神経ネットワーク推定器による認知モデルの潜在変数配列識別
Ti-Fen Pan1, Jing-Jing Li2, Bill Thompson3
1Department of Psychology, University of California, Berkeley, USA. tfpan@berkeley.edu.
Behavior research methods
|August 28, 2025
まとめ
この研究は,複合的で難解な可能性を持つものであっても,認知モデルからダイナミックな潜在変数を抽出するために,再発性ニューラルネットワークを用いた新しいシミュレーションベースのアプローチを導入し,認知プロセス研究を進めています.
科学分野:
- 認知科学
- 計算神経科学
- 機械学習
背景:
- ダイナミックな認知プロセスの理解には 時間によって変化する潜在的変数を抽出することが重要です
- 現在の方法は特定の認知モデルに限られており, 難解な可能性を持つものは除外されています.
研究 の 目的:
- 潜伏変数配列を推論するための再発性ニューラルネットワーク (RNN) を使用したシミュレーションベースのアプローチを開発する.
- 難解な確率を持つ認知モデルの既存の方法の限界を克服する.
- コンピュータによる認知モデルのより広範な探求を可能にします.
主な方法:
- リキュアントニューラルネットワーク (RNN) を活用したシミュレーションベースのアプローチ.
- 実験データを潜伏変数空間に直接マッピングする.
- 訓練と検証のためのシミュレーションデータを使用します.
主要な成果:
- シミュレーションでは,確率操作可能なモデルと不操作可能なモデルの両方の潜在変数配列を推論する競争力のある性能を達成しました.
- リアルなデータセットでの応用が証明されている.
- このアプローチは個々のデータに対して実用的で,一般化可能であり,連続的/離散的な潜伏空間に適応可能である.
結論:
- 研究者が分析できる 認知モデルの範囲を広げています
- 複雑なモデルでの推論を可能にすることで,より広範な認知理論のテストを容易にする.
- RNNとシミュレートされたデータを組み合わせて,強固な潜伏変数抽出を行います.
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