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发现基金提出的计划是为了更好地理解我们如何处理动态场景，以及条件可能如何影响这种处理。我的研究计划的短期目标是：1)确定立体视觉在动态场景处理中的基础作用。2)确定增加的生理(即疲劳)负荷对动态场景处理学习功能的影响。3)研究运动负荷增加对动态场景加工学习功能的影响。4)确定在感知-认知学习和迁移方面表现优秀的人群，如专业的、高水平的业余运动员和非运动受试者之间的区别因素。5)通过脑电测量评估在这些任务的训练过程中大脑活动的变化。长期目标是：1)更好地了解用于处理与社会相关的复杂动态场景的不同元素，如行人-人群运动或体育活动。2)确定使用隔离动力学的训练程序(例如3D-MOT)是否优于其他类型的学习，例如用于处理动态复杂场景的视频游戏训练。3)建立从培训计划到与日常活动相关的更一般能力的可转换性程度。4)比较不同的精英表现人群，以确定在精英运动员学习方面的差异是否也可以在其他人群中观察到，如空军飞行员、职业司机或任何必须利用来自多个同时来源的信息快速做出决定的人，以试图评估与专业知识相关的需求所涉及的基本机制是否在更广泛的人类活动中是共同的5)发展知识，这也将导致新的和改进的方法以及与各种合作伙伴的许可，以便我们的总部在学术界内外有更多的就业机会。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 discovery grant is to build a better understanding of how we process dynamic scenes and how conditions may affect this processing. The short-term objectives of my research program are: 1) To determine the fundamental role of stereoscopy on dynamic scene processing. 2) To determine the impact of increased physiological (i.e. fatigue) load on the learning functions for dynamic scene processing. 3) To determine the impact of increased motor load on the learning functions for dynamic scene processing. 4) To determine what are the distinguishing factors between elite-performance populations, such as professional, high-level amateur athletes, and non-athletic subjects for perceptual-cognitive learning and transfer. 5) To assess the changes in brain activity as a function of training on these tasks with EEG measures. Long-term objectives are: 1) Build a better understanding of the different elements used for processing socially relevant complex dynamic scenes such as pedestrian-crowd movements or sports activities. 2) Establish whether training programs using isolated dynamics such as 3D-MOT are superior to other types of learning such as video gaming training for processing dynamic complex scenes. 3) Establish the extent of transferability from training programs to more general abilities related to daily-activities. 4) Compare different elite-performance populations to determine whether the differences seen in elite-athletes learning can also be observed for other populations such as air force pilots, professional drivers or anyone that has to make rapid decisions with information coming from multiple simultaneous sources in an attempt to assess whether the fundamental mechanisms involved in expertise-related demands are common across a broader spectrum of human activities 5) Develop knowledge that will also lead to new and improved methodologies and licensing with a variety of partners to allow further job opportunities to our HQP within and outside of academia. 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.