Experience-based and intentional suppression of distracting information

基于经验和有意抑制分散注意力的信息

• 批准号: 10717468
• 负责人: Viola S. Stoermer
• 金额: $ 40.88万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-11 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10717468
• 关键词: Address; Appearance; Attention; Attention deficit hyperactivity disorder; Automobile Driving; Behavior; Behavioral; Bypass; Clinical; Clutterings; Cognition; Cognition Disorders; Computer Models; Cues; Development; Diagnosis; Dissociation; Electrophysiology (science); Environment; Event-Related Potentials; Goals; Grant; Human; Impairment; Individual; Intervention; Knowledge; Learning; Link; Measures; Mental Depression; Mental disorders; Methods; Modeling; Outcome; Participant; Perception; Perceptual Disorders; Performance; Persons; Population; Predictive Factor; Process; Psychophysics; Research; Resource Allocation; Resources; Schizophrenia; Sensory; Series; Shapes; Speed; Structure; System; Testing; Time; Translating; Variant; Visual evoked cortical potential; Work; Workplace; attentional control; brain electrical activity; cognitive control; diagnostic tool; distraction; experience; experimental study; information processing; insight; neural; neuromechanism; novel; response; selective attention; sensory input; sensory system; theories; visual search

Project Summary Human sensory systems cannot process all available inputs in a structured and meaningful way. Thus, selecting relevant information and filtering out irrelevant and distracting information is critical to survive and thrive. Decades of research on selective attention have investigated the neural mechanisms underlying the ability to focus processing resources on relevant information, demonstrating that processing of information within the focus of attention is enhanced. Much less clear is how task-irrelevant and distracting information is effectively ignored, albeit major theories of attention proposing that efficient filtering of irrelevant information is essential for many aspects of higher-level cognition. Thus, there is a critical need to identify the mechanisms that support the effective ignoring of distracting information. Without such knowledge, models of attention are incomplete, and it will remain difficult to help people avoid distractions in everyday lives. This proposal aims to identify the cortical processes involved in effective distractor suppression, focusing on two modes of attention: Experience-based attention, where based on statistical regularities in the environment processing resources are biased towards or away from relevant or irrelevant information, respectively, and volitional attention, where processing resources are allocated towards relevant or withdrawn from irrelevant information based on an individual’s intentions and explicit task goals. Recent theories of cortical information processing indicate the importance of dissociating between these two types of attention because they each influence information processing in distinct ways. Here, we test the hypothesis that experience-based attention induced via statistical regularities will be more effective relative to volitional attention when ignoring distracting information. Our approach will combine psychophysics, electrophysiological methods (EEG) and computational modeling to determine how experience and intentions influence the temporal dynamics of cortical information processing and how they shape the quality of the perceptual representations of to-be-ignored inputs. Critically, these neural measures will be directly linked to behavioral performance with the goal to identify the neural mechanisms responsible for successful distractor ignoring. Collectively, this work will provide key insights into how different modes of attentional control processes interact to shape perception and behavior, and will more broadly test general models of attention and cognitive control. Furthermore, the results of this proposal have the potential to help support people’s abilities to reduce distraction in everyday tasks, such as driving and at the workplace, and elucidate on why certain populations have particular difficulties in avoiding distractions, thereby enabling more targeted diagnoses and interventions in clinical settings.

项目概要 人类感觉系统无法以结构化且有意义的方式处理所有可用的输入。因此，选择 相关信息并过滤掉不相关和分散注意力的信息对于生存和发展至关重要。 数十年的选择性注意研究已经调查了选择性注意能力背后的神经机制 将处理资源集中在相关信息上，证明信息处理在 注意力集中度得到增强。不太清楚的是，与任务无关和分散注意力的信息如何有效地发挥作用 尽管主要的注意力理论提出有效过滤不相关信息对于 高级认知的许多方面。因此，迫切需要确定支持的机制 有效地忽略分散注意力的信息。如果没有这些知识，注意力模型就是不完整的，并且 帮助人们避免日常生活中的干扰仍然很困难。该提案旨在确定皮质 有效抑制干扰因素的过程，重点关注两种注意力模式： 基于经验 注意，根据环境处理资源的统计规律偏向或 分别远离相关或不相关的信息和意志注意，其中处理资源 根据个人的意图分配给相关信息或从不相关信息中撤回 明确的任务目标。最近的皮质信息处理理论表明了分离的重要性 这两种类型的注意力之间存在差异，因为它们各自以不同的方式影响信息处理。 在这里，我们测试了这样一个假设：通过统计规律引起的基于经验的注意力将是 当忽略分散注意力的信息时，相对于意志注意力更有效。我们的方法将结合 心理物理学、电生理学方法 (EEG) 和计算模型来确定体验如何 和意图影响皮质信息处理的时间动态以及它们如何塑造质量 被忽略的输入的感知表征。至关重要的是，这些神经测量将直接相关 行为表现，目标是确定成功分散注意力的神经机制 忽略。总的来说，这项工作将为不同模式的注意力控制过程提供重要的见解 相互作用以塑造感知和行为，并将更广泛地测试注意力和认知的一般模型 控制。此外，该提案的结果有可能帮助支持人们减少 日常任务（例如驾驶和工作场所）中的分心，并阐明为什么某些人群 特别难以避免分心，从而实现更有针对性的诊断和干预 在临床环境中。