Advancing NeuroAI through developmental alignment
Vladislav Ayzenberg1, Michael F Bonner2, Laurie Bayet3
1Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA.
Neuron
|July 21, 2026
Summary
Modeling child development is key to understanding adult intelligence. Incorporating developmental principles into artificial intelligence (AI) can create more capable and plausible AI models.
Area of Science:
- Neuroscience
- Artificial Intelligence (AI)
- Developmental Psychology
Background:
- Current artificial intelligence (AI) models in neuroscience (NeuroAI) primarily simulate adult intelligence, neglecting the developmental trajectory.
- Understanding the emergence of human-level intelligence requires examining the process of development, not just the end state.
Purpose of the Study:
- To advocate for modeling the developmental process of intelligence to better understand adult intelligence.
- To illustrate how principles of child development can inform AI engineering.
- To propose early childhood capacities as benchmarks for evaluating AI models.
Main Methods:
- Analyzing developmental changes in children's neural architecture, experiences, and learning objectives.
- Integrating developmental principles into AI engineering frameworks.
- Identifying and describing early developing capacities in children.
Main Results:
- Developmental changes in children are adaptively suited for rapid learning.
- Incorporating developmental principles can enhance AI engineering processes.
- Early childhood capacities offer valuable benchmarks for AI model evaluation.
Conclusions:
- Modeling the developmental pathway to intelligence is crucial for creating mechanistically plausible AI.
- A developmental approach to NeuroAI can improve the capabilities of artificial intelligence.
- Child development provides a roadmap for advancing AI research and application.
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