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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Is the description-experience gap a gap in attention?
Mon-B22-Talk III-04
Presented by: Veronika Zilker
Veronika Zilker, Thorsten Pachur
Chair of Behavioral Research Methods, School of Management, Technical University of Munich
Preferences in risky choice often differ systematically depending on whether people learn about the options based on abstract descriptions of outcomes and probabilities (decisions from description), or by repeatedly sampling the options' payoff distributions (decisions from experience). This description-experience gap is often formalized in terms of differences in the weighting of probabilistic events between description and experience. However, it is not clear how such differences might come about. Here we test a mechanistic, attentional account of differences choice behavior and probability weighting between description and experience. We demonstrate that people attend systematically more to risky options (vs. safe options) in experience compared to description. Attending more to the safe option was linked to a higher tendency to choose this option in both paradigms. Moreover, attention allocation was linked to the elevation and curvature of probability weighting functions in experience and in description. Therefore, differences in attention allocation between description and experience mediated differences in choice behavior and probability weighting between the paradigms. These analyses offer a novel process-based understanding of how the ways in which people learn about risky prospects may shape attention allocation, and thereby give rise to differences in preferences and probability weighting patterns indicative of a description-experience gap.
Keywords: attention, risky choice, description-experience gap, Cumulative Prospect Theory, probability weighting, cognitive modeling, computational modeling
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