Why are we social? Mapping the development of social motivation through adaptive sampling

婴儿天生就有与他人互动的动力。在一年之内，这种动力使他们从一个被动的新生儿变成一个微笑，会说话的蹒跚学步的孩子。我们的目标塑造了我们的社交能力以及我们在整个生命周期中与谁交往，因此是社会心理学的基础（Over，2016）。然而，婴儿选择互动的原因仍然是一个谜。测量动机是困难的，因为它是由孩子产生的，而传统的实验方法测量被动的反应，由实验者产生的刺激。我们研究婴儿社会动机的变革性方法受到广告创新的启发。在过去的二十年里，广告已经通过使用人工智能（AI）进行了革命性的改变。与传统的根据创意人员认为消费者想要的东西来创建通用广告系列的模式不同，在互联网上，广告商现在可以通过尝试不同的广告并衡量个人客户对它们的反应来确定到底是什么激励了个人客户。例如，如果您点击毛里求斯度假的广告，您将在稍后访问的其他网站上看到度假胜地的广告。我们的目标是利用这种方法的原理来确定是什么促使婴儿与其他人互动。 研究1：识别与社会动机相关的大脑信号和网络。作为第一步，我们需要确定大脑中核心社会奖励网络的读数;测量大脑（而不是行为）使我们能够在整个发展过程中使用相同的信号来测量社会动机。我们可以使用功能性磁共振成像（fMRI）非常精确地测量这些网络，但这不适合清醒的婴儿。功能性近红外光谱（fNIRS）是一种替代成像方法，与fMRI非常相似，但可用于清醒的婴儿。我们将使用fNIRS和fMRI的组合来识别参与核心社会奖励网络的大脑网络的大脑信号，然后我们可以在研究2和3中单独使用fNIRS进行测量。研究2：识别婴儿发现最大奖励的社会线索。我们将使用使用眼动追踪方法的社交任务。这项技术精确地跟踪婴儿在屏幕上看的地方，婴儿的观看行为甚至会触发屏幕上的视觉事件（例如，如果婴儿看向一张脸，这将触发社会奖励，如微笑或说话的脸）。当婴儿观看屏幕并完成任务时，算法将能够从社会奖励网络中了解哪些任务会产生更大的大脑信号。这使我们能够确定哪种类型的社会互动对婴儿特别有益，以及随着婴儿的成长，这种互动会如何变化。例如，非常小的婴儿可能对眼睛凝视和微笑特别感兴趣，但当他们成长为幼儿并开始说话时，语言对他们来说可能更有趣。研究3：开发在现实生活中使用我们的方法的工具。基于屏幕的社交任务非常有用，但是在屏幕上观看社交刺激与人互动的动态性质非常不同。在这里，我们将测量婴儿与社交伙伴互动时的大脑反应。当婴儿与他们的伴侣互动时，算法将识别他们认为特别有益的社交线索类型。然后，该算法将提示受过训练的社交伙伴参与这些最大限度地奖励社交互动（例如眼神接触，微笑或触摸）。这将展示我们的工具如何用于影响社会动机的儿童的定制干预设计中，如自闭症。总之，我们的工作旨在产生新的工具，以改变我们对婴儿与他人交往的理解，并帮助弱势儿童充分发挥其潜力。

Babies are born with a drive to interact with other people. Within a year, this drive takes them from a passive newborn to a smiling, talking toddler. Our goals shape how sociable we are and who we socialise with across the lifespan, and are thus fundamental to social psychology (Over, 2016). However, the reasons why babies choose to interact remains a mystery. Measuring motivation is difficult because it is generated by the child, whilst traditional experimental methods measure passive responses to stimuli produced by the experimenter. Our transformative approach to studying infant social motivation is inspired by innovations in advertising. In the last twenty years, advertising has been revolutionised by the use of artificial intelligence (AI). Rather than the traditional model of creating generic campaigns based on what creatives thought consumers wanted, on the internet advertisers can now identify what exactly motivates individual customers by trying out different adverts and measuring an individual customer's reaction to them. For example, if you click on an advert for a holiday in Mauritius, you will then see adverts for holiday resorts on other websites that you later visit. We aim to use the principles of this approach to determine what motivates babies to interact with other people. Study 1: Identify brain signals and networks related to social motivation. As a first step, we need to identify readouts of core social reward networks in the brain; measuring the brain (rather than behaviour) allows us to measure social motivation using the same signals across development. We can measure these networks very precisely using functional magnetic resonance imaging (fMRI), but this isn't suitable for babies who are awake. Functional Near-Infrared Spectroscopy (fNIRS) is an alternative imaging method that is very similar to fMRI but that can be used with babies who are awake. We will use a combination of fNIRS and fMRI to identify brain signals of the brain networks that are involved in the core social reward networks, which we can then measure with fNIRS alone in Studies 2 and 3. Study 2: Identify the social cues infants find maximally rewarding. We will use social tasks that use eye tracking methodology. This technology follows exactly where infants look at on a screen, with infants' looking behaviour even triggering visual events on the screen (e.g., if infants look towards a face, this will trigger a social reward such as a smiling or talking face). As the infant watches the screen and completes the tasks, the algorithm will be able to learn which tasks produce a larger brain signal from the social reward networks. This then allows us to determine which type of social interaction is particularly rewarding for the infants and how this may change as babies grow up. For example, very young babies may be particularly interested in eye gaze and smiling, but as they grow into toddlers and begin to talk, language may be more interesting for them. Study 3: Develop tools for using our approach within real-life interaction. Screen based social tasks are extremely useful, but watching social stimuli on a screen is very different from the dynamic nature of interacting with people. Here, we will measure infants' brain responses whilst they interact with a social partner. As the infant interacts with their partner, the algorithm will identify the type of social cues that they find particularly rewarding. The algorithm will then prompt the trained social partner to engage in these maximally rewarding social interactions (such as eye contact, smiling or touch). This will provide a demonstration of how our tools can be used within a custom intervention design for children with conditions that affect social motivation, like autism.Taken together, our work is designed to produce new tools to transform our understanding of why babies socialise with other people, and to help vulnerable children to reach their full potential.