Bridging the Gap Between Subjective Probability and Probability Judgments: The Quantum Sequential Sampler.
One of the most important challenges in decision theory has been how to reconcile the normative expectations from Bayesian theory with the apparent fallacies that are common in probabilistic reasoning. Recently, Bayesian models have been driven by the insight that apparent fallacies are due to sampl...
| Publicado en: | Psychological Review Vol. 132; no. 4; pp. 916 - 956 |
|---|---|
| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
American Psychological Association
Jul2025
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=185306800&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 185306800 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0033295X PYV jtl: Psychological Review issn: 0033295X maglogo: N pubinfo: dt: Jul2025 vid: 132 iid: 4 pid: 34 pub: American Psychological Association artinfo: ui: 185306800 10.1037/rev0000489 ppf: 916 ppct: 40 formats: tig: atl: Bridging the Gap Between Subjective Probability and Probability Judgments: The Quantum Sequential Sampler. aug: au: Huang, Jiaqi Busemeyer, Jerome R. Ebelt, Zo Pothos, Emmanuel M. affil: Department of Cognitive Science, Indiana University Department of Psychology, City, University of London su: Empirical research Quantum theory Bayesian analysis Decision theory Bayes' estimation sug: subj: Empirical research Quantum theory Bayesian analysis Decision theory Bayes' estimation keyword: Bayesian probabilistic fallacies probabilistic reasoning quantum cognition sequential sampling Bayesian probabilistic fallacies probabilistic reasoning quantum cognition sequential sampling ab: One of the most important challenges in decision theory has been how to reconcile the normative expectations from Bayesian theory with the apparent fallacies that are common in probabilistic reasoning. Recently, Bayesian models have been driven by the insight that apparent fallacies are due to sampling errors or biases in estimating (Bayesian) probabilities. An alternative way to explain apparent fallacies is by invoking different probability rules, specifically the probability rules from quantum theory. Arguably, quantum cognitive models offer a more unified explanation for a large body of findings, problematic from a baseline classical perspective. This work addresses two major corresponding theoretical challenges: first, a framework is needed which incorporates both Bayesian and quantum influences, recognizing the fact that there is evidence for both in human behavior. Second, there is empirical evidence which goes beyond any current Bayesian and quantum model. We develop a model for probabilistic reasoning, seamlessly integrating both Bayesian and quantum models of reasoning and augmented by a sequential sampling process, which maps subjective probabilistic estimates to observable responses. Our model, called the Quantum Sequential Sampler, is compared to the currently leading Bayesian model, the Bayesian Sampler (J. Zhu et al., 2020) using a new experiment, producing one of the largest data sets in probabilistic reasoning to this day. The Quantum Sequential Sampler embodies several new components, which we argue offer a more theoretically accurate approach to probabilistic reasoning. Moreover, our empirical tests revealed a new, surprising systematic overestimation of probabilities. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|