Dewitt, SH;
Adler, N;
Li, C;
Stoilova, E;
Fenton, NE;
Lagnado, DA;
(2023)
Categorical Updating in a Bayesian Propensity Problem.
Cognitive Science
, 47
(7)
, Article e13313. 10.1111/cogs.13313.
Preview |
Text
Categorical Updating in a Bayesian Propensity Problem.pdf - Published Version Download (2MB) | Preview |
Abstract
We present three experiments using a novel problem in which participants update their estimates of propensities when faced with an uncertain new instance. We examine this using two different causal structures (common cause/common effect) and two different scenarios (agent-based/mechanical). In the first, participants must update their estimate of the propensity for two warring nations to successfully explode missiles after being told of a new explosion on the border between both nations. In the second, participants must update their estimate of the accuracy of two early warning tests for cancer when they produce conflicting reports about a patient. Across both experiments, we find two modal responses, representing around one-third of participants each. In the first, "Categorical" response, participants update propensity estimates as if they were certain about the single event, for example, certain that one of the nations was responsible for the latest explosion, or certain about which of the two tests is correct. In the second, "No change" response, participants make no update to their propensity estimates at all. Across the three experiments, the theory is developed and tested that these two responses in fact have a single representation of the problem: because the actual outcome is binary (only one of the nations could have launched the missile; the patient either has cancer or not), these participants believe it is incorrect to update propensities in a graded manner. They therefore operate on a "certainty threshold" basis, whereby, if they are certain enough about the single event, they will make the "Categorical" response, and if they are below this threshold, they will make the "No change" response. Ramifications are considered for the "categorical" response in particular, as this approach produces a positive-feedback dynamic similar to that seen in the belief polarization/confirmation bias literature.
| Type: | Article |
|---|---|
| Title: | Categorical Updating in a Bayesian Propensity Problem |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1111/cogs.13313 |
| Publisher version: | https://doi.org/10.1111/cogs.13313 |
| Language: | English |
| Additional information: | Copyright © 2023 The Authors. Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society (CSS). This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Bayesian network, Belief polarization, Causality, Confirmation bias, Propensity, Uncertainty |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Experimental Psychology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10174050 |
Archive Staff Only
 |
View Item |
