Infants’ motivation to engage with the social world depends on the interplay between individual brain’s characteristics and previous exposure to social cues such as the parent’s smile or eye contact. Different hypotheses about why specific combinations of emotional expressions and gaze direction engage children have been tested with group-level approaches rather than focusing on individual differences in the social brain development. Here, a novel Artificial Intelligence-enhanced brain-imaging approach, Neuroadaptive Bayesian Optimisation (NBO), was applied to infant electro-encephalography (EEG) to understand how selected neural signals encode social cues in individual infants. EEG data was acquired from 42 6- to 9-month-old infants looking at images of their parent’s face, analysed in real-time and selected by a Bayesian Optimisation algorithm to identify which combination of gaze and emotional expression of the parent’s face produces the strongest brain activation in the child. This individualised approach supported the theory that the infant’s brain is maximally engaged by communicative cues with a negative valence such as direct gaze and angry facial expressions. Moreover, we evaluated whether results also capture individual differences in behaviour. We found that infants attending preferentially to faces with direct gaze had increased positive affectivity and decreased negative affectivity compared to infants preferentially attending to faces with averted gaze. This work supports the idea that infants’ attentional preferences for social cues are heterogeneous and lays the foundation for the development of neuroimaging-informed personalized experiments to study diversity in neurodevelopmental trajectories of social skills.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 customers 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 is not 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 6 and 9 months took part in a study combining real-time analysis of EEG data with machine learning (Neuroadaptive Bayesian Optimisation). Parents filled in online questionnaires about infant behaviour (Infant Behaviour Questionnaire; Vineland Adaptive Behaviour Scales) and mood (Positive and Negative Affect Schedule). The used pre-processing pipeline has been initially created and tested in a proof-of-principle study (da Costa et al., 2021, see preprint attached) and subsequently piloted in more infants. The entire pipeline, EEG testing Standard Operating Procedures and scripts are available online [view-only mode]: https://osf.io/8yfv2/?view_only=c341e03e7838489f820c059b3a5bd632