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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.
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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Generalization, Discrimination, and Extinction01:24

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
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Another way in which a group presence can affect performance is social loafing—the exertion of less effort by a person working together with a group. Social loafing occurs when our individual performance cannot be evaluated separately from the group. Thus, group performance declines on easy tasks (Karau & Williams, 1993). Essentially individual group members loaf and let other group members pick up the slack. Because each individual’s efforts cannot be evaluated,...
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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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While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
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Updated: Sep 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning.

Teresa Salazar, Joao Gama, Helder Araujo

    IEEE Transactions on Neural Networks and Learning Systems
    |September 1, 2025
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    Summary
    This summary is machine-generated.

    This study introduces group-specific concept drift in machine learning, where fairness erodes as only some groups experience data changes. An adapted federated learning approach addresses this challenge to improve equitable AI outcomes.

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    Area of Science:

    • Machine Learning
    • Artificial Intelligence Ethics
    • Fairness in AI

    Background:

    • Ensuring group fairness in machine learning is crucial for unbiased decision-making.
    • Group fairness requires equitable outcomes across demographic groups (e.g., gender, race).
    • Existing fairness research often overlooks dynamic data changes like concept drift.

    Purpose of the Study:

    • To formalize and introduce the problem of group-specific concept drift.
    • To address the challenges of group-specific concept drift within federated learning (FL).
    • To adapt existing methods for maintaining fairness in dynamic, distributed environments.

    Main Methods:

    • Formalization of group-specific concept drift and its distributed form.
    • Adaptation of a distributed concept drift adaptation algorithm for FL.
    • Implementation of a multi-model approach with local drift detection and model clustering.

    Main Results:

    • Group-specific concept drift can decrease fairness even with stable overall accuracy.
    • Federated learning environments amplify fairness challenges due to independent client drift.
    • The adapted algorithm shows promise in tackling distributed group-specific concept drift.

    Conclusions:

    • Addressing group-specific concept drift is vital for advancing AI fairness.
    • The proposed methods offer a pathway to maintain fairness in dynamic, distributed ML systems.
    • Further research is needed to fully understand and mitigate these fairness challenges.