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Related Experiment Video

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Propensities and Second Order Uncertainty: A Modified Taxi Cab Problem.

Stephen H Dewitt¹, Norman E Fenton², Alice Liefgreen¹

¹Department of Experimental Psychology, University College London, London, United Kingdom.

Frontiers in Psychology

|November 16, 2020

Summary

People often struggle with causal probabilistic reasoning, especially when witness accuracy is uncertain. This study shows that a witness report can decrease perceived accuracy, yet many participants incorrectly updated their beliefs.

Keywords:

causal Bayesian networks confirmation bias propensity second order uncertainty uncertainty

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Area of Science:

Cognitive Psychology
Decision Science
Bayesian Reasoning

Background:

Causal probabilistic reasoning often relies on fixed probability estimates.
Real-world uncertainty (second-order uncertainty) means estimates are distributions, not fixed points.
Witness accuracy estimates should update based on new evidence.

Purpose of the Study:

To model causal probabilistic reasoning with second-order uncertainty using a Bayesian Network.
To investigate how people update beliefs about event probability and witness accuracy.
To identify common reasoning errors in probabilistic judgment.

Main Methods:

Developed a Bayesian Network model for the taxi-cab problem with second-order uncertainty.
Presented participants with a scenario where a witness identified a less common event.
Collected participant estimates of event probability and witness accuracy, alongside reasoning explanations.

Main Results:

A witness report for a less common event decreases estimated witness accuracy.
Many participants failed to update beliefs normatively, with some showing circular logic (confirmation bias).
A significant portion of participants did not adjust their estimates, suggesting a misunderstanding of evidence relevance.

Conclusions:

Human probabilistic reasoning is susceptible to biases when dealing with second-order uncertainty.
Understanding how people update beliefs about evidence reliability is crucial for cognitive science.
The study highlights the need for improved probabilistic reasoning education and debiasing strategies.