2021
DOI: 10.1016/j.tics.2020.10.007
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Sources of Metacognitive Inefficiency

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Cited by 99 publications
(145 citation statements)
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References 90 publications
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“…This, in turn, prevents it from exhibiting error monitoring. Furthermore, and inconsistent with empirical observations of dissociations between performance and metacognition (Rahnev et al, 2020;Shekhar & Rahnev, 2020), it ensures the actor and the rater's accuracy remain coupled, as we will discuss in more detail below. 1 1 Our postdecisional model somewhat extends Fleming and Daw (2017) but is using a formally equivalent architecture.…”
Section: Tablementioning
confidence: 87%
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“…This, in turn, prevents it from exhibiting error monitoring. Furthermore, and inconsistent with empirical observations of dissociations between performance and metacognition (Rahnev et al, 2020;Shekhar & Rahnev, 2020), it ensures the actor and the rater's accuracy remain coupled, as we will discuss in more detail below. 1 1 Our postdecisional model somewhat extends Fleming and Daw (2017) but is using a formally equivalent architecture.…”
Section: Tablementioning
confidence: 87%
“…However, it is of course also limiting. Decision noise is ubiquitous in behaviour (Mueller & Weidemann, 2008;Wilson et al, 2014), and noisy computations offer a different lens for understanding metacognitive inefficiencies (Shekhar & Rahnev, 2020) and exploration (Findling, Skvortsova, Dromnelle, Palminteri, & Wyart, 2019). In our task, consider an agent which would have the opportunity to collect very accurate information, but has difficulty translating this information to good actions (for example through a low softmax-temperature or high trembling hand parameter).…”
Section: Ambiguity Computational Noise Uncertainty and Normativitymentioning
confidence: 99%
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