Hierarchical control of sequential skills: Using EEG to decode the underlying representations

顺序技能的分层控制：使用脑电图解码底层表征

• 批准号: 1734264
• 负责人: Ulrich Mayr
• 金额: $ 49.87万
• 依托单位: University of Oregon Eugene
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734264&HistoricalAwards=false
• 关键词: Hierarchical; control; sequential; skills; Using

Many of the most interesting human activities result from basic building blocks of behavior being structured into appropriate sequences. For example, speaking involves the combination of individual syllables into words and sentences; while, performing music involves the combination of notes into melody. The mind must represent these activities in terms of the hierarchical structures that combine the basic elements. Deficits in the ability to implement such structures, due to neurological disorders or aging, can have wide-ranging consequences for real-world functioning. Little is currently known about the mental representations that enable these sequential skills, or about how deficits in fluent sequential performance arise from disruptions to these representations. This project develops new methods for tracking these mental representations using electrophysiological signals recorded at the scalp (EEG). These methods will allow the pinpointing of the exact sources of individual and age-related differences and provide initial hints for the origin of deficits due to disease or brain insults. The project provides training and educational opportunities in sophisticated brain-imaging and data-analytic techniques for undergraduate and graduate students and contains a science-education component for underprivileged high-school students.Cognitive scientists have long assumed that the critical, hierarchically-organized structures that support sequenced behaviors rely on a special set of mental representations that organize complex sequences into smaller parts (often referred to as chunks), or that mark the position within a chunk. Yet, because of their abstract nature, it has been very difficult to empirically track these hidden representations. The central hypothesis tested in this project is that rhythmic, electrophysiological signals contain robust information about how strongly these mental representations are activated at a given point in time. The planned experiments will examine the validity and robustness of new, EEG-based methods of tracking mental representations. Individual experiments will determine how exactly positions or chunks are represented and will test specific hypotheses about how individual or adult, age-related differences in cognitive resources (e.g., working-memory capacity) affect sequential representations. This project will equip researchers with refined theories about how sequential information is processed, and more generally with a novel set of analytic tools to characterize complex, cognitive performance.

许多最有趣的人类活动都是由行为的基本组成部分组成的，它们被组织成适当的序列。例如，说话涉及将单个音节组合成单词和句子;而演奏音乐涉及将音符组合成旋律。心智必须以将基本元素联合收割机结合起来的层级结构来表征这些活动。由于神经系统疾病或衰老，实现这种结构的能力不足，可能会对现实世界的功能产生广泛的影响。目前我们对这些序列技能的心理表征知之甚少，也不知道这些表征的中断是如何导致流畅序列表现的缺陷的。该项目开发了新的方法来跟踪这些心理表征使用记录在头皮（EEG）的电生理信号。这些方法将允许精确定位个体和年龄相关差异的确切来源，并为疾病或大脑损伤引起的缺陷的起源提供初步提示。该项目为本科生和研究生提供复杂的脑成像和数据分析技术的培训和教育机会，并包含针对贫困高中生的科学教育部分。支持有序行为的分层组织结构依赖于一组特殊的心理表征，这些心理表征将复杂的序列组织成更小的部分（通常称为块），或者标记块内的位置。然而，由于它们的抽象性质，很难从经验上追踪这些隐藏的表征。在这个项目中测试的中心假设是，有节奏的电生理信号包含了关于这些心理表征在给定时间点被激活的强度的强大信息。计划中的实验将检查新的，基于EEG的跟踪心理表征的方法的有效性和鲁棒性。个体实验将确定位置或组块是如何被准确地表示的，并将测试关于个体或成人在认知资源（例如，工作记忆容量）影响顺序表征。该项目将为研究人员提供有关序列信息如何处理的精细理论，以及更普遍的一套新颖的分析工具来表征复杂的认知表现。

项目成果

Conjunctive representations that integrate stimuli, responses, and rules are critical for action selection

• DOI: 10.1073/pnas.1922166117
• 发表时间: 2020-05-12
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Kikumoto, Atsushi;Mayr, Ulrich
• 通讯作者: Mayr, Ulrich

The Role of Conjunctive Representations in Stopping Actions

联合表征在停止动作中的作用

• DOI: 10.1177/09567976211034505
• 发表时间: 2022
• 期刊: Psychological Science
• 影响因子: 8.2
• 作者: Kikumoto, Atsushi;Sameshima, Tesufuaishin;Mayr, Ulrich
• 通讯作者: Mayr, Ulrich