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.

中文翻译：

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通过神经自适应贝叶斯优化证明和提高婴儿研究的可靠性

婴儿研究领域也不能幸免于认知科学和心理学的可重复性危机。在他们最近的方法论文章中，Byers-Heinlein 等人。(2021) 邀请婴儿研究人员通过报告他们感兴趣的变量的可靠性指标、提高数据质量和数量以及转向更复杂的范式和分析来承诺产生可靠的研究结果。我们提出了一种新的人工智能丰富的个性化方法，在我们看来，这种方法特别有希望为婴儿和儿童的发展提供新的视角，并促进该领域的良好研究实践；神经自适应贝叶斯优化（NBO）。NBO 是一种变革性方法，其中收集的大脑或行为数据被实时处理，并用于识别最大化个人反应的刺激。将 NBO 应用于婴儿研究朝着 Byers-Heinlein 等人提出的方向发展。（2021），此外，该方法需要仔细的先验选择，这些选择有效地对应于预注册实验设计和分析管道。在本评论中，我们研究了 NBO 方法如何嵌入六种建议的解决方案以实现更可靠的婴儿研究，鼓励计划分析的透明度和结果的稳健性。