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

项目摘要 积极效价和消极效价之间的区别是精神病学的核心。一个看似明确的 对许多人来说，追求奖励与努力避免惩罚之间的区别似乎很重要。 精神功能障碍的症状，并在如何划分诊断类别和 RDoC中交叉诊断结构的定义。然而，尽管在以下方面取得了重大进展， 了解奖励如何驱动学习和行动以及潜在的神经机制， 更不用说理解损失和惩罚影响行为的机制了。的确， 关于奖赏和损失的神经机制是否 完全不可分离对寻求奖励与避免损失的神经基础的研究得出了各种各样的结论。 结果，表现在关于共享与单独神经回路的不一致发现，以及令人惊讶的 在精神病人群中的结果，例如，在精神病患者中显示出奖励处理异常， 表面上看起来是由回避驱动的条件（例如强迫症和焦虑）。这使得 几乎不可能解决定义有效的衡量奖励寻求与损失的关键问题 避免分开，更不用说理解他们之间的平衡和他们与其他人的关系， 维度结构和精神病理学我们在此应对这一挑战。我们建立在一个计算的 一个框架，通过正式规定如何避免损失来解决现有结果中的不一致性 在某些情况下，在某些人-被重新定义为奖励。在这里，我们提出假设， 使用计算方法来量化和隔离这种主观的重新构建将使我们能够更好地 解开奖励寻求与损失避免的相对，隐蔽的贡献，并澄清他们的神经 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。 测量（与公开奖励和损失行为的简单测量相比）跨任务，测量和 重复测试特别是，我们处理两个具体目标。首先，我们试图比较神经和 使用计算的方法，对任务和参与者之间的奖励寻求和损失避免的行为测量 模型和功能性磁共振成像在大样本的参与者。第二，我们要检查个人 奖赏寻求和损失回避学习的差异及其与精神心理维度的关系 使用大量在线样本的免疫学。这两个目标利用两个平行和互补的 实验任务，每个测试奖励寻求，损失避免，以及在何种程度上的平衡， 两者之间的差异受到对回报或损失的基线期望的差异的影响。这些研究一起 提供了一个综合的计算框架来测试奖励寻求措施的结构效度， 避免损失，它们之间的关系，它们的相对重构的新结构，以及如何 这些结构中的个体差异在整个群体的大脑和行为中是明显的。

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.