Proving and improving the reliability of infant research with neuroadaptive Bayesian optimization

The field of infant research is not immune from the reproducibility crisis in cognitive science and psychology. In their recent methodological article, Byers-Heinlein et al. (2021) invited infant researchers to commit to produce robust findings by reporting reliability metrics for their variables of interest, improving data quality and quantity, and moving towards more sophisticated paradigms and analyses. We present a novel artificial intelligence-enriched individualized approach that, in our view, is particularly promising to shed new light on infant and child development and promote good research practice in the field; neuroadaptive Bayesian optimization (NBO). NBO is a transformative method where the collected brain or behavioural data are processed in real time and used to identify the stimuli that maximize the individual's response. Applying NBO to infant research goes in the direction proposed by Byers-Heinlein et al. (2021) and further, the method requires careful a priori choices that effectively correspond to preregistering the experimental design and analytic pipeline. In this commentary, we examine how the NBO approach embeds the six proposed solutions for more reliable infant research, encouraging transparency of the planned analyses and robustness of findings.