The paper proposes a text-mining based analytical framework aiming at the cognitive organization of complex scientific discourses. The approach is based on science mapping models recently developed in infoscience, being a generalization of the so-called Science Overlay Mapping methodology, referred to as Topic Overlay Mapping (TOM). It is shown that via applications of TOM in visualization, document clustering, time series analysis etc. the in-depth exploration and even the measurement of cognitive complexity and its dynamics is feasible for scientific domains. The methodology allows for an ICT-based understanding of contexts induced by large-scale scientific text collections that deeply enhance and drive further, detailed exploration of their contents (augmented cognition). As a use case, an empirical study is presented, focusing on the discovery of a policy-relevant discourse within SSH based on the proposed toolbox of TOM.