A cognitive-computational model of avoidance learning

学会避免威胁和寻求安全是一种基本的心理功能。它使我们能够灵活地适应不断变化的环境。这种学习过程也被用于暴露疗法，这是一种治疗焦虑症的常见临床干预手段。最近的研究为我们提供了前所未有的关于潜在神经解剖学的细节，以及一系列新的临床条件的候选干预措施。然而，有两个关键的缺点损害了我们对厌恶学习的理论理解和临床应用。首先是缺乏能够预测实验范式之外的行为的机制模型。因此，即使是很小的程序更改（在许多应用程序设置中不可避免）也会对干预的成功产生重大影响。二是侧重于威胁预测，而不是避免威胁。这意味着大多数研究使用的是实验范式，无法避免。然而，至关重要的是，生物学数据表明，至少有两个部分独立的学习系统用于预测和避免。从功能、生物学和临床的角度来看，回避可以说比预测更重要。因此，研究回避学习及其与预测学习的关系是必要的。本提案旨在通过开发包含威胁预测和避免的计算学习模型来填补这两个空白。使用虚拟现实方法，结合威胁预测和威胁避免测量，我们将首先在实验范式中收集大量数据（N = 800），用于诊断底层学习系统。这些数据将公开提供给社区。然后，我们将开发一系列可以解释这些数据的计算学习模型。为了在进一步的诊断实验中消除这些模型之间的歧义，我们将利用贝叶斯实验设计方法。由于行为数据可能是模糊的，我们将进一步为最终的学习模型提供神经证据。为此，我们将使用对学习量的神经信号重要的脑干区域的7t功能性神经成像。最后，我们将通过获取未用于模型开发的新实验范式中的数据来对已确定的学习模型进行基准测试。为了提供直接的应用潜力，这些实验的设计将着眼于最大限度地减少模型中的回避。因此，由此产生的实验范例可以用于临床干预措施的开发。这项研究计划将为威胁预测和避免提供计算学习模型，以解释在未知学习情况下的行为。这将极大地促进我们对威胁学习的心理理解，从而形成改进学习理论的基石。作为一项强大的应用，它将使我们离临床干预的定量发展更近一步，并且已经有可能提供临床程序的蓝图。最后，它将提供一个大而深的数据集（总N bbb1000），可用于学术界的计算模型，理论和测量方法的发展。

Learning to avoid threat and to seek safety is a fundamental psychological function. It allows us to flexibly adapt to ever-changing environments. This learning process is also leveraged in exposure therapy, a common clinical intervention for anxiety disorders. Recent research has provided us with unprecedented detail on the underlying neuroanatomy, and with a range of novel candidate interventions for clinical conditions. However, there are two crucial shortcomings that impair both our theoretical understanding of aversive learning, and a clinical application. The first is a dearth of mechanistic models that predict behaviour outside of well-characterised experimental paradigms. As a consequence, even small procedural changes - unavoidable in many application settings - can have a major impact on the success of an intervention. The second is a focus on threat prediction, rather than avoidance of threat. This means that most research has used experimental paradigms that don't afford avoidance. Crucially, however, biological data suggests there are at least two partly independent learning systems for prediction and avoidance. Avoidance is arguably more relevant than prediction from a functional, biological, and clinical perspective. Hence, research into avoidance learning, and its relation to prediction learning is warranted. This proposal seeks to fill both of these gaps, by developing a computational learning model that encompasses both threat prediction and avoidance. Using a virtual reality approach with combined threat prediction and threat avoidance measurement, we will first gather a large body of data (N = 800) in experimental paradigms that are diagnostic on the underlying learning systems. These data will be made publicly available for the community. We will then develop an array of computational learning models that can explain these data. To disambiguate between these models in further diagnostic experiments, we will leverage a Bayesian experimental design approach. Because behavioural data can be ambiguous, we will further provide neural evidence for the final learning model. To this end, we will use 7 T functional neuroimaging of brain stem regions important for neural signalling of learning quantities. Finally, we will benchmark the identified learning model by acquiring data in new experimental paradigms that were not used for model development. To provide direct application potential, these experiments will be designed with a view to maximising avoidance reduction in the model. Thus, the resulting experimental paradigms could be leveraged for development of clinical interventions. This research proposal will provide computational learning models for threat prediction and avoidance that explain behaviour in unseen learning situations. This will crucially contribute to our psychological understanding of threat learning, and could thus form the cornerstone of an improved learning theory. As a powerful application, it will bring us a step closer to quantitative development of clinical interventions, and already has a potential to provide the blueprint of a clinical procedure. Finally, it will furnish a big and deep data set (overall N > 1000), which can be used by the academic community for development of computational models, theory, and measurement methods.