Estimation of Unidentified Cognitive Models with Physiological Data

用生理数据估计未知的认知模型

• 批准号: 1658303
• 负责人: Joachim Vandekerckhove
• 金额: $ 33.7万
• 依托单位: University of California-Irvine
• 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: 2017-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1658303&HistoricalAwards=false
• 关键词: Estimation; Unidentified; Cognitive; Models; Physiological

Understanding the role of changes in brain activity over time is extremely difficult. Moreover, relating these changes to the psychology of decision making is even more challenging, but important to understand to predict and explain behavior. In particular, brain activity can vary in relationship to the speed of a behavioral response, but these variations are difficult to measure systematically. The goal of this project is to measure brain electrical activity together with behavioral data in order to develop new methods to statistically model the relationships and to aid in detection of subtle brain changes occurring with behavior. The project will result in a new method for analyzing these complex relationships and allow for better combination of different forms of data generally. This will provide a more accurate view of the effect of experimental manipulations and treatments. All analytic procedures will be extensively documented along with the experimental data and these will be freely available online. A crucial tool in this project is the new technique of joint modeling of behavioral and physiological data. An advantage of joint modeling that has thus far been underexploited is the capacity to construct genuine neurocognitive models that are informed by both behavioral and neural data. Indeed, joint estimation opens up the possibility to construct new models whose parameters are only estimable given more than one type of information. This project will lead to the development of a multimodal sequential accumulation model that makes predictions about the combination of reaction time, accuracy, and EEG data, and that allows for conclusions not possible from either type of data individually. Specifically, the project involves experimental studies to generate data that will, in combination with the new statistical framework, allow the disentanglement of parameters of a cognitive model that cannot be estimated without the use of both neural and behavioral data. The parametric neurocognitive model will involve specific neural markers that connect behavioral parameters to EEG activity measures. The newly collected data will also provide a direct test of the classical modeling assumption that visual encoding, decision-making, and executing a motor response are sequential processes.

理解大脑活动随时间变化的作用是非常困难的。 此外，将这些变化与决策心理学联系起来更具挑战性，但对于理解预测和解释行为很重要。特别是，大脑活动可以根据行为反应的速度而变化，但这些变化很难系统地测量。 该项目的目标是测量脑电活动与行为数据，以开发新的方法来统计建模的关系，并帮助检测微妙的大脑变化发生的行为。 该项目将产生一种分析这些复杂关系的新方法，并允许更好地组合不同形式的数据。 这将为实验操作和治疗的效果提供更准确的视图。 所有分析程序将与实验数据一起沿着广泛记录，这些数据将在网上免费提供。 该项目的一个关键工具是行为和生理数据联合建模的新技术。联合建模的一个优点是，迄今为止尚未充分利用的能力，以构建真正的神经认知模型的行为和神经数据的通知。事实上，联合估计开辟了可能性，以构建新的模型，其参数只能估计给定的一种以上类型的信息。该项目将导致多模态连续累积模型的开发，该模型对反应时间，准确性和EEG数据的组合进行预测，并允许单独从任何类型的数据中得出不可能的结论。具体而言，该项目涉及实验研究，以生成数据，这些数据将与新的统计框架相结合，允许在不使用神经和行为数据的情况下无法估计的认知模型参数的解开。参数神经认知模型将涉及将行为参数连接到EEG活动测量的特定神经标记。新收集的数据还将提供对经典建模假设的直接测试，即视觉编码，决策和执行运动响应是顺序过程。

项目成果

Cortico-Brainstem Mechanisms of Biased Perceptual Decision-Making in the Context of Pain

• DOI: 10.1016/j.jpain.2021.11.006
• 发表时间: 2022-04-02
• 期刊: JOURNAL OF PAIN
• 影响因子: 4
• 作者: Wiech，Katja;Eippert，Falk;Tracey，Irene
• 通讯作者: Tracey，Irene

Individual Differences in Cortical Processing Speed Predict Cognitive Abilities: a Model-Based Cognitive Neuroscience Account

皮层处理速度的个体差异预测认知能力：基于模型的认知神经科学解释

• DOI: 10.1007/s42113-018-0021-5
• 发表时间: 2019
• 期刊: Computational Brain & Behavior
• 作者: Schubert, Anna-Lena;Nunez, Michael D.;Hagemann, Dirk;Vandekerckhove, Joachim
• 通讯作者: Vandekerckhove, Joachim

The latency of a visual evoked potential tracks the onset of decision making

视觉诱发电位的潜伏期追踪决策的开始

• DOI: 10.1016/j.neuroimage.2019.04.052
• 发表时间: 2019
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Nunez, Michael D.;Gosai, Aishwarya;Vandekerckhove, Joachim;Srinivasan, Ramesh
• 通讯作者: Srinivasan, Ramesh