预测,解释和控制:在动态环境中使用心理模型
Roman Tikhonov1, Simon DeDeo1,2
1Department of Social & Decision Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.
Open mind : discoveries in cognitive science
|December 6, 2023
概括
预测,解释和控制等认知能力可能源自一个共同的心理模型. 控制系统的培训将技能转移到预测和解释,而不是相反.
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:
- 人类对预测,解释和控制的认知能力可以来自统一的表示或不同的过程.
- 研究概率思维模型在这些任务中的作用对于理解认知灵活性至关重要.
研究的目的:
- 探索个人如何利用概率思维模型来预测,解释和控制不同复杂度和不确定性的情况.
- 检查任务绩效,能力之间的相关性,以及跨任务的技能转移.
主要方法:
- 开发了一个新的实验范式,使用由有限状态机器定义的聊天机器.
- 评估参与者在预测,解释和控制任务中的表现,使用完整和隐藏的信息.
- 进行交叉培训实验,以评估技能转移和能力之间的相关性.
主要成果:
- 控制任务在复杂性方面最强大,而解释在完整信息方面最具挑战性.
- 隐藏的信息导致了绩效均等化和另一种忽视偏见.
- 交叉训练揭示了不对称性:控制训练一般化为预测和解释,而不是相反.
结论:
- 研究结果表明,心理模型与特定任务的启发式模型之间存在复杂的相互作用,特别是在部分信息的情况下.
- 基于控制的训练促进了可普遍化的认知能力,这表明心理模型具有潜在的统一作用.
- 突出了认知能力和通用心理模型获取的新研究方向.
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