A Quantum Question Order Model Supported by Empirical Tests of an<i>A Priori</i>and Precise Prediction

Wang, Zheng; Busemeyer, Jerome R.

doi:10.1111/tops.12040

Cited by 183 publications

(310 citation statements)

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“…A more detailed discussion of the explanatory scope of quantum models is provided in the General Discussion section. (Wang & Busemeyer, 2013). Research has also examined the mechanistic foundations of quantum probability models of cognition (Fuss & Navarro, 2013), showing how complex (classical) cognitive architectures can give rise to behavior that is best described in terms of quantum theory.…”

Section: A Quantum Probability Framework For Human Probabilistic Infementioning

confidence: 99%

A quantum probability framework for human probabilistic inference.

Trueblood

Yearsley

Pothos

2017

Journal of Experimental Psychology: General

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThere is considerable variety in human inference (e.g., a doctor inferring the presence of a disease, a juror inferring the guilt of a defendant, or someone inferring future weight loss based on diet and exercise). As such, people display a wide range of behaviors when making inference judgments.Sometimes, people's judgments appear Bayesian (i.e., normative), but in other cases, judgments deviate from the normative prescription of classical probability theory. How can we combine bothBayesian and non-Bayesian influences in a principled way? We propose a unified explanation of human inference using quantum probability theory. In our approach, we postulate a hierarchy of mental representations, from 'fully' quantum to 'fully' classical, which could be adopted in different situations. In our hierarchy of models, moving from the lowest level to the highest involves changing assumptions about compatibility (i.e., how joint events are represented). Using results from three experiments, we show that our modeling approach explains five key phenomena in human inference including order effects, reciprocity (i.e., the inverse fallacy), memorylessness, violations of the Markov condition, and anti-discounting. As far as we are aware, no existing theory or model can explain all five phenomena. We also explore transitions in our hierarchy, examining how representations change from more quantum to more classical. We show that classical representations provide a better account of data as individuals gain familiarity with a task.We also show that representations vary between individuals, in a way that relates to a simple measure of cognitive style, the Cognitive Reflection Test.Keywords: Human judgment, quantum probability theory, Bayes' rule, order effects, Markov condition A Quantum Framework for Probabilistic Inference 3 A Quantum Probability Framework for Human Probabilistic InferenceEveryday we face situations where we must make inferences about the world around us. For example, a doctor must determine the likelihood that a patient has a disease based on a set of symptoms. A juror must decide the probability that a defendant is guilty after hearing the cases made by the prosecution and defense. Or, maybe you want to judge the likelihood that you will weigh less next month if you start exercising more regularly and you improve your diet. In general, the inference problem involves judging the likelihood of some hypothesis (e.g., presence of a disease, guilt of a defendant, future weight loss) based on a series of evidence (e.g., medicalsymptoms, prosecution and defense cases, changes in your diet and exercise).Bayesian inference is widely accepted as the normative approach to inference. However, decades of research in human judgment and decision-making have suggested that people's judgments often violate the rules of Bayesian inference and classical probability theory (Tversky...

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Section: A Quantum Probability Framework For Human Probabilistic Infementioning

confidence: 99%

A quantum probability framework for human probabilistic inference.

Trueblood

Yearsley

Pothos

2017

Journal of Experimental Psychology: General

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“…This comes from the projection postulate that modifies the state of belief when an answer is given to a question. This order effect property of the quantum-like models is well known, and it has actually been used to provide a quantum-like account of order effect (see for example Conte et al 2009;Busemeyer et al 2009Busemeyer et al , 2011Atmanspacher and Römer 2012;Pothos and Busemeyer 2013;Wang and Busemeyer 2013;Wang et al 2014;Boyer-Kassem et al 2016)-thus, the same models are at the basis of the account of order effect and of the conjunction fallacy. Fig.…”

Section: Order Effectmentioning

confidence: 99%

“…The tradition in the literature is to test the null of absence of order effect (e.g. Wang and Busemeyer 2013;Wang et al 2014). Table 4 reports the adjusted p values of the log-likelihood ratio test with a Bonferroni correction for such a test.…”

Section: Test Of the Order Effectmentioning

confidence: 99%

“…To test the QQ equality, we adopt the statistical test proposed in Wang and Busemeyer (2013) and Wang et al (2014), based on the log-likelihood ratio test, commonly used to compare the goodness of fit of two models. 14 The two models are an unconstrained one and a constrained one by the QQ equality.…”

Section: Test Of the Qq Equalitymentioning

confidence: 99%

“…For instance, quantum-like models of judgments have been proposed to account for order effect, i. e. when the answers given to two questions depend on the order of presentation of these questions (Atmanspacher and Römer 2012;Busemeyer and Bruza 2012;Wang and Busemeyer 2013;Wang et al 2014); for the violation of the sure thing principle, which states that if an agent prefers choosing action A-B under a specific state of the world and also prefers choosing A-B in the complementary state, then she should choose A over B regardless of the state of the world (Busemeyer et al 2006a, b;Busemeyer and Wang 2007;Khrennikov and Haven 2009; for Ellsberg's paradox (Ellsberg 1961) more specifically, cf. Aerts et al 2011Aerts et al , 2014Aerts and Sozzo 2013;for Allais' paradox (Allais 1953), cf.…”

Section: Introductionmentioning

confidence: 99%

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Quantum-like models cannot account for the conjunction fallacy

2016

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Human agents happen to judge that a conjunction of two terms is more probable than one of the terms, in contradiction with the rules of classical probabilities-this is the conjunction fallacy. One of the most discussed accounts of this fallacy is currently the quantum-like explanation, which relies on models exploiting the mathematics of quantum mechanics. The aim of this paper is to investigate the empirical adequacy of major quantum-like models which represent beliefs with quantum states. We first argue that they can be tested in three different ways, in a question order effect configuration which is different from the traditional conjunction fallacy experiment. We then carry out our proposed experiment, with varied methodologies from experimental economics. The experimental results we get are at odds with the predictions of the quantum-like models. This strongly suggests that this quantum-like account of the conjunction fallacy fails. Future possible research paths are discussed.

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Measurement effects in decision‐making

Burns

Hohnemann

2022

Behavioral Decision Making

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When participants are shown a series of stimuli, their responses differ depending on whether they respond after each stimulus or only at the end of the series, in what we call a measurement effect. These effects have received paltry attention compared with more well‐known order effects and pose a unique challenge to theories of decision‐making. In a series of two preregistered experiments, we consistently find measurement effects such that responding to a stimulus reduces its impact on later stimuli. While previous research has found such effects in noncumulative tasks, where participants are instructed only to respond to the most recent stimulus, this may be the first demonstration of these effects when participants are asked to combine information across either two or four stimuli. We present modeling results showing that although several extant classical and quantum models fail to predict the direction of these effects, new versions can be created that can do so. Ways in which these effects can be described using either quantum or classical models are discussed, as well as potential connections with other well‐known phenomena like the dilution effect.

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A Quantum Question Order Model Supported by Empirical Tests of anA Prioriand Precise Prediction

Cited by 183 publications

References 46 publications

A quantum probability framework for human probabilistic inference.

A quantum probability framework for human probabilistic inference.

Quantum-like models cannot account for the conjunction fallacy

Measurement effects in decision‐making

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