For artificial entities to achieve true autonomy and display complex life-like behaviour they will need to exploit appropriate adaptable learning algorithms. In this sense adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviours. We examine the use of constructivism-inspired mechanisms within a neural learning classifier system architecture, which exploits parameter self-adaptation as an approach to realise such behaviour. The system uses a rule structure in which each is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in Markov, nonstationary and nonMarkov simulated mazes.