Autonomous learning is the ability to learn effectively without much external assistance, which is a desirable characteristic in both engineering and computational-modeling. We extend a constructive neural-learning algorithm, sibling-descendant cascade-correlation, to monitor lack of progress in learning in order to autonomously abandon unproductive learning. The extended algorithm simulates results of recent experiments with infants who abandon learning on difficult tasks. It also avoids network overtraining effects in a more realistic manner than conventional use of validation test sets. Some contributions and limitations of constructive neural networks for achieving autonomy in learning are briefly assessed.