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Related Concept Videos

Associative Learning01:27

Associative Learning

525
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...
525
Cognitive Learning01:21

Cognitive Learning

480
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...
480
Observational Learning01:12

Observational Learning

281
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...
281
Modeling in Therapy01:26

Modeling in Therapy

139
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
139
Operant Conditioning Intervention01:24

Operant Conditioning Intervention

104
Operant conditioning serves as a foundational principle in therapeutic interventions aimed at modifying maladaptive behaviors. Central to this approach is the notion that behaviors, both adaptive and maladaptive, are learned through reinforcement. By analyzing the environmental factors that reinforce problematic behaviors, clinicians can design interventions to weaken these reinforcements and replace maladaptive behaviors with healthier alternatives.
In operant conditioning, behaviors that are...
104
False Memories01:18

False Memories

133
False memories represent a cognitive distortion in which individuals recall events that did not happen, or remember them in an altered form. This phenomenon highlights the brain's constructive nature in processing and recalling memories, emphasizing that memory is not a perfect representation of past events but rather a dynamic reconstruction influenced by various factors.
One primary source of false memories is misattribution, where individuals incorrectly associate external information...
133

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Related Experiment Video

Updated: Aug 26, 2025

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

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Learning Transactional Behavioral Representations for Credit Card Fraud Detection.

Yu Xie, Guanjun Liu, Chungang Yan

    IEEE Transactions on Neural Networks and Learning Systems
    |October 5, 2022
    PubMed
    Summary

    This study introduces a novel model for credit card fraud detection by learning user transaction representations. The method effectively distinguishes fraudulent from legitimate behaviors, improving detection accuracy.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Credit card fraud detection is complex due to subtle fraudulent patterns within large datasets.
    • Accurate detection requires understanding evolving user transaction behaviors.

    Purpose of the Study:

    • To develop a novel model for enhanced credit card fraud detection.
    • To learn effective transaction representations capturing user behavioral changes and periodicity.

    Main Methods:

    • Proposed a model enhancing Long Short-Term Memory (LSTM) with a time-aware gate.
    • Incorporated a current-historical attention module for transactional behavior analysis.
    • Utilized an interaction module for comprehensive behavioral representation learning.

    Main Results:

    • The model demonstrated a clear distinction between legitimate and fraudulent transactions.
    • Achieved superior fraud detection performance compared to existing state-of-the-art methods.
    • Validated on large-scale real-world and public transaction datasets.

    Conclusions:

    • The learned behavioral representations are effective for accurate fraud detection.
    • The proposed model offers a significant advancement in automated credit card fraud detection systems.