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

Xue, Kai

doi:10.31234/osf.io/buahk

Cited by 6 publications

(13 citation statements)

References 28 publications

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“…An alternative to the normal distribution is a double exponential distribution, which allows for tail asymmetry. In particular, I here consider the Gumbel distribution which has a pronounced right tail, a property that fits recent observations regarding the skewed nature of metacognitive noise (Shekhar and Rahnev, 2021; Xue et al, 2021). Mathematical definitions of all distributions are listed in Appendix 2—table 1.…”

Section: Resultssupporting

confidence: 58%

“…The very same factor (metacognitive noise) that therefore plausibly introduces interindividual differences in metacognitive performance, might obviate a type-1-performance-independent measurement of metacognitive efficiency in this way. Apart from type 1 performance, a recent study has shown that in empirical data the overall level of confidence likewise affects M ratio (Xue et al, 2021) − a confound that may be caused by different levels of metacognitive noise when overall confidence is low or high (Shekhar and Rahnev, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Reverse engineering of metacognition

Guggenmos

2021

Preprint

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The human ability to introspect on thoughts, perceptions or actions – metacognitive ability – has become a focal topic of both cognitive basic and clinical research. At the same time it has become increasingly clear that currently available quantitative tools are limited in their ability to make unconfounded inferences about metacognition. As a step forward, the present work introduces a comprehensive framework and model of metacognition that allows for inferences about metacognitive noise and metacognitive biases during the readout of type 1 decision values or at the confidence reporting stage. The model assumes that confidence results from a continuous but noisy and potentially biased transformation of decision values, described by a confidence link function. A canonical set of metacognitive noise distributions is introduced which differ, amongst others, in their predictions about metacognitive sign flips of type 1 decision values. Successful recovery of model parameters is demonstrated and the model is validated on an empirical data set. In particular, it is shown that metacognitive noise and bias parameters correlate with conventional behavioral measures. Crucially, in contrast to these conventional measures, metacognitive noise parameters inferred from the model are shown to be independent of type 1 performance. This work is accompanied by a toolbox (ReMeta) that allows researchers to estimate key parameters of metacognition in confidence datasets.

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Section: Resultssupporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Reverse engineering of metacognition

Guggenmos

2021

Preprint

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“…Additionally, people undertaking tasks involving trialby-trial ratings of confidence, such as during measurement of local metacognition, can be characterised by their tendency to give high confidence or low confidence ratings in general, termed metacognitive bias. Biases in confidence again need to be accounted for when deriving local metacognitive efficiency parameters -a problem which can be solved via type 2 signal-detection theory (Maniscalco and Lau, 2012, Fleming, 2017, Xue et al, 2021.…”

Section: Introductionmentioning

confidence: 99%

Age-related decreases in global metacognition are independent of local metacognition and task performance

McWilliams¹,

Bibbey²,

Steinbeis³

et al. 2022

Preprint

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Metacognition refers to a capacity to reflect on and control other cognitive processes, commonly quantified as the extent to which confidence tracks objective performance. There is conflicting evidence about how metacognition changes across the lifespan and it is unknown whether “local” metacognition (monitoring of individual judgments) and “global” metacognition (estimates of self-ability) change. Additionally, the degree to which metacognition generalises across cognitive domains may itself change over time due to increased experience with one’s own abilities. Using a gamified metacognitive task we measured local and global metacognition during performance-controlled memory and visual perception tasks in an age-stratified sample of 304 healthy volunteers (18-83 years; N=50 in each of 6 age groups). We calculated both local and global metrics of metacognition and charted domain-generality with age. Task performance was stable with age. Global self-performance estimates and local metacognitive bias decreased with age, indicating overall lower confidence. Local metacognitive efficiency was unrelated to age and remained correlated across the two cognitive domains. A stability of local metacognition indicates distinct mechanisms contributing to local and global metacognition. Our study provides both a profile of local and global metacognition across the lifespan and a benchmark against which disease-related changes in metacognition can be compared.

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“…Ideally, one’s metacognitive bias should be independent of one’s estimated metacognitive ability because metacognitive bias can be manipulated at will (i.e., one can easily increase or decrease their reported level of confidence). However, Shekhar and Rahnev found that higher confidence led to higher Mratio values (the finding was subsequently replicated and extended by Xue et al, 2021). This led them to propose a new model of metacognition with signal-dependent, lognormally-distributed metacognitive noise that could explain this relationship.…”

Section: The Need For Synergy In Research On Measures Models and Neur...mentioning

confidence: 84%

“…An important issue that requires additional research is the fuller characterization of the psychometric properties of Mratio and other measures of metacognitive ability. For example, beyond their dependence on the primary task performance, it is important to empirically assess whether these measures are independent from both response bias and metacognitive bias (the tendency to give either high or low confidence ratings) with recent research suggesting that Mratio may be confounded with metacognitive bias (Shekhar & Rahnev, 2021a; Xue et al, 2021). In addition, fundamental features of these measures such as their test–retest reliability are just beginning to be examined (Guggenmos, 2021).…”

Section: Measures Of Visual Metacognitive Abilitymentioning

confidence: 99%

Visual metacognition: Measures, models, and neural correlates.

Rahnev

2021

American Psychologist

Self Cite

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

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

Cited by 6 publications

References 28 publications

Reverse engineering of metacognition

Reverse engineering of metacognition

Age-related decreases in global metacognition are independent of local metacognition and task performance

Visual metacognition: Measures, models, and neural correlates.

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