Dynamic scene processing in humans

当有人在繁忙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，该人必须能够感知，并且必须能够避开人群中的行人。其中许多事件都是同时发生的。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。类似的类比也可以用在体育上，因为体育的情况有时甚至比上面的例子更苛刻。无论这些活动在我们的日常生活中可能有多么重要，我们仍然对如何处理这类信息知之甚少，因为它本质上是复杂的。我为我的NSERC提出的计划是为了更好地理解我们是如何处理复杂的动态刺激的，以及需要什么大脑机制来完成这些任务。我的研究计划的短期目标是：1)以三维物体跟踪任务为基准，更好地描述与我们如何处理复杂的动态刺激相关的大脑系统。2)确定在不同条件下，如何以及何时使用像阿尔法峰值频率这样的脑电注意标记来增强不同条件下的学习功能。3)将对侧延迟范式应用于MOT类型任务，以了解工作记忆在加工复杂动态刺激时的作用；4)确定闭环系统训练是否与通常的学习功能相比较，以及对元状态转换的影响。长期目标是：1)提出一个我们如何处理动态视觉场景的通用模型。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，例如行人-人群移动、体育活动或飞行的飞机。3)确定从培训计划到与日常活动相关的更一般能力的可转移性程度(远距离迁移)。建议的项目是可行的，是我之前由NSERC资助的研究的直接延续。这项计划在制作HQP培训方面非常成功，预计它将在这方面继续取得成功。

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.