Gradual sensory maturation promotes abstract representation learning
Human infants begin life with limited visual capacities, such as low acuity and poor color sensitivity, due to gradual sensory maturation. In contrast, machine learning models are trained on high-fidelity inputs from the outset, often leading to shortcut learning and overfitting to spurious correlations. Here, we show that early sensory immaturity plays a critical role in shaping bias-resistant, abstract visual representations that conventional models struggle to develop. Using neural network simulations and human psychophysics experiments, we demonstrate that gradual sensory development supports the emergence of robust and generalizable internal representations, reduces reliance on superficial cues, and promotes disentangled representations that enable compositional reconstruction and visual imagination. Comparative analyses of human and model behavior reveal shared patterns of bias resistance and adaptive generalization, including resilience to misleading information. Our findings suggest that gradual sensory maturation is not merely a developmental constraint, but rather a key mechanism that enables abstract representation learning.