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推断心灵从感知决策中漫步的推断.

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概括
此摘要是机器生成的。

研究人员开发了一个计算模型来追踪任务期间的思维流浪 (MW). 这种方法测量了注意力逐试验的转移,而不会打断参与者,为认知状态提供了新的见解.

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科学领域:

  • 认知心理学 认知心理学
  • 神经科学是一个神经科学.
  • 计算建模 计算建模

背景情况:

  • 保持专注的注意力对于目标实现至关重要,但注意力往往由于心灵漫游 (MW) 而失效.
  • 传统的方法,如思维探测,是不常见的和破坏性的,而其他技术则假设固定的任务/非任务时间分割.
  • 需要持续的,非侵入性的方法来测量认知任务期间的MW.

研究的目的:

  • 开发和验证一个计算框架,以逐个试验的基础上推断思维漂泊.
  • 评估使用计算建模来追踪注意状态的时间变化是否可行.
  • 在不依赖破坏性思维探测的情况下,提供对MW动态的更细致的理解.

主要方法:

  • 使用随机点运动任务与操纵的证据和重复的响应偏差.
  • 一个隐藏的马尔科夫模型与通用线性模型 (GLM-HMM) 被安装到参与者的性能数据中.
  • 偶尔会使用思维探测来验证模型对内部状态的分类.

主要成果:

  • 非任务试验的特点是更高的准确性和更短的反应时间,这表明刺激处理减少,对响应偏差的依赖增加.
  • 两种状态的GLM-HMM成功地预测了在非任务期间的较低反应时间.
  • 该模型显示,在任务的过程中,思想流浪的增加与自我报告的焦点水平相关.

结论:

  • 计算建模,特别是GLM-HMM,可以有效地测量注意力状态的试验对试验变化.
  • 这种方法可以在不中断认知任务的情况下,持续评估心灵漂泊.
  • 这些发现为研究注意力和认知控制的动态开辟了新的途径.