Advancing NeuroAI through developmental alignment

The goal of artificial intelligence (AI) modeling in neuroscience, or NeuroAI, is to uncover the factors that give rise to human-level intelligence. However, current models overwhelmingly focus on simulating adulthood, the end state of intelligence, and often do not consider how this state was achieved in the first place. We argue that, to understand adult intelligence, it is important to model the developmental process by which intelligence arose. Here, we describe how developmental changes in children's neural architecture, experiences, and learning objectives are adaptively suited to support rapid learning and illustrate how these principles can be incorporated into the AI engineering process. Finally, we describe the early developing capacities of children and how these capacities provide an ideal set of benchmarks for evaluating AI models. Together, by modeling the developmental process by which humans achieve intelligence, we may build more mechanistically plausible models as well as improve the capabilities of AI.