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.

现实世界的环境不是完全随机的，而是充满规律性的。我们可以使用这些规律性来形成有关未来相关事件的预测，并主动指导我们的行为。使用静态视觉搜索任务的研究，参与者在其他分散注意力的项目中搜索目标，这表明我们可以在繁忙的视觉场景中学习规律性。此外，我们可以使用这些规律性来预测目标的出现，或者它们的外观，并因此指导我们对这些位置或这些视觉特征的项目的关注，以更有效地找到目标。但是，与这些任务中的视觉搜索阵列不同，实际场景是动态的。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直到最近才开始研究。最初的研究是使用动态视觉搜索任务进行的，其中某些目标可以预见出现在同一位置和试验期间的同时出现。其他目标和干扰素出现在不可预测的位置和时间。在这里，参与者比时空不可预测的目标更频繁地发现了时空可预测的目标。这些发现表明，参与者在这里学习了时间规律，并结合了空间规律性，并利用这些知识来指导他们注意某些位置的注意力，有时会出现目标。最初的证据表明，人类可以在视觉搜索过程中使用基于常规性的时间预测作为注意力指导的有用来源。但是，我们对可能发生的情况或该时间依赖于指导的本质了解一无所知。该项目旨在洞悉这些问题。例如，我将调查我们是否可以使用时间规律性在空间位置独立于操作时指导搜索，并与颜色等非空间特征结合使用。此外，我对参与者在搜索任务期间的运动响应如何拖欠他们的学习和使用时间规律感兴趣。我将使用动态的视觉搜索任务进行调查，而其他问题，其中目标和干扰因素在试验期间的不同时间出现，有时可以预见。根据手头的问题，这些任务的要素，例如目标的可预测属性（例如位置和/或颜色和/或时间），或任务所需的电动机响应会有所不同。我还将开发新的方法来获取和分析时间序列数据，以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。与典型的准确性或响应时间措施相比，这些措施将更具信息性，以了解如何确切地基于规律性的时间预测随着时间的流逝而塑造注意力指导。例如，我将随着时间的流逝衡量参与者固定的可能性。我将评估这种可能性如何在目标分享特征与该干扰器的目标分享之前的时刻发生变化。这里的分心可能性的增加将反映出目标特征的注意力优先级的增加。在这里，看到这个增加的增加可能会开始，这将是特别有趣的。总体而言，该项目旨在扩大和加深我们对人类如何在环境中学习和使用时间规律的理解，以形成预测并随着时间的流逝而自适应地指导他们的视觉关注。该项目将有助于我们理解如何在繁忙而充满活力的现实世界中有效地执行任务。此外，这将有助于更广泛的研究，以调查选择性视觉关注的预时主题。