Understanding the neural and cognitive mechanisms of attributional styles and credit assignment in depression

抑郁症是最常见的精神疾病。然而，其原因仍然知之甚少，尽管大量的研究。我提出了一种新的方法来理解抑郁症，即调查信用分配的作用-或因果归因-在抑郁症。学分分配是关于学习事件的原因时，有不同的潜在原因。我的实验将包括测量人类参与者的信用分配行为，以及使用现代脑成像技术评估潜在的神经过程。简而言之，我将研究抑郁症患者是否在正确地将积极或消极结果归因于不同来源方面存在问题-他们是否倾向于错误地认为自己是消极结果的来源？但另一方面，积极的结果是由于外部原因，如环境或其他人？他们只是在识别外部结果的来源方面有问题，还是这延伸到了他们自己的情绪，比如情绪低落？任何这些异常的信用分配过程可以解释抑郁症如何保持自己不断加强消极的看法和态度，对自我，从而导致低mood.To调查什么类型的信用分配改变了抑郁症，我提出了一套范式，每个实验测试的信用分配在抑郁症的不同方面。为此，我将使用计算精神病学方法。这意味着，我将开发模型来描述我的范例中数学定义方面的信用分配的复杂过程。例如，首先做出一个决定，期望它会导致某种结果。然后评估实际结果与您的期望相比如何。最后，把功劳归于看起来最有可能的内部或外部原因。然后，我可以评估哪些不同的方面更具体地影响抑郁症。 为了测试神经效应，我将联合收割机结合这些行为测量和使用功能性磁共振成像（fMRI）测量大脑活动。我将使用功能性磁共振成像技术来识别大脑中与学分分配的特定方面特别相关的区域。随后，我可以测试抑郁症中信用分配的缺陷如何与特定的大脑区域联系起来。在过去，认知神经科学在分离认知子过程方面特别富有成效，因为它们通常映射到非常不同的大脑区域。总而言之，我提出了一系列实验，研究抑郁症患者在有多种原因的情况下如何将功劳归于不同的结果。我将研究这个问题的神经和使用定量计算模型。因此，我的建议是试图弥合精神病学概念与认知神经科学和心理学的机械模型之间的差距。特别是，我的工作将有助于理解抑郁症发生变化的一些神经和行为机制，并尝试使用多学科方法治疗它。

Depression is the most common psychiatric illness. Yet, its causes remain poorly understood, despite considerable amount of research. I propose a novel approach to understanding depression, namely to investigate the role of credit assignment - or causal attribution - in depression. Credit assignment is about learning the causes of events when there are different potential causes. My experiments will involve measuring credit assignment behaviour in human participants, as well as assessing the underlying neural processes using modern brain imaging techniques.In a nutshell, I will examine whether in depression, patients have problems with correctly attributing positive or negative outcomes to different sources - do they have a tendency to falsely belief that they are the source of negative outcomes? But that on the other hand, positive outcomes are due to external causes, such as the environment or other people? Do they only have problems identifying the source for outcomes external to them or does this extend to their own emotions, such as low mood? Any of these aberrant credit assignment processes could explain how depression maintains itself by continuously reinforcing negative views and attitudes about the self, thus leading to low mood.To investigate what types of credit assignment are changed in depression, I propose a set of paradigms each testing experimentally a different aspect of credit assignment in depression. For this, I will use a computational psychiatry approach. This means, I will develop models describing the complex process of credit assignment in my paradigms in terms of mathematically defined aspects. For example, firstly making a decision in the expectation that it will lead to a certain outcome. Then assessing how the actual outcome compares to your expectation. And finally assigning credit to the internal or external causes that seem most likely. I can then assess which of the different aspects are more specifically affected in depression. To test the neural effects, I will combine these measurements of behaviour with measurements of brain activity using functional magnetic resonance imaging (fMRI). I will use fMRI to identify the brain areas that are particularly engaged by specific aspects of credit assignment. Subsequently, I can test how deficits in credit assignment in depression can be linked to specific brain areas. In the past, cognitive neuroscience has been particularly fruitful in dissociating cognitive sub-processes as they often map onto very distinct brain regions. To summarise, I am proposing a series of experiments looking at how patients with depression assign credit to different outcomes when there are multiple causes. I will investigate this question neurally and using quantitative computational modelling. Thus my proposal is an attempt to bridge the gap between psychiatric concepts and mechanistic models from cognitive neuroscience and psychology. In particular, my work will contribute to understanding some of the neural and behavioural mechanisms of the changes occurring in depression and attempts to treat it, using a multi-disciplinary approach.