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. Expected final online publication date for the Annual Review of Psychology, Volume 75 is January 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Metacognition and Confidence: A Review and Synthesis.

Determining the neural basis 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 roadmap 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 both 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 developed. 

Metacognition and confidence: A review and synthesis

On why we lack confidence in some signal-detection-based analyses of confidence

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, turned out to predict confidence in a direction opposite to previous findings; and (2) two other models previously reported to equivalently predict confidence showed stark differences. 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 many other domains. 

Computational rationality approaches to arbitrating models of cognition: a case study using perceptual metacognition

Signal-detection theory (SDT) is one of the most popular frameworks for analyzing data from studies of human behavior – including investigations of confidence. SDT-based analyses of confidence deliver both standard estimates of sensitivity (d’), and a second estimate based only on high-confidence decisions – meta d’. The extent to which meta d’ estimates fall short of d’ estimates is regarded as a measure of metacognitive inefficiency, quantifying the contamination of confidence by additional noise. These analyses rely on a key but questionable assumption – that repeated exposures to an input will evoke a normally-shaped distribution of perceptual experiences (the normality assumption). Here we show, via analyses inspired by an experiment and modelling, that when distributions of experiences do not conform with the normality assumption, meta d’ can be systematically underestimated relative to d’. Our data therefore highlight that SDT-based analyses of confidence do not provide a ground truth measure of human metacognitive inefficiency. Public Significance Statement Signal-detection theory is one of the most popular frameworks for analysing data from experiments of human behaviour – including investigations of confidence. The authors show that the results of these analyses cannot be regarded as ground truth. If a key assumption of the framework is inadvertently violated, analyses can encourage conceptually flawed conclusions.

/pdf/on-why-we-lack-confidence-in-signal-detection-based-analyses-1s9nckvh.pdf

On why we lack confidence in signal-detection-based analyses of confidence

Perceptual confidence typically corresponds to accuracy. However, observers can be overconfident relative to accuracy, termed “subjective inflation.” Inflation is stronger in the visual periphery relative to central vision, especially under conditions of peripheral inattention. Previous literature suggests inflation stems from errors in estimating noise (i.e., “variance misperception”). However, despite previous Bayesian hypotheses about metacognitive noise estimation, no work has systematically explored how noise estimation may critically depend on empirical noise statistics, which may differ across the visual field, with central noise distributed symmetrically but peripheral noise positively skewed. Here, we examined central and peripheral vision predictions from five Bayesian-inspired noise-estimation algorithms under varying usage of noise priors, including effects of attention. Models that failed to optimally estimate noise exhibited peripheral inflation, but only models that explicitly used peripheral noise priors—but used them incorrectly—showed increasing peripheral inflation under increasing peripheral inattention. Further, only one model successfully captured previous empirical results, which showed a selective increase in confidence in incorrect responses under performance reductions due to inattention accompanied by no change in confidence in correct responses; this was the model that implemented Bayesian estimation of peripheral noise, but using an (incorrect) symmetric rather than the correct positively skewed peripheral noise prior. Our findings explain peripheral inflation, especially under inattention, and suggest future experiments that might reveal the noise expectations used by the visual metacognitive system.

Variance misperception under skewed empirical noise statistics explains overconfidence in the visual periphery.

Perceptual confidence typically corresponds to accuracy. However, observers can be overconfident relative to accuracy, termed 9subjective inflation9. Inflation is stronger in the visual periphery relative to central vision, especially under conditions of peripheral inattention. Previous literature suggests inflation stems from errors in estimating noise, i.e. 9variance misperception9. However, despite previous Bayesian hypotheses about metacognitive noise estimation, no work has systematically explored how noise estimation may critically depend on empirical noise statistics which may differ across the visual field, with central noise distributed symmetrically but peripheral noise positively skewed. Here we examined central and peripheral vision predictions from five Bayesian-inspired noise-estimation algorithms under varying usage of noise priors, including effects of attention. Models that failed to optimally estimate noise exhibited peripheral inflation, but only models that explicitly used peripheral noise priors -- but used them incorrectly -- showed increasing peripheral inflation under increasing peripheral inattention. Further, only one model successfully captured previous empirical results which showed a selective increase in confidence in incorrect responses under performance reductions due to inattention accompanied by no change in confidence in correct responses; this was the model that implemented Bayesian estimation of peripheral noise, but using an (incorrect) symmetric rather than the correct positively skewed peripheral noise prior. Our findings explain peripheral inflation, especially under inattention, and suggest future experiments that might reveal the noise expectations used by the visual metacognitive system.

https://biorxiv.org/content/10.1101/2021.01.08.425966v1.full.pdf

Variance misperception under skewed empirical noise statistics explains overconfidence in the visual periphery

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Variance misperception under skewed empirical noise statistics explains overconfidence in the visual periphery.

Variance misperception under skewed empirical noise statistics explains overconfidence in the visual periphery