人間の認知とニューラルネットワークにおける並列のトレードオフ:イン・コンテキストとイン・ウェイト学習のダイナミックな相互作用
Jacob Russin1,2, Ellie Pavlick1, Michael J Frank2,3
1Department of Computer Science, Brown University, Providence, RI 02906.
まとめ
人間の学習には 2つのモードがあります 規則を素早く推論することと 試行錯誤をゆっくり行うことです この研究は,神経ネットワークにおけるインコンテキスト・ラーニング (ICL) が,インウェイト・ラーニング (IWL) をどのように補完するかを示し,二重学習プロセスを説明することによってそれらを統合しています.
科学分野:
- 認知科学
- 神経科学
- 人工知能
背景:
- 規則に基づく学習と ゆっくりと進んだ学習という 二元性を表しています
- 心理学の理論はこれらの学習モードに 異なるシステムを提案しています
- 主にインクリメンタルアップデートを用いるニューラルネットワークと これらの二重システムを調和させることは 困難でした
研究 の 目的:
- 神経ネットワークモデルと人間の学習の二重プロセス理論を調和させる.
- 神経ネットワークにおけるインコンテキスト学習 (ICL) が人間の学習現象とどのように関係しているかを調査する.
- 認知学習のメカニズムの統合的な視点を提供すること
主な方法:
- メタラーニングのニューラルネットワークと大型言語モデルを研究した.
- 文脈学習 (ICL) と重量学習 (IWL) の相互作用を調べました.
- カリキュラム効果,構成性,柔軟性維持のトレードオフを分析した.
主要な成果:
- ICLとIWLの相互作用が人間の様々な学習現象を自然に説明することを示した.
- ニューラルネットワークにおける新興ICLは,IWLとは異なる学習特性を提供することを示した.
- ICLは人間の作業記憶と 類似した活性化ベースのダイナミクスと 結びついていることがわかりました
結論:
- ICLとIWLの統合は,人間の認知における二重プロセス理論を理解するための統一された枠組みを提供します.
- ニューラルネットワークにおける新興ICL機能は,従来のIWLと共存することができます.
- この研究は 学習の計算モデルと 人間の認知に関する 心理学的理論を結びつけています
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