How does one aim to understand neural information processing? One of the difficult first challenges is to identify the roles of the network's elements. To this end a functional contribution analysis (FCA) method has been developed and applied for studying the neurocontrollers of evolutionary autonomous agents (EAAs). The FCA processes data composed of multiple lesion experiments and the corresponding performance levels that the agent obtains under these lesions. It calculates the contribution values (CVs) of the network's elements such that the ability to predict the agent's performance under new, unseen lesions is maximized. Previous analysis has found a strong dependence of the CVs and the prediction error on the specific type of lesioning method used, i.e. on the way in which the activity of lesioned neurons is disrupted. We present a new, informational lesioning method (ILM), which views a lesion as a noisy channel and applies a controlled lesion to the network by varying the lesioning level from large to arbitrarily small magnitudes. Studying the ILM within the FCA framework, our main results are threefold: first, that lower lesioning levels permit more accurate FCA predictions; second, that the usage of minute ILM lesioning levels can uncover the long-term effects of elements on the network's functioning; and third, that as the lesioning level decreases, the CVs tend to approach limit values, reflecting the importance of these elements in the intact, normal-functioning neurocontroller.