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

被引:7
作者
Fernandez-Vargas, Jacobo [1 ]
Tremmel, Christoph [1 ]
Valeriani, Davide [2 ,3 ]
Bhattacharyya, Saugat [1 ,4 ]
Cinel, Caterina [1 ]
Citi, Luca [1 ]
Poli, Riccardo [1 ]
机构
[1] Univ Essex, Sch Comp Sci & Elect Engn, Brain Comp Interfaces & Neural Engn Lab, Colchester, Essex, England
[2] Massachusetts Eye & Ear, Dept Otolaryngol Head & Neck Surg, Boston, MA USA
[3] Harvard Med Sch, Dept Otolaryngol Head & Neck Surg, Boston, MA 02115 USA
[4] Ulster Univ, Sch Comp Engn & Intelligent Syst, Coleraine, Londonderry, North Ireland
基金
英国工程与自然科学研究理事会;
关键词
BCI; confidence; EEG; decision-making; transfer-learning; neural correlate; BRAIN-COMPUTER INTERFACES; EVIDENCE ACCUMULATION; VIRTUAL-REALITY; COMMUNICATION; METACOGNITION; AUGMENTATION; COMPUTATION; WORKLOAD; GEOMETRY; CHOICE;
D O I
10.1088/1741-2552/abf2e4
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
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.
引用
收藏
页数:16
相关论文
共 64 条
  • [1] Comparing Bayesian and non-Bayesian accounts of human confidence reports
    Adler, William T.
    Ma, Wei Ji
    [J]. PLOS COMPUTATIONAL BIOLOGY, 2018, 14 (11)
  • [2] Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making
    Aitchison, Laurence
    Bang, Dan
    Bahrami, Bahador
    Latham, Peter E.
    [J]. PLOS COMPUTATIONAL BIOLOGY, 2015, 11 (10)
  • [3] [Anonymous], 2008, Oxford Psychology Series, DOI DOI 10.1093/ACPROF:OSO/9780195070019.001.0001
  • [4] THE CALIBRATION AND RESOLUTION OF CONFIDENCE IN PERCEPTUAL JUDGMENTS
    BARANSKI, JV
    PETRUSIC, WM
    [J]. PERCEPTION & PSYCHOPHYSICS, 1994, 55 (04): : 412 - 428
  • [5] How the brain integrates costs and benefits during decision making
    Basten, Ulrike
    Biele, Guido
    Heekeren, Hauke R.
    Fiebach, Christian J.
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2010, 107 (50) : 21767 - 21772
  • [6] Berka C, 2007, AVIAT SPACE ENVIR MD, V78, pB231
  • [7] Bhattacharyya S, 2019, IEEE ENG MED BIO, P3099, DOI [10.1109/EMBC.2019.8856309, 10.1109/embc.2019.8856309]
  • [8] Bhattacharyya S, 2019, I IEEE EMBS C NEUR E, P159, DOI [10.1109/ner.2019.8717146, 10.1109/NER.2019.8717146]
  • [9] Breaking the silence: Brain-computer interfaces (BCI) for communication and motor control
    Birbaumer, Niels
    [J]. PSYCHOPHYSIOLOGY, 2006, 43 (06) : 517 - 532
  • [10] Shared Neural Markers of Decision Confidence and Error Detection
    Boldt, Annika
    Yeung, Nick
    [J]. JOURNAL OF NEUROSCIENCE, 2015, 35 (08) : 3478 - 3484