Inspired by information systems theory, Semiotic Cognitive Information Processing (SCIP) is grounded in (natural/artificial) systemenvironment situations. SCIP systems' knowledgebased processing of information makes it cognitive, their sign and symbol generation, manipulation, and understanding capabilities render it semiotic. Based upon structures whose representational status is not a presupposition to, but a result from recursive processing, SCIP algorithms initiate and modify the structures

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... they are operating on to realize (rather than simulate) language understanding by meaning constitution. Thus, the symbolic (de)composition of propositional structures in traditional semantics is complemented by SCIP, which models learning and understanding dynamically by visualizing what is understood in a perception-based, sub-symbolic, multi-resolutional way of processing natural language discourse. An experimental 2-dim scenario with object locations described relative to a mobile agent's varying positions allows to test SCIP systems' performance against human natural language understanding in a controlled way 1 . The author is indebted to two anonymous referees whose comments helped to improve the written version of the lecture and, hopefully, its readability. All errors are, as always, my own. 1 The implementation of the SCIP system-environment testbed is due to my PhD-students, Christoph Flores and Daniel John, whose design and programming proficiencies are thankfully appreciated.