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Related Concept Videos

Introduction to Psychological Disorders01:19

Introduction to Psychological Disorders

606
Abnormal behavior, often referred to as mental illness, results from changes in brain function that influence thought patterns, behaviors, and social interactions. Psychologists and psychiatrists typically assess abnormal behavior using three primary criteria: deviance, maladaptation, and personal distress, particularly when these traits persist over long periods.
Deviant Behavior
Deviance in behavior refers to actions or thought patterns that significantly diverge from societal norms or...
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Deindividuation00:57

Deindividuation

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Deindividuation is a form of social influence on an individual’s behavior such that the individual engages in unusual or non-normal behavior while in a group setting. Why? Because in these group settings, the individual no longer sees themselves as an individual anymore, disinhibiting their behavior and personal restraint.
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Positive Symptoms of Schizophrenia: Hallucinations and Delusions01:30

Positive Symptoms of Schizophrenia: Hallucinations and Delusions

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Schizophrenia is a complex mental health disorder that can manifest with various positive symptoms, including thought, movement, and behavior disorders. These symptoms significantly disrupt cognitive and motor functions, leading to profound effects on an individual's ability to engage with the world.
Thought Disorders
Disorganized and unusual thought processes mark thought disorders in schizophrenia. One key feature is disorganized speech, where an individual's conversation includes...
238
Behavior Modification01:21

Behavior Modification

299
Behavioral approaches have often been criticized for ignoring mental processes and focusing solely on observable behavior. However, these approaches provide an optimistic perspective for individuals seeking to change their behaviors. Rather than concentrating on intrinsic personality traits, behavioral approaches suggest that even longstanding habits can be modified by changing the reward contingencies that maintain them.
A real-world application of operant conditioning principles is applied...
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Modeling in Therapy01:26

Modeling in Therapy

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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...
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Theoretical Approaches to Psychological Disorder01:29

Theoretical Approaches to Psychological Disorder

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The development of psychological disorders, which are characterized by deviant, maladaptive, and personally distressing behaviors, has been explored through several theoretical approaches.
Biological approach
The biological approach posits that internal, organic factors are the primary causes of such disorders. This perspective emphasizes brain structure and function, genetic predispositions, and neurotransmitter imbalances. For example, schizophrenia has been associated with both genetic...
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Related Experiment Video

Updated: Oct 15, 2025

Decoding Natural Behavior from Neuroethological Embedding
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An End-to-End Human Abnormal Behavior Recognition Framework for Crowds With Mentally Disordered Individuals.

Yixue Hao, Zaiyang Tang, Bander Alzahrani

    IEEE Journal of Biomedical and Health Informatics
    |October 26, 2021
    PubMed
    Summary
    This summary is machine-generated.

    Detecting abnormal behavior in individuals with mental disorders is crucial. This study introduces a novel framework using Graph Convolutional Networks (GCN) and 3D Convolutional Neural Networks (3DCNN) for improved detection accuracy.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Behavioral Science

    Background:

    • Abnormal and violent behaviors by individuals with mental disorders pose risks in public spaces.
    • Current motion recognition technologies face challenges in accurately detecting abnormal human behavior, particularly in this population.
    • Visual surveillance systems are essential for monitoring but require advanced detection capabilities.

    Purpose of the Study:

    • To propose an end-to-end framework for detecting abnormal behavior using Graph Convolutional Networks (GCN) and 3D Convolutional Neural Networks (3DCNN).
    • To enhance the accuracy of abnormal behavior detection by modeling clip similarities and correcting noisy labels.
    • To specifically improve the detection of violent behaviors in individuals with mental disorders.

    Main Methods:

    • A one-class classifier was trained for feature extraction and abnormality score estimation.
    • Graph Convolutional Network (GCN) was employed to model video clip similarities and refine labels.
    • 3D Convolutional Neural Network (3DCNN) was utilized for spatiotemporal feature extraction and behavior classification.

    Main Results:

    • The proposed framework demonstrated improved performance in detecting abnormal behaviors.
    • GCN effectively corrected noisy labels by modeling similarities between video clips.
    • On the UCF-Crime dataset, the classification accuracy reached 37.9%, surpassing existing state-of-the-art methods.

    Conclusions:

    • The integrated GCN and 3DCNN framework offers a promising approach for abnormal behavior detection.
    • This method shows significant potential for enhancing public safety through improved surveillance.
    • Further research can build upon this framework for more nuanced detection of specific abnormal behaviors.