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Updated: Jan 15, 2026

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经典和现代连接主义的元理论
Olivia Guest1, Andrea E Martin1
1Donders Institute for Brain, Cognition, and Behaviour, Radboud University.
Psychological review
|October 16, 2025
概括
这项研究分析了认知科学中的人工神经网络 (ANN),区分了古典和现代的连接主义. 更深入地参与ANN元理论可以防止AI"冬季"和"夏季"的周期.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
背景情况:
- 连接主义是认知科学中极具影响力的框架,塑造了当代人工智能 (AI).
- 人工神经网络 (ANN) 是人工智能的成功和挑战的核心.
- 之前的工作将ANN与认知,大脑和行为进行了比较.
研究的目的:
- 分析ANN作为科学工具和科学时代精神的仲裁者.
- 在连接主义中描述两个不同的趋势:古典和现代.
- 为了识别和解决现场矛盾的实践,由于不完整的理论参与.
主要方法:
- 超理论分析技术,包括形式逻辑.
- 基于目标,机制和科学问题的古典和现代连接主义倾向的表征.
- 分析该领域与围绕ANN的 (元) 理论的接触.
主要成果:
- 在连接主义 (古典和现代) 中发现了两种不同的趋势.
- 该领域经常未能充分追求理论论证,导致矛盾的实践.
- ANN既是科学研究的工具,也是塑造科学话语的有影响力的力量.
结论:
- 对ANN的 (元) 理论进行更深入的研究对于科学进步至关重要.
- 了解古典和现代连接主义的不同属性可以改进研究.
- 通过强大的理论基础,可以避免人工智能"冬季"和"夏季"的周期性.
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