Structural mapping in statistical word problems: A relational reasoning approach to Bayesian inference

Presenting natural frequencies facilitates Bayesian inferences relative to using percentages. Nevertheless, many people, including highly educated and skilled reasoners, still fail to provide Bayesian responses to these computationally simple problems. We show that the complexity of relational reaso...

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Detalles Bibliográficos
Autores: Johnson, Eric D., Tubau Sala, Elisabet
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2017
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/108348
Acceso en línea:https://hdl.handle.net/2445/108348
Access Level:acceso abierto
Palabra clave:Estadística bayesiana
Raonament (Psicologia)
Bayesian statistical decision
Reasoning (Psychology)
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spelling Structural mapping in statistical word problems: A relational reasoning approach to Bayesian inferenceJohnson, Eric D.Tubau Sala, ElisabetEstadística bayesianaRaonament (Psicologia)Bayesian statistical decisionReasoning (Psychology)Presenting natural frequencies facilitates Bayesian inferences relative to using percentages. Nevertheless, many people, including highly educated and skilled reasoners, still fail to provide Bayesian responses to these computationally simple problems. We show that the complexity of relational reasoning (e.g., the structural mapping between the presented and requested relations) can help explain the remaining difficulties. With a non-Bayesian inference that required identical arithmetic but afforded a more direct structural mapping, performance was universally high. Furthermore, reducing the relational demands of the task through questions that directed reasoners to use the presented statistics, as compared with questions that prompted the representation of a second, similar sample, also significantly improved reasoning. Distinct error patterns were also observed between these presented- and similar-sample scenarios, which suggested differences in relational-reasoning strategies. On the other hand, while higher numeracy was associated with better Bayesian reasoning, higher-numerate reasoners were not immune to the relational complexity of the task. Together, these findings validate the relational-reasoning view of Bayesian problem solving and highlight the importance of considering not only the presented task structure, but also the complexity of the structural alignment between the presented and requested relations.Springer Verlag2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://hdl.handle.net/2445/108348Articles publicats en revistes (Cognició, Desenvolupament i Psicologia de l'Educació)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésVersió postprint del document publicat a: https://doi.org/10.3758/s13423-016-1159-6Psychonomic Bulletin & Review, 2017, vol. 24, num. 3, p. 964-971https://doi.org/10.3758/s13423-016-1159-6(c) Springer Verlag, 2017info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1083482026-05-27T06:46:51Z
dc.title.none.fl_str_mv Structural mapping in statistical word problems: A relational reasoning approach to Bayesian inference
title Structural mapping in statistical word problems: A relational reasoning approach to Bayesian inference
spellingShingle Structural mapping in statistical word problems: A relational reasoning approach to Bayesian inference
Johnson, Eric D.
Estadística bayesiana
Raonament (Psicologia)
Bayesian statistical decision
Reasoning (Psychology)
title_short Structural mapping in statistical word problems: A relational reasoning approach to Bayesian inference
title_full Structural mapping in statistical word problems: A relational reasoning approach to Bayesian inference
title_fullStr Structural mapping in statistical word problems: A relational reasoning approach to Bayesian inference
title_full_unstemmed Structural mapping in statistical word problems: A relational reasoning approach to Bayesian inference
title_sort Structural mapping in statistical word problems: A relational reasoning approach to Bayesian inference
dc.creator.none.fl_str_mv Johnson, Eric D.
Tubau Sala, Elisabet
author Johnson, Eric D.
author_facet Johnson, Eric D.
Tubau Sala, Elisabet
author_role author
author2 Tubau Sala, Elisabet
author2_role author
dc.subject.none.fl_str_mv Estadística bayesiana
Raonament (Psicologia)
Bayesian statistical decision
Reasoning (Psychology)
topic Estadística bayesiana
Raonament (Psicologia)
Bayesian statistical decision
Reasoning (Psychology)
description Presenting natural frequencies facilitates Bayesian inferences relative to using percentages. Nevertheless, many people, including highly educated and skilled reasoners, still fail to provide Bayesian responses to these computationally simple problems. We show that the complexity of relational reasoning (e.g., the structural mapping between the presented and requested relations) can help explain the remaining difficulties. With a non-Bayesian inference that required identical arithmetic but afforded a more direct structural mapping, performance was universally high. Furthermore, reducing the relational demands of the task through questions that directed reasoners to use the presented statistics, as compared with questions that prompted the representation of a second, similar sample, also significantly improved reasoning. Distinct error patterns were also observed between these presented- and similar-sample scenarios, which suggested differences in relational-reasoning strategies. On the other hand, while higher numeracy was associated with better Bayesian reasoning, higher-numerate reasoners were not immune to the relational complexity of the task. Together, these findings validate the relational-reasoning view of Bayesian problem solving and highlight the importance of considering not only the presented task structure, but also the complexity of the structural alignment between the presented and requested relations.
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/108348
url https://hdl.handle.net/2445/108348
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Versió postprint del document publicat a: https://doi.org/10.3758/s13423-016-1159-6
Psychonomic Bulletin & Review, 2017, vol. 24, num. 3, p. 964-971
https://doi.org/10.3758/s13423-016-1159-6
dc.rights.none.fl_str_mv (c) Springer Verlag, 2017
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) Springer Verlag, 2017
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Verlag
publisher.none.fl_str_mv Springer Verlag
dc.source.none.fl_str_mv Articles publicats en revistes (Cognició, Desenvolupament i Psicologia de l'Educació)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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