable solution for privacy-conscious machine learning applications.