Estimation of Unidentified Cognitive Models with Physiological Data

随着时间的推移，理解大脑活动变化的作用是极其困难的。此外，将这些变化与决策心理学联系起来更具挑战性，但了解这些变化以预测和解释行为是很重要的。特别是，大脑活动可能会随着行为反应的速度而变化，但这些变化很难系统地衡量。这个项目的目标是测量大脑的电活动和行为数据，以便开发新的方法来对关系进行统计建模，并帮助检测随着行为发生的细微大脑变化。该项目将产生一种分析这些复杂关系的新方法，并允许更好地结合不同形式的数据。这将提供对实验操作和治疗效果的更准确的看法。所有的分析程序将与实验数据一起被广泛地记录下来，这些数据将在网上免费获得。该项目的一个重要工具是行为和生理数据联合建模的新技术。到目前为止，联合建模的一个优势是能够构建真正的神经认知模型，该模型由行为和神经数据提供信息。事实上，联合估计开启了构建新模型的可能性，这些模型的参数只有在给定一种以上类型的信息时才是可估计的。该项目将导致开发一种多峰顺序累积模型，该模型对反应时间、准确度和脑电数据的组合进行预测，并允许从这两种类型的数据中单独得出不可能的结论。具体地说，该项目涉及生成数据的实验研究，这些数据将与新的统计框架结合起来，允许分离认知模型的参数，而如果不使用神经和行为数据，这些参数是无法估计的。参数神经认知模型将涉及特定的神经标记物，将行为参数与脑电活动测量联系起来。新收集的数据还将提供对经典建模假设的直接测试，该假设认为视觉编码、决策和执行运动响应是顺序过程。

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.