Proceedings of the Annual Meeting of the Cognitive Science Society

Cognitive science has often modeled cognition as an efficiency-driven process, assuming that mental computation aims at optimizing performance under resource constraints. While this view has been successful in many domains, it struggles to explain cognitive phenomena in which inefficiency, ambiguity, and non-convergence play a functional role, such as creative insight, exploratory problem solving, and conceptual restructuring. In this paper, we argue that these forms of cognitive inefficiency are not mere by-products of limited resources, but reflect a deeper computational organization of cognition. We propose a quantum-algorithmic perspective in which cognitive processes are understood as inherently incomplete computations, where superposition, interference, and delayed resolution contribute to flexibility and context sensitivity. From this viewpoint, inefficiency emerges as a structural feature rather than a deviation from optimality. By rethinking cognitive computation beyond output-driven optimization, we introduce a process-based notion of cognitive optimality and discuss its implications for cognitive modeling and computer science.