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Related Concept Videos

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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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
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Crossing the Cleft: Communication Challenges Between Neuroscience and Artificial Intelligence.

Frances S Chance1, James B Aimone1, Srideep S Musuvathy1

  • 1Department of Cognitive and Emerging Computing, Sandia National Laboratories, Albuquerque, NM, United States.

Frontiers in Computational Neuroscience
|June 2, 2020
PubMed
Summary
This summary is machine-generated.

Neuroscience and artificial intelligence (AI) share historical links, but cultural differences hinder synergy. Addressing divergent priorities can unlock new AI advancements from neuroscience data.

Keywords:
artificial intelligenceartificial neural networkdeep learningneural-inspired algorithmsneuromorphic

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Area of Science:

  • Neuroscience and Artificial Intelligence (AI)

Background:

  • Historically, AI was influenced by neuroscience, exemplified by the perceptron model's impact on artificial neural networks.
  • Current AI trends, like reinforcement learning, align more with cognitive neuroscience and psychology, focusing on abstract functional levels.
  • Neuroscience is entering an era of large-scale data, focusing on neural mechanisms, which may seem distant from AI's functional focus.

Purpose of the Study:

  • To discuss cultural differences and divergent priorities between neuroscience and AI.
  • To identify challenges in leveraging modern neuroscience for AI development.
  • To propose cultural shifts to enhance synergy between the fields.

Main Methods:

  • Comparative analysis of historical and current trends in neuroscience and AI.
  • Discussion of field-specific priorities and application requirements.
  • Identification of cultural differences impacting interdisciplinary collaboration.

Main Results:

  • Significant cultural differences and divergent priorities exist between neuroscience and AI.
  • Conflicting perspectives arise from the distinct applications driving each field.
  • Leveraging large-scale neuroscience data for AI requires addressing these cultural gaps.

Conclusions:

  • Directly applying modern neuroscience to AI is challenging due to field-specific cultures.
  • Small, targeted cultural shifts can significantly improve synergy between neuroscience and AI.
  • Enhanced collaboration could lead to novel AI approaches informed by deep neuroscience insights.