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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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Observational Learning01:12

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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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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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Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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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.
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Avoidance Learning and Learned Helplessness01:14

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Related Experiment Video

Updated: Jan 16, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

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Reliable Active Learning via Influence Functions.

Meng Xia1, Ricardo Henao2

  • 1Department of Electrical & Computer Engineering, University of Duke.

Transactions on Machine Learning Research
|October 2, 2025
PubMed
Summary
This summary is machine-generated.

Insufficient labeled data hinders deep learning (DL) model performance. This study introduces a reliable active learning (AL) framework using influence functions to efficiently select valuable data, improving model accuracy and overcoming AL unreliability.

Related Experiment Videos

Last Updated: Jan 16, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.0K

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computer Science

Background:

  • High costs and time requirements for labeled data collection pose challenges for deep learning (DL) model development.
  • Insufficient labeled data negatively impacts DL model performance in real-world applications.
  • Existing active learning (AL) algorithms demonstrate unreliable performance for DL architectures, sometimes performing worse than random selection.

Purpose of the Study:

  • To address the unreliability of current active learning algorithms in deep learning.
  • To propose a theoretically motivated active learning framework for deep learning architectures.
  • To enhance the efficiency and effectiveness of data selection in active learning.

Main Methods:

  • Proposed a novel active learning framework for deep learning architectures.
  • Utilized influence functions, pseudo-labels, and diversity selection to estimate sample value.
  • Focused on selecting samples that improve overall model performance on the entire dataset, including unlabeled data.

Main Results:

  • The proposed framework, Reliable Active Learning via Influence Functions (RALIF), consistently outperforms random selection.
  • RALIF demonstrates superior performance compared to other existing and state-of-the-art active learning approaches.
  • The method efficiently estimates the performance impact of unlabeled data samples.

Conclusions:

  • The proposed RALIF framework offers a reliable and effective solution for active learning in deep learning.
  • This approach mitigates the performance unreliability issues observed in previous active learning methods.
  • RALIF provides a practical method to reduce the need for extensive labeled datasets in deep learning.