Human and model growth as interacting learning processes [ftip-00NC]
Human and model growth as interacting learning processes [ftip-00NC]
A realistic comparison needs more than the contrast between a person who grows and a model that reads text. Human experience is selective and changes with the learner's actions and abilities. Models also inherit large bodies of human-produced data and can learn from video, interaction, feedback, generated tasks and earlier model generations. The relevant asymmetry is the particular developmental access and retained structure available in the specified comparison.
Smith and colleagues' 2018 review, The developing infant creates a curriculum for statistical learning, describes head-camera and eye-tracking evidence that infants' visual inputs change with posture, mobility and object manipulation. It proposes that these changing inputs can support a developmental curriculum. The authors leave the causal benefit of particular ordering and its relation to learning mechanisms as research questions. This motivates modelling learner-dependent experience; it does not establish an adult mathematical advantage or a lower bound for artificial learners.
Artificial learning already combines different forms of experience. In V-JEPA 2, Assran and colleagues pretrain visual representations on large-scale video, then train an action-conditioned predictor with robot interaction trajectories. The result supports planning for evaluated manipulation tasks. The relevant lesson is that recorded observations, action information and learned simulation can play different roles within one model's development. These results do not by themselves concern mathematical concept discovery.
The SIMA 2 report of November 2025 describes learning in new game environments using self-generated experience, with Gemini supplying tasks and estimated rewards, after initial learning from demonstrations. This is an example of a model lineage gaining skills through further interaction. Its teacher, environment access and retained experience belong in the baseline when available. The reported remaining difficulties with long tasks and memory are properties of that system, not universal limits of model growth.
These observations suggest comparing concrete interventions: preserve or shuffle a developmental sequence; provide selected traces or interactive access; add a bounded teacher; allow the lineage to design its own curriculum. Fix total resources and evaluate fresh-task acquisition after removing help. Such comparisons can identify which experience matters and guide a mathematical family. Even a large measured gap applies only to the tested procedures; proving the closed inequality still requires the broader argument in § [ftip-00N8].