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

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 electroencephalography (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 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-locked activity from confounding 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.

A meta-learning BCI for estimating decision confidence

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.

Separable neural signatures of confidence during perceptual decisions

Perceptual confidence is an evaluation of the validity of perceptual decisions. While there is behavioural evidence that confidence evaluation differs from perceptual decision-making, disentangling these two processes remains a challenge at the neural level. Here, we examined the electrical brain activity of human participants in a protracted perceptual decision-making task where observers tend to commit to perceptual decisions early whilst continuing to monitor sensory evidence for evaluating confidence. Premature decision commitments were revealed by patterns of spectral power overlying motor cortex, followed by an attenuation of the neural representation of perceptual decision evidence. A distinct neural representation was associated with the computation of confidence, with sources localised in the superior parietal and orbitofrontal cortices. In agreement with a dissociation between perception and confidence, these neural resources were recruited even after observers committed to their perceptual decisions, and thus delineate an integral neural circuit for evaluating perceptual decision confidence.

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

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.

Frontal scalp potentials foretell perceptual choice confidence

When making decisions, people naturally ask two implicit questions: how soon can I make a decision, and how certain am I? In perception, people's confidence (how certain?) shows a nonmonotonic relationship with response time (how soon?), such that choice confidence can either increase or decrease with response time. Although a frontoparietal network has been implicated as a neural substrate that binds choice confidence and action (e.g., response time), the dynamic interplay between choice behaviors within such a network has not been clarified. Here, we show that frontal event-related potentials (ERPs) reflect choice confidence before a decision. Specifically, we report a second positive peak of the stimulus-locked frontal ERP at ~500 ms that scales with confidence but not stimulus level, whereas the centroparietal ERP amplitude covaries inversely with response time. This frontal ERP component occurs before the response, which helps explain the inverse relationship between choice confidence and response time (i.e., higher confidence for shorter response time) when choice accuracy is emphasized over speed. Our findings provide the first early neural representation of confidence, consistent with the temporal precedence for its causal role in the current decision-making task: "I decided earlier because I am confident."NEW & NOTEWORTHY We report novel neural correlates of predecisional choice confidence in frontal scalp potential in humans. In conjunction with the centroparietal choice-action event-related potential component, this new frontal choice confidence component further elucidates the dynamics of the frontoparietal decision-making neural circuitry.

Confidence tracks sensory- and decision-related ERP dynamics during auditory detection

Recent research has focused on measuring neural correlates of metacognitive judgments in decision and post-decision processes during memory retrieval and categorization. However, many tasks (e.g., stimulus detection) may require monitoring of earlier sensory processing. Here, participants indicated which of two intervals contained an 80-ms pure tone embedded in white noise. One frequency (e.g., 1000 Hz) was presented on ∼80% of all trials (i.e., 'primary' trials). Another frequency (e.g., 2500 Hz) was presented on ∼20% of trials (i.e., 'probe' trials). The event-related potential (ERP) was used to investigate the processing stages related to confidence. Tone-locked N1, P2, and P3 amplitudes were larger for trials rated with high than low confidence. Interestingly, a P3-like late positivity for the tone-absent interval showed high amplitude for low confidence. No 'primary' vs. 'probe' differences were found. However, confidence rating differences between primary and probe trials were correlated with N1 and tone-present P3 amplitude differences. We suggest that metacognitive judgments can track both sensory- and decision-related processes (indexed by the N1 and P3, respectively). The particular processes on which confidence judgments are based likely depend upon the task an individual is faced with and the information at hand (e.g., presence or absence of a signal).

Metacognition for strategy selection during arithmetic problem-solving in young and older adults

We examined participants' strategy choices and metacognitive judgments during arithmetic problem-solving. Metacognitive judgments were collected either prospectively or retrospectively. We tested whether metacognitive judgments are related to strategy choices on the current problems and on the immediately following problems, and age-related differences in relations between metacognition and strategy choices. Data showed that both young and older adults were able to make accurate retrospective, but not prospective, judgments. Moreover, the accuracy of retrospective judgments was comparable in young and older adults when participants had to select and execute the better strategy. Metacognitive accuracy was even higher in older adults when participants had to only select the better strategy. Finally, low-confidence judgments on current items were more frequently followed by better strategy selection on immediately succeeding items than high-confidence judgments in both young and older adults. Implications of these findings to further our understanding of age-related differences and similarities in adults' metacognitive monitoring and metacognitive regulation for strategy selection in the context of arithmetic problem solving are discussed.

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

The combined effect of fundamental results about neurocognitive processes and advancements in decoding mental states from ongoing brain signals has brought forth a whole range of potential neurotechnological applications. In this article, we review our developments in this area and put them into perspective. These examples cover a wide range of maturity levels with respect to their applicability. While we assume we are still a long way away from integrating Brain-Computer Interface (BCI) technology in general interaction with computers, or from implementing neurotechnological measures in safety-critical workplaces, results have already now been obtained involving a BCI as research tool. In this article, we discuss the reasons why, in some of the prospective application domains, considerable effort is still required to make the systems ready to deal with the full complexity of the real world.

But I Was So Sure! Metacognitive Judgments Are Less Accurate Given Prospectively than Retrospectively

Prospective and retrospective metacognitive judgments have been studied extensively in the field of memory; however, their accuracy has not been systematically compared. Such a comparison is important for studying how metacognitive judgments are formed. Here, we present the results of an experiment aiming to investigate the relation between performance in an anagram task and the accuracy of prospective and retrospective confidence judgments. Participants worked on anagrams and were then asked to respond whether a presented word was the solution. They also rated their confidence, either before or after the response and either before or after seeing the suggested solution. The results showed that although response accuracy always correlated with confidence, this relationship was weaker when metacognitive judgements were given before the response. We discuss the theoretical and methodological implications of this finding for studies on metacognition and consciousness.