A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings.

By Brian Maniscalco, H. Lau. Consciousness and Cognition, 2012

817 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cbc21c7e0525653526264a68ba1687e6782030fe

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In Cognitive Neuroscience

Citations in the corpus

The Cognitive Neuroscience of Metacognition (2014, methodology): “A similar decline option test was used by Kiani and Shadlen [40] in macaque monkeys, who made binary decisions about the direction of visual motion.”
Self-Evaluation of Decision-Making: A General Bayesian Framework for Metacognitive Computation (2017, methodology): “For instance, metacognitive reports may require a neural “readout” of confidence from decision circuitry (Insabato, Pannunzi, Rolls, & Deco, 2010; Maniscalco & Lau, 2012; Shimamura, 2000).”
How can we measure awareness? An overview of current methods (2015, background): “…to as d'') is the application of signal detection theory (SDT) to awareness ratings: where d' represents the sensitivity of a system to states of the outside world, meta-d' represents the sensitivity of a system to its own internal states (Galvin et al. 2003; Maniscalco and Lau 2012, 2014).”
Metacognition in Psychology (2019, methodology): “By using Signal Detection Theory, one can calculate both a performance score, that is, how well a person can discriminate between seen and not seen stimuli, and a bias score (see, for example, Barrett, Dienes, Seth, Appelbaum, & Harlow, 2013; Maniscalco & Lau, 2012).”
Prefrontal contributions to metacognition in perceptual decision-making (2012, background): “Behaviorally, we replicate previous findings that variation in metacognitive ability can be dissociated from variation in metacognition due to task performance (Song et al., 2011; Maniscalco and Lau, 2012).”
How to measure metacognition (2014, neutral): “The recently developed meta-d′ measure (Maniscalco and Lau, 2012, 2014) exploits the fact that given Gaussian variance assumptions at the type 1 level, the shapes of the type 2 distributions are known even if they are not themselves Gaussian (Galvin et al., 2003).”
The effects of task difficulty, novelty and the size of the search space on intrinsically motivated exploration (2014, result): “…that we tapped into here—sensorimotor learning that was implicit and slow over the course of the session—and is consistent with a large body of literature showing that meta-cognitive reports are unreliable under such conditions (Efklides, 2006; Fleming et al., 2012a,b; Maniscalco and Lau, 2012).”
How Bayesian statistics are needed to determine whether mental states are unconscious (2015, neutral): “For example, the relation may be expressed in terms of signal detection theory (SDT) as type 2 d’ (Tunney and Shanks 2003) or meta-d’ (Maniscalco and Lau 2012); or by the slope of accuracy regressed on confidence.”