dc.contributor.author | Mei, Ning | |
dc.contributor.author | Rahnev, Dobromir | |
dc.contributor.author | Soto, David | |
dc.date.accessioned | 2024-04-18T14:04:48Z | |
dc.date.available | 2024-04-18T14:04:48Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Mei, N., Rahnev, D., & Soto, D. (2023). Using serial dependence to predict confidence across observers and cognitive domains. Psychonomic Bulletin & Review, 30(4):1596-1608. Doi:10.3758/s13423-023-02261-x | es_ES |
dc.identifier.citation | Psychonomic Bulletin and Review | |
dc.identifier.issn | 1069-9384 | |
dc.identifier.uri | http://hdl.handle.net/10810/66770 | |
dc.description | Published online 7 March 2023 | es_ES |
dc.description.abstract | Our perceptual system appears hardwired to exploit regularities of input features across space and time in seemingly stable
environments. This can lead to serial dependence effects whereby recent perceptual representations bias current perception.
Serial dependence has also been demonstrated for more abstract representations, such as perceptual confidence. Here, we ask
whether temporal patterns in the generation of confidence judgments across trials generalize across observers and different
cognitive domains. Data from the Confidence Database across perceptual, memory, and cognitive paradigms was reanalyzed.
Machine learning classifiers were used to predict the confidence on the current trial based on the history of confidence judgments
on the previous trials. Cross-observer and cross-domain decoding results showed that a model trained to predict confidence
in the perceptual domain generalized across observers to predict confidence across the different cognitive domains.
The recent history of confidence was the most critical factor. The history of accuracy or Type 1 reaction time alone, or in
combination with confidence, did not improve the prediction of the current confidence. We also observed that confidence
predictions generalized across correct and incorrect trials, indicating that serial dependence effects in confidence generation
are uncoupled to metacognition (i.e., how we evaluate the precision of our own behavior). We discuss the ramifications of
these findings for the ongoing debate on domain-generality versus domain-specificity of metacognition. | es_ES |
dc.description.sponsorship | D.S. acknowledges support from the Basque Government
through the BERC 2022-2025 program, from the Spanish
State Research Agency, through the 'Severo Ochoa' Programme for
Centres/Units of Excellence in R&D (CEX2020-001010-S). This project
was funded by project grant PID2019-105494GB-I00 from the
Spanish State Research Agency (DS). We thank Megan Peters and Alan
Lee for the helpful feedback on a previous version of the manuscript. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | SPRINGER | es_ES |
dc.relation | info:eu-repo/grantAgreement/GV/BERC2022-2025 | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/CEX2020-001010-S | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/PID2019-105494GB-I00 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | Confidence | es_ES |
dc.subject | Perception | es_ES |
dc.subject | Memory | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Metacognition | es_ES |
dc.title | Using serial dependence to predict confidence across observers and cognitive domains | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © The Psychonomic Society, Inc. 2023 | es_ES |
dc.relation.publisherversion | https://link.springer.com/journal/13423 | es_ES |
dc.identifier.doi | 10.3758/s13423-023-02261-x | |