A Neurobiologically-based Neural Network Model of Risky Decision-making

基于神经生物学的风险决策神经网络模型

基本信息

批准号：
8991712
负责人：
LYNN C MILLER
金额：
$ 44.71万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-01-05 至 2018-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8991712
关键词：
Accidents Accounting Address Adolescence Adolescent Advanced Development Alcohol abuse Behavior Behavioral Biological Biological Models Brain Characteristics Cognitive Complex Computer Analysis Computer Simulation Data Data Set Decision Making Development Drug abuse Etiology Feeling Foundations Functional disorder Future Goals Health Human Image Incentives Individual Individual Differences Injury Intervention Knowledge Learning Legal Literature Medical Modeling National Institute of Drug Abuse Neural Network Simulation Outcome Pattern Personal Satisfaction Play Preventive Intervention Process Public Health Punishment Research Resources Review Literature Rewards Risk Risk Reduction Risk-Taking Sampling Science Scientist Self-control as a personality trait Signal Transduction Structure System Testing Time Translating Validation Work adaptive learning base behavioral response computerized tools contextual factors design dynamic system economic cost effective intervention executive function experience men who have sex with men methamphetamine use methamphetamine user neural circuit neurobiological mechanism neuroimaging neuromechanism neurotransmission novel peer protective behavior relating to nervous system research study response simulation simulation game social social learning theories time use virtual young men who have sex with men

项目摘要

DESCRIPTION (provided by applicant): Risky decision-making leads to pervasive negative health outcomes (e.g., alcohol and drug abuse, risky sexual decisions, accidents). One central characteristic of such individuals (e.g., risky men who have sex with men (MSM)) is that they continue to engage in behaviors with very rewarding short-term consequences, but extremely negative long-term consequences, including medical, social and legal problems. Why do they have such difficulties making the right choices? A growing body of research suggests that motivated human decision-making is the result of a dynamic interplay among three systems: (1) a relatively automatic appetitive system, which has been called the "Impulsive System", (2) an executive control system, which has been called the "Reflective System"[11], and (3) a neural system that translates interoceptive signals into what one experiences as a feeling of desire, or urge [5,12] that may help propel individuals towards reward, and inhibit cognitive resources needed for self-control. Unfortunately, we lack a systematic understanding of how these complex neural systems interact with each other and with various social and contextual factors to produce risk-taking, when, for whom, and why. This gap impedes more rapid advancements in prevention and intervention science. Adequate computational tools could help address this critical barrier, and better advance a cumulative science, but they are currently lacking. This project aims to address this gap by developing generalizable computational tools: A validated neurobiologically based, neural network model of the interaction of these systems could transform our ability to advance theory and effective interventions. To this end, a team of social scientists, neuroscientists, and computational neuroscientists will (a) develop biologically-based computational models that leverage and integrate existing neural network models that view behavior as emergent from approach and avoid motivational structures[13,14] and, at a different level of scale, neural network models that simulate the underlying biological basis of incentive processing and learning, executive function, and decision-making [15,16]; (b) test, validate, and refine the model by predicting the neural and behavioral responses of a subsample from 180 young MSM (sexually risky, sexually risky methamphetamine users, and non-risky) from a completed NIDA imaging study on risky decision-making; (c) assess its generalizability via focused tests, and cross validate with additional NIDA data subsamples; and (d) conduct exploratory computational analyses aimed at concurrently predicting MSM's sequential neural and behavioral dynamics in a virtual date simulation over time, and using the model to explore what interventions, when, and for whom might more effectively reduce risk-taking. A deeper understanding of these neural systems and their interactions, will transform our ability to advance theory, design effective risk-reduction interventions and enhance societal health and well-being, while reducing economic costs.

描述（由申请人提供）：危险的决策会导致无处不在的负面健康结果（例如，酒精和药物滥用，危险的性决策，事故）。此类人的一个核心特征（例如，与男性发生性关系的有风险的男人（MSM））继续从事具有非常有意义的短期后果但非常负面的长期后果的行为，包括医疗，社会和法律问题。他们为什么要做出正确的选择遇到如此困难？越来越多的研究表明，动机的人类决策是三个系统之间动态相互作用的结果：（1）相对自动自动的食欲系统，被称为“冲动系统”，（2）一个被称为“反射性系统”的执行控制系统[11]和（3）一个可能会在某种程度上向某种欲望的神经系统施加了一种欲望，或者敦促某种欲望的人，或者敦促某种欲望，或者是对欲望的感觉，或者是对某种欲望的感觉。奖励，并抑制自我控制所需的认知资源。不幸的是，我们对这些复杂的神经系统如何相互相互作用以及各种社会和上下文因素，以产生冒险，何时，为谁以及原因有系统地了解。这一差距阻碍了预防和干预科学方面的进步。足够的计算工具可以帮助解决这一关键障碍，并更好地提高累积科学，但目前缺乏。该项目旨在通过开发可推广的计算工具来解决这一差距：这些系统相互作用的基于神经生物学的神经网络模型可以改变我们推进理论和有效干预措施的能力。为此，一个社会科学家，神经科学家和计算神经科学家团队将（a）开发基于生物学的计算模型，以利用和整合现有的神经网络模型，将现有的神经网络模型视为从方法中出现的行为并避免动机结构[13,14] [13,14]，并在不同级别的尺度上，在不同级别的尺度上，这些模型，以模拟生物学上的生物学上的生物学上的15.执行和学习的生物学上和学习，并进行了依赖的依据。（b）通过预测从危险的决策危险的NIDA成像研究中，从180个年轻的MSM（有性风险，有性风险的甲基苯丙胺使用者和非危险）中预测子样本的神经和行为反应，以测试，验证和完善该模型；（c）通过集中测试评估其概括性，并与其他NIDA数据子样本进行交叉验证；（d）进行探索性计算分析，旨在同时预测MSM随时间的虚拟日期模拟中MSM的顺序神经和行为动力学，并使用模型探索哪些干预措施，何时以及更有效地减少风险的干预措施。对这些神经系统及其相互作用的更深入了解，将改变我们提高理论的能力，设计有效的风险降低干预措施，增强社会健康和福祉，同时降低经济成本。