Critical tests of neurocognitive relationships

神经认知关系的关键测试

• 批准号: 1850849
• 负责人: Joachim Vandekerckhove
• 金额: $ 67.48万
• 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: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1850849&HistoricalAwards=false
• 关键词: Critical; tests; neurocognitive; relationships

The link between patterns of activity in the brain and human actions has been studied for a long time. Careful study of these patterns has led to great scientific and technological progress. Brain-computer interfaces and brain-controlled prosthetic limbs are two examples. These technologies depend strongly on the power to tie brain signals to (intended) actions. In this project, we use state-of-the-art methods in cognitive science to develop a precise mathematical model of the link between brain activity and human behavior. Our theory now needs to be tested with new experimental data in adverse conditions. The challenges that will test whether this model can predict the brain-behavior link in new experiments, new behaviors, and new measures of brain activity. A strong model of brain and behavior will improve our knowledge of the brain and help future research and technological development. It can also improve the accuracy of science or technology that uses brain signals, including brain-computer interfaces. The project will benefit researchers outside our lab in other ways. We will document analyses and experiments as part of a series of video lectures. We will also freely share our data, code, and methods online. This will help other researchers to verify our findings and to educate members of the general public who have an interest in cognitive neuroscience. Finally, the project will involve junior scientists who will receive training and start a career in neuroscience or cognitive science.The primary advantage of joint modeling is that it improves researchers' ability to estimate parameters of neural, cognitive, or behavioral models by using constraints imposed by one or more additional data modes. This has already allowed us to construct genuine neurocognitive models that are jointly informed by behavioral and neural data. We have developed a multimodal sequential accumulation model that makes predictions about the combination of reaction time, accuracy, and neuroelectric data, and that allows for conclusions not possible from either type of data individually. We will now test the generalizability of this model to other contexts. After first training a model on a relatively small data set, we will extrapolate its predictions to (a) new tasks by the same participants; (b) new participants in the same task; (c) new paradigms (i.e., tasks with new response modalities) by the same participants; and (d) new tasks by new participants. For a rigorous test of the linkage between the neural and behavioral data, the experiments will involve manipulations that selectively affect the cognitive components (visual preprocessing, motor preparation time, evidence accumulation) as well as corresponding human behavior (reaction times, accuracies, and choice behavior) and electrophysiological signals (ERP latencies and magnitudes and EMG muscle preparation signatures).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

大脑活动模式和人类行为之间的联系已经研究了很长时间。 对这些模式的仔细研究导致了巨大的科学和技术进步。 脑机接口和脑控假肢就是两个例子。 这些技术在很大程度上依赖于将大脑信号与（预期）动作联系起来的能力。 在这个项目中，我们使用认知科学中最先进的方法来开发大脑活动和人类行为之间联系的精确数学模型。 我们的理论现在需要在不利条件下用新的实验数据来检验。 这些挑战将测试该模型是否可以在新的实验、新的行为和新的大脑活动测量中预测大脑行为联系。 一个强大的大脑和行为模型将提高我们对大脑的认识，并有助于未来的研究和技术发展。 它还可以提高使用大脑信号的科学或技术的准确性，包括脑机接口。 该项目将以其他方式使我们实验室以外的研究人员受益。 我们将记录分析和实验作为一系列视频讲座的一部分。 我们还将在网上免费分享我们的数据、代码和方法。 这将有助于其他研究人员验证我们的发现，并教育对认知神经科学感兴趣的普通公众。 最后，该项目将涉及初级科学家，他们将接受培训并开始神经科学或认知科学的职业生涯。联合建模的主要优点是，它通过使用一个或多个附加数据模式施加的约束，提高了研究人员估计神经，认知或行为模型参数的能力。 这已经使我们能够构建真正的神经认知模型，这些模型由行为和神经数据共同提供信息。 我们已经开发了一个多模态顺序累积模型，该模型可以预测反应时间，准确性和神经电数据的组合，并且可以从单独的任何类型的数据中得出不可能的结论。 我们现在将测试这个模型在其他环境中的可推广性。 首先在相对较小的数据集上训练模型后，我们将其预测外推到（a）相同参与者的新任务;（B）相同任务中的新参与者;（c）新范式（即，具有新响应模式的任务）;以及（d）新参与者的新任务。 为了对神经和行为数据之间的联系进行严格的测试，实验将涉及选择性地影响认知成分的操作（视觉预处理、运动准备时间、证据积累）以及相应的人类行为（反应时间、准确度、和选择行为）和电生理信号（ERP潜伏期和幅度以及EMG肌肉准备特征）该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.

项目成果

A cognitive modeling analysis of risk in sequential choice tasks

顺序选择任务中风险的认知模型分析

• 发表时间: 2020
• 期刊: Judgment and decision making
• 影响因子: 2.5
• 作者: Maime Guan, Ryan Stokes

Parsing memory and nonmemory contributions to age-related declines in mnemonic discrimination performance: a hierarchical Bayesian diffusion decision modeling approach.

解析记忆和非记忆对助记辨别性能与年龄相关的下降的贡献：分层贝叶斯扩散决策建模方法。

• DOI: 10.1101/lm.053838.123
• 发表时间: 2023
• 期刊: Learning & memory (Cold Spring Harbor, N.Y.)
• 作者: Chwiesko,Caroline;Janecek,John;Doering,Stephanie;Hollearn,Martina;McMillan,Liv;Vandekerckhove,Joachim;Lee,MichaelD;Ratcliff,Roger;Yassa,MichaelA
• 通讯作者: Yassa,MichaelA

The case for formal methodology in scientific reform.

• DOI: 10.1098/rsos.200805
• 发表时间: 2021-03-31
• 期刊: Royal Society open science
• 影响因子: 3.5
• 作者: Devezer B;Navarro DJ;Vandekerckhove J;Ozge Buzbas E
• 通讯作者: Ozge Buzbas E

Composing Graphical Models with Generative Adversarial Networks for EEG Signal Modeling

使用生成对抗网络构建图形模型进行脑电图信号建模

• DOI: 10.1109/icassp43922.2022.9747783
• 发表时间: 2022
• 期刊: IEEE ICASSP
• 作者: Vo, Khuong;Vishwanath, Manoj;Srinivasan, Ramesh;Dutt, Nikil;Cao, Hung
• 通讯作者: Cao, Hung