Evaluating the role of mental sampling in probability judgments
Illogical rankings occur in a predictable manner
Bibliographic Data
| ID | 21306333 |
|---|---|
| Authors | Xiaotong Liu (0000-0003-1141-9184, University of Mannheim, corresponding author), Arndt Broder (0000-0002-2658-4791, University of Mannheim), Henrik Singmann (0000-0002-4842-3657, University College London) |
| Year | 2025 |
| Volume | 263 |
| Pages | 106125 |
| Publication date | 2025-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Cognition (JOURNAL) |
| Journal identifiers | ISSN: 0010-0277 • E-ISSN: 1873-7838 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.cognition.2025.106125 |
| PMID | 40543218 |
| OpenAlex | W4411466332 |
| Language | EN |
| References cited | 37 |
People's probability judgments often appear to be probabilistically incoherent, as exemplified by the conjunction fallacy. Recently, various sampling-based models have been proposed as an integrative account for different biases and fallacies in probability judgments. In the current study, the novel Event Ranking Task was used to investigate sampling-based models of probability judgments. On each trial of the Event Ranking Task, participants were asked to provide a ranking for an event set consisting of four events, A, not-A, B, and not-B, in terms of their perceived likelihoods. Qualitative predictions were formally derived by assuming direct sampling from a fixed underlying probability distribution. Adding read-out noise in the sampling process - as suggested in the Probability Theory plus Noise model (Costello and Watts, 2014) - did not change the qualitative predictions. Two online experiments, where participants ranked twelve different event sets, yielded results in line with the qualitative predictions, providing evidence for the idea that mental sampling underlies probability judgments
Cognitive psychology · Econometrics · Event (particle physics) · Probability distribution · Ranking (information retrieval) · Sampling (signal processing) · Set (abstract data type) · Statistics · Task (project management) · Artificial Intelligence · Behavioral Health and Interventions · Computer Science · Decision-Making and Behavioral Economics · Forecasting Techniques and Applications · Mathematics · Psychology · Social Psychology
One and Done? Optimal Decisions From Very Few Samples
Extensional versus intuitive reasoning
Theoretical and empirical review of multinomial process tree modeling
Support theory
Random variation and systematic biases in probability estimation
Clarifying the relationship between coherence and accuracy in probability judgments
Evaluating the robustness of parameter estimates in cognitive models
Explaining High Conjunction Fallacy Rates
| Citation velocity | historical |
|---|---|
| Highly cited | No |