How (in)variant are subjective representations of described and experienced risk and rewards? (2016)
Authors
Abstract
Decisions under risk have been shown to differ depending on whether information on outcomes and probabilities is gleaned from symbolic descriptions or gathered through experience. To some extent, this description???experience gap is due to sampling error in experience-based choice. Analyses with cumulative prospect theory (CPT), investigating to what extent the gap is also driven by differences in people's subjective representations of outcome and probability information (taking into account sampling error), have produced mixed results. We improve on previous analyses of description-based and experience-based choices by taking advantage of both a within-subjects design and a hierarchical Bayesian implementation of CPT. This approach allows us to capture both the differences and the within-person stability of individuals??? subjective representations across the two modes of learning about choice options. Relative to decisions from description, decisions from experience showed reduced sensitivity to probabilities and increased sensitivity to outcomes. For some CPT parameters, individual differences were relatively stable across modes of learning. Our results suggest that outcome and probability information translate into systematically different subjective representations in description- versus experience-based choice. At the same time, both types of decisions seem to tap into the same individual-level regularities.
Bibliographic entry
Kellen, D., Pachur, T., & Hertwig, R. (2016). How (in)variant are subjective representations of described and experienced risk and rewards? Cognition, 157, 126-138. doi:10.1016/j.cognition.2016.08.020 (Full text)
Miscellaneous
Publication year | 2016 | |
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Document type: | Article | |
Publication status: | Published | |
External URL: | http://dx.doi.org/10.1016/j.cognition.2016.08.020 View | |
Categories: | ||
Keywords: | cumulative prospect theorydecisions from experiencehierarchical bayesian modelingrisky choice |