Dynamic scene processing in humans

人类的动态场景处理

• 批准号: RGPIN-2022-05122
• 负责人: Faubert, Jocelyn
• 金额: $ 2.4万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=761880
• 关键词: Dynamic; scene; processing; humans

When someone crosses the street at a busy intersection this person has to simultaneously process a multitude of elements, many of which are dynamic. The person must be able to perceive when the cars are moving in a collision path and must be able to avoid pedestrians in the crowd. Many of these events are happening simultaneously. The person must process all of this information, make decisions about what is happening around and subsequently physically respond in a congruent fashion to avoid a catastrophic outcome. A similar analogy can be made for sports where situations can sometimes be even more demanding then the above example. Regardless of how critical these activities may be in our daily lives, we still know relatively little about how we process this type of information, as it is inherently complex. The program I propose for my NSERC is to build a better understanding of how we process complex dynamic stimuli and what brain mechanisms are solicited to achieve such tasks. The short-term objectives of my research program are: 1) To better characterize brain systems related to how we process complex dynamic stimuli using the 3-dimensional object tracking task as a benchmark. 2) To determine how and when the use of EEG attention markers like the alpha-peak frequency, used in a closed loop paradigm can enhance the learning functions under different conditions. 3) To adapt the Contralateral delay paradigm to an MOT-type task for understanding the role of working memory when processing complex dynamic stimuli 4) To determine whether the training with a closed-loop system compares with the usual learning functions and impacts on Meta-State transitions. Long-term objectives are: 1) To propose a general model of how we process dynamic visual scenes. 2) Build a better understanding of the different elements used for processing more realistic complex dynamic scenes such as pedestrian-crowd movements, sports activities, or flying planes. 3) Establish the extent of transferability from training programs to more general abilities related to daily activities (far transfer). The projects proposed are feasible and a direct continuation with my previous NSERC-funded research. This program has been very successful in producing HQP training and it is anticipated to continue to be successful in this regard.

当有人在忙碌的十字路口过马路时，这个人必须同时处理许多元素，其中许多是动态的。当汽车在碰撞路径上移动时，人必须能够感知，并且必须能够避开人群中的行人。这些事件中有许多是同时发生的。这个人必须处理所有这些信息，对周围发生的事情做出决定，然后以一致的方式做出身体反应，以避免灾难性的结果。一个类似的类比可以为体育的情况下，有时甚至可以更苛刻，然后上述例子。无论这些活动在我们的日常生活中多么重要，我们对如何处理此类信息仍然知之甚少，因为它本质上很复杂。 我为我的NSERC提出的计划是更好地理解我们如何处理复杂的动态刺激，以及需要什么样的大脑机制来完成这些任务。我的研究计划的短期目标是：1）更好地表征大脑系统与我们如何处理复杂的动态刺激使用三维物体跟踪任务作为基准。2)为了确定如何以及何时使用EEG注意力标记，如α峰频率，在闭环范例中使用，可以在不同条件下增强学习功能。3)将双侧延迟范式应用于MOT类型任务，以理解工作记忆在复杂动态刺激加工中的作用。4）确定闭环系统训练是否与通常的学习功能相比较，以及是否影响元状态转换。长期目标是：1）提出一个我们如何处理动态视觉场景的一般模型。2)更好地理解用于处理更逼真的复杂动态场景的不同元素，例如人群运动，体育活动或飞行飞机。3)建立从培训项目到日常活动相关的更一般能力的可转移性程度（远转移）。所提出的项目是可行的，并直接延续了我以前的NSERC资助的研究。该方案在进行人力资源质量人员培训方面非常成功，预计将继续取得成功。