A low-dimensional approximation of optimal confidence.

Human decision making is accompanied by a sense of confidence. According to Bayesian decision theory, confidence reflects the learned probability of making a correct response, given available data (e.g., accumulated stimulus evidence and response time). Although optimal, independently learning these probabilities for all possible data combinations is computationally intractable. Here, we describe a novel model of confidence implementing a low-dimensional approximation of this optimal yet intractable solution. This model allows efficient estimation of confidence, while at the same time accounting for idiosyncrasies, different kinds of biases and deviation from the optimal probability correct. Our model dissociates confidence biases resulting from the estimate of the reliability of evidence by individuals (captured by parameter α), from confidence biases resulting from general stimulus independent under and overconfidence (captured by parameter ß). We provide empirical evidence that this model accurately fits both choice data (accuracy, response time) and trial-by-trial confidence ratings simultaneously. Finally, we test and empirically validate two novel predictions of the model, namely that 1) changes in confidence can be independent of performance and 2) selectively manipulating each parameter of our model leads to distinct patterns of confidence judgments. As a tractable and flexible account of the computation of confidence, our model offers a clear framework to interpret and further resolve different forms of confidence biases.

Affective valence does not reflect progress prediction errors in perceptual decisions.

Affective valence and intensity form the core of our emotional experiences. It has been proposed that affect reflects the prediction error between expected and actual states, such that better/worse-than-expected discrepancies result in positive/negative affect. However, whether the same principle applies to progress prediction errors remains unclear. We empirically and computationally evaluate the hypothesis that affect reflects the difference between expected and actual progress in forming a perceptual decision. We model affect within an evidence accumulation framework where actual progress is mapped onto the drift-rate parameter and expected progress onto an expected drift-rate parameter. Affect is computed as the difference between the expected and actual amount of accumulated evidence. We find that expected and actual progress both influence affect, but in an additive manner that does not align with a prediction error account. Our computational model reproduces both task behavior and affective ratings, suggesting that sequential sampling models provide a promising framework to model progress appraisals. These results show that although affect is sensitive to both expected and actual progress, it does not reflect the computation of a progress prediction error.

Manipulating Prior Beliefs Causally Induces Under- and Overconfidence.

Humans differ vastly in the confidence they assign to decisions. Although such under- and overconfidence relate to fundamental life outcomes, a computational account specifying the underlying mechanisms is currently lacking. We propose that prior beliefs in the ability to perform a task explain confidence differences across participants and tasks, despite similar performance. In two perceptual decision-making experiments, we show that manipulating prior beliefs about performance during training causally influences confidence in healthy adults (N = 50 each; Experiment 1: 8 men, one nonbinary; Experiment 2: 5 men) during a test phase, despite unaffected objective performance. This is true when prior beliefs are induced via manipulated comparative feedback and via manipulated training-phase difficulty. Our results were accounted for within an accumulation-to-bound model, explicitly modeling prior beliefs on the basis of earlier task exposure. Decision confidence is quantified as the probability of being correct conditional on prior beliefs, causing under- or overconfidence. We provide a fundamental mechanistic insight into the computations underlying under- and overconfidence.

Temporal Expectation Hastens Decision Onset But Does Not Affect Evidence Quality.

The ability to predict the timing of forthcoming events, known as temporal expectation, has a strong impact on human information processing. Although there is growing consensus that temporal expectations enhance the speed and accuracy of perceptual decisions, it remains unclear whether they affect the decision process itself, or non-decisional (sensory/motor) processes. Here, healthy human participants (N = 21; 18 female) used predictive auditory cues to anticipate the timing of low-contrast visual stimuli they were required to detect. Modeling of the behavioral data using a prominent sequential sampling model indicated that temporal expectations speeded up non-decisional processes but had no effect on decision formation. Electrophysiological recordings confirmed and extended this result: temporal expectations hastened the onset of a neural signature of decision formation but had no effect on its build-up rate. Anticipatory α band power was modulated by temporal expectation and co-varied with intrinsic trial-by-trial variability in behavioral and neural signatures of the onset latency of the decision process. These findings highlight how temporal predictions optimize our interaction with unfolding sensory events.SIGNIFICANCE STATEMENT Temporal expectation enhances performance, but the locus of this effect remains debated. Here, we contrasted the two dominant accounts: enhancement through (1) expedited decision onset, or (2) an increase in the quality of sensory evidence. We manipulated expectations about the onset of a dim visual target using a temporal cueing paradigm, and probed the locus of the expectation effect with two complementary approaches: drift diffusion modeling (DDM) of behavior, and estimation of the onset and progression of the decision process from a supramodal accumulation-to-bound signal in simultaneously measured EEG signals. Behavioral modeling and neural data provided strong, converging evidence for an account in which temporal expectations enhance perception by speeding up decision onset, without affecting evidence quality.

Math anxiety relates positively to metacognitive insight into mathematical decision making.

The current study reports a pre-registered investigation into the interrelations between mathematics anxiety, metacognition and mathematical decision-making. Although this question has already received some attention in previous work, reliance on self-report measures of metacognition has hindered its interpretation. Here, a novel experimental mathematical decision-making task was used in which participants solved mathematical assignments of varying difficulty, and expressed their level of confidence in the accuracy of their decision both prospectively and retrospectively. Mathematics anxiety was measured using a standardized questionnaire. Both prospective and retrospective confidence judgments predicted unique variation in accuracy; however, the explanatory effect of prospective confidence disappeared after taking task difficulty into account. This suggests that prospective, but not retrospective, confidence is largely based on easily available cues indicative of performance. Results of a multiple regression analysis indicated that individual differences in mathematics anxiety were negatively related to the overall level of confidence (both prospectively and retrospectively), and positively related to metacognitive efficiency (only prospectively). Having insight in these interrelationships is important in the context of remediating mathematics anxiety, which might in turn be useful with regard to the worldwide need for more workers with degrees in science, technology, engineering, or mathematics (STEM).

Metacognitive Awareness of Difficulty in Action Selection: The Role of the Cingulo-opercular Network.

The question whether and how we are able to monitor our own cognitive states (metacognition) has been a matter of debate for decades. Do we have direct access to our cognitive processes, or can we only infer them indirectly based on their consequences? In the current study, we wanted to investigate the brain circuits that underlie the metacognitive experience of fluency in action selection. To manipulate action-selection fluency, we used a subliminal response priming paradigm. On each trial, both male and female human participants additionally engaged in the metacognitive process of rating how hard they felt it was to respond to the target stimulus. Despite having no conscious awareness of the prime, results showed that participants rated incompatible trials (during which subliminal primes interfered with the required response) to be more difficult than compatible trials (where primes facilitated the required response), reflecting metacognitive awareness of difficulty. This increased sense of subjective difficulty was mirrored by increased activity in the rostral cingulate zone and the anterior insula, two regions that are functionally closely connected. Importantly, this reflected activations that were unique to subjective difficulty ratings and were not explained by RTs or prime-response compatibility. We interpret these findings in light of a possible grounding of the metacognitive judgment of fluency in action selection in interoceptive signals resulting from increased effort.

A Postdecisional Neural Marker of Confidence Predicts Information-Seeking in Decision-Making.

Theoretical work predicts that decisions made with low confidence should lead to increased information-seeking. This is an adaptive strategy because it can increase the quality of a decision, and previous behavioral work has shown that decision-makers engage in such confidence-driven information-seeking. The present study aimed to characterize the neural markers that mediate the relationship between confidence and information-seeking. A paradigm was used in which 17 human participants (9 male) made an initial perceptual decision, and then decided whether or not they wanted to sample more evidence before committing to a final decision and confidence judgment. Predecisional and postdecisional event-related potential components were similarly modulated by the level of confidence and by information-seeking choices. Time-resolved multivariate decoding of scalp EEG signals first revealed that both information-seeking choices and decision confidence could be decoded from the time of the initial decision to the time of the subsequent information-seeking choice (within-condition decoding). No above-chance decoding was visible in the preresponse time window. Crucially, a classifier trained to decode high versus low confidence predicted information-seeking choices after the initial perceptual decision (across-condition decoding). This time window corresponds to that of a postdecisional neural marker of confidence. Collectively, our findings demonstrate, for the first time, that neural indices of confidence are functionally involved in information-seeking decisions.SIGNIFICANCE STATEMENT Despite substantial current interest in neural signatures of our sense of confidence, it remains largely unknown how confidence is used to regulate behavior. Here, we devised a task in which human participants could decide whether or not to sample additional decision-relevant information at a small monetary cost. Using neural recordings, we could predict such information-seeking choices based on a neural signature of decision confidence. Our study illuminates a neural link between decision confidence and adaptive behavioral control.

Subjective Confidence Predicts Information Seeking in Decision Making.

There is currently little direct evidence regarding the function of subjective confidence in decision making: The tight correlation between objective accuracy and subjective confidence makes it difficult to distinguish each variable's unique contribution. Here, we created conditions in a perceptual decision task that were matched in accuracy but differed in subjective evaluation of accuracy by orthogonally varying the strength versus variability of evidence. Confidence was reduced with variable (vs. weak) evidence, even across conditions matched for difficulty. Building on this dissociation, we constructed a paradigm in which participants ( N = 20) could choose to seek further information before making their decision. The data provided clear support for the hypothesis that subjective confidence predicts information seeking in decision making: Participants were more likely to sample additional information before giving a response in the condition with low confidence, despite matched accuracy. In a preregistered replication ( N = 50), these findings were replicated with increased task difficulty levels.

Feeling the conflict: the crucial role of conflict experience in adaptation.

In the study reported here, we examined the role of conflict experience in cognitive adaptation to conflict. Although the experience of conflict is generally neglected in theoretical models of cognitive control, we demonstrated that it plays a critical role in cognitive adaptation. Using a masked-priming paradigm, we showed that conflict adaptation was present only after trials on which participants experienced response conflict. Furthermore, when subjective experience did not coincide with actual conflict, adaptation effects in the error rates were observed after the experience of conflict, not after response conflict. We conclude that the experience of conflict, and not response conflict per se, is the crucial factor underlying cognitive adaptation effects. The current findings provide a new perspective on the question of why the human cognitive system exerts cognitive control, and they suggest that a crucial role of subjective experience is to allow for top-down control of behavior.

A Bayesian Hierarchical Model of Trial-To-Trial Fluctuations in Decision Criterion.

Classical decision models assume that the parameters giving rise to choice behavior are stable, yet emerging research suggests these parameters may fluctuate over time. Such fluctuations, observed in neural activity and behavioral strategies, have significant implications for understanding decision-making processes. However, empirical studies on fluctuating human decision-making strategies have been limited due to the extensive data requirements for estimating these fluctuations. Here, we introduce hMFC (Hierarchical Model for Fluctuations in Criterion), a Bayesian framework designed to estimate slow fluctuations in the decision criterion from limited data. We first showcase the importance of considering fluctuations in decision criterion: incorrectly assuming a stable criterion gives rise to apparent history effects and underestimates perceptual sensitivity. We then present a hierarchical estimation procedure capable of reliably recovering the underlying state of the fluctuating decision criterion with as few as 500 trials per participant, offering a robust tool for researchers with typical human datasets. Critically, hMFC does not only accurately recover the state of the underlying decision criterion, it also effectively deals with the confounds caused by criterion fluctuations. Lastly, we provide code and a comprehensive demo to enable widespread application of hMFC in decision-making research.

Feeling and deciding: Subjective experiences rather than objective factors drive the decision to invest cognitive control.

When presented with the choice to invest cognitive control in a task, several signals are monitored to reach a decision. Leading theoretical frameworks argued that the investment of cognitive control is determined by a cost-benefit computation. However, previous accounts remained silent on the potential role of subjective experience in this computation. We experience confidence when giving an answer, feel the excitement of an anticipated reward, and reflect on how much effort is required for successful task performance. Two questions are investigated in the present work: how objective task parameters give rise to subjective experience and whether these drive the decision to allocate cognitive control. To this end, we designed a task in which we manipulated three objective parameters in the same sequence of events (stimulus uncertainty, physical effort, and reward prediction error). We asked participants to report their subjective experiences associated with these manipulations: confidence, subjective physical effort, and reward satisfaction. At the end of each trial, participants indicated whether they wanted to repeat that trial on the next day. In response to the first question, we demonstrate that subjective ratings are reliable and selective. Subjective experiences closely mirrored their objective manipulations. In response to the second question, we demonstrate that subjective experiences provide a better fit for the decisions on future control investments. While objective task parameters are considered when deciding, they do not always produce the expected changes in subjective experience, and when dissociations occur, it is the subjective experience that better explains the decision to allocate cognitive control.

Evidence for an age-related decline in feature-based attention.

Feature-based attention allows to efficiently guide attention to relevant information in the visual scene, but unambiguous empirical evidence on age-related effects is still limited. In this study, young and older participants performed a two-alternative forced choice task in which a response was selected based on a task-relevant number (=target) presented alone or with a task-irrelevant letter (=neutral distracter) or number (=compatible/incompatible distracter). Participants were required to select the target based on color. To compare the behavioral interference of the distracters between the age groups, data were modeled with a hierarchical drift-diffusion model. The results revealed that decreases in the rate at which information was collected in the conditions with versus without a distracter were more pronounced in the older than young age group when the distracter was compatible or incompatible. Our findings are consistent with an age-related decline in the ability to filter out distracters based on features.

Dynamic influences on static measures of metacognition.

Humans differ in their capability to judge choice accuracy via confidence judgments. Popular signal detection theoretic measures of metacognition, such as M-ratio, do not consider the dynamics of decision making. This can be problematic if response caution is shifted to alter the tradeoff between speed and accuracy. Such shifts could induce unaccounted-for sources of variation in the assessment of metacognition. Instead, evidence accumulation frameworks consider decision making, including the computation of confidence, as a dynamic process unfolding over time. Using simulations, we show a relation between response caution and M-ratio. We then show the same pattern in human participants explicitly instructed to focus on speed or accuracy. Finally, this association between M-ratio and response caution is also present across four datasets without any reference towards speed. In contrast, when data are analyzed with a dynamic measure of metacognition, v-ratio, there is no effect of speed-accuracy tradeoff.

Theta oscillations shift towards optimal frequency for cognitive control.

Cognitive control allows to flexibly guide behaviour in a complex and ever-changing environment. It is supported by theta band (4-7 Hz) neural oscillations that coordinate distant neural populations. However, little is known about the precise neural mechanisms permitting such flexible control. Most research has focused on theta amplitude, showing that it increases when control is needed, but a second essential aspect of theta oscillations, their peak frequency, has mostly been overlooked. Here, using computational modelling and behavioural and electrophysiological recordings, in three independent datasets, we show that theta oscillations adaptively shift towards optimal frequency depending on task demands. We provide evidence that theta frequency balances reliable set-up of task representation and gating of task-relevant sensory and motor information and that this frequency shift predicts behavioural performance. Our study presents a mechanism supporting flexible control and calls for a reevaluation of the mechanistic role of theta oscillations in adaptive behaviour.

Consensus Goals in the Field of Visual Metacognition.

Despite the tangible progress in psychological and cognitive sciences over the last several years, these disciplines still trail other more mature sciences in identifying the most important questions that need to be solved. Reaching such consensus could lead to greater synergy across different laboratories, faster progress, and increased focus on solving important problems rather than pursuing isolated, niche efforts. Here, 26 researchers from the field of visual metacognition reached consensus on four long-term and two medium-term common goals. We describe the process that we followed, the goals themselves, and our plans for accomplishing these goals. If this effort proves successful within the next few years, such consensus building around common goals could be adopted more widely in psychological science.

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.

Dynamic expressions of confidence within an evidence accumulation framework.

Human observers can reliably report their confidence in the choices they make. An influential framework conceptualizes decision confidence as the probability of a decision being correct, given the choice made and the evidence on which it was based. This framework accounts for three diagnostic signatures of human confidence reports, including an opposite dependence of confidence on evidence strength for correct and error trials. However, the framework does not account for the temporal evolution of these signatures, because it only describes the transformation of a static representation of evidence into choice and the associated confidence. Here, we combine this framework with another influential framework: dynamic accumulation of evidence over time, and build on the notion that confidence reflects the probability of being correct, given the choice and accumulated evidence up until that point. Critically, we show that such a dynamic model predicts that the diagnostic signatures of confidence depend on time; most critically, it predicts a stronger opposite dependence of confidence on evidence strength and choice correctness as a function of time. We tested, and confirmed, these predictions in human behaviour during random dot motion discrimination, in which confidence judgments were queried at different points in time. We conclude that human confidence reports reflect the dynamics of the probability of being correct given the accumulated evidence and choice.

The Confidence Database.

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects.

Confidence predicts speed-accuracy tradeoff for subsequent decisions.

When external feedback about decision outcomes is lacking, agents need to adapt their decision policies based on an internal estimate of the correctness of their choices (i.e., decision confidence). We hypothesized that agents use confidence to continuously update the tradeoff between the speed and accuracy of their decisions: When confidence is low in one decision, the agent needs more evidence before committing to a choice in the next decision, leading to slower but more accurate decisions. We tested this hypothesis by fitting a bounded accumulation decision model to behavioral data from three different perceptual choice tasks. Decision bounds indeed depended on the reported confidence on the previous trial, independent of objective accuracy. This increase in decision bound was predicted by a centro-parietal EEG component sensitive to confidence. We conclude that internally computed neural signals of confidence predict the ongoing adjustment of decision policies.