Hierarchical decision processes that operate over distinct timescales underlie choice and changes in strategy

Neural basis of concurrent deliberation toward a choice and degree of confidence

Decision confidence plays a key role in flexible behavior, but exactly how and when it arises in the brain remains unclear. Theoretical accounts suggest that confidence can be inferred from the same evidence accumulation process that governs choice and response time (RT), implying that a provisional confidence assessment could be updated in parallel with decision formation. We tested this using a novel RT task in nonhuman primates that measures choice and confidence with a single eye movement on every trial. Monkey behavior was well fit by a 2D bounded accumulator model instantiating parallel processing of evidence, rejecting a serial model in which choice is resolved first followed by post-decision accumulation for confidence. Neural activity in area LIP reflected concurrent accumulation, exhibiting within-trial dynamics consistent with parallel updating at near zero time lag, and significant covariation in choice and confidence signals across the population. The results demonstrate that monkeys concurrently process a single stream of evidence to arrive at a choice and level of confidence, and illuminate a candidate neural mechanism for this ability.

Flexible gating between subspaces in a neural network model of internally guided task switching

Behavioral flexibility relies on the brain's ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism that is consistent across networks trained with different hyperparameters. The networks' dynamical trajectories for different rules resided in separate subspaces of population activity; the subspaces collapsed and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, illustrating how a phenomenological description of representational subspaces is explained by a specific circuit mechanism.

Linking Cognitive Integrity to Working Memory Dynamics in the Aging Human Brain

Aging is accompanied by a decline of working memory, an important cognitive capacity that involves stimulus-selective neural activity that persists after stimulus presentation. Here, we unraveled working memory dynamics in older human adults (male and female) including those diagnosed with mild cognitive impairment (MCI) using a combination of behavioral modeling, neuropsychological assessment, and MEG recordings of brain activity. Younger adults (male and female) were studied with behavioral modeling only. Participants performed a visuospatial delayed match-to-sample task under systematic manipulation of the delay and distance between sample and test stimuli. Their behavior (match/nonmatch decisions) was fit with a computational model permitting the dissociation of noise in the internal operations underlying the working memory performance from a strategic decision threshold. Task accuracy decreased with delay duration and sample/test proximity. When sample/test distances were small, older adults committed more false alarms than younger adults. The computational model explained the participants' behavior well. The model parameters reflecting internal noise (not decision threshold) correlated with the precision of stimulus-selective cortical activity measured with MEG during the delay interval. The model uncovered an increase specifically in working memory noise in older compared with younger participants. Furthermore, in the MCI group, but not in the older healthy controls, internal noise correlated with the participants' clinically assessed cognitive integrity. Our results are consistent with the idea that the stability of working memory contents deteriorates in aging, in a manner that is specifically linked to the overall cognitive integrity of individuals diagnosed with MCI.

Flexible gating between subspaces in a neural network model of internally guided task switching

Behavioral flexibility relies on the brain's ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism that is consistent across networks trained with different hyperparameters. The networks' dynamical trajectories for different rules resided in separate subspaces of population activity; the subspaces collapsed and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, illustrating how a phenomenological description of representational subspaces is explained by a specific circuit mechanism.

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.

Hierarchical network structure as the source of hierarchical dynamics (power-law frequency spectra) in living and non-living systems: How state-trait continua (body plans, personalities) emerge from first principles in biophysics

Living systems are hierarchical control systems that display a small world network structure. In such structures, many smaller clusters are nested within fewer larger ones, producing a fractal-like structure with a 'power-law' cluster size distribution (a mereology). Just like their structure, the dynamics of living systems shows fractal-like qualities: the timeseries of inner message passing and overt behavior contain high frequencies or 'states' (treble) that are nested within lower frequencies or 'traits' (bass), producing a power-law frequency spectrum that is known as a 'state-trait continuum' in the behavioral sciences. Here, we argue that the power-law dynamics of living systems results from their power-law network structure: organisms 'vertically encode' the deep spatiotemporal structure of their (anticipated) environments, to the effect that many small clusters near the base of the hierarchy produce high frequency signal changes and fewer larger clusters at its top produce ultra-low frequencies. Such ultra-low frequencies exert a tonic regulatory pressure that produces morphological as well as behavioral traits (i.e., body plans and personalities). Nested-modular structure causes higher frequencies to be embedded within lower frequencies, producing a power-law state-trait continuum. At the heart of such dynamics lies the need for efficient energy dissipation through networks of coupled oscillators, which also governs the dynamics of non-living systems (e.q., earthquakes, stock market fluctuations). Since hierarchical structure produces hierarchical dynamics, the development and collapse of hierarchical structure (e.g., during maturation and disease) should leave specific traces in system dynamics (shifts in lower frequencies, i.e. morphological and behavioral traits) that may serve as early warning signs to system failure. The applications of this idea range from (bio)physics and phylogenesis to ontogenesis and clinical medicine.

Environmental dynamics shape perceptual decision bias

To interpret the sensory environment, the brain combines ambiguous sensory measurements with knowledge that reflects context-specific prior experience. But environmental contexts can change abruptly and unpredictably, resulting in uncertainty about the current context. Here we address two questions: how should context-specific prior knowledge optimally guide the interpretation of sensory stimuli in changing environments, and do human decision-making strategies resemble this optimum? We probe these questions with a task in which subjects report the orientation of ambiguous visual stimuli that were drawn from three dynamically switching distributions, representing different environmental contexts. We derive predictions for an ideal Bayesian observer that leverages knowledge about the statistical structure of the task to maximize decision accuracy, including knowledge about the dynamics of the environment. We show that its decisions are biased by the dynamically changing task context. The magnitude of this decision bias depends on the observer's continually evolving belief about the current context. The model therefore not only predicts that decision bias will grow as the context is indicated more reliably, but also as the stability of the environment increases, and as the number of trials since the last context switch grows. Analysis of human choice data validates all three predictions, suggesting that the brain leverages knowledge of the statistical structure of environmental change when interpreting ambiguous sensory signals.

Recurrent networks endowed with structural priors explain suboptimal animal behavior

The strategies found by animals facing a new task are determined both by individual experience and by structural priors evolved to leverage the statistics of natural environments. Rats quickly learn to capitalize on the trial sequence correlations of two-alternative forced choice (2AFC) tasks after correct trials but consistently deviate from optimal behavior after error trials. To understand this outcome-dependent gating, we first show that recurrent neural networks (RNNs) trained in the same 2AFC task outperform rats as they can readily learn to use across-trial information both after correct and error trials. We hypothesize that, although RNNs can optimize their behavior in the 2AFC task without any a priori restrictions, rats' strategy is constrained by a structural prior adapted to a natural environment in which rewarded and non-rewarded actions provide largely asymmetric information. When pre-training RNNs in a more ecological task with more than two possible choices, networks develop a strategy by which they gate off the across-trial evidence after errors, mimicking rats' behavior. Population analyses show that the pre-trained networks form an accurate representation of the sequence statistics independently of the outcome in the previous trial. After error trials, gating is implemented by a change in the network dynamics that temporarily decouple the categorization of the stimulus from the across-trial accumulated evidence. Our results suggest that the rats' suboptimal behavior reflects the influence of a structural prior that reacts to errors by isolating the network decision dynamics from the context, ultimately constraining the performance in a 2AFC laboratory task.

Flexible sensory-motor mapping rules manifest in correlated variability of stimulus and action codes across the brain

Humans and non-human primates can flexibly switch between different arbitrary mappings from sensation to action to solve a cognitive task. It has remained unknown how the brain implements such flexible sensory-motor mapping rules. Here, we uncovered a dynamic reconfiguration of task-specific correlated variability between sensory and motor brain regions. Human participants switched between two rules for reporting visual orientation judgments during fMRI recordings. Rule switches were either signaled explicitly or inferred by the participants from ambiguous cues. We used behavioral modeling to reconstruct the time course of their belief about the active rule. In both contexts, the patterns of correlations between ongoing fluctuations in stimulus- and action-selective activity across visual- and action-related brain regions tracked participants' belief about the active rule. The rule-specific correlation patterns broke down around the time of behavioral errors. We conclude that internal beliefs about task state are instantiated in brain-wide, selective patterns of correlated variability.

Neural Mechanisms That Make Perceptual Decisions Flexible

Neural mechanisms of perceptual decision making have been extensively studied in experimental settings that mimic stable environments with repeating stimuli, fixed rules, and payoffs. In contrast, we live in an ever-changing environment and have varying goals and behavioral demands. To accommodate variability, our brain flexibly adjusts decision-making processes depending on context. Here, we review a growing body of research that explores the neural mechanisms underlying this flexibility. We highlight diverse forms of context dependency in decision making implemented through a variety of neural computations. Context-dependent neural activity is observed in a distributed network of brain structures, including posterior parietal, sensory, motor, and subcortical regions, as well as the prefrontal areas classically implicated in cognitive control. We propose that investigating the distributed network underlying flexible decisions is key to advancing our understanding and discuss a path forward for experimental and theoretical investigations.

A random-object-kinematogram plugin for web-based research: implementing oriented objects enables varying coherence levels and stimulus congruency levels

One of the recent major advances in cognitive psychology research has been the option of web-based in addition to lab-based experimental research. This option fosters experimental research by increasing the pace and size of collecting data sets. Importantly, web-based research profits heavily from integrating tasks that are frequently applied in cognitive psychology into open access software. For instance, an open access random-dot kinematogram (RDK) plugin has recently been integrated into the jsPsych software for web-based research. This plugin allows researchers to implement experimental tasks with varying coherence levels (with that varying task difficulty) of moving dots or varying signal to noise ratios of colored dots. Here, we introduce the random-object kinematogram (ROK) plugin for the jsPsych software which, among other new features, enables researchers to include oriented objects (e.g., triangles or arrows) instead of dots as stimuli. This permits experiments with feature congruency (e.g., upwards-moving triangles pointing upwards) or incongruency (e.g., upwards-moving triangles pointing downwards), allowing to induce gradual degrees of stimulus interference, in addition to gradual degrees of task difficulty. We elaborate on possible set-ups with this plugin in two experiments examining participants' RTs and error rates on different combinations of coherence and congruency levels. Results showed increased RTs and error rates on trials with lower coherence percentages, and on trials with lower congruency levels. We discuss other new features of the ROK plugin and conclude that the possibility of gradually varying the coherence level and congruency level independently from each other offers novel possibilities when conducting web-based experiments.

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.

Fast rule switching and slow rule updating in a perceptual categorization task

To adapt to a changing world, we must be able to switch between rules already learned and, at other times, learn rules anew. Often we must do both at the same time, switching between known rules while also constantly re-estimating them. Here, we show these two processes, rule switching and rule learning, rely on distinct but intertwined computations, namely fast inference and slower incremental learning. To this end, we studied how monkeys switched between three rules. Each rule was compositional, requiring the animal to discriminate one of two features of a stimulus and then respond with an associated eye movement along one of two different response axes. By modeling behavior, we found the animals learned the axis of response using fast inference (rule switching) while continuously re-estimating the stimulus-response associations within an axis (rule learning). Our results shed light on the computational interactions between rule switching and rule learning, and make testable neural predictions for these interactions.

Revealing human sensitivity to a latent temporal structure of changes

Precisely timed behavior and accurate time perception plays a critical role in our everyday lives, as our wellbeing and even survival can depend on well-timed decisions. Although the temporal structure of the world around us is essential for human decision making, we know surprisingly little about how representation of temporal structure of our everyday environment impacts decision making. How does the representation of temporal structure affect our ability to generate well-timed decisions? Here we address this question by using a well-established dynamic probabilistic learning task. Using computational modeling, we found that human subjects' beliefs about temporal structure are reflected in their choices to either exploit their current knowledge or to explore novel options. The model-based analysis illustrates a large within-group and within-subject heterogeneity. To explain these results, we propose a normative model for how temporal structure is used in decision making, based on the semi-Markov formalism in the active inference framework. We discuss potential key applications of the presented approach to the fields of cognitive phenotyping and computational psychiatry.

Dynamic task-belief is an integral part of decision-making

Natural decisions involve two seemingly separable processes: inferring the relevant task (task-belief) and performing the believed-relevant task. The assumed separability has led to the traditional practice of studying task-switching and perceptual decision-making individually. Here, we used a novel paradigm to manipulate and measure macaque monkeys' task-belief and demonstrated inextricable neuronal links between flexible task-belief and perceptual decision-making. We showed that in animals, but not in artificial networks that performed as well or better than the animals, stronger task-belief is associated with better perception. Correspondingly, recordings from neuronal populations in cortical areas 7a and V1 revealed that stronger task-belief is associated with better discriminability of the believed-relevant, but not the believed-irrelevant, feature. Perception also impacts belief updating; noise fluctuations in V1 help explain how task-belief is updated. Our results demonstrate that complex tasks and multi-area recordings can reveal fundamentally new principles of how biology affects behavior in health and disease.

Confidence modulates the decodability of scene prediction during partially-observable maze exploration in humans

Prediction ability often involves some degree of uncertainty-a key determinant of confidence. Here, we sought to assess whether predictions are decodable in partially-observable environments where one's state is uncertain, and whether this information is sensitive to confidence produced by such uncertainty. We used functional magnetic resonance imaging-based, partially-observable maze navigation tasks in which subjects predicted upcoming scenes and reported their confidence regarding these predictions. Using a multi-voxel pattern analysis, we successfully decoded both scene predictions and subjective confidence from activities in the localized parietal and prefrontal regions. We also assessed confidence in their beliefs about where they were in the maze. Importantly, prediction decodability varied according to subjective scene confidence in the superior parietal lobule and state confidence estimated by the behavioral model in the inferior parietal lobule. These results demonstrate that prediction in uncertain environments depends on the prefrontal-parietal network within which prediction and confidence interact.

Gated recurrence enables simple and accurate sequence prediction in stochastic, changing, and structured environments

From decision making to perception to language, predicting what is coming next is crucial. It is also challenging in stochastic, changing, and structured environments; yet the brain makes accurate predictions in many situations. What computational architecture could enable this feat? Bayesian inference makes optimal predictions but is prohibitively difficult to compute. Here, we show that a specific recurrent neural network architecture enables simple and accurate solutions in several environments. This architecture relies on three mechanisms: gating, lateral connections, and recurrent weight training. Like the optimal solution and the human brain, such networks develop internal representations of their changing environment (including estimates of the environment’s latent variables and the precision of these estimates), leverage multiple levels of latent structure, and adapt their effective learning rate to changes without changing their connection weights. Being ubiquitous in the brain, gated recurrence could therefore serve as a generic building block to predict in real-life environments.

Decision prioritization and causal reasoning in decision hierarchies

From cooking a meal to finding a route to a destination, many real life decisions can be decomposed into a hierarchy of sub-decisions. In a hierarchy, choosing which decision to think about requires planning over a potentially vast space of possible decision sequences. To gain insight into how people decide what to decide on, we studied a novel task that combines perceptual decision making, active sensing and hierarchical and counterfactual reasoning. Human participants had to find a target hidden at the lowest level of a decision tree. They could solicit information from the different nodes of the decision tree to gather noisy evidence about the target's location. Feedback was given only after errors at the leaf nodes and provided ambiguous evidence about the cause of the error. Despite the complexity of task (with 107 latent states) participants were able to plan efficiently in the task. A computational model of this process identified a small number of heuristics of low computational complexity that accounted for human behavior. These heuristics include making categorical decisions at the branching points of the decision tree rather than carrying forward entire probability distributions, discarding sensory evidence deemed unreliable to make a choice, and using choice confidence to infer the cause of the error after an initial plan failed. Plans based on probabilistic inference or myopic sampling norms could not capture participants' behavior. Our results show that it is possible to identify hallmarks of heuristic planning with sensing in human behavior and that the use of tasks of intermediate complexity helps identify the rules underlying human ability to reason over decision hierarchies.

Bayesian inference with incomplete knowledge explains perceptual confidence and its deviations from accuracy

In perceptual decisions, subjects infer hidden states of the environment based on noisy sensory information. Here we show that both choice and its associated confidence are explained by a Bayesian framework based on partially observable Markov decision processes (POMDPs). We test our model on monkeys performing a direction-discrimination task with post-decision wagering, demonstrating that the model explains objective accuracy and predicts subjective confidence. Further, we show that the model replicates well-known discrepancies of confidence and accuracy, including the hard-easy effect, opposing effects of stimulus variability on confidence and accuracy, dependence of confidence ratings on simultaneous or sequential reports of choice and confidence, apparent difference between choice and confidence sensitivity, and seemingly disproportionate influence of choice-congruent evidence on confidence. These effects may not be signatures of sub-optimal inference or discrepant computational processes for choice and confidence. Rather, they arise in Bayesian inference with incomplete knowledge of the environment.

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.

Global visual confidence

Visual confidence is the observers’ estimate of their precision in one single perceptual decision. Ultimately, however, observers often need to judge their confidence over a task in general rather than merely on one single decision. Here, we measured the global confidence acquired across multiple perceptual decisions. Participants performed a dual task on two series of oriented stimuli. The perceptual task was an orientation-discrimination judgment. The metacognitive task was a global confidence judgment: observers chose the series for which they felt they had performed better in the perceptual task. We found that choice accuracy in global confidence judgments improved as the number of items in the series increased, regardless of whether the global confidence judgment was made before (prospective) or after (retrospective) the perceptual decisions. This result is evidence that global confidence judgment was based on an integration of confidence information across multiple perceptual decisions rather than on a single one. Furthermore, we found a tendency for global confidence choices to be influenced by response times, and more so for recent perceptual decisions than earlier ones in the series of stimuli. Using model comparison, we found that global confidence is well described as a combination of noisy estimates of sensory evidence and position-weighted response-time evidence. In summary, humans can integrate information across multiple decisions to estimate global confidence, but this integration is not optimal, in particular because of biases in the use of response-time information.

Age differences in sustained attention tasks: A meta-analysis

Many aspects of attention decline with aging. There is a current debate on how aging also affects sustained attention. In this study, we contribute to this debate by meta-analytically comparing performance on the go/no-go Sustained Attention to Response Task (SART) in younger and older adults. We included only studies in which the SART had a low proportion of no-go trials (5%–30%), there was a random or quasirandom stimulus presentation, and data on both healthy younger and older adults were available. A total of 12 studies were suitable with 832 younger adults and 690 older adults. Results showed that older adults were slower than younger adults on go trials (g = 1, 95% CI [.72, 1.27]) and more accurate than younger adults on no-go trials (g = .59, 95% CI [.32, .85]). Moreover, older adults were slower after a no-go error than younger adults (g = .79, 95% CI [.60, .99]). These results are compatible with an age-related processing speed deficit, mostly suggested by longer go RTs, but also with an increased preference for a prudent strategy, as demonstrated by fewer no-go errors and greater posterror slowing in older adults. An inhibitory deficit account could not explain these findings, as older adults actually outperformed younger adults by producing fewer false alarms to no-go stimuli. These findings point to a more prudent strategy when using attentional resources in aging that allows reducing the false-alarm rate in tasks producing a tendency for automatic responding.

Efficient Lévy walks in virtual human foraging

Efficient foraging depends on decisions that account for the costs and benefits of various activities like movement, perception, and planning. We conducted a virtual foraging experiment set in the foothills of the Himalayas to examine how time and energy are expended to forage efficiently, and how foraging changes when constrained to a home range. Two hundred players foraged the human-scale landscape with simulated energy expenditure in search of naturally distributed resources. Results showed that efficient foragers produced periods of locomotion interleaved with perception and planning that approached theoretical expectations for Lévy walks, regardless of the home-range constraint. Despite this constancy, efficient home-range foraging trajectories were less diffusive by virtue of restricting locomotive search and spending more time instead scanning the environment to plan movement and detect far-away resources. Altogether, results demonstrate that humans can forage efficiently by arranging and adjusting Lévy-distributed search activities in response to environmental and task constraints.

A Network Perspective on Sensorimotor Learning

What happens in the brain when we learn? Ever since the foundational work of Cajal, the field has made numerous discoveries as to how experience could change the structure and function of individual synapses. However, more recent advances have highlighted the need for understanding learning in terms of complex interactions between populations of neurons and synapses. How should one think about learning at such a macroscopic level? Here, we develop a conceptual framework to bridge the gap between the different scales at which learning operates, from synapses to neurons to behavior. Using this framework, we explore the principles that guide sensorimotor learning across these scales, and set the stage for future experimental and theoretical work in the field.

The neural basis of consciousness

Consciousness has evolved and is a feature of all animals with sufficiently complex nervous systems. It is, therefore, primarily a problem for biology, rather than physics. In this review, I will consider three aspects of consciousness: level of consciousness, whether we are awake or in a coma; the contents of consciousness, what determines how a small amount of sensory information is associated with subjective experience, while the rest is not; and meta-consciousness, the ability to reflect upon our subjective experiences and, importantly, to share them with others. I will discuss and compare current theories of the neural and cognitive mechanisms involved in producing these three aspects of consciousness and conclude that the research in this area is flourishing and has already succeeded to delineate these mechanisms in surprising detail.

Cross-Hemispheric Complementary Prefrontal Mechanisms during Task Switching under Perceptual Uncertainty

Flexible adaptation to changing environments is a representative executive control function implicated in the frontoparietal network that requires appropriate extraction of goal-relevant information through perception of the external environment. It remains unclear, however, how the flexibility is achieved under situations where goal-relevant information is uncertain. To address this issue, the current study examined neural mechanisms for task switching in which task-relevant information involved perceptual uncertainty. Twenty-eight human participants of both sexes alternated behavioral tasks in which they judged motion direction or color of visually presented colored dot stimuli that moved randomly. Task switching was associated with frontoparietal regions in the left hemisphere, and perception of ambiguous stimuli involved contralateral homologous frontoparietal regions. On the other hand, in stimulus-modality-dependent occipitotemporal regions, task coding information was increased during task switching. Effective connectivity analysis revealed that the frontal regions signaled toward the modality-dependent occipitotemporal regions when a relevant stimulus was more ambiguous, whereas the occipitotemporal regions signaled toward the frontal regions when the stimulus was more distinctive. These results suggest that complementary prefrontal mechanisms in the left and right hemispheres help to achieve a behavioral goal when the external environment involves perceptual uncertainty.SIGNIFICANCE STATEMENT In our daily life, environmental information to achieve a goal is not always certain, but we make judgments in such situations, and change our behavior accordingly. This study examined how the flexibility of behavior is achieved in a situation where goal-relevant information involves perceptual uncertainty. fMRI revealed that the lateral prefrontal cortex (PFC) in the left hemisphere is associated with behavioral flexibility, and the perception of ambiguous stimuli involves the PFC in the right hemisphere. These bilateral PFC signaled to stimulus-modality-dependent occipitotemporal regions, depending on perceptual uncertainty and the task to be performed. These top-down signals supplement task coding in the occipitotemporal regions, and highlight interhemispheric prefrontal mechanisms involved in executive control and perceptual decision-making.

Slow Drift of Neural Activity as a Signature of Impulsivity in Macaque Visual and Prefrontal Cortex

An animal's decision depends not only on incoming sensory evidence but also on its fluctuating internal state. This state embodies multiple cognitive factors, such as arousal and fatigue, but it is unclear how these factors influence the neural processes that encode sensory stimuli and form a decision. We discovered that, unprompted by task conditions, animals slowly shifted their likelihood of detecting stimulus changes over the timescale of tens of minutes. Neural population activity from visual area V4, as well as from prefrontal cortex, slowly drifted together with these behavioral fluctuations. We found that this slow drift, rather than altering the encoding of the sensory stimulus, acted as an impulsivity signal, overriding sensory evidence to dictate the final decision. Overall, this work uncovers an internal state embedded in population activity across multiple brain areas and sheds further light on how internal states contribute to the decision-making process.

Neural encoding of task-dependent errors during adaptive learning

Effective learning requires using errors in a task-dependent manner, for example adjusting to errors that result from unpredicted environmental changes but ignoring errors that result from environmental stochasticity. Where and how the brain represents errors in a task-dependent manner and uses them to guide behavior are not well understood. We imaged the brains of human participants performing a predictive-inference task with two conditions that had different sources of errors. Their performance was sensitive to this difference, including more choice switches after fundamental changes versus stochastic fluctuations in reward contingencies. Using multi-voxel pattern classification, we identified task-dependent representations of error magnitude and past errors in posterior parietal cortex. These representations were distinct from representations of the resulting behavioral adjustments in dorsomedial frontal, anterior cingulate, and orbitofrontal cortex. The results provide new insights into how the human brain represents errors in a task-dependent manner and guides subsequent adaptive behavior.

Formation of global self-beliefs in the human brain

Momentary feelings of confidence accompany many of our actions and decisions. In addition to such “local” feelings of confidence, we also construct “global” confidence estimates about our skills and abilities (global self-performance estimates or SPEs). Distorted SPEs may have a pervasive impact on motivation and self-evaluation, for instance affecting estimates of our competitiveness at work or in a sports team. Here, we found that components of a brain network previously implicated in the tracking of local confidence was additionally modulated by SPE level, whereas ventral striatum tracked SPEs irrespective of confidence. Our findings of a neurocognitive basis for global SPEs lay the groundwork for understanding how distorted SPEs arise in educational and clinical settings.

Humans create metacognitive beliefs about their performance across many levels of abstraction—from local confidence in individual decisions to global estimates of our skills and abilities. Despite a rich literature on the neural basis of local confidence judgements, how global self-performance estimates (SPEs) are constructed remains unknown. Using functional magnetic resonance imaging, we scanned human subjects while they performed several short blocks of tasks and reported on which task they think they performed best, providing a behavioral proxy for global SPEs. In a frontoparietal network sensitive to fluctuations in local confidence, we found that activity within ventromedial prefrontal cortex and precuneus was additionally modulated by global SPEs. In contrast, activity in ventral striatum was associated with subjects’ global SPEs irrespective of fluctuations in local confidence, and predicted the extent to which global SPEs tracked objective task difficulty across individuals. Our findings reveal neural representations of global SPEs that go beyond the tracking of local confidence, and lay the groundwork for understanding how a formation of global self-beliefs may go awry in conditions characterized by distorted self-evaluation.

Human confidence judgments reflect reliability-based hierarchical integration of contextual information

Our immediate observations must be supplemented with contextual information to resolve ambiguities. However, the context is often ambiguous too, and thus it should be inferred itself to guide behavior. Here, we introduce a novel hierarchical task (airplane task) in which participants should infer a higher-level, contextual variable to inform probabilistic inference about a hidden dependent variable at a lower level. By controlling the reliability of past sensory evidence through varying the sample size of the observations, we find that humans estimate the reliability of the context and combine it with current sensory uncertainty to inform their confidence reports. Behavior closely follows inference by probabilistic message passing between latent variables across hierarchical state representations. Commonly reported inferential fallacies, such as sample size insensitivity, are not present, and neither did participants appear to rely on simple heuristics. Our results reveal uncertainty-sensitive integration of information at different hierarchical levels and temporal scales.

Because our immediate observations are often ambiguous, we must use the context (prior beliefs) to guide inference, but the context may also be uncertain. Here, the authors show that humans can accurately estimate the reliability of the context and combine it with sensory uncertainty to form their decisions and estimate confidence.

The Interaction Between Elapsed Time and Decision Accuracy Differs Between Humans and Rats

A stochastic visual motion discrimination task is widely used to study rapid decision-making in humans and animals. Among trials of the same sensory difficulty within a block of fixed decision strategy, humans and monkeys are widely reported to make more errors in the individual trials with longer reaction times. This finding has posed a challenge for the drift-diffusion model of sensory decision-making, which in its basic form predicts that errors and correct responses should have the same reaction time distributions. We previously reported that rats also violate this model prediction, but in the opposite direction: for rats, motion discrimination accuracy was highest in the trials with the longest reaction times. To rule out task differences as the cause of our divergent finding in rats, the present study tested humans and rats using the same task and analyzed their data identically. We confirmed that rats' accuracy increased with reaction time, whereas humans' accuracy decreased with reaction time in the same task. These results were further verified using a new temporally local analysis method, ruling out that the observed trend was an artifact of non-stationarity in the data of either species. The main effect was found whether the signal strength (motion coherence) was varied in randomly interleaved trials or held constant within a block. The magnitude of the effects increased with motion coherence. These results provide new constraints useful for refining and discriminating among the many alternative mathematical theories of decision-making.

Oscillatory recurrent gated neural integrator circuits (ORGaNICs), a unifying theoretical framework for neural dynamics

Working memory is an example of a cognitive and neural process that is not static but evolves dynamically with changing sensory inputs; another example is motor preparation and execution. We introduce a theoretical framework for neural dynamics, based on oscillatory recurrent gated neural integrator circuits (ORGaNICs), and apply it to simulate key phenomena of working memory and motor control. The model circuits simulate neural activity with complex dynamics, including sequential activity and traveling waves of activity, that manipulate (as well as maintain) information during working memory. The same circuits convert spatial patterns of premotor activity to temporal profiles of motor control activity and manipulate (e.g., time warp) the dynamics. Derivative-like recurrent connectivity, in particular, serves to manipulate and update internal models, an essential feature of working memory and motor execution. In addition, these circuits incorporate recurrent normalization, to ensure stability over time and robustness with respect to perturbations of synaptic weights.

Optimal models of decision-making in dynamic environments

Nature is in constant flux, so animals must account for changes in their environment when making decisions. How animals learn the timescale of such changes and adapt their decision strategies accordingly is not well understood. Recent psychophysical experiments have shown humans and other animals can achieve near-optimal performance at two alternative forced choice (2AFC) tasks in dynamically changing environments. Characterization of performance requires the derivation and analysis of computational models of optimal decision-making policies on such tasks. We review recent theoretical work in this area, and discuss how models compare with subjects' behavior in tasks where the correct choice or evidence quality changes in dynamic, but predictable, ways.

Choice history biases subsequent evidence accumulation

Perceptual choices depend not only on the current sensory input but also on the behavioral context, such as the history of one's own choices. Yet, it remains unknown how such history signals shape the dynamics of later decision formation. In models of decision formation, it is commonly assumed that choice history shifts the starting point of accumulation toward the bound reflecting the previous choice. We here present results that challenge this idea. We fit bounded-accumulation decision models to human perceptual choice data, and estimated bias parameters that depended on observers' previous choices. Across multiple task protocols and sensory modalities, individual history biases in overt behavior were consistently explained by a history-dependent change in the evidence accumulation, rather than in its starting point. Choice history signals thus seem to bias the interpretation of current sensory input, akin to shifting endogenous attention toward (or away from) the previously selected interpretation.

Hierarchical reasoning by neural circuits in the frontal cortex

Humans process information hierarchically. In the presence of hierarchies, sources of failures are ambiguous. Humans resolve this ambiguity by assessing their confidence after one or more attempts. To understand the neural basis of this reasoning strategy, we recorded from dorsomedial frontal cortex (DMFC) and anterior cingulate cortex (ACC) of monkeys in a task in which negative outcomes were caused either by misjudging the stimulus or by a covert switch between two stimulus-response contingency rules. We found that both areas harbored a representation of evidence supporting a rule switch. Additional perturbation experiments revealed that ACC functioned downstream of DMFC and was directly and specifically involved in inferring covert rule switches. These results‏ reveal the computational principles of hierarchical reasoning, as implemented by cortical circuits.

Confidence Representation of Perceptual Decision by EEG and Eye Data in a Random Dot Motion Task

The Confidence of a decision could be considered as the internal estimate of decision accuracy. This variable has been studied extensively by different types of recording data such as behavioral, electroencephalography (EEG), eye and electrophysiology data. Although the value of the reported confidence is considered as one of the most important parameters in decision making, the confidence reporting phase might be considered as a restrictive element in investigating the decision process. Thus, decision confidence should be extracted by means of other provided types of information. Here, we proposed eight confidence related properties in EEG and eye data which are significantly descriptive of the defined confidence levels in a random dot motion (RDM) task. As a matter of fact, our proposed EEG and eye data properties are capable of recognizing more than nine distinct levels of confidence. Among our proposed features, the latency of the pupil maximum diameter through the stimulus presentation was established to be the most associated one to the confidence levels. Through the time-dependent analysis of these features, we recognized the time interval of 500-600 ms after the stimulus onset as an important time in correlating features to the confidence levels.

Confidence resets reveal hierarchical adaptive learning in humans

Hierarchical processing is pervasive in the brain, but its computational significance for learning under uncertainty is disputed. On the one hand, hierarchical models provide an optimal framework and are becoming increasingly popular to study cognition. On the other hand, non-hierarchical (flat) models remain influential and can learn efficiently, even in uncertain and changing environments. Here, we show that previously proposed hallmarks of hierarchical learning, which relied on reports of learned quantities or choices in simple experiments, are insufficient to categorically distinguish hierarchical from flat models. Instead, we present a novel test which leverages a more complex task, whose hierarchical structure allows generalization between different statistics tracked in parallel. We use reports of confidence to quantitatively and qualitatively arbitrate between the two accounts of learning. Our results support the hierarchical learning framework, and demonstrate how confidence can be a useful metric in learning theory.

Forming global estimates of self-performance from local confidence

Metacognition, the ability to internally evaluate our own cognitive performance, is particularly useful since many real-life decisions lack immediate feedback. While most previous studies have focused on the construction of confidence at the level of single decisions, little is known about the formation of "global" self-performance estimates (SPEs) aggregated from multiple decisions. Here, we compare the formation of SPEs in the presence and absence of feedback, testing a hypothesis that local decision confidence supports the formation of SPEs when feedback is unavailable. We reveal that humans pervasively underestimate their performance in the absence of feedback, compared to a condition with full feedback, despite objective performance being unaffected. We find that fluctuations in confidence contribute to global SPEs over and above objective accuracy and reaction times. Our findings create a bridge between a computation of local confidence and global SPEs, and support a functional role for confidence in higher-order behavioral control.

Domain-general enhancements of metacognitive ability through adaptive training

The metacognitive ability to introspect about self-performance varies substantially across individuals. Given that effective monitoring of performance is deemed important for effective behavioral control, intervening to improve metacognition may have widespread benefits, for example in educational and clinical settings. However, it is unknown whether and how metacognition can be systematically improved through training independently of task performance, or whether metacognitive improvements generalize across different task domains. Across 8 sessions, here we provided feedback to two groups of participants in a perceptual discrimination task: an experimental group (n = 29) received feedback on their metacognitive judgments, while an active control group (n = 32) received feedback on their decision performance only. Relative to the control group, adaptive training led to increases in metacognitive calibration (as assessed by Brier scores), which generalized both to untrained stimuli and an untrained task (recognition memory). Leveraging signal detection modeling we found that metacognitive improvements were driven both by changes in metacognitive efficiency (meta-d′/d′) and confidence level, and that later increases in metacognitive efficiency were positively mediated by earlier shifts in confidence. Our results reveal a striking malleability of introspection and indicate the potential for a domain-general enhancement of metacognitive abilities.

Hierarchical decision-making produces persistent differences in learning performance

Human organizations are commonly characterized by a hierarchical chain of command that facilitates division of labor and integration of effort. Higher-level employees set the strategic frame that constrains lower-level employees who carry out the detailed operations serving to implement the strategy. Typically, strategy and operational decisions are carried out by different individuals that act over different timescales and rely on different kinds of information. We hypothesize that when such decision processes are hierarchically distributed among different individuals, they produce highly heterogeneous and strongly path-dependent joint learning dynamics. To investigate this, we design laboratory experiments of human dyads facing repeated joint tasks, in which one individual is assigned the role of carrying out strategy decisions and the other operational ones. The experimental behavior generates a puzzling bimodal performance distribution–some pairs learn, some fail to learn after a few periods. We also develop a computational model that mirrors the experimental settings and predicts the heterogeneity of performance by human dyads. Comparison of experimental and simulation data suggests that self-reinforcing dynamics arising from initial choices are sufficient to explain the performance heterogeneity observed experimentally.

Sensory and decision-making processes underlying perceptual adaptation

Perceptual systems adapt to their inputs. As a result, prolonged exposure to particular stimuli alters judgments about subsequent stimuli. This phenomenon is commonly assumed to be sensory in origin. Changes in the decision-making process, however, may also be a component of adaptation. Here, we quantify sensory and decision-making contributions to adaptation in a facial expression paradigm. As expected, exposure to happy or sad expressions shifts the psychometric function toward the adaptor. More surprisingly, response times show both an overall decline and an asymmetry, with faster responses opposite the adapting category, implicating a substantial change in the decision-making process. Specifically, we infer that sensory changes from adaptation are accompanied by changes in how much sensory information is accumulated for the two choices. We speculate that adaptation influences implicit expectations about the stimuli one will encounter, causing modifications in the decision-making process as part of a normative response to a change in context.

Psychiatric Symptom Dimensions Are Associated With Dissociable Shifts in Metacognition but Not Task Performance

BACKGROUND

Distortions in metacognition-the ability to reflect on and control other cognitive processes-are thought to be characteristic of poor mental health. However, it remains unknown whether such shifts in self-evaluation are due to specific alterations in metacognition and/or a downstream consequence of changes in decision-making processes.

METHODS

Using perceptual decision making as a model system, we employed a computational psychiatry approach to relate parameters governing both decision formation and metacognitive evaluation to self-reported transdiagnostic symptom dimensions in a large general population sample (N = 995).

RESULTS

Variability in psychopathology was unrelated to either speed or accuracy of decision formation. In contrast, leveraging a dimensional approach, we revealed independent relationships between psychopathology and metacognition: a symptom dimension related to anxiety and depression was associated with lower confidence and heightened metacognitive efficiency, whereas a dimension characterizing compulsive behavior and intrusive thoughts was associated with higher confidence and lower metacognitive efficiency. Furthermore, we obtained a robust double dissociation-whereas psychiatric symptoms predicted changes in metacognition but not decision performance, age predicted changes in decision performance but not metacognition.

CONCLUSIONS

Our findings indicate a specific and pervasive link between metacognition and mental health. Our study bridges a gap between an emerging neuroscience of decision making and an understanding of metacognitive alterations in psychopathology.

Counterfactual Reasoning Underlies the Learning of Priors in Decision Making

Accurate decisions require knowledge of prior probabilities (e.g., prevalence or base rate), but it is unclear how prior probabilities are learned in the absence of a teacher. We hypothesized that humans could learn base rates from experience making decisions, even without feedback. Participants made difficult decisions about the direction of dynamic random dot motion. Across blocks of 15–42 trials, the base rate favoring left or right varied. Participants were not informed of the base rate or choice accuracy, yet they gradually biased their choices and thereby increased accuracy and confidence in their decisions. They achieved this by updating knowledge of base rate after each decision, using a counterfactual representation of confidence that simulates a neutral prior. The strategy is consistent with Bayesian updating of belief and suggests that humans represent both true confidence, which incorporates the evolving belief of the prior, and counterfactual confidence, which discounts the prior.

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People can learn base rates without feedback and apply them to make better decisions

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The estimate of base rate is updated based on the confidence in each decision

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The form of confidence used is counterfactual, as if the base rate were uninformative

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The study extends the Bayesian framework from choice to prior probability estimation

Focal optogenetic suppression in macaque area MT biases direction discrimination and decision confidence, but only transiently

Insights from causal manipulations of brain activity depend on targeting the spatial and temporal scales most relevant for behavior. Using a sensitive perceptual decision task in monkeys, we examined the effects of rapid, reversible inactivation on a spatial scale previously achieved only with electrical microstimulation. Inactivating groups of similarly tuned neurons in area MT produced systematic effects on choice and confidence. Behavioral effects were attenuated over the course of each session, suggesting compensatory adjustments in the downstream readout of MT over tens of minutes. Compensation also occurred on a sub-second time scale: behavior was largely unaffected when the visual stimulus (and concurrent suppression) lasted longer than 350 ms. These trends were similar for choice and confidence, consistent with the idea of a common mechanism underlying both measures. The findings demonstrate the utility of hyperpolarizing opsins for linking neural population activity at fine spatial and temporal scales to cognitive functions in primates.

Distinct encoding of decision confidence in human medial prefrontal cortex

Our confidence in a choice and the evidence pertaining to a choice appear to be inseparable. However, an emerging computational consensus holds that the brain should maintain separate estimates of these quantities for adaptive behavioral control. We have devised a psychophysical task to decouple confidence in a perceptual decision from both the reliability of sensory evidence and the relation of such evidence with respect to a choice boundary. Using human fMRI, we found that an area in the medial prefrontal cortex, the perigenual anterior cingulate cortex (pgACC), tracked expected performance, an aggregate signature of decision confidence, whereas neural areas previously proposed to encode decision confidence instead tracked sensory reliability (posterior parietal cortex and ventral striatum) or boundary distance (presupplementary motor area). Supporting that information encoded by pgACC is central to a subjective sense of decision confidence, we show that pgACC activity does not simply covary with expected performance, but is also linked to within-subject and between-subject variation in explicit confidence estimates. Our study is consistent with the proposal that the brain maintains choice-dependent and choice-independent estimates of certainty, and sheds light on why dysfunctional confidence often emerges following prefrontal lesions and/or degeneration.

Instance-based generalization for human judgments about uncertainty

While previous studies have shown that human behavior adjusts in response to uncertainty, it is still not well understood how uncertainty is estimated and represented. As probability distributions are high dimensional objects, only constrained families of distributions with a low number of parameters can be specified from finite data. However, it is unknown what the structural assumptions are that the brain uses to estimate them. We introduce a novel paradigm that requires human participants of either sex to explicitly estimate the dispersion of a distribution over future observations. Judgments are based on a very small sample from a centered, normally distributed random variable that was suggested by the framing of the task. This probability density estimation task could optimally be solved by inferring the dispersion parameter of a normal distribution. We find that although behavior closely tracks uncertainty on a trial-by-trial basis and resists an explanation with simple heuristics, it is hardly consistent with parametric inference of a normal distribution. Despite the transparency of the simple generating process, participants estimate a distribution biased towards the observed instances while still strongly generalizing beyond the sample. The inferred internal distributions can be well approximated by a nonparametric mixture of spatially extended basis distributions. Thus, our results suggest that fluctuations have an excessive effect on human uncertainty judgments because of representations that can adapt overly flexibly to the sample. This might be of greater utility in more general conditions in structurally uncertain environments.

Are three heavy tropical storms this year compelling evidence for climate change? A suspicious clustering of events may reflect a real change of the environment or might be due to random fluctuations because our world is uncertain. To generalize well, we should build a probability distribution over our observations defined in terms of latent causes. If data is scarce we are forced to make strong assumptions about the shape of the distribution ideally incorporating our prior knowledge. In our task, human behavior is consistent with probabilistic inference but reveals a tendency to generalize based on observed instances enhancing the effect of random patterns on behavioral judgments. The decreased reliance on available constraints through prior knowledge corresponds to a dominance of bottom-up sensory information. Maintaining a balance with expectation-driven top-down information is crucial for proper generalization. Our work provides evidence for the necessity to include graded instance-based generalization into the mathematical formulation of cognitive models. The investigation of the determinants and neural substrates of this inferential bias is expected to give insights into the richness but also fallibility of human inferences.

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.

Neural mediators of changes of mind about perceptual decisions

Changing one’s mind on the basis of new evidence is a hallmark of cognitive flexibility. To revise our confidence in a previous decision, new evidence should be used to update beliefs about choice accuracy, but how this process unfolds in the human brain remains unknown. Here we manipulated whether additional sensory evidence supports or negates a previous motion direction discrimination judgment while recording markers of neural activity in the human brain using fMRI. A signature of post-decision evidence (change in log-odds correct) was selectively observed in the activity of posterior medial frontal cortex (pMFC). In contrast, distinct activity profiles in anterior prefrontal cortex (aPFC) mediated the impact of post-decision evidence on subjective confidence, independently of changes in decision value. Together our findings reveal candidate neural mediators of post-decisional changes of mind in the human brain, and indicate possible targets for ameliorating deficits in cognitive flexibility.

Domain-General and Domain-Specific Patterns of Activity Supporting Metacognition in Human Prefrontal Cortex

Metacognition is the capacity to evaluate the success of one's own cognitive processes in various domains; for example, memory and perception. It remains controversial whether metacognition relies on a domain-general resource that is applied to different tasks or if self-evaluative processes are domain specific. Here, we investigated this issue directly by examining the neural substrates engaged when metacognitive judgments were made by human participants of both sexes during perceptual and memory tasks matched for stimulus and performance characteristics. By comparing patterns of fMRI activity while subjects evaluated their performance, we revealed both domain-specific and domain-general metacognitive representations. Multivoxel activity patterns in anterior prefrontal cortex predicted levels of confidence in a domain-specific fashion, whereas domain-general signals predicting confidence and accuracy were found in a widespread network in the frontal and posterior midline. The demonstration of domain-specific metacognitive representations suggests the presence of a content-rich mechanism available to introspection and cognitive control.

SIGNIFICANCE STATEMENT We used human neuroimaging to investigate processes supporting memory and perceptual metacognition. It remains controversial whether metacognition relies on a global resource that is applied to different tasks or if self-evaluative processes are specific to particular tasks. Using multivariate decoding methods, we provide evidence that perceptual- and memory-specific metacognitive representations coexist with generic confidence signals. Our findings reconcile previously conflicting results on the domain specificity/generality of metacognition and lay the groundwork for a mechanistic understanding of metacognitive judgments.