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Training Program for Interactionist Cognitive Neuroscience (ICoN)

互动认知神经科学培训计划 (ICoN)

基本信息

批准号：
10624876
负责人：
David Badre
金额：
$ 24.47万
依托单位：
BROWN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10624876
关键词：
Training Programs cognitive neuroscience

项目摘要

Our training program for Interactionist Cognitive Neuroscience (ICoN) seeks to provide student-focused, interdisciplinary training in computational cognitive neuroscience that integrates data from multiple scales and levels of analysis. Transformative gains in understanding the human brain and mental health require integration across multiple levels of analysis. Recent historic advances in genetics and cellular biology are paving the way for understanding fundamentals of neural function. At the other end of the spectrum, methods for imaging and stimulating human brains non-invasively have led to revolutionary advances in discovering the macro-scale organization supporting perception, motivation, and cognition. Now, a major effort at the `systems' level between these two scales is beginning to uncover the activity, connectivity, and computations of neural circuits. The advent of this systems-level progress holds the promise of linking core circuit computations to emergent human behavior and leading to detailed, transdiagnostic models of mental illness. However, as we recently argued (Badre, Frank and Moore, 2015 Neuron), fulfilling this promise requires making direct links between circuit-level computation and the emergent function of the human system. We believe that integrating systems- and human neuroscience in this way demands a systematic approach built on two key strategies. First, formal computational models must be used to provide principled links between levels of analysis; and, second, complementary methods must be applied, and in the ideal case parallel human and non-human studies conducted in coordination. Achieving these aims requires a new generation of scientists that can take full advantage of multiple techniques and data sources, and who are deeply versed in computational theory. Traditional neuroscience training relies on an apprenticeship model that limits students to a single lab and level of inquiry. Thus, a specialized training program is required to specifically equip neuroscientists for this `Interactionist' approach. ICoN will provide this training emphasizing the two tenets: I. Computation is key to translating between levels. Students must be rigorously quantitatively trained in formal theory. A close corollary is that they must be fluent in the advanced analysis methods necessary for cross-level integration (e.g., machine learning). II. Next-generation scholars must have expertise at multiple levels. Students must be trained to use and integrate multiple methods and data sources. Further, they must have the skills (and courage) to pursue ideas to their next most logical step, to be question driven and not technique limited. Students will be trained to conduct integrative research projects across domains such as human cognitive neuroscience, systems neuroscience, and computational neuroscience.

我们的互动认知神经科学（ICoN）培训计划旨在提供以学生为中心，

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Head-mounted mobile eye-tracking in the domestic dog: A new method.

DOI：
10.3758/s13428-022-01907-3
发表时间：
2023-06
期刊：
BEHAVIOR RESEARCH METHODS
影响因子：
5.4
作者：
Pelgrim, Madeline H.;Espinosa, Julia;Buchsbaum, Daphna
通讯作者：
Buchsbaum, Daphna

Feature Integration in Motor Sequences: Implications for Abstract Task Sequence Studies.

运动序列中的特征集成：对抽象任务序列研究的影响。

DOI：
10.1523/jneurosci.0638-23.2023
发表时间：
2023
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
0
作者：
Doyle,Hannah
通讯作者：
Doyle,Hannah

Make or break: The influence of expected challenges and rewards on the motivation and experience associated with cognitive effort exertion.

成败：预期挑战和奖励对与认知努力发挥相关的动机和体验的影响。