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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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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
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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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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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New Cognitive Deep-Learning CAPTCHA.

Nghia Dinh Trong1, Thien Ho Huong2, Vinh Truong Hoang2

  • 1Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. Listopadu 15/2172, 708 33 Ostrava, Czech Republic.

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Summary
This summary is machine-generated.

This study enhances CAPTCHA security by combining text, image, and cognitive elements with adversarial examples. The improved CAPTCHA design effectively defends against machine learning attacks while maintaining usability for humans.

Keywords:
CAPTCHAcognitivedeep learningsecurity

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Area of Science:

  • Cybersecurity
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Conventional text- and image-based CAPTCHAs are vulnerable to advanced machine learning (ML) attacks, including Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN).
  • Adversarial examples, imperceptible to humans, can deceive neural networks, compromising CAPTCHA security.
  • Existing CAPTCHA schemes struggle to keep pace with sophisticated botnets and cybercriminal activities.

Purpose of the Study:

  • To improve CAPTCHA security and usability against automated attacks.
  • To explore the effectiveness of combining different CAPTCHA modalities with advanced AI techniques.
  • To propose a novel CAPTCHA design resilient to ML-based threats.

Main Methods:

  • Integration of text-based, image-based, and cognitive CAPTCHA features.
  • Application of adversarial examples and neural style transfer techniques.
  • Rigorous usability and security assessments to evaluate the proposed CAPTCHA.

Main Results:

  • The proposed CAPTCHA demonstrates superior security performance compared to standard CAPTCHAs.
  • The enhanced CAPTCHA maintains a high level of usability for human users.
  • Effectiveness in defending against ML, CNN, and DNN-powered automated attacks was confirmed.

Conclusions:

  • Combining deep learning insights with cognitive principles significantly bolsters the security of traditional CAPTCHAs.
  • The proposed CAPTCHA design offers a promising new direction for creating robust human-interactive proofs.
  • This research highlights the potential of hybrid approaches in combating evolving cyber threats.