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Subject- and task-independent neural correlates and prediction of decision confidence in perceptual decision making

Fernandez-Vargas, Jacobo and Tremmel, Christoph and Valeriani, Davide and Bhattacharyya, Saugat and Cinel, Caterina and Citi, Luca and Poli, Riccardo (2021) 'Subject- and task-independent neural correlates and prediction of decision confidence in perceptual decision making.' Journal of Neural Engineering, 18 (4). 046055-046055. ISSN 1741-2552

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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.

Item Type: Article
Uncontrolled Keywords: Humans; Electroencephalography; Decision Making; Reaction Time; Brain-Computer Interfaces; Neural Networks, Computer
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 30 Sep 2021 13:24
Last Modified: 14 Jan 2022 22:01

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