Applications of statistical summary representations

统计汇总表示法的应用

• 批准号: 312436-2011
• 负责人: Oriet, Christopher
• 金额: $ 1.75万
• 依托单位: University of Regina
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=595094
• 关键词: Applications; statistical; summary; representations

Our visual environment contains a great deal of redundancy, especially where sets of similar items are concerned. For example, when searching for a parking spot in a crowded lot, we likely do not attend to and remember every car in the lot because this is not necessary for the task at hand. Instead, researchers now believe that at any one time, the brain rapidly calculates certain properties of the set that are important for understanding the meaning of the scene, and for accomplishing goals. In this example, as we are driving through the crowded parking lot watching carefully for pedestrians and other cars, it would be helpful to be able to rapidly estimate the average size of the vehicles in a portion of the lot in order to decide whether there is likely to be a space that is appropriate for our vehicle. In the proposed work, I will examine whether this rapid averaging process can be carried out when attention is unavailable, and how it might contribute to performance of everyday tasks like the one described above. In the short-term, my goal is to establish the utility of several promising indirect measures of size averaging that are less susceptible to contamination from response biases and sophisticated guessing strategies than currently-used methods. This will be accomplished by testing perception and memory for the average size of displays indirectly using reaction time measures in tasks that do not explicitly require people to estimate average size. Because size averaging is not part of the task instructions, it is unlikely that performance will reflect the use of size averaging strategies. Ultimately, I hope to integrate my work in size averaging with findings from well-established paradigms to yield a better understanding of how we use attention and perception in ordinary tasks we encounter in the real world.

我们的视觉环境包含大量冗余，尤其是在涉及相似项目集的情况下。例如，当在拥挤的停车场寻找停车位时，我们可能不会注意并记住停车场中的每辆车，因为这对于手头的任务来说不是必要的。相反，研究人员现在认为，在任何时候，大脑都会快速计算该集合的某些属性，这些属性对于理解场景的含义和实现目标非常重要。在此示例中，当我们开车穿过拥挤的停车场并仔细观察行人和其他车辆时，如果能够快速估计停车场一部分中车辆的平均大小，以便确定是否有可能是适合我们车辆的空间。在拟议的工作中，我将研究当注意力不可用时是否可以执行这种快速平均过程，以及它如何有助于执行上述日常任务。在短期内，我的目标是建立几种有前途的规模平均间接测量方法的效用，这些测量方法比目前使用的方法更不易受到响应偏差和复杂猜测策略的污染。这将通过在不明确要求人们估计平均尺寸的任务中间接使用反应时间测量来测试显示器平均尺寸的感知和记忆来实现。由于大小平均不是任务指令的一部分，因此性能不太可能反映大小平均策略的使用。最终，我希望将我在规模平均方面的工作与成熟范式的发现相结合，以便更好地理解我们如何在现实世界中遇到的普通任务中使用注意力和感知。