Previous beliefs affect Bayesian reasoning in conditions fostering gist comprehension
It has been shown that Bayesian reasoning is afected by the believability of the data, but it is unknown which conditions could potentiate or reduce such belief efect. Here, we tested the hypothesis that the belief efect would mainly be observed in conditions fostering a gist comprehension of the da...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/220822 |
| Acceso en línea: | https://hdl.handle.net/2445/220822 |
| Access Level: | acceso abierto |
| Palabra clave: | Raonament Teoria de l'estimació Probabilitats Reasoning Estimation theory Probabilities |
| Sumario: | It has been shown that Bayesian reasoning is afected by the believability of the data, but it is unknown which conditions could potentiate or reduce such belief efect. Here, we tested the hypothesis that the belief efect would mainly be observed in conditions fostering a gist comprehension of the data. Accordingly, we expected to observe a signifcant belief efect in iconic rather than in textual presentations and, in general, when nonnumerical estimates were requested. The results of three studies showed more accurate Bayesian estimates, either expressed numerically or nonnumerically, for icons than for text descriptions of natural frequencies. Moreover, in line with our expectations, nonnumerical estimates were, in general, more accurate for believable rather than for unbelievable scenarios. In contrast, the belief efect on the accuracy of the numerical estimates depended on the format and on the complexity of the calculation. The present fndings also showed that single-event posterior probability estimates based on described frequencies were more accurate when expressed nonnumerically rather than numerically, opening new avenues for the development of interventions to improve Bayesian reasoning. |
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