Alpha power and stimulus-evoked activity dissociate metacognitive reports of attention, visibility and confidence in a visual detection task

Variability in the detection and discrimination of weak visual stimuli has been linked to prestimulus neural activity. In particular, the power of oscillatory activity in the alpha-band (8-12 Hz) has been shown to impact upon the objective likelihood of stimulus detection, as well as measures of subjective visibility, attention, and decision confidence. We aimed to clarify how prestimulus alpha influences performance and phenomenology, by recording simultaneous subjective measures of attention and confidence (Experiment 1), or attention and visibility (Experiment 2) on a trial-by-trial basis in a visual detection task. Across both experiments, prestimulus alpha power was negatively and linearly correlated with the intensity of subjective attention. In contrast to this linear relationship, we observed a quadratic relationship between the strength of prestimulus alpha power and subjective ratings of confidence and visibility. We find that this same quadratic relationship links prestimulus alpha power to the strength of stimulus evoked responses. Visibility and confidence judgements corresponded to the strength of evoked responses, but confidence, uniquely, incorporated information about attentional state. As such, our findings reveal distinct psychological and neural correlates of metacognitive judgements of attentional state, stimulus visibility, and decision confidence.

Electrophysiological correlates of confidence differ across correct and erroneous perceptual decisions

Every decision we make is accompanied by an estimate of the likelihood that our decision is accurate or appropriate. This likelihood estimate is termed our degree of decision confidence. Recent work has uncovered event-related potential (ERP) correlates of confidence both during decision formation and after a decision has been made. However, the interpretation of these findings is complicated by methodological issues related to ERP amplitude measurement that are prevalent across existing studies. To more accurately characterise the neural correlates of confidence, we presented participants with a difficult perceptual decision task that elicited a broad range of confidence ratings. We identified a frontal ERP component within an onset prior to the behavioural response, which exhibited more positive-going amplitudes in trials with higher confidence ratings. This frontal effect also biased measures of the centro-parietal positivity (CPP) component at parietal electrodes via volume conduction. Amplitudes of the error positivity (Pe) component that followed each decision were negatively associated with confidence for trials with decision errors, but not for trials with correct decisions. We provide evidence for both pre- and post- decisional neural correlates of decision confidence that are observed in trials with correct and erroneous decisions, respectively. Our findings suggest that certainty in having made a correct response is associated with frontal activity during decision formation, whereas certainty in having committed an error is instead associated with the post-decisional Pe component. We additionally show that some previously reported associations between decision confidence and CPP/Pe component amplitudes are (at least partly) a consequence of ERP amplitude measurement-related confounds.

Performance-optimized neural networks as an explanatory framework for decision confidence

Previous work has sought to understand decision confidence as a prediction of the probability that a decision will be correct, leading to debate over whether these predictions are optimal, and whether they rely on the same decision variable as decisions themselves. This work has generally relied on idealized, low-dimensional modeling frameworks, such as signal detection theory or Bayesian inference, leaving open the question of how decision confidence operates in the domain of high-dimensional, naturalistic stimuli. To address this, we developed a deep neural network model optimized to assess decision confidence directly given high-dimensional inputs such as images. The model naturally accounts for a number of puzzling dissociations between decisions and confidence, suggests a principled explanation of these dissociations in terms of optimization for the statistics of sensory inputs, and makes the surprising prediction that, despite these dissociations, decisions and confidence depend on a common decision variable.

Four Simple Questions: Decision-Centered Risk and Project Management

A decision-centric approach to projects creates confidence, improves value, and shortens time to revenue. A straight-forward objective based approach to managing project decisions is presented in the form of four primary questions. Those questions are:Does the issue/threat/opportunity make a material difference to the project? (Materiality)Can anything be done to affect the outcome? (Influence)Can you afford to do anything about it? (Value)What if you are wrong? (Confidence) Materiality – An issue/threat/opportunity must make a material difference to a project decision to be worth receiving attention. The concept of materiality will vary in size and consequence from project to project, so it is important to maintain a decision focus. Understanding the variability in the project with respect to decision thresholds can provide an indication of materiality. We must also ask how different our current assessment of the project, or its environment could get before we would like to change our decision. Affective ability – Accepted risk-management options of avoidance, mitigation, transfer, and acceptance present the decision options within this category. In considering the consequences the options, decision tools such as Indifference Assessment and Pain and Regret Assessment. Avoidance, Mitigation, or Transfer – while most projects can benefit through risk reduction, such effort must make economic sense. Risk reduction paths must add value to the project through added upside or elimination of at least a portion of downside threat. The value of these efforts is aided by use of tools such as Value-of-Information, Value-of-Control, and Value-of-learning. Being wrong – Making a regretful decision is always a possibility but the source of the "wrongness" and its likely impact is often overlooked by teams. In project planning and execution, decision-makers are often presented with a plethora of issues, threats, and opportunities. From development planning through implementation significant time and resource waste can be cut by prioritizing effort to the issues that matter. Understanding the issues in the context of materiality and then what to do, if anything, about an issue, becomes key to maximizing success. This approach cuts waste and focuses the attention on what matters. Decision Intelligence not only increases the probability of making the best decisions, but it also prioritizes work to those items that matter either for value or decision path. While most of the decision tools referenced are well documented in the literature, placing them into the context of the Four Question Approach allows teams and management to focus more closely on efficiently mitigating issues, shortening workflow, and creating significantly higher decision confidence. This novel approach works well in all phases of project planning through project management implementation.

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.

Understanding neural signals of post-decisional performance monitoring: An integrative review

Performance monitoring is a key cognitive function, allowing to detect mistakes and adapt future behavior. Post-decisional neural signals have been identified that are sensitive to decision accuracy, decision confidence and subsequent adaptation. Here, we review recent work that supports an understanding of late error/confidence signals in terms of the computational process of post-decisional evidence accumulation. We argue that the error positivity, a positive-going centro-parietal potential measured through scalp electrophysiology, reflects the post-decisional evidence accumulation process itself, which follows a boundary crossing event corresponding to initial decision commitment. This proposal provides a powerful explanation for both the morphological characteristics of the signal and its relation to various expressions of performance monitoring. Moreover, it suggests that the error positivity –a signal with thus far unique properties in cognitive neuroscience – can be leveraged to furnish key new insights into the inputs to, adaptation, and consequences of the post-decisional accumulation process.

Explaining distortions in metacognition with an attractor network model of decision uncertainty

Metacognition is the ability to reflect on, and evaluate, our cognition and behaviour. Distortions in metacognition are common in mental health disorders, though the neural underpinnings of such dysfunction are unknown. One reason for this is that models of key components of metacognition, such as decision confidence, are generally specified at an algorithmic or process level. While such models can be used to relate brain function to psychopathology, they are difficult to map to a neurobiological mechanism. Here, we develop a biologically-plausible model of decision uncertainty in an attempt to bridge this gap. We first relate the model’s uncertainty in perceptual decisions to standard metrics of metacognition, namely mean confidence level (bias) and the accuracy of metacognitive judgments (sensitivity). We show that dissociable shifts in metacognition are associated with isolated disturbances at higher-order levels of a circuit associated with self-monitoring, akin to neuropsychological findings that highlight the detrimental effect of prefrontal brain lesions on metacognitive performance. Notably, we are able to account for empirical confidence judgements by fitting the parameters of our biophysical model to first-order performance data, specifically choice and response times. Lastly, in a reanalysis of existing data we show that self-reported mental health symptoms relate to disturbances in an uncertainty-monitoring component of the network. By bridging a gap between a biologically-plausible model of confidence formation and observed disturbances of metacognition in mental health disorders we provide a first step towards mapping theoretical constructs of metacognition onto dynamical models of decision uncertainty. In doing so, we provide a computational framework for modelling metacognitive performance in settings where access to explicit confidence reports is not possible.