Coordination Funds

能够从嘈杂的输入中提取信息，识别它们何时发生变化并相应地更新信念，这对心理健康至关重要。在不稳定的环境中的最佳学习需要动态向上和向下调节的学习速率取决于在一个状态内的随机性和发生状态变化的概率。如果更新过程出错，这将导致对世界状态的有偏见的内部表征，最终导致适应不良的行为。在精神障碍中，当患者对自己、他人或世界的信念不再与可观察到的经验相匹配，或者无法形成足够稳定的环境表征时，偏见的内部表征就会变得明显。然而，假设精神病理学可能产生的困难，在动态信念更新（DynBU）尚未得到系统的测试。此外，DynBU背后的神经认知机制才刚刚开始解开。我们假设，环境状态的根本变化引发了一系列更高层次的惊喜，唤醒的增强和皮层网络的重置。识别影响学习率的级联的异常将告知我们对异常信念更新的起源的理解。我们还假设，从新的结果意外事件中学习的能力将在整个个体发育过程中变化，并对环境输入作出反应。能够确定DynBU的临床相关背景影响将提供对基本适应过程的更好理解，并有助于理解新兴精神病理学的发展。在九个创新项目中，该研究单位的目标是：1）解开DynBU在动荡环境中的潜在神经认知机制，2）确定DynBU的临床相关发育和环境背景影响，以及3）使用这些知识来指定DynBU中与精神病理学相关的问题。为了实现直接的跨项目比较，每个项目将使用一个共同的变点任务，采用共享的临床评估，并采取行为，计算和神经科学的方法。将通过共享数据分析，利用先进的计算模型对数据进行整合。为了实现雄心勃勃的目标，跨学科和国际化的研究单位包括13名研究人员和一名墨卡托研究员，他们在行为建模，发展，学习机制，神经过程和精神病理学方面具有所需的专业知识。对DynBU的基础机制以及塑造它的发展和环境背景的基本见解，将大大促进我们对人们如何学习和适应的理解。这将使我们能够识别引起精神病理学的异常，并且它可以为建立在对不确定性处理的新理解基础上的基于机制和发育信息的干预开辟新的途径。

Being able to extract regularities from noisy input, recognize when they change and update beliefs accordingly is crucial to mental health. Optimal learning in volatile environments requires dynamic up- and down-regulation of the learning rate depending both on the stochasticity within a state and on the probability of a state change having occurred. If the updating process goes awry this will result in biased internal representations of the state of the world that eventually give rise to maladaptive behaviour. In mental disorders, biased internal representations become apparent when patients either hold on to beliefs about themselves, others or the world that no longer match the observable experiences or fail to form sufficiently stable representations of their environment. However, the assumption that psychopathology could arise from difficulties in dynamic belief updating (DynBU) has not been systematically tested. Also, the neurocognitive mechanisms underlying DynBU are only just beginning to be unravelled. We assume that fundamental changes in the environmental state trigger a cascade of higher-level surprise, boosts of arousal and a cortical network reset. Identifying aberrancies of this cascade that affect the learning rate will inform our understanding of the origins of aberrant belief updating. We also assume that the ability to learn from new outcome contingencies will vary throughout ontogeny and in response to environmental input. Being able to pinpoint clinically relevant contextual influences on DynBU will provide a better understanding of basic adaptive processes and will help to understand the development of emerging psychopathology. In nine innovative projects, this Research Unit aims to 1) unravel underlying neurocognitive mechanisms of DynBU in volatile environments, 2) identify clinically relevant developmental and environmental contextual influences on DynBU, and 3) use this knowledge to specify problems in DynBU related to psychopathology. To enable direct cross-project comparisons, every project will use a common change-point task, employ a shared clinical assessment and take a behavioural, computational, and neuroscientific approach. Data will be integrated by advanced computational modelling via shared data-analysis. To realize the ambitious aims, the interdisciplinary and internationally oriented Research Unit includes thirteen established researchers and a Mercator fellow with the required expertise in behavioural modelling, development, learning mechanisms, neural processes, and psychopathology. The fundamental insight into mechanisms that underlie DynBU and the developmental and environmental contexts that shape it, will significantly advance our understanding of how people learn and adapt. This will enable us to identify aberrancies that give rise to psychopathology and it can open up new avenues for mechanism-based and developmentally informed intervention built on a new understanding of uncertainty processing.