解决关系不一致的两个途径
Tomer Barak1, Yonatan Loewenstein2,3
1The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, Jerusalem, Israel. tomer.barak@mail.huji.ac.il.
Scientific reports
|August 21, 2025
概括
当面对意想不到的信息时, 人类和人工神经网络 (ANN) 可能会拒绝改变他们的核心信念. 极端违规会触发不同的学习机制,
科学领域:
- 认知科学
- 计算神经科学
- 机器学习
背景情况:
- 人们常常保持先前的期望,
- 极端违反预期的行为,
研究的目的:
- 调查面对观察违规行为的预期调整背后的机制.
- 探索人工神经网络 (ANN) 在关系学习中是否表现出类似的现象.
主要方法:
- 测试受过关系式学习任务培训的ANN的适应能力.
- 分析ANN如何根据违反观测的规模调整预期.
主要成果:
- 人类的ANN表现出类似的模式:小的违规行为导致关系期望的调整.
- 在ANN中发生的重大违规行为会引发对象表示的转变,从而绕过关系适应.
- 这表明生物和人工系统之间存在统一的学习动态.
结论:
- 在重大违规事件中, 预期的稳定性是学习动态的自然结果.
- 不需要任何额外的认知机制来解释这种观察到的稳定性.
- 中间的适应步骤可能会影响先前预期的稳定性.
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