Developmental trajectories of emotional learning and episodic memory

该奖项是作为NSF的社会，行为和经济科学博士后研究奖学金（SPRF）计划的一部分提供的。SPRF计划的目标是为学术界，工业或私营部门和政府的科学事业准备有前途的早期职业博士级科学家。SPRF的奖励包括在知名科学家的赞助下进行两年的培训，并鼓励博士后研究员进行独立研究。NSF致力于促进来自科学界各部门的科学家，包括来自代表性不足的群体的科学家参与其研究计划和活动;博士后期间被认为是实现这一目标的专业发展的重要水平。每个博士后研究员必须解决推进各自学科领域的重要科学问题。从环境中学习和记住显著信息的能力对于个体在整个生命周期中生存和发展至关重要。在向成年过渡的过程中，青少年在充满新的和经常是情感体验的动态环境中航行。在情绪敏感度提高的这段时间里，情绪化的行为可能导致决策失误和负面结果，包括增加精神病理学和可预防死亡的发生率。情绪学习的规范发展变化和情景记忆的情绪促进作用可能有助于青少年在情绪化情境中的独特行为。大量研究经典条件反射的文献强调了刺激的情感意义是如何随着环境统计数据的变化而波动的。最近的研究表明，在动态情绪学习过程中形成的认知表征的质的差异可能会调节情绪记忆的持久性。尽管如此，人们对这种变化如何在行为和大脑中出现，以及这些变化如何影响情景记忆知之甚少。拟议的研究使用经典条件反射的计算模型来检查情绪学习和情景记忆的个体差异。通过利用计算，神经成像和行为方法来更好地了解个体如何学习和记忆情绪环境结构的发展变化，这项研究可能会揭示促进动机行为健康发展的神经认知过程。从拟议的研究的见解将有影响的情感，发展和计算认知神经科学各自独立，并为桥接三个研究领域。拟议的研究使用了一个正式的经典条件反射的计算模型，以检查在情绪学习和情节记忆的发展过程中的个体差异。该项目将通过调查扩展并整合几条研究路线：1）情绪学习中个体差异的发展轨迹，2）条件反射计算模型的实用性，用于预测情绪学习中神经回路的参与，以及3）情绪学习中的个体差异如何影响学习期间呈现的类别特定项目的情景记忆。我们将实施心理生理学情感学习数据的计算建模，分析情感学习过程中的神经活动和功能连接，并研究情感学习如何影响整个发展过程中的情景记忆。我们推测，在青春期观察到的情绪驱动行为的增加可能反映了情绪学习和记忆过程的认知结构的规范性发展变化。这项研究计划将为研究人类从童年到成年的情绪学习和潜在神经回路的稀疏文献提供重要贡献，不断建模年龄，以确定与年龄相关的变化的不同模式。这些数据也将是第一个表征人类发展过程中威胁学习计算模型的神经相关性的数据，并将模型预测扩展到情感学习对情景记忆的影响。这个项目建立在先前研究消极情绪的基础上，但我们预测结果将与研究积极情绪相似。这项工作将作为未来研究的基础，使用计算建模方法来研究个体差异的认知结构的学习和记忆的发展。这些数据最终可能有助于确定利用情绪学习和记忆中的个体差异和发展变化的方法，以帮助优化学习和促进健康发展。

This award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. The ability to learn from and remember salient information in the environment is essential for an individual to survive and thrive throughout the lifespan. During the transition to adulthood, adolescents navigate a dynamic environment filled with new and often emotional experiences. Actions in emotional situations, during this time of heightened emotional sensitivity, can result in poor decision-making and negative outcomes, including increased incidence of psychopathology and preventable death. Normative developmental changes in emotional learning and the emotional facilitation of episodic memory may contribute to adolescents' unique behavior in emotionally charged contexts. A rich literature examining classical conditioning has highlighted how the emotional significance of stimuli fluctuates as the statistics of the environment change. Recent work suggests that qualitative differences in the cognitive representations formed during dynamic emotional learning may modulate the persistence of emotional memories. Still, little is known about how this variation emerges, in both behavior and brain, across development or how these changes may influence episodic memory. The proposed research uses a computational model of classical conditioning to examine individual differences in emotional learning and episodic memory across development. By leveraging computational, neuroimaging, and behavioral approaches to better understand developmental changes in how individuals learn and remember the structure of the emotional environment, this research may shed light on the neurocognitive processes that promote healthy development of motivated behaviors. The insights from the proposed research will have implications for affective, developmental, and computational cognitive neuroscience each independently, and for bridging the three research fields. The proposed research uses a formalized computational model of classical conditioning to examine individual differences in emotional learning and episodic memory across development. This project will expand on and integrate several lines of research by investigating: 1) developmental trajectories of individual differences in emotional learning, 2) the utility of a computational model of conditioning for making predictions about engagement of neural circuitry underlying emotional learning, and 3) how individual differences in emotional learning may influence episodic memory for category-specific items presented during learning. We will implement computational modeling of psychophysiological emotional learning data, analyze neural activity and functional connectivity during emotional learning, and examine how emotional learning influences episodic memory across development. We hypothesize that the increase in emotionally driven behaviors observed during adolescence may be reflective of normative developmental changes in the cognitive structure of emotional learning and memory processes. This research program will provide an important contribution to the sparse literature examining emotional learning and underlying neural circuitry in humans from childhood to adulthood, modeling age continuously in order to determine distinct patterns of age-related change. These data will also be the first to characterize the neural correlates of a computational model of threat learning across development in humans and to extend the model predictions to the effects of emotional learning on episodic memory, also across development. This project builds on prior work examining negative emotion, but we predict results will be similar examining positive emotion. This work will serve as a foundation for future studies using computational modeling approaches to study individual differences in the cognitive structure of learning and memory across development. These data may ultimately help identify ways to take advantage of individual differences and developmental changes in emotional learning and memory to help optimize learning and bolster healthy development.