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• People experience greater confidence when they make good as opposed to inaccurate perceptual decisions. • This is known as perceptual metacognition. • Current gold standard approaches to measuring perceptual metacognition are subject to known interpretive ambiguities. • The authors describe a better measure of metacognitive sensitivity, and provide scripts to implement this new approach. Humans experience feelings of confidence in their decisions. In perception, these feelings are typically accurate – we tend to feel more confident about correct decisions. The degree of insight people have into the accuracy of their decisions is known as metacognitive sensitivity. Currently popular methods of estimating metacognitive sensitivity are subject to interpretive ambiguities because they assume people have normally shaped distributions of different experiences when they are repeatedly exposed to a single input. If this normality assumption is violated, calculations can erroneously underestimate metacognitive sensitivity. Here, we describe a means of estimating metacognitive sensitivity that is more robust to violations of the normality assumption. This improved method can easily be added to standard behavioral experiments, and the authors provide Matlab code to help researchers implement these analyses and experimental procedures. [ABSTRACT FROM AUTHOR]

Humans experience levels of confidence in perceptual decisions that tend to scale with the precision of their judgements; but not always. Sometimes precision can be held constant while confidence changes--leading researchers to assume precision and confidence are shaped by different types of information (e.g. perceptual and decisional). To assess this, we examined how visual adaptation to oriented inputs changes tilt perception, perceptual sensitivity and confidence. Some adaptors had a greater detrimental impact on measures of confidence than on precision. We could account for this using an observer model, where precision and confidence rely on different magnitudes of sensory information. These data show that differences in perceptual sensitivity and confidence can therefore emerge, not because these factors rely on different types of information, but because they rely on different magnitudes of sensory information. [ABSTRACT FROM AUTHOR]

• 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 assume that repeated exposures to an input will evoke a normally-shaped distribution of perceptual experiences. • We show that when distributions of experiences do not conform with the normality assumption, SDT based analyses of confidence can underestimate how well informed our feelings of confidence are. 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 informed by 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 experience do not conform with the normality assumption, meta d' can be systematically underestimated relative to d'. Our data highlight that SDT-based analyses of confidence do not provide a ground truth measure of human metacognitive inefficiency. We explain why deviance from the normality assumption is especially a problem for some popular SDT-based analyses of confidence, in contrast to other analyses inspired by the SDT framework, which are more robust to violations of the normality assumption. [ABSTRACT FROM AUTHOR]

Humans intuitively evaluate their decisions by forming different levels of confidence. Despite being highly correlated, decisional confidence and sensitivity can be differentiated. The computational processes underlying this remain unknown. Here we find that, for visual judgments concerning global direction, signal range has a greater impact on confidence than it does sensitivity. We equated sensitivity for stimuli containing different degrees of directional variability. This failed, however, to equate confidence--participants were less confident when judging more variable signals despite constant sensitivity. When stimuli were instead calibrated to equate confidence, participants were more sensitive when judging more variable signals. Directional range had no impact on an unrelated judgment of brightness, helping to establish that these results cannot be attributed to a simple decisional confound. Our complementary results show that directional sensitivity and decisional confidence rely on independent transformations of sensory input. We propose that confidence will generally be shaped by the range of differently tuned neural mechanisms responsive to input during evidence accumulation, with this having a lesser impact on sensitivity. [ABSTRACT FROM AUTHOR]