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Neuroadaptive Bayesian Optimisation to Identify which Combination of Gaze and Emotion in the Parent Face Maximises Attention in the Individual Infant, 2023-2024
Creator
Jones, E, Birkbeck, University of London
Gui, A, Birkbeck, University of London
Throm, E, Birkbeck, University of London
Study number / PID
856957 (UKDA)
10.5255/UKDA-SN-856957 (DOI)
Data access
Restricted
Series
Not available
Abstract
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...
Terminology used is generally based on DDI controlled vocabularies: Time Method, Analysis Unit, Sampling Procedure and Mode of Collection, available at CESSDA Vocabulary Service.
Methodology
Data collection period
28/06/2023 - 19/01/2024
Country
United Kingdom
Time dimension
Not available
Analysis unit
Individual
Group
Universe
Not available
Sampling procedure
Not available
Kind of data
Numeric
Text
Video
Software
Data collection mode
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
Funding information
Grant number
Unknown
Access
Publisher
UK Data Service
Publication year
2024
Terms of data access
The Data Collection is available for download to users registered with the UK Data Service.