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.

项目摘要 人类的感官系统不能以结构化和有意义的方式处理所有可用的输入。因此，选择 相关信息和过滤掉无关和令人分心的信息是生存和蓬勃发展的关键。 几十年来对选择性注意的研究已经探索了潜在的神经机制 将处理资源集中在相关信息上，演示了在 关注的焦点增强了。不太清楚的是，与任务无关的、分散注意力的信息是如何有效的 被忽视，尽管主要的注意理论提出，有效地过滤无关信息对于 更高层次的认知的许多方面。因此，迫切需要确定支持 有效地忽略了令人分心的信息。没有这样的知识，注意力的模型是不完整的，而且它 仍然很难帮助人们避免在日常生活中分心。这项建议旨在确定大脑皮质 有效抑制分心物的过程，关注两种注意力模式：基于经验 注意，如果基于环境中的统计规则，加工资源偏向于或 分别远离相关或不相关的信息和意志注意，在那里加工资源 根据个人意图分配给相关信息或从无关信息中撤回 明确的任务目标。大脑皮层信息处理的最新理论表明，分离的重要性 这两种类型的注意力之间的差异，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，即通过统计规则诱导的基于经验的注意将是 当忽略分散注意力的信息时，相对于意志性的注意更有效。我们的方法将结合 心理物理学、电生理学方法(EEG)和计算建模来确定体验 意图影响大脑皮层信息处理的时间动力学，以及它们如何塑造质量 对不被忽视的输入的知觉表征。关键的是，这些神经测量将直接联系在一起 行为表现，目的是确定导致成功分心的神经机制 视而不见。总而言之，这项工作将为不同的注意力控制过程模式提供关键的见解 互动以塑造感知和行为，并将更广泛地测试注意力和认知的一般模型 控制力。此外，这项提案的结果有可能帮助支持人们减少 在日常工作中分心，如开车和在工作场所，并解释为什么某些人群 在避免分心方面有特别困难，从而能够进行更有针对性的诊断和干预 在临床环境中。