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Inspired by information systems theory, semiotic cognitive information processing (SCIP) is grounded in (natural/artificial) system-environment situations. SCIP systems' knowledge-based 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 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, subsymbolic, multiresolutional 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 the SCIP systems' performance against human natural language understanding in a controlled way. 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.