Estimation of Unidentified Cognitive Models with Physiological Data

理解大脑活动随时间变化的作用是极其困难的。此外，将这些变化与决策心理联系起来更具挑战性，但对理解、预测和解释行为很重要。特别是，大脑活动可以随着行为反应的速度而变化，但这些变化很难系统地测量。这个项目的目标是测量脑电活动和行为数据，以开发新的方法来统计模型的关系，并帮助检测细微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观点。所有的分析过程将与实验数据一起广泛地记录下来，这些将在网上免费提供。这个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。迄今为止尚未充分利用的联合建模的一个优势是能够构建由行为和神经数据提供信息的真正的神经认知模型。事实上，联合估计开辟了构建新模型的可能性，这些模型的参数只能在给定不止一种类型的信息的情况下才可估计。该项目将导致多模态顺序积累模型的发展，该模型可以预测反应时间、准确性和脑电图数据的组合，并允许单独从任何一种类型的数据中得出不可能的结论。具体来说，该项目涉及实验研究，以生成数据，结合新的统计框架，允许解开不使用神经和行为数据就无法估计的认知模型参数的纠缠。参数化神经认知模型将涉及特定的神经标记，将行为参数与脑电图活动测量相连接。新收集的数据还将为经典建模假设提供直接测试，即视觉编码，决策和执行运动反应是连续的过程。

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.