Neuro-computational mechanisms underlying the effects of anxiety and motivation on biased attentional and learning processes

结合神经成像技术的计算模型正越来越多地被应用于精神疾病的研究。然而，它们对健康个体的应用仍未被探索，这些健康个体可能代表着患上这些疾病的风险人群。在这里，我们建议揭示神经计算机制，在健康的人认知偏差，可以产生严重和衰弱的症状，通过焦虑和抑郁障碍。焦虑和抑郁是全球最常见的心理健康问题之一，据估计，英国每年要花费770亿英镑。通过将神经科学和计算建模技术结合起来，我的目标是进一步理解在健康的个人中构建认知偏见的机制，这些偏见导致这些问题的幸福。一个越来越有影响力的观点提出，大脑通过比较先前的信念和感觉证据来学习环境，目的是对未来状态产生越来越准确的预测。使用贝叶斯推理的计算模型可以更好地理解这一过程，贝叶斯推理解释了大脑如何对其环境状态进行推理。具体地说，这些模型提出，通过根据可靠性(或精确度)对感觉数据和先前信念进行加权，大脑可以节俭地解释传入的感觉信息。这一提议旨在表明，感知的预测性与我们的长期记忆、动机和情感同步运行，创造了认知偏差，可以将计算简约转化为计算病理学。因此，我将调查内在动机和焦虑等日常因素如何改变对归因于先前信念或感官数据的精确度或可靠性的估计，从而使感知和学习产生偏差。分析神经生理和行为数据，结合贝叶斯建模，将提供对这一以前未探索的非最佳推理过程的机械理解。此外，通过使用一类专门引入来评估个体差异的贝叶斯模型，我最终的目标是确定个体间精确度估计的差异如何解释不同程度的焦虑和内在动机下个体学习的差异。

Computational models in combination with neuroimaging techniques are increasingly being applied to the study of psychiatric disorders. However, their application to healthy individuals that may represent a population at risk to develop these disorders remains unexplored. Here we propose to reveal the neuro-computational mechanisms subserving cognitive biases in healthy individuals that can generate serious and debilitating symptoms through anxiety and depressive disorders. Anxiety and depression are amongst the most common mental health problems worldwide, costing the UK an estimated £77 billion a year. By combining neuroscientific and computational modelling techniques, I aim to further our understanding of the mechanisms which construct cognitive biases within healthy individuals that lead to these problems in well-being.An increasingly influential view proposes that the brain learns about its environment by comparing prior beliefs with sensory evidence, with the aim to generate increasingly accurate predictions about future states. This process can be best understood using computational models of Bayesian inference, which account for how the brain makes inferences about the state of its environment. Specifically, these models propose that by weighting both sensory data and prior belief according to their reliability (or precision), the brain can parsimoniously explain away incoming sensory information. This proposal intends to show that the predictive nature of perception runs in tandem with our long-term memories, motivations and emotions, creating cognitive biases that can transform computational parsimony, into computational pathology. Accordingly, I will investigate how everyday factors such as intrinsic motivation and anxiety can alter estimates of precision or reliability ascribed to prior belief or sensory data, thereby biasing perception and learning. Analysing neurophysiological and behavioural data in combination with Bayesian modelling will provide a mechanistic understanding of this previously unexplored non-optimal inference process. Moreover, by using a class of Bayesian models specifically introduced to assess individual differences, I ultimately aim to determine how inter-individual variation in estimates of precision can account for variability in individual learning under different degrees of anxiety and intrinsic motivation.