We compare the acquisition of knowledge in humans and machines. Research from
the field of developmental psychology indicates, that human-employed hypothesis
are initially guided by simple rules, before evolving into more complex
theories. This observation is shared across many tasks and domains. We
investigate whether stages of development in artificial learning systems are
based on the same characteristics. We operationalize developmental stages as
the size of the data-set, on which the artificial system is trained. For our
analysis we look at the developmental progress of Bayesian Neural Networks on
three different data-sets, including occlusion, support and quantity comparison
tasks. We compare the results with prior research from developmental psychology
and find agreement between the family of optimized models and pattern of
development observed in infants and children on all three tasks, indicating
common principles for the acquisition of knowledge
