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

婴儿生来就有与他人互动的动力。在一年内，这种驱动力将他们从一个被动的新生儿变成了一个微笑、会说话的蹒跚学步的孩子。我们的目标决定了我们的社交能力以及我们一生中与谁交往，因此也是社会心理学的基础(见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，因为它是由儿童产生的，而传统的实验方法测量的是对实验者产生的刺激的被动反应。我们研究婴儿社会动机的变革性方法受到了广告创新的启发。在过去的二十年里，人工智能(AI)的使用给广告业带来了革命性的变化。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别客户对它们的反应，来确定究竟是什么激励了个别客户，而不是基于创意人士认为消费者的需求来创建通用广告的传统模式。例如，如果你点击毛里求斯度假的广告，你就会在以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：确定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)使我们能够在发育过程中使用相同的信号来衡量社会动机。我们可以使用功能磁共振成像(FMRI)非常精确地测量这些网络，但这不适合醒着的婴儿。功能性近红外光谱(FNIRS)是一种与fMRI非常相似的替代成像方法，但可以用于醒着的婴儿。我们将结合使用fNIRS和fMRI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中仅使用fNIRS进行测量。研究2：确定婴儿认为最有回报的社交线索。我们将使用使用眼球跟踪方法的社交任务。这项技术完全遵循婴儿在屏幕上看什么，婴儿的注视行为甚至会在屏幕上触发视觉事件(例如，如果婴儿看向一张脸，这将触发一张微笑或说话的脸等社交奖励)。当婴儿看着屏幕并完成任务时，该算法将能够从社会奖励网络中学习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交对婴儿特别有益，以及随着婴儿的成长，这种情况可能会发生怎样的变化。例如，很小的婴儿可能对眼睛凝视和微笑特别感兴趣，但随着他们成长为蹒跚学步的孩子并开始说话，他们可能会对语言更感兴趣。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，但在屏幕上观看社交刺激与与人互动的动态本质是非常不同的。在这里，我们将测量婴儿在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，该算法将识别他们认为特别有价值的社交线索的类型。然后，算法将促使受过训练的社交伙伴参与这些回报最高的社交互动(如眼神接触、微笑或触摸)。这将展示我们的工具如何在针对患有自闭症等社交动机的儿童的定制干预设计中使用。我们的工作旨在共同开发新的工具，以改变我们对婴儿为什么与他人交往的理解，并帮助易受伤害的儿童充分发挥他们的潜力。

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.