Mingzhou Ding

Advancing NeuroAI through developmental alignment cell.com/neuron/home SUMMARY: 1. Model the development of intelligence, not just its adult state. Current AI models primarily aim to replicate adult-level abilities. NeuroAI should also investigate how intelligence develops from infancy to adulthood to understand the mechanisms that produce sophisticated cognition. 2. Build AI architectures inspired by brain development. The human brain develops hierarchically, with sensory regions maturing before higher-level association areas. AI could adopt this principle by training early layers first and progressively developing higher-level layers, potentially improving robustness and learning efficiency. 3. Use developmental learning as a curriculum. Children encounter information in a natural progression from simple to complex. Training AI with gradually increasing complexity, such as blurry-to-clear images or simple-to-complex visual experiences, may improve generalization, robustness, and data efficiency. 4. Learn from children's limited but meaningful experiences. Unlike AI models trained on enormous datasets, children learn remarkably well from relatively few experiences. Training models on realistic, child-centered sensory data and age-appropriate experiences may help reduce their dependence on massive datasets. 5. Incorporate curiosity-driven learning and exploration. Children actively seek information that is neither too easy nor too difficult to understand. AI models could use predictive learning, information gain, and the ability to select their own experiences to develop more efficient and adaptive learning strategies. 6. Treat developmental milestones as benchmarks for AI. Children's perceptual and cognitive abilities emerge in a characteristic sequence. Evaluating whether AI models acquire similar capacities over training—not merely whether they achieve adult-level performance—could reveal whether their learning mechanisms resemble those of humans. 7. Use NeuroAI to discover the mechanisms of intelligence. By manipulating neural architecture, training experiences, and learning objectives in AI models, researchers can test which factors are necessary or sufficient for particular cognitive abilities. This approach can also help explain brain development, identify mechanisms of dysfunction, and inform future clinical research. 8. Integrate architecture, experience, and learning objectives. These three factors interact throughout development and should not be optimized independently. A developmentally informed approach could produce AI systems that are more biologically plausible, robust, energy-efficient, and capable of generalizing beyond their training data. Central message: To understand and reproduce human intelligence, NeuroAI must study not only what the adult brain can do, but also how the developing brain acquires those abilities. Childhood development offers a powerful blueprint for building more capable and biologically grounded AI systems.

Toward an automated science of the mind cell.com/neuron/fulltext/S08… Summary Artificial intelligence is opening the door to a new era of scientific discovery: the automated science of the mind. Traditionally, cognitive science has relied on a cycle in which researchers design experiments, collect human behavioral data, develop computational models, and refine theories through scientific debate. Although this approach has advanced our understanding of human cognition, it is time-consuming, dependent on human intervention, and often limited by researchers’ existing assumptions about experiments and models. The emerging field of automated cognitive science aims to accelerate this process by integrating AI across four stages of discovery. First, AI can generate and represent diverse experimental paradigms, expanding the range of questions researchers can investigate. Second, cognitive foundation models can simulate human behavior, enabling researchers to explore hypotheses before conducting costly experiments. Third, AI-driven program synthesis can generate interpretable computational models that explain observed behavior and reveal new cognitive mechanisms. Finally, automated systems can close the discovery loop by evaluating competing theories, identifying informative experiments, and proposing the next steps in scientific investigation. However, significant challenges remain. Synthetic behavior may reproduce shortcuts or population-specific biases rather than genuine human cognition. Automatically generated models may be difficult to interpret, sensitive to assumptions, or capable of exploiting evaluation metrics without capturing the underlying mechanisms. Moreover, defining scientific novelty, theoretical importance, and meaningful progress requires careful validation and human oversight. Ultimately, automated cognitive science is not simply about using AI to analyze data faster. It represents a shift toward AI systems that actively generate experiments, simulate behavior, construct theories, and iteratively test new hypotheses. If successful, this approach could transform cognitive science from a largely researcher-driven process into a more scalable, continuous, and data-informed discovery cycle—while keeping empirical evidence, interpretability, and scientific rigor at its core.