- •Preface
- •Contents
- •Contributors
- •Modeling Meaning Associated with Documental Entities: Introducing the Brussels Quantum Approach
- •1 Introduction
- •2 The Double-Slit Experiment
- •3 Interrogative Processes
- •4 Modeling the QWeb
- •5 Adding Context
- •6 Conclusion
- •Appendix 1: Interference Plus Context Effects
- •Appendix 2: Meaning Bond
- •References
- •1 Introduction
- •2 Bell Test in the Problem of Cognitive Semantic Information Retrieval
- •2.1 Bell Inequality and Its Interpretation
- •2.2 Bell Test in Semantic Retrieving
- •3 Results
- •References
- •1 Introduction
- •2 Basics of Quantum Probability Theory
- •3 Steps to Build an HSM Model
- •3.1 How to Determine the Compatibility Relations
- •3.2 How to Determine the Dimension
- •3.5 Compute the Choice Probabilities
- •3.6 Estimate Model Parameters, Compare and Test Models
- •4 Computer Programs
- •5 Concluding Comments
- •References
- •Basics of Quantum Theory for Quantum-Like Modeling Information Retrieval
- •1 Introduction
- •3 Quantum Mathematics
- •3.1 Hermitian Operators in Hilbert Space
- •3.2 Pure and Mixed States: Normalized Vectors and Density Operators
- •4 Quantum Mechanics: Postulates
- •5 Compatible and Incompatible Observables
- •5.1 Post-Measurement State From the Projection Postulate
- •6 Interpretations of Quantum Mechanics
- •6.1 Ensemble and Individual Interpretations
- •6.2 Information Interpretations
- •7 Quantum Conditional (Transition) Probability
- •9 Formula of Total Probability with the Interference Term
- •9.1 Växjö (Realist Ensemble Contextual) Interpretation of Quantum Mechanics
- •10 Quantum Logic
- •11 Space of Square Integrable Functions as a State Space
- •12 Operation of Tensor Product
- •14 Qubit
- •15 Entanglement
- •References
- •1 Introduction
- •2 Background
- •2.1 Distributional Hypothesis
- •2.2 A Brief History of Word Embedding
- •3 Applications of Word Embedding
- •3.1 Word-Level Applications
- •3.2 Sentence-Level Application
- •3.3 Sentence-Pair Level Application
- •3.4 Seq2seq Application
- •3.5 Evaluation
- •4 Reconsidering Word Embedding
- •4.1 Limitations
- •4.2 Trends
- •4.4 Towards Dynamic Word Embedding
- •5 Conclusion
- •References
- •1 Introduction
- •2 Motivating Example: Car Dealership
- •3 Modelling Elementary Data Types
- •3.1 Orthogonal Data Types
- •3.2 Non-orthogonal Data Types
- •4 Data Type Construction
- •5 Quantum-Based Data Type Constructors
- •5.1 Tuple Data Type Constructor
- •5.2 Set Data Type Constructor
- •6 Conclusion
- •References
- •Incorporating Weights into a Quantum-Logic-Based Query Language
- •1 Introduction
- •2 A Motivating Example
- •5 Logic-Based Weighting
- •6 Related Work
- •7 Conclusion
- •References
- •Searching for Information with Meet and Join Operators
- •1 Introduction
- •2 Background
- •2.1 Vector Spaces
- •2.2 Sets Versus Vector Spaces
- •2.3 The Boolean Model for IR
- •2.5 The Probabilistic Models
- •3 Meet and Join
- •4 Structures of a Query-by-Theme Language
- •4.1 Features and Terms
- •4.2 Themes
- •4.3 Document Ranking
- •4.4 Meet and Join Operators
- •5 Implementation of a Query-by-Theme Language
- •6 Related Work
- •7 Discussion and Future Work
- •References
- •Index
- •Preface
- •Organization
- •Contents
- •Fundamentals
- •Why Should We Use Quantum Theory?
- •1 Introduction
- •2 On the Human Science/Natural Science Issue
- •3 The Human Roots of Quantum Science
- •4 Qualitative Parallels Between Quantum Theory and the Human Sciences
- •5 Early Quantitative Applications of Quantum Theory to the Human Sciences
- •6 Epilogue
- •References
- •Quantum Cognition
- •1 Introduction
- •2 The Quantum Persuasion Approach
- •3 Experimental Design
- •3.1 Testing for Perspective Incompatibility
- •3.2 Quantum Persuasion
- •3.3 Predictions
- •4 Results
- •4.1 Descriptive Statistics
- •4.2 Data Analysis
- •4.3 Interpretation
- •5 Discussion and Concluding Remarks
- •References
- •1 Introduction
- •2 A Probabilistic Fusion Model of Trust
- •3 Contextuality
- •4 Experiment
- •4.1 Subjects
- •4.2 Design and Materials
- •4.3 Procedure
- •4.4 Results
- •4.5 Discussion
- •5 Summary and Conclusions
- •References
- •Probabilistic Programs for Investigating Contextuality in Human Information Processing
- •1 Introduction
- •2 A Framework for Determining Contextuality in Human Information Processing
- •3 Using Probabilistic Programs to Simulate Bell Scenario Experiments
- •References
- •1 Familiarity and Recollection, Verbatim and Gist
- •2 True Memory, False Memory, over Distributed Memory
- •3 The Hamiltonian Based QEM Model
- •4 Data and Prediction
- •5 Discussion
- •References
- •Decision-Making
- •1 Introduction
- •1.2 Two Stage Gambling Game
- •2 Quantum Probabilities and Waves
- •2.1 Intensity Waves
- •2.2 The Law of Balance and Probability Waves
- •2.3 Probability Waves
- •3 Law of Maximal Uncertainty
- •3.1 Principle of Entropy
- •3.2 Mirror Principle
- •4 Conclusion
- •References
- •1 Introduction
- •4 Quantum-Like Bayesian Networks
- •7.1 Results and Discussion
- •8 Conclusion
- •References
- •Cybernetics and AI
- •1 Introduction
- •2 Modeling of the Vehicle
- •2.1 Introduction to Braitenberg Vehicles
- •2.2 Quantum Approach for BV Decision Making
- •3 Topics in Eigenlogic
- •3.1 The Eigenlogic Operators
- •3.2 Incorporation of Fuzzy Logic
- •4 BV Quantum Robot Simulation Results
- •4.1 Simulation Environment
- •5 Quantum Wheel of Emotions
- •6 Discussion and Conclusion
- •7 Credits and Acknowledgements
- •References
- •1 Introduction
- •2.1 What Is Intelligence?
- •2.2 Human Intelligence and Quantum Cognition
- •2.3 In Search of the General Principles of Intelligence
- •3 Towards a Moral Test
- •4 Compositional Quantum Cognition
- •4.1 Categorical Compositional Model of Meaning
- •4.2 Proof of Concept: Compositional Quantum Cognition
- •5 Implementation of a Moral Test
- •5.2 Step II: A Toy Example, Moral Dilemmas and Context Effects
- •5.4 Step IV. Application for AI
- •6 Discussion and Conclusion
- •Appendix A: Example of a Moral Dilemma
- •References
- •Probability and Beyond
- •1 Introduction
- •2 The Theory of Density Hypercubes
- •2.1 Construction of the Theory
- •2.2 Component Symmetries
- •2.3 Normalisation and Causality
- •3 Decoherence and Hyper-decoherence
- •3.1 Decoherence to Classical Theory
- •4 Higher Order Interference
- •5 Conclusions
- •A Proofs
- •References
- •Information Retrieval
- •1 Introduction
- •2 Related Work
- •3 Quantum Entanglement and Bell Inequality
- •5 Experiment Settings
- •5.1 Dataset
- •5.3 Experimental Procedure
- •6 Results and Discussion
- •7 Conclusion
- •A Appendix
- •References
- •Investigating Bell Inequalities for Multidimensional Relevance Judgments in Information Retrieval
- •1 Introduction
- •2 Quantifying Relevance Dimensions
- •3 Deriving a Bell Inequality for Documents
- •3.1 CHSH Inequality
- •3.2 CHSH Inequality for Documents Using the Trace Method
- •4 Experiment and Results
- •5 Conclusion and Future Work
- •A Appendix
- •References
- •Short Paper
- •An Update on Updating
- •References
- •Author Index
- •The Sure Thing principle, the Disjunction Effect and the Law of Total Probability
- •Material and methods
- •Experimental results.
- •Experiment 1
- •Experiment 2
- •More versus less risk averse participants
- •Theoretical analysis
- •Shared features of the theoretical models
- •The Markov model
- •The quantum-like model
- •Logistic model
- •Theoretical model performance
- •Model comparison for risk attitude partitioning.
- •Discussion
- •Authors contributions
- •Ethical clearance
- •Funding
- •Acknowledgements
- •References
- •Markov versus quantum dynamic models of belief change during evidence monitoring
- •Results
- •Model comparisons.
- •Discussion
- •Methods
- •Participants.
- •Task.
- •Procedure.
- •Mathematical Models.
- •Acknowledgements
- •New Developments for Value-based Decisions
- •Context Effects in Preferential Choice
- •Comparison of Model Mechanisms
- •Qualitative Empirical Comparisons
- •Quantitative Empirical Comparisons
- •Neural Mechanisms of Value Accumulation
- •Neuroimaging Studies of Context Effects and Attribute-Wise Decision Processes
- •Concluding Remarks
- •Acknowledgments
- •References
- •Comparison of Markov versus quantum dynamical models of human decision making
- •CONFLICT OF INTEREST
- •Endnotes
- •FURTHER READING
- •REFERENCES
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quantum machine learning |
The Power of Distraction: An Experimental Test of Quantum Persuasion |
37 |
no new information of relevance for the choice was provided and yet it did a ect the choice.
5 Discussion and Concluding Remarks
In the experiment we performed, incompatible information i.e., a change of focus, was shown to a ect revealed preferences for uncertain alternatives which we interpret as distraction a ecting beliefs (rather than preferences). Because the two projects are classical objects our results are not consistent with rationality. When Receiver processes information about a classical object as if it was a quantum system, she is mistaken. But as amply evidenced by Kahneman’s best selling book “Thinking Fast and Slow” [11], information processing is not always disciplined by rational thinking when the brain processes information quickly. Moreover a learning process adapted to the quantum-like world may be appropriate when you are interested in actions/decisions produced by other people as they would also have a quantum-like representation of the world. So, while fast quantum-like information processing is inappropriate when dealing with simple decision involving classical objects, it may be suitable in many situations involving human beings. We believe we should not dismiss quantum-like information processing as overly irrational.
Finally, we do not believe that the quantum approach is an alternative to all other behavioral theories. Instead we believe that it provides rigorous foundations to a number of them as argued for instance in [15].
Acknowledgements. We would like to thank Jerome Busemeyer for a very valuable suggestion on the design of the experiment.
References
1.Akerlof, G.A., Shiller, R.J.: Phishing for Phools: The Economics of Manipulation and Deception. Princeton University Press, Princeton (2015)
2.Baron, R.S., Baron, P.H., Miller, N.: The relation between distraction and persuasion. Psychol. Bull. 80(4), 310–323 (1973)
3.Busemeyer, J.R., Bruza, P.D.: Quantum Models of Cognition and Decision. Cambridge University Press, Cambridge (2012)
4.Chong, D., Druckman, J.N.: Framing theory. Annu. Rev. Polit. Sci. 10, 103–126 (2007)
5.Danilov, V., Lambert-Mogiliansky, A.: Preparing a (quantum) belief system. Theor. Comput. Sci. 752, 97–103 (2018)
6.Danilov, V., Lambert-Mogiliansky, A.: Targeting in quantum persuasion problem. J. Math. Econ. 78, 142–149 (2018)
7.Danilov, V., Lambert-Mogiliansky, A., Vergopoulos, V.: Dynamic consistency of expected utility under non-classical (quantum) uncertainty. Theor. Decis. 84(4), 645–670 (2018)
8.DellaVigna, S., List, J.A., Malmendier, U.: Testing for altruism and social pressure in charitable giving. Q. J. Econ. 127(1), 1–56 (2012)
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38A. Lambert-Mogiliansky et al.
9.Festinger, L., Maccoby, N.: On resistance to persuasive communications. J. Abnorm. Soc. Psychol. 68(4), 359–366 (1964)
10.Haven, E., Khrennikov, A.: A brief introduction to quantum formalism. In: The Palgrave Handbook of Quantum Models in Social Science, pp. 1–17 (2017)
11.Kahneman, D.: Thinking, Fast and Slow. Macmillan, London (2011)
12.Kamenica, E., Gentzkow, M.: Bayesian persuasion. Am. Econ. Rev. 101(6), 2590– 2615 (2011)
13.Kees, J., Berry, C., Burton, S., Sheehan, K.: An analysis of data quality: professional panels, student subject pools, and Amazon’s Mechanical Turk. J. Advert. 46(1), 141–155 (2017)
14.Kupor, D.M., Tormala, Z.L.: Persuasion, interrupted: the e ect of momentary interruptions on message processing and persuasion. J. Consum. Res. 42(2), 300– 315 (2015)
15.Lambert-Mogiliansky, A., Busemeyer, J.: Quantum type indeterminacy in dynamic decision-making: self-control through identity management. Games 3(2), 97–118 (2012)
16.Petty, R.E., Cacioppo, J.T.: The elaboration likelihood model of persuasion. In: Petty, R.E., Cacioppo, J.T. (eds.) Communication and Persuasion: Central and Peripheral Routes to Attitude Change, pp. 1–24. Springer, New York (1986). https://doi.org/10.1007/978-1-4612-4964-1 1
17.White, L.C., Pothos, E.M., Busemeyer, J.R.: Insights from quantum cognitive models for organizational decision making. J. Appl. Res. Mem. Cogn. 4(3), 229–238 (2015)
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quantum machine learning |
Are Decisions of Image Trustworthiness
Contextual? A Pilot Study
Peter D. Bruza(B) and Lauren Fell
School of Information Systems,
Queensland University of Technology, Brisbane, Australia
p.bruza@qut.edu.au
Abstract. This article documents an empirical pilot study conducted to determine whether decisions of image trustworthiness are contextual. Contextuality is an active area of investigation in quantum cognition, however there has been little compelling evidence of its presence in human information processing. A Bell scenario experimental design was employed which manipulated both content and representational features in order to minimize the di erence in marginal probabilities across experimental conditions. In addition, participants were subjected to time pressure in order to promote more spontaneous decisions. Results revealed no significant di erences in marginal probabilities, however, no evidence of contextuality was found. The study revealed a tension between the requirement for minimizing the di erence in marginal probabilities and the need to produce the strong correlations required to empirically ascertain contextuality.
1 Introduction
Understanding of trust is pivotal in today’s environment characterized by claims of fake news and deliberate digital misdirection [15]. It is from concerns regarding the edge of what is acceptable or not acceptable that reputed organizations with image archives such Getty Images (www.gettimages.com) and Reuters (http:// www.reuters.com/) have a zero tolerance policy on photo manipulations. A photographer who modified a golfing image to remove a background bystander was terminated by Getty Images in accordance with this policy [12]. Similarly, a Pulitzer prize-winning photographer was fired after he had admitted to altering an image of the conflict in Syria by photoshopping a camera out of the image. In both cases the stance taken is that an image must be a totally true and accurate depiction of reality. Naturally, much hinges on how accuracy is interpreted and where a human subject sets the threshold for an image being “accurate enough” to be judged trustworthy. For example, a human subject might still judge the Pulitzer prize winner’s photograph as trustworthy, knowing the camera had been photoshopped out, simply because the object erased did not influence the photograph’s resemblance to an actual war scene in Syria. This example attempts to demonstrate that judgments of image trustworthiness are cognitively situated. It turns out that visual fluency is an important factor.
c Springer Nature Switzerland AG 2019
B. Coecke and A. Lambert-Mogiliansky (Eds.): QI 2018, LNCS 11690, pp. 39–50, 2019. https://doi.org/10.1007/978-3-030-35895-2_3
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quantum machine learning |
40 P. D. Bruza and L. Fell
Visual fluency is based on the principle that any visual stimulus requires cognitive resources to process; the more work required, the less fluent the process. Cognitive work is determined by perceptual processing of image clarity, contrast, etc. [17], and is also determined by the evaluation of aspects of content, resemblance to what is expected, representation (e.g., geometric and artistic depictions), etc [16]. Images that cohere with background beliefs on any of these factors are more visually fluent than properties that surprise or confuse us. The amount of cognitive work can be measured by the speed and accuracy of visual processing as well as the reported ease or di culty of visual judgments [8]. As a consequence, manipulated images are less detectable the more they conform to largely unconscious rules of visual fluency. Ease of visual processing results in an illusion of accuracy, perhaps because perceptual fluency elicits a feeling of familiarity, and hence trust. For example, [13] found that people are more likely to say that they ’liked’ a person shown in an image, if that image was high in visual fluency. Conversely, raucous interruptions to visual fluency are sometimes deployed in image manipulations in order to generate humor, or shock. Recent experimental findings show evidence that both the subject of the image as well as its representational features (features of the image itself as a representation of the subject) were involved when subjects judge the trustworthiness of images such as that of a smiling Vladimir Putin [7]. Although participants in this study were specifically instructed to judge the trustworthiness of an image itself, a dichotomy appeared between participants who were making a deliberative decision based on the content of the image (e.g. Vladimir Putin), and participants making a decision based on representational features of the image, which could be explained in terms of visual fluency. Due to the uncertainty experienced in evaluations of trustworthiness, as well as the potentially high processing di - culty associated with images that were low in visual fluency, the employment of ‘Hot’ processing was likely in play in this experiment. This system is described as one half of Dual Process Theory, which posits that humans employ two distinct systems for information processing [9]. System 1, or ‘Hot’ system, is intuitive, fast, and prone to cognitive biases. System 2, or ‘Cold’ system, is deliberative, slow, and requires significant cognitive resources. The Hot system is often relied upon in circumstances where cognitive resources are sparse [1], uncertainty is present [10], and where a time pressure exists [11].
A conventional way to model the preceding situation is to develop a fusion model whereby an assessment of trust in the content of an image is fused with an assessment of trust in the image as a representation of the content into a single overall assessment of trust. However, is such a reduction into disparate content and representational subsystems valid? This article aims to address this question by determining whether decision making is contextual.