Jiaqi Huang, Jerome R Busemeyer, Zo Ebelt, Emmanuel M Pothos
{"title":"缩小主观概率与概率判断之间的差距:量子顺序采样器","authors":"Jiaqi Huang, Jerome R Busemeyer, Zo Ebelt, Emmanuel M Pothos","doi":"10.1037/rev0000489","DOIUrl":null,"url":null,"abstract":"<p><p>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. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>","PeriodicalId":21016,"journal":{"name":"Psychological review","volume":" ","pages":""},"PeriodicalIF":5.1000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bridging the gap between subjective probability and probability judgments: The quantum sequential sampler.\",\"authors\":\"Jiaqi Huang, Jerome R Busemeyer, Zo Ebelt, Emmanuel M Pothos\",\"doi\":\"10.1037/rev0000489\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>\",\"PeriodicalId\":21016,\"journal\":{\"name\":\"Psychological review\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":5.1000,\"publicationDate\":\"2024-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Psychological review\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1037/rev0000489\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Psychological review","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1037/rev0000489","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
决策理论中最重要的挑战之一,就是如何协调贝叶斯理论的规范性预期与概率推理中常见的明显谬误。近来,贝叶斯模型受到这样一种观点的推动,即表面谬误是由于抽样误差或估计(贝叶斯)概率时的偏差造成的。另一种解释明显谬误的方法是援引不同的概率规则,特别是量子理论中的概率规则。可以说,量子认知模型为大量从基线经典视角来看存在问题的研究结果提供了更为统一的解释。这项工作解决了两大相应的理论挑战:首先,需要一个同时包含贝叶斯和量子影响的框架,承认人类行为中同时存在这两种影响的证据这一事实。其次,经验证据超越了任何现有的贝叶斯和量子模型。我们开发了一个概率推理模型,无缝整合了贝叶斯推理模型和量子推理模型,并通过顺序采样过程进行增强,将主观概率估计映射到可观察的反应。我们的模型被称为量子顺序采样器(Quantum Sequential Sampler),通过一项新的实验与目前领先的贝叶斯模型--贝叶斯采样器(J. Zhu et al.量子序列采样器包含几个新的组成部分,我们认为它们为概率推理提供了一种理论上更精确的方法。此外,我们的实证测试还发现了一种新的、令人惊讶的系统性概率高估。(PsycInfo Database Record (c) 2024 APA, 版权所有)。
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 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. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
期刊介绍:
Psychological Review publishes articles that make important theoretical contributions to any area of scientific psychology, including systematic evaluation of alternative theories.