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Subject- and task-independent neural correlates and prediction of decision confidence in perceptual decision making.
Fernandez-Vargas, Jacobo; Tremmel, Christoph; Valeriani, Davide; Bhattacharyya, Saugat; Cinel, Caterina; Citi, Luca; Poli, Riccardo.
Afiliação
  • Fernandez-Vargas J; Brain-Computer Interfaces and Neural Engineering laboratory, School of Computer Science and Electronic Engineering, University of Essex, Essex, United Kingdom.
  • Tremmel C; Brain-Computer Interfaces and Neural Engineering laboratory, School of Computer Science and Electronic Engineering, University of Essex, Essex, United Kingdom.
  • Valeriani D; Department of Otolaryngology | Head and Neck Surgery, Massachusetts Eye and Ear, Boston, MA, United States of America.
  • Bhattacharyya S; Department of Otolaryngology | Head and Neck Surgery, Harvard Medical School, Boston, MA, United States of America.
  • Cinel C; Brain-Computer Interfaces and Neural Engineering laboratory, School of Computer Science and Electronic Engineering, University of Essex, Essex, United Kingdom.
  • Citi L; School of Computing, Engineering & Intelligent Systems, Ulster University, Londonderry, United Kingdom.
  • Poli R; Brain-Computer Interfaces and Neural Engineering laboratory, School of Computer Science and Electronic Engineering, University of Essex, Essex, United Kingdom.
J Neural Eng ; 18(4)2021 05 13.
Article em En | MEDLINE | ID: mdl-33780913
ABSTRACT
Objective.In many real-world decision tasks, the information available to the decision maker is incomplete. To account for this uncertainty, we associate a degree of confidence to every decision, representing the likelihood of that decision being correct. In this study, we analyse electroencephalography (EEG) data from 68 participants undertaking eight different perceptual decision-making experiments. Our goals are to investigate (1) whether subject- and task-independent neural correlates of decision confidence exist, and (2) to what degree it is possible to build brain computer interfaces that can estimate confidence on a trial-by-trial basis. The experiments cover a wide range of perceptual tasks, which allowed to separate the task-related, decision-making features from the task-independent ones.Approach.Our systems train artificial neural networks to predict the confidence in each decision from EEG data and response times. We compare the decoding performance with three training approaches (1) single subject, where both training and testing data were acquired from the same person; (2) multi-subject, where all the data pertained to the same task, but the training and testing data came from different users; and (3) multi-task, where the training and testing data came from different tasks and subjects. Finally, we validated our multi-task approach using data from two additional experiments, in which confidence was not reported.Main results.We found significant differences in the EEG data for different confidence levels in both stimulus-locked and response-locked epochs. All our approaches were able to predict the confidence between 15% and 35% better than the corresponding reference baselines.Significance.Our results suggest that confidence in perceptual decision making tasks could be reconstructed from neural signals even when using transfer learning approaches. These confidence estimates are based on the decision-making process rather than just the confidence-reporting process.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Eletroencefalografia / Interfaces Cérebro-Computador Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Neural Eng Assunto da revista: NEUROLOGIA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Eletroencefalografia / Interfaces Cérebro-Computador Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Neural Eng Assunto da revista: NEUROLOGIA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Reino Unido