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，一种常见的焦虑症临床干预措施。最近的研究为我们提供了关于潜在神经解剖学的前所未有的细节，以及一系列用于临床条件的新型候选干预措施。然而，有两个关键的缺点，既损害了我们对厌恶学习的理论理解，也损害了临床应用。第一个是缺乏机制模型来预测在特征鲜明的实验范式之外的行为。因此，即使是很小的程序变化-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。第二个重点是威胁预测，而不是避免威胁。这意味着大多数研究都使用了无法避免的实验范式。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，回避可能比预测更相关。因此，研究回避学习及其与预测学习的关系是必要的。该提案旨在通过开发一个包含威胁预测和避免的计算学习模型来填补这两个空白。使用结合威胁预测和威胁规避测量的虚拟现实方法，我们将首先在实验范例中收集大量数据（N = 800），这些数据对底层学习系统进行诊断。这些数据将向社会公开。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7 T功能神经成像的脑干区域重要的神经信号的学习量。最后，我们将通过在未用于模型开发的新实验范式中获取数据来对所识别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开发临床干预措施。这项研究提案将提供用于威胁预测和避免的计算学习模型，以解释在看不见的学习情况下的行为。这将对我们对威胁学习的心理理解做出至关重要的贡献，从而可能成为改进学习理论的基石。作为一个强大的应用程序，它将使我们更接近临床干预措施的定量发展，并且已经有潜力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 > 1000），可供学术界用于开发计算模型，理论和测量方法。

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.