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Related Concept Videos

Concepts and Prototypes01:24

Concepts and Prototypes

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The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
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Introduction to Cognitive Psychology01:20

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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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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
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The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Cognitive psychology emerged as a significant field in the mid-20th century. It focused on understanding humans' internal mental processes. This approach emphasizes how people perceive, remember, think, and solve problems—elements critical to human cognition.
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Human-like cognitive generalization for large models via mental representation-guided supervision.

Jiaxuan Chen1,2, Yu Qi3,4, Yueming Wang1

  • 1College of Computer Science and Technology, Zhejiang University, Hangzhou, China.

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Summary
This summary is machine-generated.

This study introduces mental representation-guided supervised learning to enhance deep neural networks (DNNs). This method uses brain signals to improve DNNs

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Neuroscience

Background:

  • Deep neural networks (DNNs) show progress in perception and language tasks.
  • Scaling DNNs improves capabilities but struggles with abstract reasoning and novel scenarios.
  • Human cognition involves complex abilities like abstract concept understanding and adaptation.

Purpose of the Study:

  • To enhance deep neural networks' cognitive abilities using human conceptual structures.
  • To improve DNN comprehension of abstract and unseen concepts.
  • To augment complex cognitive functions in artificial systems.

Main Methods:

  • Utilized mental representation-guided supervised learning.
  • Employed a small set of brain signals to transfer human conceptual structures.
  • Applied the method to large-scale language models.

Main Results:

  • Significantly enhanced DNN comprehension of abstract and unseen concepts.
  • Achieved substantial performance gains in few-shot/zero-shot learning and out-of-distribution recognition.
  • Yielded highly interpretable concept representations.

Conclusions:

  • Mental representation-guided supervision effectively augments complex cognitive abilities in DNNs.
  • This approach offers a pathway toward more human-like artificial cognitive systems.
  • Brain signal-guided learning enhances DNNs' adaptability and interpretability.