Human eye movements as a model of visual prediction

人眼运动作为视觉预测的模型

• 批准号: RGPIN-2018-04987
• 负责人: Spering, Miriam
• 金额: $ 3.42万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=713469
• 关键词: Human; eye; movements; model; visual

Eye movements have an important function to stabilize gaze during self-motion, and to shift gaze to locations or objects of interest. Because of their close connection to visual perception, eye movements provide an excellent model system to answer fundamental questions about brain mechanisms underlying vision and movement. Most of our research relies on smooth pursuit eye movementsthe eyes' key response to visual-object motion. In our previous NSERC-funded research, we have characterized and compared the visual signals that drive perception and pursuit, and discovered similarities and striking dissociations between perception and action. Recently, we found that pursuit eye movements provide an important performance advantage during tasks that require prediction. Many real-world movements, such as intercepting prey or hitting a ball, involve prediction of an object's trajectory from a brief glance at its motion. The decision whether, when and where to intercept is based on the integration of current sensory object information, like a baseball's spin or speed, with past sensory experience, like the probability of a curveball. The efficacy and timescale of the mechanisms underlying the integration of current and past sensory signals are poorly understood. We will develop novel experimental paradigms and rely on a combination of technological approaches including human visual psychophysics, eye tracking, hand tracking, and computational modeling to investigate these integration mechanisms. We will manipulate sensory experience at different levels of processingfrom low-level expectations about the occurrence of a particular object motion to high-level rules and heuristics. Establishing the temporal dynamics of integrating and accumulating information during visual prediction will significantly contribute to our understanding of visual and motor control. Moreover, our experimental paradigms have high ecological validity for many natural tasks such as ball sports, and can be translated into portable applications for skill testing and training. The diverse combination of techniques and our multi-disciplinary and collaborative research approach will provide trainees with an ideal research environment.

眼动具有重要的功能，可以在自我运动过程中稳定凝视，并将目光转移到感兴趣的位置或对象。由于它们与视觉感知的紧密联系，眼动提供了一个出色的模型系统，可以回答有关视力和运动的大脑机制的基本问题。我们的大多数研究都依赖于光滑的追随眼动，眼睛对视觉对象运动的关键反应。在我们以前的NSERC资助研究中，我们表征并比较了引起感知和追求的视觉信号，并发现了感知和动作之间的相似性和引人注目的分离。 最近，我们发现追捕眼运动在需要预测的任务中提供了重要的性能优势。许多现实世界的动作，例如拦截猎物或击球，都涉及对物体的轨迹的预测，从短暂的一眼就能浏览其运动。决定是否基于当前感觉对象信息的整合，例如棒球的旋转或速度，以及过去的感官体验，例如曲线球的概率。对当前和过去感觉信号整合的机制的功效和时间尺度知之甚少。 我们将开发新颖的实验范式，并依靠技术方法的组合，包括人类视觉心理物理学，眼睛跟踪，手动跟踪和计算建模来研究这些整合机制。我们将在不同级别的处理中操纵感官体验，从低级期望，即对特定物体运动的发生到高级规则和启发式方法。 在视觉预测期间建立整合和累积信息的时间动态将极大地有助于我们对视觉和运动控制的理解。此外，我们的实验范例对于许多自然任务（例如球运动）具有很高的生态有效性，并且可以转化为可移植应用程序进行技能测试和训练。技术和我们的多学科和协作研究方法的各种组合将为受训者提供理想的研究环境。