How can humans adaptively use temporal regularities within their environment to help guide attention?

人类如何适应性地利用环境中的时间规律来帮助引导注意力？

• 批准号: 2760336
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2760336
• 关键词: How; humans; adaptively; use; temporal

Real-world environments are not entirely random but imbued with regularities. We can use these regularities to form predictions about future relevant events and proactively guide our behaviour. Research using static visual-search tasks, where participants search for targets amongst other, distracting items, has shown that we can learn regularities within busy visual scenes. Further, we can use these regularities to predict where targets will appear, or what they will look like, and accordingly guide our attention towards items in these locations, or with these visual features, to find targets more efficiently. However, unlike the visual-search arrays in these tasks, real-world scenes are dynamic. Thus, it is imperative to consider the effects of time, and temporal regularities, on visual search. Effects of temporal regularities on search have only recently begun to be investigated. Initial studies have done so using a dynamic visual-search task where certain targets predictably appeared in the same location and at the same time during trials. Other targets, and distractors, appeared at unpredictable locations and times. Here, participants identified spatiotemporally predictable targets significantly more often, and significantly faster, than spatiotemporally unpredictable targets. These findings suggest that participants learnt temporal regularities, here combined with spatial regularities, and used this knowledge to guide their attention towards certain locations at times when targets were expected to appear there. This initial evidence suggests that humans can use regularity-based temporal predictions as a helpful source of attentional guidance during visual search. However, we know little about the circumstances under which this can occur, or the nature of this time-dependent guidance. This project aims to provide insight into these questions. For example, I will investigate whether we can use temporal regularities to guide search when they operate independently of spatial location, and in combination with non-spatial features like colour. Further, I am interested in how participants' motor responses during search tasks may scaffold their learning and use of temporal regularities. I will investigate these questions, and others, using dynamic visual-search tasks in which targets and distractors appear at different times during trials, sometimes predictably. Depending on the question at hand, elements of these tasks, such as the predictable properties of targets (e.g., location and/or colour and/or time), or the motor responses the tasks require, will vary. I will also develop new methods to acquire and analyse time series data capturing continuous behavioural and brain measures while people perform the dynamic visual-search tasks. These measures will be more informative for understanding how exactly regularity-based temporal predictions may shape attentional guidance over time, compared to typical accuracy or response-time measures. For example, I will measure the likelihood of participants fixating, with their eyes, a particular distractor over time. I will assess how this likelihood changes in the moments before a target sharing features with that distractor predictably appears. An increase in fixation likelihood of the distractor here would reflect an increase in attentional prioritisation of target features. Here, it will be particularly interesting to see how soon before a temporally predictable target appears this increase may begin. Overall, this project aims to expand and deepen our understanding of how humans can learn and use temporal regularities in their environment to form predictions and adaptively guide their visual attention over time. This project will contribute to our understanding of how we can perform tasks efficiently and effectively in the busy and dynamic real world. Further, it will contribute to wider research investigating the previously neglected topic of timing in selective visual attention.

现实世界的环境并不完全随机，而是充满规律性。我们可以利用这些规律来预测未来相关事件并主动指导我们的行为。使用静态视觉搜索任务的研究表明，我们可以在繁忙的视觉场景中学习规律性，其中参与者在其他分散注意力的项目中搜索目标。此外，我们可以利用这些规律来预测目标将出现在哪里，或者它们会是什么样子，并相应地引导我们对这些位置中的物品的注意力，或者利用这些视觉特征，更有效地找到目标。然而，与这些任务中的视觉搜索阵列不同，现实世界的场景是动态的。因此，必须考虑时间和时间规律对视觉搜索的影响。时间规律对搜索的影响最近才开始被研究。初步研究使用动态视觉搜索任务来做到这一点，其中某些目标可预测地在试验期间同时出现在同一位置。其他目标和干扰物出现在不可预测的地点和时间。在这里，参与者比时空不可预测的目标更频繁、更快地识别时空可预测的目标。这些发现表明，参与者学习了时间规律，并与空间规律相结合，并利用这些知识在目标预计出现的时候引导他们的注意力集中到某些位置。这一初步证据表明，人类可以使用基于规律性的时间预测作为视觉搜索过程中注意力引导的有用来源。然而，我们对这种情况发生的情况或这种依赖时间的指导的性质知之甚少。该项目旨在深入了解这些问题。例如，我将研究当时间规律独立于空间位置运行并与颜色等非空间特征相结合时，我们是否可以使用时间规律来指导搜索。此外，我感兴趣的是参与者在搜索任务期间的运动反应如何支撑他们对时间规律的学习和使用。我将使用动态视觉搜索任务来研究这些问题和其他问题，其中目标和干扰物在试验期间的不同时间出现，有时是可预测的。根据当前的问题，这些任务的元素，例如目标的可预测属性（例如位置和/或颜色和/或时间）或任务所需的运动反应，将会有所不同。我还将开发新方法来获取和分析时间序列数据，捕获人们执行动态视觉搜索任务时的连续行为和大脑测量结果。与典型的准确性或响应时间测量相比，这些测量将提供更多信息，有助于理解基于规律性的时间预测如何随着时间的推移准确地塑造注意力指导。例如，我将测量参与者随着时间的推移用眼睛注视特定干扰物的可能性。我将评估在与该干扰因素共享特征的目标按预期出现之前，这种可能性如何变化。这里干扰物的注视可能性的增加将反映出目标特征的注意力优先顺序的增加。在这里，特别有趣的是看看在时间上可预测的目标出现之前多久这种增加可能会开始。总体而言，该项目旨在扩展和加深我们对人类如何学习和使用环境中的时间规律来形成预测并随着时间的推移自适应地引导他们的视觉注意力的理解。该项目将有助于我们了解如何在繁忙和动态的现实世界中高效且有效地执行任务。此外，它将有助于更广泛的研究，调查以前被忽视的选择性视觉注意的时间主题。