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

Fernández-Vargas, Jacobo; Tremmel, Christoph; Valeriani, Davide; Bhattacharyya, Saugat; Cinel, Caterina; Citi, Luca; Poli, Roberto

doi:10.1088/1741-2552/abf2e4

Cited by 9 publications

(16 citation statements)

References 62 publications

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“…In this study we investigate whether a suitably tailored adaptation of a recently introduced transfer-learning technique based on meta-learning-meta-learning by biased regularisation [59]-could improve the performance of BCIs for decision-confidence prediction with respect to other learning methods. We chose a linear algorithm for this problem since the amplitude of the ERPs associated with varying levels of confidence is approximately linearly related to confidence [16,36,62]. The algorithm has two phases: first, it iteratively trains a BCI with data from previous participants to learn domain-invariant features and then it uses data from a new participant to quickly finetune the BCI.…”

Section: Contributions Of This Studymentioning

confidence: 99%

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A meta-learning BCI for estimating decision confidence

et al. 2022

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Objective: We investigated whether a recently introduced transfer-learning technique based on meta-learning could improve the performance of Brain-Computer Interfaces (BCIs) for decision-confidence prediction with respect to more traditional machine learning methods. Approach: We adapted the meta-learning by biased regularisation algorithm to the problem of predicting decision confidence from EEG and EOG data on a decision-by-decision basis in a difficult target discrimination task based on video feeds. The method exploits previous participants’ data to produce a prediction algorithm that is then quickly tuned to new participants. We compared it with with the traditional single-subject training almost universally adopted in BCIs, a state-of-the-art transfer learning technique called Domain Adversarial Neural Networks (DANN), a transfer-learning adaptation of a zero-training method we used recently for a similar task, and with a simple baseline algorithm. Main results: The meta-learning approach was significantly better than other approaches in most conditions, and much better in situations where limited data from a new participant are available for training/tuning. Meta-learning by biased regularisation allowed our BCI to seamlessly integrate information from past participants with data from a specific user to produce high-performance predictors. Its robustness in the presence of small training sets is a real-plus in BCI applications, as new users need to train the BCI for a much shorter period. Significance: Due to the variability and noise of EEG/EOG data, BCIs need to be normally trained with data from a specific participant. This work shows that even better performance can be obtained using our version of meta-learning by biased regularisation.

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Section: Contributions Of This Studymentioning

confidence: 99%

“…As discussed in [36], just considering the error of the prediction is not enough to evaluate the quality of the confidence prediction. In particular, given that confidence is a (subjective) evaluation of task performance, we need to evaluate how well the confidence is modulated by decision accuracy.…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

A meta-learning BCI for estimating decision confidence

et al. 2022

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“…When using BCI confidence to weigh individual opinions, groups become more accurate than equally-sized groups based on standard majority or on subjective confidence judgments in a variety of decisionmaking tasks, including visual search [28] and face recognition [13]. These BCI decoders rely on neural correlates of confidence that are common across tasks and people [29] and are extracted using spatio-temporal transformations of multielectrode EEG activity, such as common spatial patterns [30]. Yet, in these studies, a different BCI was trained for each user to promote decoding accuracy and user training [31].…”

Section: Optimal Brain-computer Interfacesmentioning

confidence: 99%

Optimized Collaborative Brain-Computer Interfaces for Enhancing Face Recognition

Salvatore

Valeriani²,

Piccialli

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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The aim of this study is to maximize group decision performance by optimally adapting EEG confidence decoders to the group composition. We train linear support vector machines to estimate the decision confidence of human participants from their EEG activity. We then simulate groups of different size and membership by combining individual decisions using a weighted majority rule. The weights assigned to each participant in the group are chosen solving a small-dimension, mixed, integer linear programming problem, where we maximize the group performance on the training set. We therefore introduce optimized collaborative brain-computer interfaces (BCIs), where the decisions of each team member are weighted according to both the individual neural activity and the group composition. We validate this approach on a face recognition task undertaken by 10 human participants. The results show that optimal collaborative BCIs significantly enhance team performance over other BCIs, while improving fairness within the group. This research paves the way for practical applications of collaborative BCIs to realistic scenarios characterized by stable teams, where optimizing the decision policy of a single group may lead to significant long-term benefits of team dynamics.

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“…Regarding the first question, prior work has shown that decision confidence is reflected in neural activity, and can be decoded at above-chance levels [39,[41][42][43][44][45]. However, it is still largely unclear whether the neural encoding of confidence is stimulus-locked or response-locked.…”

Section: Introductionmentioning

confidence: 99%

Post-stimulus encoding of decision confidence in EEG: toward a brain–computer interface for decision making

Sadras,

Sani,

Ahmadipour

et al. 2023

J. Neural Eng.

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Objective. When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain-computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time. Although prior work has shown that decision confidence is represented in brain signals, it is unclear if the representation is stimulus-locked or response-locked, and whether stimulus-locked pre-response decoding is sufficiently accurate for enabling such a BCI. Approach. We investigate the neural correlates of confidence by collecting high-density EEG during a perceptual decision task with realistic stimuli. Importantly, we design our task to include a post-stimulus gap that prevents the confounding of stimulus-locked activity by response-locked activity and vice versa, and then compare with a task without this gap. Main Results. We perform event-related potential (ERP) and source-localization analyses. Our analyses suggest that the neural correlates of confidence are stimulus-locked, and that an absence of a post-stimulus gap could cause these correlates to incorrectly appear as response-locked. By preventing response-related activity to confound stimulus-locked activity, we then show that confidence can be reliably decoded from single-trial stimulus-locked pre-response EEG alone. We also identify a high-performance classification algorithm by comparing a battery of algorithms. Lastly, we design a simulated BCI framework to show that the EEG classification is accurate enough to build a BCI and that the decoded confidence could be used to improve decision making performance particularly when the task difficulty and cost of errors are high. Significance. Our results show feasibility of non-invasive EEG-based BCIs to improve human decision making.

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

Cited by 9 publications

References 62 publications

A meta-learning BCI for estimating decision confidence

A meta-learning BCI for estimating decision confidence

Optimized Collaborative Brain-Computer Interfaces for Enhancing Face Recognition

Post-stimulus encoding of decision confidence in EEG: toward a brain–computer interface for decision making

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