A Quantum Probability Framework for Human Probabilistic Inference

There 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. So...

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Veröffentlicht in:Journal of experimental psychology. General 2017-09, Vol.146 (9), p.1307-1341
Hauptverfasser: Trueblood, Jennifer S., Yearsley, James M., Pothos, Emmanuel M.
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container_title Journal of experimental psychology. General
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creator Trueblood, Jennifer S.
Yearsley, James M.
Pothos, Emmanuel M.
description There 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 both Bayesian 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 3 experiments, we show that our modeling approach explains 5 key phenomena in human inference including order effects, reciprocity (i.e., the inverse fallacy), memorylessness, violations of the Markov condition, and antidiscounting. As far as we are aware, no existing theory or model can explain all 5 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.
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subjects Bayes Theorem
Bayesian analysis
Cognitive Style
Experimental psychology
Human
Humans
Inference
Judgment
Memory
Probability Judgment
Probability Theory
Quantum Theory
Reciprocity
Statistical Probability
Theories
Thinking - physiology
title A Quantum Probability Framework for Human Probabilistic Inference
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