The Confidence Database

Objectively quantifying subjective phenomena: Measuring the flashed face distortion effect

Objectively quantifying subjective phenomena like visual illusions is challenging. We address this issue in the context of the Flashed Face Distortion Effect (FFDE), where faces presented in succession appear distorted and grotesque. We first show that the traditional method of quantifying FFDE - via subjective ratings of the level of distortion - is subject to substantial biases. Motivated by this finding, we develop an objective method for quantifying FFDE by introducing two design innovations. First, we create artificially distorted faces and ask subjects to discriminate between undistorted and objectively distorted faces. Second, we employ both an illusion condition, which includes a succession of 15 face flashes, and a control condition, which includes a single face flash and does not induce an illusion. Using these innovations, we quantify the strength of the face distortion illusion by comparing the response bias for identifying distorted faces between the illusion and control conditions. We find that our method successfully quantifies the face distortion, with subjects exhibiting a more liberal response bias in the illusion condition. Finally, we apply our new method to evaluate how the face distortion illusion is modulated by face eccentricity, face inversion, the temporal frequency of the face flashes, and presence of temporal gaps between consecutive faces. Our results demonstrate the utility of our objective method in quantifying the subjective illusion of face distortion. Critically, the method is general and can be applied to other phenomena that are inherently subjective.

Altered temporal awareness during Covid-19 pandemic

Social isolation during the COVID-19 pandemic had profound effects on human well-being. A handful of studies have focused on how time perception was altered during the COVID-19 pandemic, while no study has tested whether temporal metacognition is also affected by the lockdown. We examined the impact of long-term social isolation during the COVID-19 pandemic on the ability to monitor errors in timing performance. We recruited 1232 participants from 12 countries during lockdown, 211 of which were retested "post-pandemic" for within-group comparisons. We also tested a new group of 331 participants during the "post-pandemic" period and compared their data to those of 1232 participants tested during the lockdown (between-group comparison). Participants produced a 3600 ms target interval and assessed the magnitude and direction of their time production error. Both within and between-group comparisons showed reduced metric error monitoring performance during the lockdown, even after controlling for government-imposed stringency indices. A higher level of reported social isolation also predicted reduced temporal error monitoring ability. Participants produced longer duration during lockdown compared to post-lockdown (again controlling for government stringency indices). We reason that these effects may be underlain by altered biological and behavioral rhythms during social isolation experienced during the COVID-19 pandemic. Understanding these effects is crucial for a more complete characterization of the cognitive consequences of long-term social isolation.

Neural mechanisms of resource allocation in working memory

To mitigate capacity limits of working memory, people allocate resources according to an item's relevance. However, the neural mechanisms supporting such a critical operation remain unknown. Here, we developed computational neuroimaging methods to decode and demix neural responses associated with multiple items in working memory with different priorities. In striate and extrastriate cortex, the gain of neural responses tracked the priority of memoranda. Higher-priority memoranda were decoded with smaller error and lower uncertainty. Moreover, these neural differences predicted behavioral differences in memory prioritization. Remarkably, trialwise variability in the magnitude of delay activity in frontal cortex predicted differences in decoded precision between low and high-priority items in visual cortex. These results suggest a model in which feedback signals broadcast from frontal cortex sculpt the gain of memory representations in visual cortex according to behavioral relevance, thus, identifying a neural mechanism for resource allocation.

Confidence ratings do not distinguish imagination from reality

Perceptual reality monitoring refers to the ability to distinguish internally triggered imagination from externally triggered reality. Such monitoring can take place at perceptual or cognitive levels-for example, in lucid dreaming, perceptual experience feels real but is accompanied by a cognitive insight that it is not real. We recently developed a paradigm to reveal perceptual reality monitoring errors during wakefulness in the general population, showing that imagined signals can be erroneously attributed to perception during a perceptual detection task. In the current study, we set out to investigate whether people have insight into perceptual reality monitoring errors by additionally measuring perceptual confidence. We used hierarchical Bayesian modeling of confidence criteria to characterize metacognitive insight into the effects of imagery on detection. Over two experiments, we found that confidence criteria moved in tandem with the decision criterion shift, indicating a failure of reality monitoring not only at a perceptual but also at a metacognitive level. These results further show that such failures have a perceptual rather than a decisional origin. Interestingly, offline queries at the end of the experiment revealed global, task-level insight, which was uncorrelated with local, trial-level insight as measured with confidence ratings. Taken together, our results demonstrate that confidence ratings do not distinguish imagination from reality during perceptual detection. Future research should further explore the different cognitive dimensions of insight into reality judgments and how they are related.

Probabilistic causal reasoning under time pressure

While causal reasoning is a core facet of our cognitive abilities, its time-course has not received proper attention. As the duration of reasoning might prove crucial in understanding the underlying cognitive processes, we asked participants in two experiments to make probabilistic causal inferences while manipulating time pressure. We found that participants are less accurate under time pressure, a speed-accuracy-tradeoff, and that they respond more conservatively. Surprisingly, two other persistent reasoning errors-Markov violations and failures to explain away-appeared insensitive to time pressure. These observations seem related to confidence: Conservative inferences were associated with low confidence, whereas Markov violations and failures to explain were not. These findings challenge existing theories that predict an association between time pressure and all causal reasoning errors including conservatism. Our findings suggest that these errors should not be attributed to a single cognitive mechanism and emphasize that causal judgements are the result of multiple processes.

How do humans give confidence? A comprehensive comparison of process models of perceptual metacognition

Humans have the metacognitive ability to assess the accuracy of their decisions via confidence judgments. Several computational models of confidence have been developed but not enough has been done to compare these models, making it difficult to adjudicate between them. Here, we compare 14 popular models of confidence that make various assumptions, such as confidence being derived from postdecisional evidence, from positive (decision-congruent) evidence, from posterior probability computations, or from a separate decision-making system for metacognitive judgments. We fit all models to three large experiments in which subjects completed a basic perceptual task with confidence ratings. In Experiments 1 and 2, the best-fitting model was the lognormal meta noise (LogN) model, which postulates that confidence is selectively corrupted by signal-dependent noise. However, in Experiment 3, the positive evidence (PE) model provided the best fits. We evaluated a new model combining the two consistently best-performing models-LogN and the weighted evidence and visibility (WEV). The resulting model, which we call logWEV, outperformed its individual counterparts and the PE model across all data sets, offering a better, more generalizable explanation for these data. Parameter and model recovery analyses showed mostly good recoverability but with important exceptions carrying implications for our ability to discriminate between models. Finally, we evaluated each model's ability to explain different patterns in the data, which led to additional insight into their performances. These results comprehensively characterize the relative adequacy of current confidence models to fit data from basic perceptual tasks and highlight the most plausible mechanisms underlying confidence generation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Metacognition and Confidence: A Review and Synthesis

Determining the psychological, computational, and neural bases of confidence and uncertainty holds promise for understanding foundational aspects of human metacognition. While a neuroscience of confidence has focused on the mechanisms underpinning subpersonal phenomena such as representations of uncertainty in the visual or motor system, metacognition research has been concerned with personal-level beliefs and knowledge about self-performance. I provide a road map for bridging this divide by focusing on a particular class of confidence computation: propositional confidence in one's own (hypothetical) decisions or actions. Propositional confidence is informed by the observer's models of the world and their cognitive system, which may be more or less accurate-thus explaining why metacognitive judgments are inferential and sometimes diverge from task performance. Disparate findings on the neural basis of uncertainty and performance monitoring are integrated into a common framework, and a new understanding of the locus of action of metacognitive interventions is developed.

Democrats are better than Republicans at discerning true and false news but do not have better metacognitive awareness

Insight into one's own cognitive abilities is one important aspect of metacognition. Whether this insight varies between groups when discerning true and false information has yet to be examined. We investigated whether demographics like political partisanship and age were associated with discernment ability, metacognitive efficiency, and response bias for true and false news. Participants rated the veracity of true and false news headlines and provided confidence ratings for each judgment. We found that Democrats and older adults were better at discerning true and false news than Republicans and younger adults. However, all demographic groups maintained good insight into their discernment ability. Although Republicans were less accurate than Democrats, they slightly outperformed Democrats in metacognitive efficiency when a politically equated item set was used. These results suggest that even when individuals mistake misinformation to be true, they are aware that they might be wrong.

Sensory processing in humans and mice fluctuates between external and internal modes

Perception is known to cycle through periods of enhanced and reduced sensitivity to external information. Here, we asked whether such slow fluctuations arise as a noise-related epiphenomenon of limited processing capacity or, alternatively, represent a structured mechanism of perceptual inference. Using 2 large-scale datasets, we found that humans and mice alternate between externally and internally oriented modes of sensory analysis. During external mode, perception aligns more closely with the external sensory information, whereas internal mode is characterized by enhanced biases toward perceptual history. Computational modeling indicated that dynamic changes in mode are enabled by 2 interlinked factors: (i) the integration of subsequent inputs over time and (ii) slow antiphase oscillations in the impact of external sensory information versus internal predictions that are provided by perceptual history. We propose that between-mode fluctuations generate unambiguous error signals that enable optimal inference in volatile environments.

A uniform human multimodal dataset for emotion perception and judgment

Face perception is a fundamental aspect of human social interaction, yet most research on this topic has focused on single modalities and specific aspects of face perception. Here, we present a comprehensive multimodal dataset for examining facial emotion perception and judgment. This dataset includes EEG data from 97 unique neurotypical participants across 8 experiments, fMRI data from 19 neurotypical participants, single-neuron data from 16 neurosurgical patients (22 sessions), eye tracking data from 24 neurotypical participants, behavioral and eye tracking data from 18 participants with ASD and 15 matched controls, and behavioral data from 3 rare patients with focal bilateral amygdala lesions. Notably, participants from all modalities performed the same task. Overall, this multimodal dataset provides a comprehensive exploration of facial emotion perception, emphasizing the importance of integrating multiple modalities to gain a holistic understanding of this complex cognitive process. This dataset serves as a key missing link between human neuroimaging and neurophysiology literature, and facilitates the study of neuropsychiatric populations.

Retrospective judgments of confidence in a complex span task

Although the study of metamemory monitoring originated in predictions for simple span tasks, the study of metacognition for working memory (WM) has been somewhat neglected in comparison with long-term memory. We aimed to fill this gap by exploring the ability to self-assess WM operations. Thirty-four participants performed 16 series of complex span tasks and rated their confidence in a verbal recall paradigm. We manipulated the cognitive load based on the TBRS model in order to analyze the role of attentional resources on both WM and metacognitive evaluations. As expected, we found that recall is affected by cognitive load and we found standard serial position effects. Interestingly, metacognitive evaluations followed the same pattern, and measures of metacognitive sensitivity suggest that participants are able to make item-by-item retrospective judgments reflective of their performance. We discuss how these results contribute to our understanding of metacognitive access to newly-formed WM contents.

Toward 'Computational-Rationality' Approaches to Arbitrating Models of Cognition: A Case Study Using Perceptual Metacognition

Perceptual confidence results from a metacognitive process which evaluates how likely our percepts are to be correct. Many competing models of perceptual metacognition enjoy strong empirical support. Arbitrating these models traditionally proceeds via researchers conducting experiments and then fitting several models to the data collected. However, such a process often includes conditions or paradigms that may not best arbitrate competing models: Many models make similar predictions under typical experimental conditions. Consequently, many experiments are needed, collectively (sub-optimally) sampling the space of conditions to compare models. Here, instead, we introduce a variant of optimal experimental design which we call a computational-rationality approach to generative models of cognition, using perceptual metacognition as a case study. Instead of designing experiments and post-hoc specifying models, we began with comprehensive model comparison among four competing generative models for perceptual metacognition, drawn from literature. By simulating a simple experiment under each model, we identified conditions where these models made maximally diverging predictions for confidence. We then presented these conditions to human observers, and compared the models' capacity to predict choices and confidence. Results revealed two surprising findings: (1) two models previously reported to differently predict confidence to different degrees, with one predicting better than the other, appeared to predict confidence in a direction opposite to previous findings; and (2) two other models previously reported to equivalently predict confidence showed stark differences in the conditions tested here. Although preliminary with regards to which model is actually 'correct' for perceptual metacognition, our findings reveal the promise of this computational-rationality approach to maximizing experimental utility in model arbitration while minimizing the number of experiments necessary to reveal the winning model, both for perceptual metacognition and in other domains.

Evidence for a metacognitive awareness of autobiographical memory organisation

Models of autobiographical memory (AM) recall posit some form of control process, but the extent to which we can reflect on this form of retrieval is under-researched. Here we propose a method for measuring such metacognitive awareness in AM. Since the verification of personal facts is difficult, we based our design on AM organisation. AMs are proposed to be organised into a coherent life story, that is, a subjective chronology reflecting the goals of the individual over time. We investigated the metacognitive awareness of this coherence. Eighty-three participants generated AMs and made two judgements of order for pairs of memories and gave a confidence rating. We found that participants were indeed able to distinguish pairs of memories that were coherent with their life story chronology from pairs which were not. We also found a significant effect of response time and task difficulty on confidence, suggesting that judgement of order fluency was determinant for metacognitive evaluation. This suggests common properties between metacognitive abilities related to autobiographical memory and those related to other forms of memory.

Preserved Metacognition for Undetected Visuomotor Deviations

Humans can successfully correct deviations of movements without conscious detection of such deviations, suggesting limited awareness of movement details. We ask whether such limited awareness impairs confidence (metacognition). We recorded functional magnetic resonance imaging data while 31 human female and male participants detected cursor deviations during a visuomotor reaching task and rated their confidence retrospectively. We show that participants monitor a summary statistic of the unfolding visual feedback (the peak cursor error) to detect visuomotor deviations and adjust their confidence ratings, even when they report being unaware of a deviation. Crucially, confidence ratings were as metacognitively efficient for aware and unaware deviations. At the neural level, activity in the ventral striatum tracks high confidence, whereas a broad network encodes cursor error but not confidence. These findings challenge the notion of limited conscious action monitoring and uncover how humans monitor their movements as they unfold, even when unaware of ongoing deviations.SIGNIFICANCE STATEMENT We are unaware of the small corrections we apply to our movements as long as our goals are achieved. Here, although we replicate the finding that participants deny perceiving small deviations they correct, we show that their confidence reliably reflects the presence or absence of a deviation. This observation shows they can metacognitively monitor the presence of a deviation, even when they deny perceiving it. We also describe the hemodynamic correlates of confidence ratings. Our study questions the extent to which humans are unaware of the details of their movements; describes a plausible mechanism for metacognition in a visuomotor task, along with its neural correlates; and has important implications for the construction of the sense of self.

Using serial dependence to predict confidence across observers and cognitive domains

Our perceptual system appears hardwired to exploit regularities of input features across space and time in seemingly stable environments. This can lead to serial dependence effects whereby recent perceptual representations bias current perception. Serial dependence has also been demonstrated for more abstract representations, such as perceptual confidence. Here, we ask whether temporal patterns in the generation of confidence judgments across trials generalize across observers and different cognitive domains. Data from the Confidence Database across perceptual, memory, and cognitive paradigms was reanalyzed. Machine learning classifiers were used to predict the confidence on the current trial based on the history of confidence judgments on the previous trials. Cross-observer and cross-domain decoding results showed that a model trained to predict confidence in the perceptual domain generalized across observers to predict confidence across the different cognitive domains. The recent history of confidence was the most critical factor. The history of accuracy or Type 1 reaction time alone, or in combination with confidence, did not improve the prediction of the current confidence. We also observed that confidence predictions generalized across correct and incorrect trials, indicating that serial dependence effects in confidence generation are uncoupled to metacognition (i.e., how we evaluate the precision of our own behavior). We discuss the ramifications of these findings for the ongoing debate on domain-generality versus domain-specificity of metacognition.

Confidence response times: Challenging postdecisional models of confidence

Even though the nature of confidence computations has been the topic of intense interest, little attention has been paid to what confidence response times (cRTs) reveal about the underlying confidence computations. Several previous studies found cRTs to be negatively correlated with confidence in the group as a whole and consequently hypothesized the existence of an intrinsic relationship of cRT with confidence for all subjects. This hypothesis was further used to support postdecisional models of confidence that predict that cRT and confidence should always be negatively correlated. Here we test the alternative hypothesis that cRT is driven by the frequency of confidence responses such that the most frequent confidence ratings are inherently made faster regardless of whether they are high or low. We examined cRTs in three large data sets from the Confidence Database and found that the lowest cRTs occurred for the most frequent confidence rating. In other words, subjects who gave high confidence ratings most frequently had negative confidence-cRT relationships, whereas subjects who gave low confidence ratings most frequently had positive confidence-cRT relationships. In addition, we found a strong across-subject correlation between response time and cRT, suggesting that response speed for both the decision and the confidence rating is influenced by a common factor. Our results show that cRT is not intrinsically linked to confidence and strongly challenge several postdecisional models of confidence.

Illusion of knowledge in statistics among clinicians: evaluating the alignment between objective accuracy and subjective confidence, an online survey

Healthcare professionals' statistical illiteracy can impair medical decision quality and compromise patient safety. Previous studies have documented clinicians' insufficient proficiency in statistics and a tendency in overconfidence. However, an underexplored aspect is clinicians' awareness of their lack of statistical knowledge that precludes any corrective intervention attempt. Here, we investigated physicians', residents' and medical students' alignment between subjective confidence judgments and objective accuracy in basic medical statistics. We also examined how gender, profile of experience and practice of research activity affect this alignment, and the influence of problem framing (conditional probabilities, CP vs. natural frequencies, NF). Eight hundred ninety-eight clinicians completed an online survey assessing skill and confidence on three topics: vaccine efficacy, p value and diagnostic test results interpretation. Results evidenced an overall consistent poor proficiency in statistics often combined with high confidence, even in incorrect answers. We also demonstrate that despite overconfidence bias, clinicians show a degree of metacognitive sensitivity, as their confidence judgments discriminate between their correct and incorrect answers. Finally, we confirm the positive impact of the more intuitive NF framing on accuracy. Together, our results pave the way for the development of teaching recommendations and pedagogical interventions such as promoting metacognition on basic knowledge and statistical reasoning as well as the use of NF to tackle statistical illiteracy in the medical context.

Measuring memory is harder than you think: How to avoid problematic measurement practices in memory research

We argue that critical areas of memory research rely on problematic measurement practices and provide concrete suggestions to improve the situation. In particular, we highlight the prevalence of memory studies that use tasks (like the "old/new" task: "have you seen this item before? yes/no") where quantifying performance is deeply dependent on counterfactual reasoning that depends on the (unknowable) distribution of underlying memory signals. As a result of this difficulty, different literatures in memory research (e.g., visual working memory, eyewitness identification, picture memory, etc.) have settled on a variety of fundamentally different metrics to get performance measures from such tasks (e.g., A', corrected hit rate, percent correct, d', diagnosticity ratios, K values, etc.), even though these metrics make different, contradictory assumptions about the distribution of latent memory signals, and even though all of their assumptions are frequently incorrect. We suggest that in order for the psychology and neuroscience of memory to become a more cumulative, theory-driven science, more attention must be given to measurement issues. We make a concrete suggestion: The default memory task for those simply interested in performance should change from old/new ("did you see this item'?") to two-alternative forced-choice ("which of these two items did you see?"). In situations where old/new variants are preferred (e.g., eyewitness identification; theoretical investigations of the nature of memory signals), receiver operating characteristic (ROC) analysis should be performed rather than a binary old/new task.

Can individual subjective confidence in training questions predict group performance in test questions?

When people have to solve many tasks, they can aggregate diverse individuals' judgments using the majority rule, which often improves the accuracy of judgments (wisdom of crowds). When aggregating judgments, individuals' subjective confidence is a useful cue for deciding which judgments to accept. However, can confidence in one task set predict performance not only in the same task set, but also in another? We examined this issue through computer simulations using behavioral data obtained from binary-choice experimental tasks. In our simulations, we developed a "training-test" approach: We split the questions used in the behavioral experiments into "training questions" (as questions to identify individuals' confidence levels) and "test questions" (as questions to be solved), similar to the cross-validation method in machine learning. We found that (i) through analyses of behavioral data, confidence in a certain question could predict accuracy in the same question, but not always well in another question. (ii) Through a computer simulation for the accordance of two individuals' judgments, individuals with high confidence in one training question tended to make less diverse judgments in other test questions. (iii) Through a computer simulation of group judgments, the groups constructed from individuals with high confidence in the training question(s) generally performed well; however, their performance sometimes largely decreased in the test questions especially when only one training question was available. These results suggest that when situations are highly uncertain, an effective strategy is to aggregate various individuals regardless of confidence levels in the training questions to avoid decreasing the group accuracy in test questions. We believe that our simulations, which follow a "training-test" approach, provide practical implications in terms of retaining groups' ability to solve many tasks.

Confidence reflects a noisy decision reliability estimate

Decisions vary in difficulty. Humans know this and typically report more confidence in easy than in difficult decisions. However, confidence reports do not perfectly track decision accuracy, but also reflect response biases and difficulty misjudgements. To isolate the quality of confidence reports, we developed a model of the decision-making process underlying choice-confidence data. In this model, confidence reflects a subject's estimate of the reliability of their decision. The quality of this estimate is limited by the subject's uncertainty about the uncertainty of the variable that informs their decision ('meta-uncertainty'). This model provides an accurate account of choice-confidence data across a broad range of perceptual and cognitive tasks, investigated in six previous studies. We find meta-uncertainty varies across subjects, is stable over time, generalizes across some domains and can be manipulated experimentally. The model offers a parsimonious explanation for the computational processes that underlie and constrain the sense of confidence.

Intertemporal choice reflects value comparison rather than self-control: insights from confidence judgements

Intertemporal decision-making has long been assumed to measure self-control, with prominent theories treating choices of smaller, sooner rewards as failed attempts to override immediate temptation. If this view is correct, people should be more confident in their intertemporal decisions when they 'successfully' delay gratification than when they do not. In two pre-registered experiments with built-in replication, adult participants (n = 117) made monetary intertemporal choices and rated their confidence in having made the right decisions. Contrary to assumptions of the self-control account, confidence was not higher when participants chose delayed rewards. Rather, participants were more confident in their decisions when possible rewards were further apart in time-discounted subjective value, closer to the present, and larger in magnitude. Demonstrating metacognitive insight, participants were more confident in decisions that better aligned with their separate valuation of possible rewards. Decisions made with less confidence were more prone to changes-of-mind and more susceptible to a patience-enhancing manipulation. Together, our results establish that confidence in intertemporal choice tracks uncertainty in estimating and comparing the value of possible rewards-just as it does in decisions unrelated to self-control. Our findings challenge self-control views and instead cast intertemporal choice as a form of value-based decision-making about future possibilities. This article is part of the theme issue 'Thinking about possibilities: mechanisms, ontogeny, functions and phylogeny'.

The relationship between response dynamics and the formation of confidence varies across the lifespan

Accurate metacognitive judgments, such as forming a confidence judgment, are crucial for goal-directed behavior but decline with older age. Besides changes in the sensory processing of stimulus features, there might also be changes in the motoric aspects of giving responses that account for age-related changes in confidence. In order to assess the association between confidence and response parameters across the adult lifespan, we measured response times and peak forces in a four-choice flanker task with subsequent confidence judgments. In 65 healthy adults from 20 to 76 years of age, we showed divergent associations of each measure with confidence, depending on decision accuracy. Participants indicated higher confidence after faster responses in correct but not incorrect trials. They also indicated higher confidence after less forceful responses in errors but not in correct trials. Notably, these associations were age-dependent as the relationship between confidence and response time was more pronounced in older participants, while the relationship between confidence and response force decayed with age. Our results add to the notion that confidence is related to response parameters and demonstrate noteworthy changes in the observed associations across the adult lifespan. These changes potentially constitute an expression of general age-related deficits in performance monitoring or, alternatively, index a failing mechanism in the computation of confidence in older adults.

Metacognition tracks sensitivity following involuntary shifts of visual attention

Salient, exogenous cues have been shown to induce a temporary boost of perceptual sensitivity in their immediate vicinity. In two experiments involving uninformative exogenous cues presented at various times before a target stimulus, we investigated whether human observers (N = 100) were able to monitor the involuntary increase in performance induced by such transients. We found that an increase of perceptual sensitivity (in a choice task) and encoding precision (in a free-estimation task) occurred approximately 100 ms after cue onset, and was accompanied by an increase in confidence about the perceptual response. These simultaneous changes in sensitivity and confidence resulted in stable metacognition across conditions. These results suggest that metacognition efficiently tracks the effects of a reflexive attentional mechanism known to evade voluntary control, and illustrate a striking ability of high-level cognition to capture fleeting, low-level sensory modulations.

Metacognitive Domains Are Not Aligned along a Dimension of Internal-External Information Source

It is still debated whether metacognition, or the ability to monitor our own mental states, relies on processes that are “domain-general” (a single set of processes can account for the monitoring of any mental process) or “domain-specific” (metacognition is accomplished by a collection of multiple monitoring modules, one for each cognitive domain). It has been speculated that two broad categories of metacognitive processes may exist: those that monitor primarily externally generated versus those that monitor primarily internally generated information. To test this proposed division, we measured metacognitive performance (using m-ratio, a signal detection theoretical measure) in four tasks that could be ranked along an internal-external axis of the source of information, namely memory, motor, visuomotor, and visual tasks. We found correlations between m-ratios in visuomotor and motor tasks, but no correlations between m-ratios in visual and visuomotor tasks, or between motor and memory tasks. While we found no correlation in metacognitive ability between visual and memory tasks, and a positive correlation between visuomotor and motor tasks, we found no evidence for a correlation between motor and memory tasks. This pattern of correlations does not support the grouping of domains based on whether the source of information is primarily internal or external. We suggest that other groupings could be more reflective of the nature of metacognition and discuss the need to consider other non-domain task-features when using correlations as a way to test the underlying shared processes between domains.

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.

Reduced choice-confidence in negative numerals

Negative numbers are central in math. However, they are abstract, hard to learn, and manipulated slower than positive numbers regardless of math ability. It suggests that confidence, namely the post-decision estimate of being correct, should be lower than positives. We asked participants to pick the larger single-digit numeral in a pair and collected their implicit confidence with button pressure (button pressure was validated with three empirical signatures of confidence). We also modeled their choices with a drift-diffusion decision model to compute the post-decision estimate of being correct. We found that participants had relatively low confidence with negative numerals. Given that participants compared with high accuracy the basic base-10 symbols (0–9), reduced confidence may be a general feature of manipulating abstract negative numerals as they produce more uncertainty than positive numerals per unit of time.

Reverse engineering of metacognition

The human ability to introspect on thoughts, perceptions or actions − metacognitive ability − has become a focal topic of both cognitive basic and clinical research. At the same time it has become increasingly clear that currently available quantitative tools are limited in their ability to make unconfounded inferences about metacognition. As a step forward, the present work introduces a comprehensive modeling framework of metacognition that allows for inferences about metacognitive noise and metacognitive biases during the readout of decision values or at the confidence reporting stage. The model assumes that confidence results from a continuous but noisy and potentially biased transformation of decision values, described by a confidence link function. A canonical set of metacognitive noise distributions is introduced which differ, amongst others, in their predictions about metacognitive sign flips of decision values. Successful recovery of model parameters is demonstrated, and the model is validated on an empirical data set. In particular, it is shown that metacognitive noise and bias parameters correlate with conventional behavioral measures. Crucially, in contrast to these conventional measures, metacognitive noise parameters inferred from the model are shown to be independent of performance. This work is accompanied by a toolbox (ReMeta) that allows researchers to estimate key parameters of metacognition in confidence datasets.

Metacognition is a person’s ability to think about their own thoughts. For example, imagine you are walking in a dark forest when you see an elongated object. You think it is a stick rather than a snake, but how sure are you? Reflecting on one’s certainty about own thoughts or perceptions – confidence – is a prime example of metacognition. While our ability to think about our own thoughts in this way provides many, perhaps uniquely human, advantages, confidence judgements are prone to biases. Often, humans tend to be overconfident: we think we are right more often than we actually are. Internal noise of neural processes can also affect confidence.

Understanding these imperfections in metacognition could shed light on how humans think, but studying this phenomenon is challenging. Current methods are lacking either mechanistic insight about the sources of metacognitive biases and noise or rely on unrealistic assumptions. A better model for how metacognition works could provide a clearer picture.

Guggenmos developed a mathematical model and a computer toolbox to help researchers investigate how humans or animals estimate confidence in their own thoughts and resulting decisions . The model splits metacognition apart, allowing scientists to explore biases and sources of noise at different phases in the process. It takes two kinds of data: the decisions study participants make, and how sure they are about their decision being correct. It then recreates metacognition in three phases: the primary decision, the metacognitive readout of the evidence, and the confidence report. This allows investigators to see where and when noise and bias come into play. Guggenmos tested the model using independent data from a visual discrimination task and found that it was able to predict how confident participants reported to be in their decisions.

Metacognitive ability can change in people with mental illness. People with schizophrenia have often been found to be overconfident in their decisions, while people with depression can be underconfident. Using this model to separate the various facets of metacognition could help to explain why. It could also shed light on human thinking in general.

On the assumptions behind metacognitive measurements: Implications for theory and practice

Theories of visual confidence have largely been grounded in the gaussian signal detection framework. This framework is so dominant that idiosyncratic consequences from this distributional assumption have remained unappreciated. This article reports systematic comparisons of the gaussian signal detection framework to its logistic counterpart in the measurement of metacognitive accuracy. Because of the difference in their distribution kurtosis, these frameworks are found to provide different perspectives regarding the efficiency of confidence rating relative to objective decision (the logistic model intrinsically gives greater meta-d'/d' ratio than the gaussian model). These frameworks can also provide opposing conclusions regarding the metacognitive inefficiency along the internal evidence continuum (whether meta-d' is larger or smaller for higher levels of confidence). Previous theories developed on these lines of analysis may need to be revisited as the gaussian and logistic metacognitive models received somewhat equivalent support in our quantitative model comparisons. Despite these discrepancies, however, we found that across-condition or across-participant comparisons of metacognitive measures are relatively robust against the distributional assumptions, which provides much assurance to conventional research practice. We hope this article promotes the awareness for the significance of hidden modeling assumptions, contributing to the cumulative development of the relevant field.

Internal noise measures in coarse and fine motion direction discrimination tasks and the correlation with autism traits

Motion perception is essential for visual guidance of behavior and is known to be limited by both internal additive noise (i.e., a constant level of random fluctuations in neural activity independent of the stimulus) and motion pooling (global integration of local motion signals across space). People with autism spectrum disorder (ASD) display abnormalities in motion processing, which have been linked to both elevated noise and abnormal pooling. However, to date, the impact of a third limit-induced internal noise (internal noise that scales up with increases in external stimulus noise)-has not been investigated in motion perception of any group. Here, we describe an extension on the double-pass paradigm to quantify additive noise and induced noise in a motion paradigm. We also introduce a new way to experimentally estimate motion pooling. We measured the impact of induced noise on direction discrimination, which we ascribe to fluctuations in decision-related variables. Our results are suggestive of higher internal noise in individuals with high ASD traits only on coarse but not fine motion direction discrimination tasks. However, we report no significant correlations between autism traits and additive noise, induced noise, or motion pooling in either task. We conclude that, under some conditions, the internal noise may be higher in individuals with pronounced ASD traits and that the assessment of induced internal noise is a useful way of exploring decision-related limits on motion perception, irrespective of ASD traits.

Psychiatrically relevant signatures of domain-general decision-making and metacognition in the general population

Human behaviours are guided by how confident we feel in our abilities. When confidence does not reflect objective performance, this can impact critical adaptive functions and impair life quality. Distorted decision-making and confidence have been associated with mental health problems. Here, utilising advances in computational and transdiagnostic psychiatry, we sought to map relationships between psychopathology and both decision-making and confidence in the general population across two online studies (N's = 344 and 473, respectively). The results revealed dissociable decision-making and confidence signatures related to distinct symptom dimensions. A dimension characterised by compulsivity and intrusive thoughts was found to be associated with reduced objective accuracy but, paradoxically, increased absolute confidence, whereas a dimension characterized by anxiety and depression was associated with systematically low confidence in the absence of impairments in objective accuracy. These relationships replicated across both studies and distinct cognitive domains (perception and general knowledge), suggesting that they are reliable and domain general. Additionally, whereas Big-5 personality traits also predicted objective task performance, only symptom dimensions related to subjective confidence. Domain-general signatures of decision-making and metacognition characterise distinct psychological dispositions and psychopathology in the general population and implicate confidence as a central component of mental health.

Across-subject correlation between confidence and accuracy: A meta-analysis of the Confidence Database

If one friend confidently tells us to buy Product A while another friend thinks that Product B is better but is not confident, we may go with the advice of our confident friend. Should we? The relationship between people's confidence and accuracy has been of great interest in many fields, especially in high-stakes situations like eyewitness testimony. However, there is still little consensus about how much we should trust someone's overall confidence level. Here, we examine the across-subject relationship between average accuracy and average confidence in 213 unique datasets from the Confidence Database. This approach allows us to empirically address this issue with unprecedented statistical power and check for the presence of various moderators. We find an across-subject correlation between average accuracy and average confidence of R = .22. Importantly, this relationship is much stronger for memory than for perception tasks ("domain effect"), as well as for confidence scales with fewer points ("granularity effect"). These results show that we should take one's confidence seriously (and perhaps buy Product A) and suggest several factors that moderate the relative consistency of how people make confidence judgments.

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.

Distinct Generation of Subjective Vividness and Confidence during Naturalistic Memory Retrieval in Angular Gyrus

Our subjective experience of remembering guides and monitors the reconstruction of past and simulation of the future, which enables us to identify mistakes and adjust our behavior accordingly. However, it remains incompletely understood what underlies the process of subjective mnemonic experience. Here, we combined behavior, repetitive TMS, and functional neuroimaging to probe whether vividness and confidence are generated differently during retrieval. We found that preretrieval repetitive TMS targeting the left angular gyrus (AnG) selectively attenuated the vividness efficiency compared with control stimulation while keeping metacognitive efficiency and objective memory accuracy unaffected. Using trialwise data, we showed that AnG stimulation altered the mediating role of vividness in confidence in the accuracy of memory judgment. Moreover, resting-state functional connectivity of hippocampus and AnG was specifically associated with vividness efficiency, but not metacognitive efficiency across individuals. Together, these results identify the causal involvement of AnG in gauging the vividness, but not the confidence, of memory, thereby suggesting a differentiation account of conscious assessment of memory by functionally and anatomically dissociating the monitoring of vividness from confidence.

The Impact of Feedback on Perceptual Decision-Making and Metacognition: Reduction in Bias but No Change in Sensitivity

It is widely believed that feedback improves behavior, but the mechanisms behind this improvement remain unclear. Different theories postulate that feedback has either a direct effect on performance through automatic reinforcement mechanisms or only an indirect effect mediated by a deliberate change in strategy. To adjudicate between these competing accounts, we performed two large experiments on human adults (total N = 518); approximately half the participants received trial-by-trial feedback on a perceptual task, whereas the other half did not receive any feedback. We found that feedback had no effect on either perceptual or metacognitive sensitivity even after 7 days of training. On the other hand, feedback significantly affected participants' response strategies by reducing response bias and improving confidence calibration. These results suggest that the beneficial effects of feedback stem from allowing people to adjust their strategies for performing the task and not from direct reinforcement mechanisms, at least in the domain of perception.

Visual metacognition: Measures, models, and neural correlates

Visual metacognition is the ability to evaluate one's performance on visual perceptual tasks. The field of visual metacognition unites the long tradition of visual psychophysics with the younger field of metacognition research. This article traces the historical roots of the field and reviews progress in the areas of (a) constructing appropriate measures of metacognitive ability, (b) developing computational models, and (c) revealing the neural correlates of visual metacognition. First, I review the most popular measures of metacognitive ability with an emphasis on their psychophysical properties. Second, I examine the empirical targets for modeling, the dominant modeling frameworks and the assumed computations underlying visual metacognition. Third, I explore the progress on understanding the neural correlates of visual metacognition by focusing on anatomical and functional studies, as well as causal manipulations. What emerges is a picture of substantial progress on constructing measures, developing models, and revealing the neural correlates of metacognition, but very little integration between these three areas of inquiry. I then explore the deep, intrinsic links between the three areas of research and argue that continued progress requires the recognition and exploitation of these links. Throughout, I discuss the implications of progress in visual metacognition for other areas of metacognition research, and pinpoint specific advancements that could be adopted by researchers working in other subfields of metacognition. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Measuring metacognitive performance: type 1 performance dependence and test-retest reliability

Research on metacognition-thinking about thinking-has grown rapidly and fostered our understanding of human cognition in healthy individuals and clinical populations. Of central importance is the concept of metacognitive performance, which characterizes the capacity of an individual to estimate and report the accuracy of primary (type 1) cognitive processes or actions ensuing from these processes. Arguably one of the biggest challenges for measures of metacognitive performance is their dependency on objective type 1 performance, although more recent methods aim to address this issue. The present work scrutinizes the most popular metacognitive performance measures in terms of two critical characteristics: independence of type 1 performance and test-retest reliability. Analyses of data from the Confidence Database (total N = 6912) indicate that no current metacognitive performance measure is independent of type 1 performance. The shape of this dependency is largely reproduced by extending current models of metacognition with a source of metacognitive noise. Moreover, the reliability of metacognitive performance measures is highly sensitive to the combination of type 1 performance and trial number. Importantly, trial numbers frequently employed in metacognition research are too low to achieve an acceptable level of test-retest reliability. Among common task characteristics, simultaneous choice and confidence reports most strongly improved reliability. Finally, general recommendations about design choices and analytical remedies for studies investigating metacognitive performance are provided.

A robust confidence-accuracy dissociation via criterion attraction

Many studies have shown that confidence and accuracy can be dissociated in a variety of tasks. However, most of these dissociations involve small effect sizes, occur only in a subset of participants, and include a reaction time (RT) confound. Here, I develop a new method for inducing confidence-accuracy dissociations that overcomes these limitations. The method uses an external noise manipulation and relies on the phenomenon of criterion attraction where criteria for different tasks become attracted to each other. Subjects judged the identity of stimuli generated with either low or high external noise. The results showed that the two conditions were matched on accuracy and RT but produced a large difference in confidence (effect appeared for 25 of 26 participants, effect size: Cohen's d = 1.9). Computational modeling confirmed that these results are consistent with a mechanism of criterion attraction. These findings establish a new method for creating conditions with large differences in confidence without differences in accuracy or RT. Unlike many previous studies, however, the current method does not lead to differences in subjective experience and instead produces robust confidence-accuracy dissociations by exploiting limitations in post-perceptual, cognitive processes.

A Bayesian inference model for metamemory

The dual-basis theory of metamemory suggests that people evaluate their memory performance based on both processing experience during the memory process and their prior beliefs about overall memory ability. However, few studies have proposed a formal computational model to quantitatively characterize how processing experience and prior beliefs are integrated during metamemory monitoring. Here, we introduce a Bayesian inference model for metamemory (BIM) which provides a theoretical and computational framework for the metamemory monitoring process. BIM assumes that when people evaluate their memory performance, they integrate processing experience and prior beliefs via Bayesian inference. We show that BIM can be fitted to recall or recognition tasks with confidence ratings on either a continuous or discrete scale. Results from data simulation indicate that BIM can successfully recover a majority of generative parameter values, and demonstrate a systematic relationship between parameters in BIM and previous computational models of metacognition such as the stochastic detection and retrieval model (SDRM) and the meta-d' model. We also show examples of fitting BIM to empirical data sets from several experiments, which suggest that the predictions of BIM are consistent with previous studies on metamemory. In addition, when compared with SDRM, BIM could more parsimoniously account for the data of judgments of learning (JOLs) and memory performance from recall tasks. Finally, we discuss an extension of BIM which accounts for belief updating, and conclude with a discussion of how BIM may benefit metamemory research. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Examining the robustness of the relationship between metacognitive efficiency and metacognitive bias

We recently found a positive relationship between estimates of metacognitive efficiency and metacognitive bias. However, this relationship was only examined on a within-subject level and required binarizing the confidence scale, a technique that introduces methodological difficulties. Here we examined the robustness of the positive relationship between estimates of metacognitive efficiency and metacognitive bias by conducting two different types of analyses. First, we developed a new within-subject analysis technique where the original n-point confidence scale is transformed into two different (n-1)-point scales in a way that mimics a naturalistic change in confidence. Second, we examined the across-subject correlation between metacognitive efficiency and metacognitive bias. Importantly, for both types of analyses, we not only established the direction of the effect but also computed effect sizes. We applied both techniques to the data from three tasks from the Confidence Database (N > 400 in each). We found that both approaches revealed a small to medium positive relationship between metacognitive efficiency and metacognitive bias. These results demonstrate that the positive relationship between metacognitive efficiency and metacognitive bias is robust across several analysis techniques and datasets, and have important implications for future research.

Metacognitive resources for adaptive learning⋆

Biological organisms display remarkably flexible behaviours. This is an area of active investigation, in particular in the fields of artificial intelligence, computational and cognitive neuroscience. While inductive biases and broader cognitive functions are undoubtedly important, the ability to monitor and evaluate one's performance or oneself -- metacognition -- strikes as a powerful resource for efficient learning. Often measured as decision confidence in neuroscience and psychology experiments, metacognition appears to reflect a broad range of abstraction levels and downstream behavioural effects. Within this context, the formal investigation of how metacognition interacts with learning processes is a recent endeavour. Of special interest are the neural and computational underpinnings of confidence and reinforcement learning modules. This review discusses a general hierarchy of confidence functions and their neuro-computational relevance for adaptive behaviours. It then introduces novel ways to study the formation and use of meta-representations and nonconscious mental representations related to learning and confidence, and concludes with a discussion on outstanding questions and wider perspectives.

Neural correlates of metacognition across the adult lifespan

Metacognitive accuracy describes the degree of overlap between the subjective perception of one's decision accuracy (i.e. confidence) and objectively observed performance. With older age, the need for accurate metacognitive evaluation increases; however, error detection rates typically decrease. We investigated the effect of ageing on metacognitive accuracy using event-related potentials (ERPs) reflecting error detection and confidence: the error/correct negativity (N_e/c) and the error/correct positivity (P_e/c). Sixty-five healthy adults (20 to 76 years) completed a complex Flanker task and provided confidence ratings. We found that metacognitive accuracy declined with age beyond the expected decline in task performance, while the adaptive adjustment of behaviour was well preserved. P_e amplitudes following errors varied by confidence rating, but they did not mirror the reduction in metacognitive accuracy. N_e amplitudes decreased with age for low confidence errors. The results suggest that age-related difficulties in metacognitive evaluation could be related to an impaired integration of decision accuracy and confidence information processing. Ultimately, training the metacognitive evaluation of fundamental decisions in older adults might constitute a promising endeavour.

Response Bias Reflects Individual Differences in Sensory Encoding

Humans exhibit substantial biases in their decision making even in simple two-choice tasks, but the origin of these biases remains unclear. I hypothesized that one source of bias could be individual differences in sensory encoding. Specifically, if one stimulus category gives rise to an internal-evidence distribution with higher variability, then responses should optimally be biased against that stimulus category. Therefore, response bias may reflect a previously unappreciated subject-to-subject difference in the variance of the internal-evidence distributions. I tested this possibility by analyzing data from three different two-choice tasks (ns = 443, 443, and 498). For all three tasks, response bias moved in the direction of the optimal criterion determined by each subject's idiosyncratic internal-evidence variability. These results demonstrate that seemingly random variations in response bias can be driven by individual differences in sensory encoding and are thus partly explained by normative strategies.

Chimpanzees (Pan troglodytes) show subtle signs of uncertainty when choices are more difficult

Humans can tell when they find a task difficult. Subtle uncertainty behaviors like changes in motor speed and muscle tension precede and affect these experiences. Theories of animal metacognition likewise stress the importance of endogenous signals of uncertainty as cues that motivate metacognitive behaviors. However, while researchers have investigated second-order behaviors like information seeking and declining difficult trials in nonhuman animals, they have devoted little attention to the behaviors that express the cognitive conflict that gives rise to such behaviors in the first place. Here we explored whether three chimpanzees would, like humans, show hand wavering more when faced with more difficult choices in a touch screen transitive inference task. While accuracy was very high across all conditions, all chimpanzees wavered more frequently in trials that were objectively more difficult, demonstrating a signature behavior which accompanies experiences of difficulty in humans. This lends plausibility to the idea that feelings of uncertainty, like other emotions, can be studied in nonhuman animals. We propose to routinely assess uncertainty behaviors to inform models of procedural metacognition in nonhuman animals.

Confidence and central tendency in perceptual judgment

This paper theoretically and empirically investigates the role of noisy cognition in perceptual judgment, focusing on the central tendency effect: the well-known empirical regularity that perceptual judgments are biased towards the center of the stimulus distribution. Based on a formal Bayesian framework, we generate predictions about the relationships between subjective confidence, central tendency, and response variability. Specifically, our model clarifies that lower subjective confidence as a measure of posterior uncertainty about a judgment should predict (i) a lower sensitivity of magnitude estimates to objective stimuli; (ii) a higher sensitivity to the mean of the stimulus distribution; (iii) a stronger central tendency effect at higher stimulus magnitudes; and (iv) higher response variability. To test these predictions, we collect a large-scale experimental data set and additionally re-analyze perceptual judgment data from several previous experiments. Across data sets, subjective confidence is strongly predictive of the central tendency effect and response variability, both correlationally and when we exogenously manipulate the magnitude of sensory noise. Our results are consistent with (but not necessarily uniquely explained by) Bayesian models of confidence and the central tendency.