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 人类的感觉系统无法以结构化和有意义的方式处理所有可用的输入。因此，选择 相关的信息和过滤掉无关和分散注意力的信息是生存和发展的关键。 数十年来对选择性注意力的研究已经调查了这种能力背后的神经机制 将处理资源集中在相关信息上，表明 注意力的集中得到加强。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 忽视，虽然主要的注意力理论提出，有效过滤无关信息是必不可少的， 高层次认知的许多方面。因此，迫切需要确定支持《公约》的机制， 有效地忽略分散注意力的信息。没有这些知识，注意力模型是不完整的， 仍然很难帮助人们在日常生活中避免分心。这项建议旨在识别大脑皮层 过程参与有效的干扰抑制，专注于两种模式的注意：经验为基础的 注意，根据环境中的统计数据，处理资源偏向或 远离相关或不相关的信息，分别，和意志的注意，在处理资源 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。最近的皮层信息处理理论表明， 这两种类型的注意力之间的区别，因为它们各自以不同的方式影响信息处理。 在这里，我们测试的假设，即经验为基础的注意力诱导通过统计学上的错误， 当忽略分散注意力的信息时，相对于意志注意力更有效。我们的方法将联合收割机 心理物理学，电生理学方法（EEG）和计算建模，以确定经验如何 和意图影响皮层信息处理的时间动力学以及它们如何塑造质量 被忽略的输入的感知表征。重要的是，这些神经测量将直接与 行为表现的目标，以确定神经机制负责成功的分心 无视总的来说，这项工作将提供关键的见解，如何不同的模式注意力控制过程 相互作用，以塑造感知和行为，并将更广泛地测试一般模型的注意力和认知 控制此外，该提案的结果有可能帮助支持人们减少 日常任务中的分心，如驾驶和工作场所，并阐明为什么某些人群 特别难以避免分心，从而能够进行更有针对性的诊断和干预 在临床环境中。