协作技能获取的综合模型:预测,控制调整和角色采用
Cvetomir M Dimov1,2, John R Anderson1, Shawn A Betts1
1Department of Psychology, Carnegie Mellon University.
Cognitive science
|July 24, 2023
概括
这项研究探讨了人们如何在没有沟通的情况下学会在动态游戏中协作. 研究结果显示,玩家自然地采取角色并预测未来的任务,这一过程是由新的认知架构建模的.
科学领域:
- 认知科学 认知科学
- 人与计算机的交互
- 人工智能的人工智能
背景情况:
- 在复杂的任务中,协作技能获取至关重要.
- 了解无协作背后的认知过程是具有挑战性的.
- 以前的模型往往缺乏整合预测和角色采用机制.
研究的目的:
- 在一个动态的,沟通有限的环境中调查协作技能获取.
- 开发和验证协作学习的综合认知模型.
- 识别参与学习协作的关键认知过程.
主要方法:
- 利用游戏合作空间堡垒进行动态任务设置.
- 在ACT-R架构中开发了一个认知模型.
- 实施强化学习用于角色采用和运动推断用于预测.
- 在ACT-R中纳入了一种新的监督学习程序.
主要成果:
- 观察到受试者在技能学习过程中发展出一致的,未经沟通的角色.
- 证明了受试者预测未来任务状态的能力.
- 该ACT-R模型成功地复制了观察到的学科技能获取轨迹.
- 该模型有效地整合了角色采用和预测过程.
结论:
- 开发的ACT-R模型是第一个协作技能获取的综合认知模型.
- 这些发现提供了对角色采用,预测和协作学习的相互作用的见解.
- 这项研究推动了我们对有效团队合作的认知基础的理解.
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