Differentiating reward seeking and loss avoidance with reference-dependent learning models

通过参考依赖学习模型区分奖励寻求和损失避免

基本信息

批准号：
10449209
负责人：
Nathaniel Douglass Daw
金额：
$ 52.66万
依托单位：
COLUMBIA UNIV NEW YORK MORNINGSIDE
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：
2019-09-10 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10449209
关键词：
Accounting Address Affect Anorexia Nervosa Anterior Anxiety Appetite Disorder Avoidance Learning Behavior Behavior Disorders Behavioral Binge Eating Brain Case-Control Studies Categories Choice Behavior Cognitive Complex Computer Models Computing Methodologies Decision Making Desire for food Diagnostic Dimensions Disease Equilibrium Evaluation Expectancy Exposure to Factor Analysis Functional Magnetic Resonance Imaging Functional disorder General Population Goals Health Heterogeneity Individual Individual Differences Insula of Reil Learning Measurement Measures Mental Health Mental disorders Modeling Moods Negative Valence Obsessive-Compulsive Disorder Outcome Participant Patient Self-Report Pattern Performance Persons Population Positive Valence Process Psychiatry Psychopathology Punishment Reaction Time Research Research Domain Criteria Rewards Role Sampling Site Symptoms System Testing Time Training Validity of Results Variant Ventral Striatum base behavior measurement brain behavior cognitive neuroscience comorbidity computational neuroscience computer framework expectation improved insight neural circuit neuromechanism psychiatric symptom relating to nervous system response reward processing symptomatology virtual

项目摘要

Project Summary The differentiation between positive and negative valence is central to psychiatry. A seemingly categorical distinction between the drive toward rewards vs. the effort to avoid punishment appears central to many symptoms of psychiatric dysfunction and is evident in both how diagnostic categories are delineated and in the definition of cross-diagnostic constructs in RDoC. However, while there has been major progress in understanding how reward drives learning and actions and the underlying neural mechanisms, there has been much less progress in understanding the mechanisms by which loss and punishment affect behavior. Indeed, there has been continued controversy about whether the neural mechanisms of reward and loss are dissociable at all. Studies of the neural bases of reward seeking vs. loss avoidance have yielded mixed results, manifested both in inconsistent findings about shared vs. separate neural circuitry, and in surprising results in psychiatric populations, for instance showing reward processing abnormalities in psychiatric conditions that appear at face value to be driven by avoidance (e.g. OCD and anxiety). This has made it virtually impossible to address the critical question of defining valid measurements for reward seeking vs. loss avoidance separately, let alone for understanding the balance between them and their relation to other dimensional constructs and psychopathology. Here we address this challenge. We build on a computational framework that resolves the inconsistency in existing results by formalizing how avoiding a loss can – in certain circumstances and in some people – be reframed as a reward. Here we advance the hypothesis that using computational methods for quantifying and isolating this subjective reframing will allow us better to disentangle the relative, covert contributions of reward seeking vs. loss avoidance, and clarify their neural underpinnings. We propose to test this hypothesis by rigorously assessing the validity of the resulting measures (compared to simpler measures of overt reward and loss behavior) across tasks, measures, and test-retest replications. In particular, we address two specific aims. First, we seek to compare neural and behavioral measures of reward seeking and loss avoidance across tasks and participants using computational models and functional MRI in a large sample of participants. Second, we seek to examine individual differences in reward seeking and loss avoidance learning and their relationship to dimensions of psychiatric symptomatology using a large online sample. Both aims make use of two parallel and complementary experimental tasks which each test reward seeking, loss avoidance, and the extent to which the balance between the two is affected by differences in baseline expectations of reward or loss. Together, these studies offer an integrative computational framework to test the construct validity of measures of reward seeking and loss avoidance, the relationship between them, the new construct of their relative reframing, and how individual differences in these constructs are manifest across the population in brain and behavior.

项目摘要正价和负价之间的区别是精神病学的核心。看似绝对的在奖励的动力与避免惩罚的努力之间的区别似乎是许多人的核心精神病功能障碍的症状，是诊断类别的描述和在 RDOC中跨诊断构建体的定义。但是，尽管取得了重大进展了解奖励如何驱动学习和行动以及潜在的神经机制，理解损失和惩罚影响行为的机制的进展要少得多。的确，关于奖励和丧失的神经机制是否是完全可以解散。对寻求奖励的神经基础与避免损失的研究混合了结果表现出了关于共享与单独神经电路的不一致的发现，以及令人惊讶的导致精神病学人群的结果，例如在精神病中显示奖励处理异常避免驱动（例如OCD和动画）驱动的面值的条件。这做到了几乎不可能解决定义有效测量的关键问题，以寻求奖励与损失分别避免，更不用说了解他们与他们与其他人之间的关系维度结构和心理病理学。在这里，我们应对这个挑战。我们以计算为基础通过格式化避免损失的方式，可以解决现有结果的不一致的框架 - 在在某些情况下，在某些人中 - 被重塑为奖励。在这里，我们提出了一个假设使用计算方法来量化和隔离此主观重塑将使我们更好解散寻求奖励与避免损失的亲戚，秘密的贡献，并确定其中立基础。我们建议通过严格评估结果的有效性来检验这一假设跨任务，措施和测试重复复制。特别是，我们解决了两个具体目标。首先，我们试图比较神经和使用计算大量参与者样本中的模型和功能性MRI。其次，我们试图检查个人寻求奖励和避免损失的学习及其与精神科维度的关系的差异使用大型在线样本的症状学。两个目标都使用两个平行和完整的每个测试奖励，避免损失以及平衡程度的实验任务两者之间受到奖励或损失的基线期望的差异的影响。在一起，这些研究提供一个集成的计算框架来测试奖励寻求奖励和避免损失，他们之间的关系，其相对重新构架的新结构以及如何这些结构中的个体差异在大脑和行为中都表现出来。