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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.
Tolman introduced the idea that behavior is influenced by...
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Magnetic Declination01:19

Magnetic Declination

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Magnetic declination is the angle between true north, which aligns with the Earth's rotational axis, and magnetic north, which follows the direction of the Earth's magnetic field. This discrepancy exists because the magnetic poles do not coincide with the geographic poles. The value of magnetic declination depends on the observer's location on Earth and is subject to changes over time due to the dynamic nature of the Earth's magnetic field.The declination is called eastern when magnetic north...
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Cognitive Dissonance01:38

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Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
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Conservation of declining population focuses on ways of detecting, diagnosing, and halting a population decline. The approach uses methods to prevent populations from going extinct.
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On comparing the reactivity of silver and lead, it is observed that the two ionic species, Ag+ (aq) and Pb2+ (aq), show a difference in their redox reactivity towards copper: the silver ion undergoes spontaneous reduction, while the lead ion does not. This relative redox activity can be easily quantified in electrochemical cells by a property called cell potential. This property is commonly known as cell voltage in electrochemistry, and it is a measure of the energy which accompanies the charge...
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VSEPR Theory for Determination of Electron Pair Geometries
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Updated: Feb 12, 2026

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説明可能な深層学習を用いたEEGベースの認知機能低下予測の可能性を探る

Anna Josefine Grillenberger1, Nelly Shenton2, Martin Lauritzen3

  • 1Department of Health Technology, Technical University of Denmark (DTU), Kongens Lyngby, 2800, Denmark.

Computers in biology and medicine
|February 10, 2026
PubMed
まとめ

本研究では、脳波（EEG）データを用いた早期アルツハイマー病検出のための新規深層学習アルゴリズムを紹介する。これらの費用対効果の高い非侵襲的モデルは、軽度認知障害（MCI）および前臨床的認知機能低下の特定に有望である。

キーワード:
アルツハイマー病認知機能低下深層学習EEG説明可能性自己注意

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科学分野:

  • 神経科学
  • 人工知能
  • 医療診断

背景:

  • アルツハイマー病（AD）の早期検出は、効果的な治療と神経細胞損傷の予防のために不可欠です。
  • 現在のAD診断方法は、しばしば侵襲的で高価です。
  • 早期認知機能低下検出のための費用対効果の高い非侵襲的方法の必要性が存在します。

主な方法:

  • 健常者およびMCI患者からの安静時脳波（EEG）データの公開利用可能なデータセットを利用しました。
  • 自己注意メカニズムを組み込んだ2つの新しいDLアルゴリズムを開発しました。
  • 従来の畳み込みニューラルネットワーク（CNN）と比較して、MCIおよび認知機能低下を予測するモデルのパフォーマンスを評価しました。

結論:

  • 開発されたDLモデルは、MCI分類において最先端の結果を達成し、前臨床的認知機能低下の予測において進歩を示しました。
  • 本研究は、認知スコアに基づいて健常者を分類し、微妙な脳の変化を特定するために、DLアテンションモデルの使用を開拓しています。
  • 本研究の結果は、早期ADバイオマーカーの発見と、解釈可能なアテンションメカニズムを通じた医療におけるAIの検証のための新しい道を提供します。