Metacognition

Metacognition is the umbrella term for the processes that allow us to reflect on and self-evaluate mental and physical function — and to use those evaluations to control behaviour.¹ In the laboratory, the term most often denotes metacognitive monitoring: the ongoing assessment of the reliability or quality of a first-order process such as perception, memory or decision-making, whose canonical expression is propositional confidence — an estimate of performance in a self-directed frame of reference (“I think I will remember this word”, rather than “this word is probably on the list”).¹ This self-directedness distinguishes metacognitive quantities from the world-directed uncertainties studied in perception and memory research, and makes the design of genuinely metacognitive tasks a subtle problem.¹

The topic connects to several neighbouring wiki pages: active-inference covers the Bayesian and precision-based treatment of uncertainty and self-evaluation in behaviour; analog-cognition-consciousness covers the relationship between higher-order representation and consciousness; cognitive-judgement-bias covers affect-driven biases in judgement. The synthesis below is centred on Fleming’s 2026 Nature Reviews Neuroscience review, supported by the companion literature.¹

Definition and scope

• Monitoring and control. The classical framework (Nelson & Narens, 1990) distinguishes a meta-level that monitors and controls object-level cognition — still the organising scheme for the field.¹²
• Propositional confidence. Computational work distinguishes world-directed uncertainty (about the stimulus or state of the world) from self-directed confidence (about one’s own behaviour); the two serve different goals, and confidence is not simply a copy of uncertainty.¹³
• The measurement requirement. Any metacognition task needs two components: a first-order process to be monitored, and a way of eliciting a second-order evaluation of it. How that second-order judgement is elicited — explicit ratings in humans, implicit opt-out or wagering measures in animals — defines the main methodological split across species.¹
• A standing caution. Not all behaviour that looks metacognitive is metacognitive: selectively avoiding difficult choices can be solved by tracking stimulus properties, rather than by representing to oneself that a decision is difficult.¹

A brief history

• 1971–1979 — origin. John Flavell coins “metamemory” (1971) and then “metacognition” (1979), defined as “knowledge and cognition about cognitive phenomena”; his distinction between metacognitive knowledge and metacognitive experience foreshadows the modern global/local distinction.⁴¹
• 1980s — first neural wave. Shimamura & Squire find that people with Korsakoff’s syndrome show selectively impaired feeling-of-knowing despite largely spared recognition memory, identifying the prefrontal cortex (PFC) as a key neural substrate; observational studies then link metacognitive sensitivity to brain structure, function and connectivity.¹
• 1990 — the organising framework. Nelson & Narens formulate the meta/object-level architecture and its monitoring–control relations.¹
• 2000s — second neural wave. Functional imaging isolates medial frontal (dorsal anterior cingulate, dACC) and lateral frontopolar activation for metacognitive judgements; confidence correlates are found across frontal and parietal cortex in monkeys, humans and rodents; the meta-d′ model supplies a performance-corrected metric (2012); visual metacognition consolidates as a subfield.¹⁴
• 2010s–2020s — computational and comparative turn. Bayesian second-order frameworks, cross-species convergence from rodent to human, clinical translation, and metacognition in artificial systems.¹⁵

Measuring metacognition

Tasks. Three families dominate, differing in how the second-order judgement is elicited: explicit confidence ratings (or confidence forced-choice, in which the participant picks the trial they did better on); opt-out paradigms in animals, where the animal is trained to select an uncertain/escape response — with the caveat that an effective opt-out can be solved by tracking stimulus strength unless the opt-out option is offered unpredictably; and post-/peri-decision wagering, including the rodent “wait for reward, give up early” variant also used with human infants, in which the optimal strategy is to wait longer when confident.¹³ Prospective measures (judgements of learning, feelings of knowing) extend the same logic to future performance.¹

Metrics. The field summarises metacognitive performance with three constructs, distinguished most clearly by Fleming and Lau:⁶²

Construct	Meaning	Typical metric
Metacognitive bias	Level of confidence relative to performance (calibration)	mean confidence; calibration curves
Metacognitive sensitivity	Trial-by-trial correspondence between confidence and accuracy	meta-d′; area under the type-2 ROC; confidence–accuracy correlation
Metacognitive efficiency	Sensitivity corrected for first-order performance and bias	meta-d′/d′

Metacognitive sensitivity estimates are easily confounded with changes in first-order performance or bias; the meta-d′ model expresses sensitivity on the same scale as first-order d′, and correcting sensitivity for performance is now the field standard.⁶⁴ A large community resource, the Confidence Database, pools confidence experiments from many laboratories across perception and memory paradigms for reuse and meta-analysis.⁷⁴

Computational models

• First-order (direct-access) models assume the subject has perfect access to the evidence underpinning the first-order decision; they predict minimal dissociations and act mainly as a null hypothesis — such accounts are arguably not metacognitive at all.¹
• Static models characterise quantitative relationships between performance and self-evaluation. Their best-known signature is the “folded-X” pattern: confidence increases with evidence strength for correct trials and decreases for errors — observed in rodent orbitofrontal firing.¹ The CASANDRE model proposes that metacognition is limited by meta-uncertainty (imprecision in one’s own estimate of reliability), imposing a ceiling on metacognitive performance even when first-order sensitivity is intact; a parallel variant allows independent evidence to “boost” confidence beyond performance.¹
• Dynamic models build on evidence-accumulation (EA) frameworks and make predictions about timing. Serial models add post-decisional accumulation; parallel models read confidence continuously from evidence available before the decision. Behavioural and neural evidence exists for both, and for pre-decisional contributions: monkeys’ lateral intraparietal (LIP) firing predicts opt-out and wagers; human EEG centroparietal positivity tracks confidence; a second, post-decisional component tracks error detection. Which architecture dominates shifts with when the judgement is collected (retrospective vs concurrent), and in naturalistic settings both pre- and post-decisional dynamics likely contribute.¹⁸
• Bayesian second-order framework. Fleming & Daw formalise self-evaluation as a second-order inference on a coupled but distinct decision system — computationally equivalent to inferring the performance of another actor. This framework unifies confidence and error detection and predicts characteristic influences of one’s own actions on metacognitive judgements.⁵

Neural basis

Convergent prefrontal core. Across species, metacognitive judgements recruit a network centred on hubs in the PFC, interacting with domain-specific areas:¹

Species	Regions linked to metacognitive quantities	Key evidence
Rodent	V1; orbitofrontal cortex (OFC); anterior cingulate cortex (ACC)	folded-X confidence signals in OFC firing; OFC/ACC inactivation reduces metacognitive sensitivity while sparing first-order performance13
Monkey	V1; lateral intraparietal cortex (LIP); dorsolateral PFC; medial agranular PFC; frontopolar cortex (FPC)	EA signals predict opt-out and peri-decision wagers; FPC inactivation causes selective metacognitive deficits1
Human	V1; intraparietal sulcus/parietal cortex; pgACC/dACC/dmPFC; vmPFC; insula; frontopolar/anterior PFC	metacognition-selective fMRI/EEG signatures; anterior PFC lesions and TMS impair metacognitive efficiency with intact first-order performance14

Where the information comes from. World-directed uncertainty must be extracted downstream of sensory areas to inform metacognition. Decoding studies show sensory uncertainty represented in visual cortex feeding confidence-related signals in PFC; in monkeys, nonlinear decoders applied to V1 populations predict confidence better than they predict choice, implying an additional transformation of sensory signals; mnemonic uncertainty is read out within and after working-memory representations. Motor/action uncertainty and memory for one’s own actions also shape confidence — making initial choices explicit improves subsequent metacognitive judgements.¹⁸

An integrated account. The review’s working hypothesis (its Fig. 4): sensory and motor cortices carry task-specific uncertainties; these are transformed into a self-directed format in downstream parietal and prefrontal association cortex, with dynamic confidence linked to EA signals; multimodal PFC regions (insula, medial PFC) represent propositional confidence and broadcast it to anterior PFC for self-regulation and communication; and a self-model — sharing machinery with social cognition — modulates confidence formation. A posterior-to-anterior gradient within PFC may support more stable, global self-beliefs.¹

Timescales. Metacognition operates from moment to moment (local) to long-run self-beliefs about skill and ability (global). Local confidence “leaks” between trials; local and global estimates interact and are integrated into a self-model; oscillatory activity has been linked to priors on confidence.¹²

Functional roles of metacognition

• Self-regulation. On short timescales, confidence regulates error correction, evidence gathering, information seeking and sequential decisions; it can act as a substitute feedback signal when none is available, and in monkeys dopamine teaching signals scale with confidence. Over longer timescales, a “common currency” for confidence — represented in medial PFC in humans and rodents — supports strategic arbitration between tasks and strategies, and feeds motivation and self-worth.¹
• Social regulation. Sharing confidence between individuals improves joint performance; confidence can be strategically overstated or understated in public, with the FPC carrying the adjustment for public communication; and other-directed metacognition (estimating others’ reliability) recruits networks shared with theory of mind, with partial overlap in vmPFC between metacognition and mentalising meta-analyses.¹

Comparative and animal metacognition

Animal metacognition is studied through uncertainty responses (opt-out) and wagering. The “two comparative psychologies” debate frames the interpretive stakes: associationist accounts seek low-level mechanisms, while higher-order accounts take the flexibility and generalisation of uncertainty responses as evidence of metacognitive representation.⁹ The review’s cross-species synthesis finds striking commonalities — sensitivity to early-sensory uncertainty fluctuations, EA-linked confidence signals, domain-general medial-PFC confidence/error signals, and causal contributions of anterior/lateral granular PFC in both primates and humans — but also differences to explain: findings of PFC involvement are more common in human studies (partly a sampling-bias issue that whole-brain monkey fMRI has corrected), explicit reports may recruit self-reflective areas, and matched tasks can reveal different algorithmic solutions across species. Rodents lack the granular lateral PFC of primates but share agranular frontal cortex and insula; even honeybees display opt-out behaviour, raising the question of minimal circuits for metacognition.¹

Clinical relevance

• Psychiatric conditions. Disturbances of metacognition have been linked to anxiety, depression, obsessive–compulsive disorder (classically a “disorder of doubt”) and psychosis. In transdiagnostic studies, anxiety-depression maps onto lower local confidence (a bias change), while compulsive-intrusive symptom dimensions show local overconfidence combined with global doubt; schizophrenia has been characterised by poor domain-general metacognitive sensitivity and impaired insight. Self-report metrics require care in this literature.¹
• Neurological conditions. Anosognosia — a lack of awareness of one’s own cognitive problems, common in prefrontal dysfunction — can be viewed as a failure to update global self-beliefs; around 75% of people with frontotemporal dementia show impaired illness awareness. Metacognitive sensitivity has been linked to tau burden and to prefrontal change in substance-use disorder, and is not yet captured by standard neuropsychological assessments.¹
• Interventions. Metacognitive training for psychosis (“planting the seeds of doubt”) reduces delusions and is recommended in national treatment guidelines in Australia, New Zealand and Germany. Brain stimulation shows causal leverage — FPC stimulation improves metacognitive efficiency while dlPFC stimulation shifts bias, and a meta-analysis of stimulation studies highlights anterior/lateral PFC — and pharmacological work (e.g. noradrenaline blockade improving metacognitive efficiency) explores neuromodulatory entry points. Whether metacognitive disturbance is a cause or a marker of clinical symptoms remains an open causal question.¹

Metacognition and consciousness

Higher-order theories of consciousness hold that becoming conscious of a mental state involves forming a metacognitive representation of it; consistent with this, metacognitive sensitivity is “consciousness-selective” — people monitor performance better for information they are conscious of. Reality monitoring (distinguishing internally generated from externally driven activity) recruits frontopolar regions similar to explicit metacognition, and failures of reality monitoring are linked to hallucinations and to conditions such as aphantasia. The topic is a bridge between circuit-level mechanisms and subjective experience (review Box 2).¹⁸

Open questions and frontiers

• Measurement and theory. Psychometric characterisation of metacognitive metrics lags their computational use; the field is moving beyond retrospective confidence in two-alternative tasks toward naturalistic and information-theoretic measures, and a reframing of the “ground truth” for metacognition from objective accuracy to self-consistency (the probability of making the same choice again).¹
• Meta-metacognition. Humans can evaluate the reliability of their own confidence (third- or fourth-order judgements) — recursive self-modelling remains largely unstudied neurally.¹
• Cross-species integration. Which aspects of metacognition are conserved, and which depend on uniquely human self-modelling? Progress requires matched tasks, performance-controlled assays, and careful homology mapping.¹
• Artificial intelligence. Metacognition is becoming a design concern for AI: large language models show a “calibration gap” between confidence and accuracy, and artificial networks can develop calibrated confidence (and human-like biases) and use internal states for self-evaluation. As AI first-order competence grows, demands on human metacognition — knowing when to offload work and when to trust output — will grow with it (review Box 1).¹

Related pages

• active-inference — uncertainty, precision and self-evaluative computation in the free-energy framework
• analog-cognition-consciousness — higher-order representation and the consciousness interface
• cognitive-judgement-bias — affect-driven biases in judgement and evaluation

Footnotes

1. raw/papers/fleming-2026-integrative-metacognition.md ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹ ↩¹⁰ ↩¹¹ ↩¹² ↩¹³ ↩¹⁴ ↩¹⁵ ↩¹⁶ ↩¹⁷ ↩¹⁸ ↩¹⁹ ↩²⁰ ↩²¹ ↩²² ↩²³ ↩²⁴ ↩²⁵ ↩²⁶ ↩²⁷ ↩²⁸ ↩²⁹ ↩³⁰ ↩³¹ ↩³² ↩³³ ↩³⁴ ↩³⁵ ↩³⁶ ↩³⁷

2. raw/papers/fleming-2024-metacognition-confidence.md ↩ ↩² ↩³

3. raw/papers/kepecs-mainen-2012-confidence-framework.md ↩ ↩² ↩³

4. raw/papers/rahnev-2021-visual-metacognition.md ↩ ↩² ↩³ ↩⁴ ↩⁵

5. raw/papers/fleming-daw-2017-self-evaluation.md ↩ ↩²

6. raw/papers/fleming-lau-2014-how-to-measure-metacognition.md ↩ ↩²

7. raw/papers/rahnev-2020-confidence-database.md ↩

8. raw/papers/goris-2025-visual-confidence.md ↩ ↩² ↩³

9. raw/papers/smith-2014-animal-metacognition.md ↩