Naturalistic Hybrid Search in Autism Spectrum Disorder: Co-recording EEG and eye-tracking data to investigate behaviour and brain activity differences

在现实生活中，当人们在人群中寻找朋友，或者开车时，往往会在记忆中寻找各种可能的目标之一。包括通过记忆搜索目标，并用视觉搜索，这被称为混合搜索(HS)。在自闭症谱系障碍(ASD)中，一些证据表明，与典型个体相比，ASD患者的搜索方式可能不同，但其潜在机制仍不清楚。ASD是一种神经发育障碍，在英国发病率不断上升，其特征是社交困难、感觉运动异常以及兴趣和行为受限。最近的一项理论试图调和关于自闭症潜在认知机制的证据，提出自闭症的非典型过程可能源于内部假设(也称为“先天”)比典型个体更少使用，而不是异常感知。当感知一种体验时，个体既受到传入的感官信息的影响，也受到他们对世界的先验知识的影响。在ASD中，这一先验知识的发展或使用来理解一种体验，例如，一个人在寻找特定朋友的场景，可能会受到影响。然而，到目前为止，混合搜索实验范式通常涉及在空白背景下搜索抽象刺激，这意味着它们不能捕捉真实世界的背景，并限制了对此类建议的研究。自然场景具有背景信息，个人使用这些信息来指导他们在场景的特定区域搜索不同的目标。因此，如果我们想要更好地理解ASD中搜索的认知过程，那么对现实世界场景中存在的语境信息的研究就显得尤为重要。因此，这个项目旨在检查行为和大脑成像数据，以调查神经典型和神经多样化的大脑如何使用更接近现实世界经验的刺激和范例来协调自然主义混合搜索的复杂步骤的潜在差异。为了检验支持混合搜索的机制，这个项目将需要在参与者执行操纵刺激类型和背景特征的修改后的HS任务时，共同记录脑电(EEG)、大脑电活动的记录和眼球跟踪数据。参与者将是患有和不患有自闭症谱系疾病的成年人，年龄、性别和非语言智商都是匹配的。共同记录脑电和眼球跟踪以了解神经典型和神经多样性大脑中的混合搜索机制是一项新的研究。如上所述，它将弥合几个知识差距。虽然ASD是一个很好的群体来研究和对比支持混合搜索的机制，因为它们很可能是非典型的，但这项实验工作也对理解ASD潜在的认知和神经机制有意义。实际上，这个项目可能会对现实世界产生影响，比如，帮助创建针对许多关键的现实世界混合搜索任务的ASD针对性培训，比如驾驶。

In real life, when searching for friends in crowds, or driving, individuals often look for one of various possible targets kept in memory. Involving searching targets through one's memory, and searching them visually, this is known as hybrid search (HS). In autism spectrum disorder (ASD), some evidence has shown that individuals with ASD may approach search differently compared to typical individuals, however the underlying mechanisms remain unclear. ASD is a neurodevelopmental disorder of rising incidence in the UK that is characterised by social difficulties, sensorimotor abnormalities, and restricted interests and behaviours. A recent theory attempting to reconcile the evidence about the latent cognitive mechanisms in ASD proposes that, rather than abnormal perception, atypical processes in ASD may stem from internal assumptions (also known as "priors") being less used than in typical individuals. When perceiving an experience, individuals are impacted by both inbound sensory information and their prior knowledge about the world. In ASD, the development or usage of this prior knowledge to understand an experience, for instance, a scene where one is searching for a specific friend, can be affected. However, hybrid search experimental paradigms so far have typically involved searching for an abstract stimulus in blank backgrounds, which means they do not capture real-world settings and limit the investigation of such proposal. Natural scenes have contextual information that individuals use to guide their search to specific areas of the scene for different targets. Therefore, research that accounts for contextual information present in real-world scenes is particularly important if we want to better understand the cognitive processes of search in ASD. Thus, this project aims to examine behavioural and brain-imaging data to investigate potential differences in how neurotypical and neurodiverse brains coordinate the complex steps of naturalistic hybrid search, using stimuli and paradigms that are closer to real-world experience. To examine the mechanisms underpinning hybrid search, this project will entail co-recording an electroencephalogram (EEG), a recording of the brain's electrical activity, and eye-tracking data while participants perform a modified HS task that manipulates stimuli type and contextual features. Participants will be adults with and without an autism spectrum condition, matched in age, gender, and non-verbal IQ. Co-recording EEG and eye-tracking to understand hybrid search's mechanisms in the neurotypical and neurodiverse brain is a novel investigation. It will bridge several knowledge gaps as aforementioned. While ASD is a sound population to investigate and contrast mechanisms underpinning hybrid search in general, because they are likely to be atypical, this experimental work also has implications for understanding underlying cognitive and neural mechanisms in ASD. Practically, this project could have real-world implications, such as, helping create ASD targeted training for many critical real-world hybrid search tasks, like driving.