16:30 - 18:00
Mon-B22-Talk III-
Mon-Talk III-
Room: B22
Chair/s:
Thorsten Pachur, Chris Donkin
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.
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Modeling Quantitative Judgments of Realistic Stimuli
Mon-B22-Talk III-05
Presented by: David Izydorczyk
David Izydorczyk, Arndt Bröder
University of Mannheim
Studies of processes of multiple-cue judgments usually rely on artificial stimuli with predefined cue structures. One reason for using these simple and artificial stimuli is that the cognitive models used in this area of research require that the cues and cue values are known. This limitation makes it difficult to apply the models to research questions with complex stimuli with an unknown cue structure. Drawing on early categorization research, in two studies we demonstrate how cues and cue values of complex stimuli can be extracted from pairwise similarity ratings with a multidimensional scaling analysis. These extracted cues can then be used in a state-of-the-art hierarchical Bayesian model of quantitative judgments. As a proof-of-concept, in the first study, we show that an MDS analysis of similarity ratings well recovers predefined cue structures of artificial stimuli and that using these MDS-based attributes as cues in a cognitive model to analyse data from an existing experiment leads to the same inferences as when the original cue values were used. In the second study, we use the same procedure to replicate previous findings from multiple-cue judgment literature, using complex stimuli with an unknown a priori cue structure.
Keywords: quantitative judgments
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