Computational modeling provides a powerful tool to study and measure the cognitive underpinnings of behavior. This symposium features recent advances in the application of computational modeling in experimental psychology, showcasing its immense value for learning about cognitive processing across a wide range of applications. Florian Bolenz presents an analysis with the computational framework of metareasoning to model differences between younger and older adults in boundedly rational strategy selection during risky choice. The contribution by Ann-Katrin Hosch features a new evidence-accumulation exemplar model of category learning that allows an examination of how the variance of sampled examplars influences categorization. Chris Donkin presents a project that uses computational modeling to distinguish basic memory processes and strategic response in the DRM paradigm, highlighting the often neglected role of reasoning processes in recognition memory research. Veronika Zilker integrates attentional processes in the computational modeling of decision making with cumulative prospect theory; specifically, she examines whether attentional processes might be key drivers of the description-experience gap in risky choice. In the contribution by David Izydorczyk, a blending model of exemplar-based and rule-based judgment is used to model the cognitive processes underlying quantitative judgment of complex stimuli. Benjamin Kowialiewski presents a connectionist model of visuospatial working memory to study the impact of visuospatial proximity on memory performance. The symposium will bring together researchers from various research groups in Europe, reflecting the increasing popularity of cognitive modeling in experimental psychology.