Computational and neural mechanisms of human safety decisions

该奖项是作为NSF的社会，行为和经济科学（SBE）博士后研究奖学金（SPRF）计划的一部分提供的，并得到了SBE的决策，风险和管理科学（DRMS）和认知神经科学（CogNeuro）计划的支持。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。在加州理工学院的Dean Mobbs博士的赞助下，该博士后奖学金支持一位研究人类安全决策机制的早期职业科学家。每天，人类都在参与复杂的决策过程，通过获得保护来促进生存。保护的获取从简单的决定（如服用维生素以抵御疾病）到更复杂的行为（如建立和训练军队以抵御未来的威胁）。尽管观察到许多不同的个人安全决策的例子，科学家们还没有确定促进保护获取的认知和神经系统。了解人类如何实现保护自己的目标是很重要的，因为获得保护使我们能够保持安全，并花费资源从事其他重要的追求，如创造力和合作。这项研究的目的是为大脑如何支持自适应安全决策的统一模型做出贡献。此外，更好地了解人类如何实现安全有可能改善对心理障碍的治疗，包括焦虑，其特征是无法识别安全。这一建议将为决策理论做出重要的基础科学贡献，并将为未来在面临威胁时促进健康决策的努力提供信息。该提案将研究支持自适应安全获取、奖励获取和威胁避免的计算决策控制系统。这项工作将实现三个主要目标：（1）定义支持安全决策的计算机制，（2）识别支持安全获取的神经回路，（3）将安全获取的神经基质与经典条件反射进行比较。在第（1）部分中，该提案将确定安全决策与自反性无模型学习和更有效的目标导向的基于模型的学习相关联的程度。通过比较从积极奖励到消极威胁的效价谱中的决策，本研究将测试动机如何影响决策控制系统的使用。在第（2）部分中，学习的计算模型将与神经成像相结合，以促进对大脑如何支持自适应安全决策的理解。识别参与安全决策的神经回路是治疗以适应不良的安全决策为特征的焦虑症的必要前提。在第三部分中，将安全获取决策与经典条件下无决策的安全学习进行比较，为建立一个全面的安全处理模型奠定基础。通过结合这些方法，这项研究有可能确定重叠和不同的神经系统参与识别安全和主动决策，以获得safety.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

This award was provided as part of NSF's Social, Behavioral and Economic Sciences (SBE) Postdoctoral Research Fellowships (SPRF) program with support from SBE's Decision, Risk, and Management Sciences (DRMS) and Cognitive Neurosciences (CogNeuro) programs. 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. Under the sponsorship of Dr. Dean Mobbs at California Institute of Technology, this postdoctoral fellowship award supports an early career scientist investigating the mechanisms of human safety decisions. Every day humans engage in complex decision processes that promote survival by acquiring protection. Protection acquisition varies from simple decisions such as taking vitamins to ward off illness, to more complex behaviors such as building and training armies to defend against future threats. Despite observing numerous and diverse examples of safety decisions across individuals, scientists have not identified the cognitive and neural systems that promote protection acquisition. It is important to understand how humans achieve the goal of protecting ourselves because acquiring protection allows us to maintain safety and spend resources to engage in other important pursuits like creativity and cooperation. The purpose of this research is to contribute to a unified model of how the brain supports adaptive safety decisions. Additionally, a better understanding of how humans achieve safety has the potential to improve treatments for psychological disorders, including anxiety, which is characterized by an inability to recognize safety. This proposal will make important basic science contributions to theories of decision making and will inform future efforts to promote healthy decision making in the face of threat. This proposal will examine the computational decision control systems that support adaptive safety acquisition, reward acquisition, and threat avoidance. This work will achieve three main aims (1) define computational mechanisms underpinning safety decision, (2) identify neural circuitry supporting safety acquisition, (3) compare neural substrates to safety acquisition to classical conditioning. In part (1), this proposal will identify the extent to which safety decisions are associated with reflexive model-free learning and more effortful goal-directed model-based learning. By comparing decisions across the valence spectrum from positive reward to negative threat, this research will test how motivation shapes use of decision control systems. In part (2), computational models of learning will be paired with neuroimaging to advance understanding of how the brain supports adaptive safety decisions. Identifying neurocircuitry involved in safety decisions is a necessary precursor to treating anxiety disorders characterized by maladaptive safety decisions. In part (3), safety acquisition decisions will be compared with safety learning in the absence of decision making via classical condition to lay the groundwork for a comprehensive model of safety processing. By combining these methods, this research has the potential to identify overlapping and distinct neural systems involved in recognizing safety and making proactive decisions to acquire safety.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.