dc.contributor.author | Mei, Ning | |
dc.contributor.author | Santana, Roberto | |
dc.contributor.author | Soto, David ![ORCID](/themes/Mirage2//images/orcid_16x16.png) | |
dc.date.accessioned | 2022-06-08T14:09:22Z | |
dc.date.available | 2022-06-08T14:09:22Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Mei, N., Santana, R. & Soto, D. Informative neural representations of unseen contents during higher-order processing in human brains and deep artificial networks. Nat Hum Behav 6, 720–731 (2022). https://doi.org/10.1038/s41562-021-01274-7 | es_ES |
dc.identifier.citation | Nature Human Behaviour | |
dc.identifier.issn | 2397-3374 | |
dc.identifier.uri | http://hdl.handle.net/10810/56860 | |
dc.description | Published: 03 February 2022 | es_ES |
dc.description.abstract | A framework to pinpoint the scope of unconscious processing is critical to improve models of visual consciousness. Previous research observed brain signatures of unconscious processing in visual cortex, but these were not reliably identified. Further, whether unconscious contents are represented in high-level stages of the ventral visual stream and linked parieto-frontal areas remains unknown. Using a within-subject, high-precision functional magnetic resonance imaging approach, we show that unconscious contents can be decoded from multi-voxel patterns that are highly distributed alongside the ventral visual pathway and also involving parieto-frontal substrates. Classifiers trained with multi-voxel patterns of conscious items generalized to predict the unconscious counterparts, indicating that their neural representations overlap. These findings suggest revisions to models of consciousness such as the neuronal global workspace. We then provide a computational simulation of visual processing/representation without perceptual sensitivity by using deep neural networks performing a similar visual task. The work provides a framework for pinpointing the representation of unconscious knowledge across different task domains. | es_ES |
dc.description.sponsorship | D.S. acknowledges support from the Basque Government through the BERC 2018-2021
programme, from the Spanish Ministry of Economy and Competitiveness, through
the ‘Severo Ochoa’ Programme for Centres/Units of Excellence in R & D (CEX2020-
001010-S) and also from project grants PSI2016-76443-P and PID2019-105494GB-I00 from MINECO. R.S. acknowledges support by the Basque Government (IT1244-19 and
ELKARTEK programmes), and the Spanish Ministry of Economy and Competitiveness
MINECO (project TIN2016-78365-R). The funders had no role in study design, data
collection and analysis, decision to publish or preparation of the manuscript. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | NATURE RESEARCH | es_ES |
dc.relation | info:eu-repo/grantAgreement/GV/BERC2018-2021 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/CEX2020-001010-S | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/PSI2016-76443-P | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/PID2019-105494GB-I00 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/TIN2016-78365-R | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | Consciousness | es_ES |
dc.subject | Human behaviour | es_ES |
dc.title | Informative neural representations of unseen contents during higher-order processing in human brains and deep artificial networks | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | Copyright © 2022, The Author(s), under exclusive licence to Springer Nature Limited | es_ES |
dc.relation.publisherversion | https://www.nature.com/nathumbehav/ | es_ES |
dc.identifier.doi | 10.1038/s41562-021-01274-7 | |