Developmental trajectories of emotional learning and episodic memory

该奖项是美国国家科学基金会社会、行为和经济科学博士后研究奖学金（SPRF）计划的一部分。SPRF计划的目标是为学术界、工业界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奖励包括在知名科学家的赞助下进行为期两年的培训，并鼓励博士后进行独立研究。美国国家科学基金会寻求促进科学界各阶层的科学家，包括那些未被充分代表的群体的科学家，参与其研究项目和活动；博士后阶段被认为是实现这一目标的一个重要的专业发展阶段。每个博士后必须解决各自学科领域的重要科学问题。从环境中学习和记忆重要信息的能力对于个体在一生中生存和发展至关重要。在向成年过渡的过程中，青少年在充满新体验和情感体验的动态环境中穿行。在这种情绪高度敏感的时期，情绪化的行为可能导致糟糕的决策和负面结果，包括增加精神病理和可预防的死亡的发生率。情绪学习的规范性发展变化和情景记忆的情绪促进可能有助于青少年在情绪环境中的独特行为。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。然而，对于这种变化是如何在行为和大脑中出现的，以及这些变化是如何影响情景记忆的，我们知之甚少。该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在整个发展过程中的个体差异。通过利用计算、神经成像和行为方法来更好地理解个体如何学习和记忆情绪环境结构的发展变化，本研究可能会揭示促进动机行为健康发展的神经认知过程。该研究的见解将对情感神经科学、发展神经科学和计算认知神经科学产生影响，并将这三个研究领域连接起来。本研究采用经典条件反射的形式化计算模型来考察情绪学习和情景记忆在发展过程中的个体差异。本项目将扩展并整合以下几方面的研究：1)情绪学习中个体差异的发展轨迹；2)利用条件反射计算模型预测情绪学习中神经回路的参与情况；3)情绪学习中的个体差异如何影响学习过程中特定类别项目的情景记忆。我们将实现心理生理情绪学习数据的计算建模，分析情绪学习过程中的神经活动和功能连接，并研究情绪学习如何影响整个发展过程中的情景记忆。我们假设，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。该研究项目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，为确定年龄相关变化的独特模式而持续建模年龄。这些数据也将是第一个描述人类跨发展的威胁学习计算模型的神经相关性，并将模型预测扩展到情感学习对情景记忆的影响，也是跨发展。这个项目建立在先前研究消极情绪的基础上，但我们预测研究积极情绪的结果将是相似的。这项工作将为未来使用计算建模方法研究跨发展的学习和记忆认知结构的个体差异奠定基础。这些数据可能最终有助于确定利用情绪学习和记忆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.