This proof-of-principle study extends the novel experimental approach of Neuroadaptive Bayesian Optimisation (NBO) to infant EEG data to study individual infants' engagement with social stimuli. In particular, the Negative central event-related potential component was optimised across a range of familiar and nonfamiliar faces. Previous group-level research suggested an initial attentional preference for parent’s vs. stranger’s face around 6 months with a subsequent change towards enhanced attention to the stranger’s face. The present individualised NBO study included n=62 infants aged 5-12 months who were presented with faces linearly varying in similarity to parent’s face. Results showed lower-than-usual attrition rate, and an equal proportion of infants preferably attending to parent and stranger, with the individual’s probability of preferably attending to parent’s face increasing between 5 and 12 months but being unrelated to parent-reported social behaviour. This study proves the feasibility of the NBO approach with infant neurophysiological data to identify among a range of cues the one that maximally triggers social brain activity in the individual infant. Further, this study suggests that on the individual level, infants differ in whether they preferably attend to parent or stranger in the second half of the first year of life.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.

Typically developing infants aged between 5 and 12 months took part in a study combining real-time analysis of EEG data with machine learning (Neuroadaptive Bayesian Optimisation). Parents filled in questionnaires about their child's behaviour, environment as well as parental mood.