在认知任务中分配心理努力:在ACT-R认知架构中的动机模型
1Department of Psychology, University of Washington.
Topics in cognitive science
|November 21, 2023
概括
本研究介绍了ACT-R认知架构中的详细动机框架,整合了预期控制价值 (EVC) 理论. 这种新模型可以实现最佳的努力分配,增强认知控制研究.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 动机驱动行为,并与认知功能相互作用.
- 计算模型通常将动机作为成本效益分析.
- 预期控制价值 (EVC) 理论将认知努力,成本和回报联系在一起.
研究的目的:
- 提出一个精细的动机框架,将EVC原则整合到ACT-R认知架构中.
- 将动机表示为目标缓冲器中的标量值 (M),影响奖励 (Rt).
- 测试框架在解释最佳努力分配和认知现象方面的有效性.
主要方法:
- 在ACT-R认知架构中开发了一个新的动机机制.
- 实施的动机作为目标缓冲区中的标量值 (M).
- 使用两个计算模型测试框架:一个是抽象的,一个是增强现有的西蒙任务模型.
主要成果:
- 建议的激励框架成功地在抽象模型中复制了预期控制价值 (EVC) 预测.
- 增强的ACT-R模型展示了最佳的努力分配,并在西蒙任务中复制了已知的现象.
- 该机制在模拟动机,努力和奖励之间的相互作用方面被证明是有效的.
结论:
- 综合动机框架为理解认知控制提供了更详细,更易于计算的方法.
- 这项工作通过结合动机的细微表现来推进ACT-R架构.
- 这些发现对建模决策,基于努力的学习和以目标为导向的行为有影响.
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