Neural correlates of metacognition: A critical perspective on current tasks

Insabato, Andrea; Pannunzi, Mario; Deco, Gustavo

doi:10.1016/j.neubiorev.2016.08.030

Cited by 15 publications

(17 citation statements)

References 47 publications

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“…Because of their non-generality, these signatures are therefore not necessary conditions of Bayesian confidence. Furthermore, they may be observed under non-Bayesian models, indicating that they are also not sufficient conditions 10,14 .…”

Section: Discussionmentioning

confidence: 98%

“…with increasing evidence discriminability, the relative frequency of low-confidence percepts increases while III Kepecs and Mainen 17 , Insabato et al 16 , and Fleming and Daw 11 call it the (folded) "X-pattern." IV Our finding is distinct from that of Insabato et al 16 , who show that the divergence signature would not be predicted under a non-Bayesian model in which the observer uses two measurements on each trial. Our analyses only concern Bayesian models in which the observer has a single measurement on each trial.…”

Section: Relevant Assumption In Hangya Et Almentioning

confidence: 99%

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Limitations of proposed signatures of Bayesian confidence

Adler

2017

Preprint

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The Bayesian model of confidence posits that confidence is the observer's posterior probability that the decision is correct. It has been proposed that researchers can gain evidence in favor of the Bayesian model by deriving qualitative signatures of Bayesian confidence, i.e., patterns that one would expect to see if an observer was Bayesian, and looking for those signatures in human or animal data. We examine two proposed qualitative signatures, showing that their derivations contain hidden assumptions that limit their applicability, and that they are neither necessary nor sufficient conditions for Bayesian confidence. One signature is an average confidence of 0.75 for trials with neutral evidence. This signature only holds when class-conditioned stimulus distributions do not overlap and internal noise is very low. Another signature is that, as stimulus magnitude increases, confidence increases on correct trials but decreases on incorrect trials. This signature is also dependent on stimulus distribution type. There is an alternative form of this signature that has been applied in the literature; we find no indication that it is expected under Bayesian confidence, which resolves an ostensible discrepancy. We conclude that, to determine the nature of the computations underlying confidence reports, there may be no shortcut to quantitative model comparison.

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Section: Discussionmentioning

confidence: 98%

Section: Relevant Assumption In Hangya Et Almentioning

confidence: 99%

Limitations of proposed signatures of Bayesian confidence

Adler

2017

Preprint

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“…Nevertheless, a recent study suggested that the Bayesian calculation of the posterior probability of being correct does not necessarily imply the folded X-pattern [26]. The inverse is also not correct as the folded X-pattern does not necessary imply the Bayesian calculation of confidence [27–29].…”

Section: Introductionmentioning

confidence: 99%

The folded X-pattern is not necessarily a statistical signature of decision confidence

Rausch¹,

Zehetleitner²

2019

PLoS Comput Biol

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Recent studies have traced the neural correlates of confidence in perceptual choices using statistical signatures of confidence. The most widely used statistical signature is the folded X-pattern, which was derived from a standard model of confidence assuming an objective definition of confidence as the posterior probability of making the correct choice given the evidence. The folded X-pattern entails that confidence as the subjective probability of being correct equals the probability of 0.75 if the stimulus in neutral about the choice options, increases with discriminability of the stimulus in correct trials, and decreases with discriminability in incorrect trials. Here, we show that the standard model of confidence is a special case in which there is no reliable trial-by-trial evidence about discriminability itself. According to a more general model, if there is enough evidence about discriminability, objective confidence is characterised by different pattern: For both correct and incorrect choices, confidence increases with discriminability. In addition, we demonstrate the consequence if discriminability is varied in discrete steps within the standard model: confidence in choices about neutral stimuli is no longer .75. Overall, identifying neural correlates of confidence by presupposing the folded X-pattern as a statistical signature of confidence is not legitimate.

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“…is also formalized in Moreno-Bote's (2010) model. Animal experiments on decision confidence have 26 received some criticism, primarily claiming that the tasks could potentially be solved without requiring any meta-cognition, for example, by treating tasks with a sure bet as a multi-alternative decision task (Insabato et al, 2016(Insabato et al, , 2017. However, Kepecs and Mainen (2012) pointed out that the same scrutiny should then also be applied to human tasks.…”

Section: Relationship With Decision Confidencementioning

confidence: 99%

Second Guessing in Perceptual Decision-Making

2020

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Human subjects of both sexes were asked to make a perceptual decision between multiple directions of visual motion. In addition to reporting a primary choice, they also had to report a second guess, indicating which of the remaining options they would rather bet on, assuming that they got their primary choice wrong. The second guess was clearly informed by the amounts of sensory evidence that were provided for the different options. A single computational integration-to-threshold model, based on the assumption that the second guess is determined by the rank ordering of accumulated evidence at or shortly after the time of the decision, was able to explain the distribution of primary choices, associated response times, and the distribution of second guesses. This suggests that the decisionmaker has access to how well supported unchosen options are by the sensory evidence. SIGNIFICANCE STATEMENT Perceptual decisions require conversion of sensory evidence into a discrete choice. Computational models based on the accumulation of evidence to a decision threshold can explain the distribution of choices and associated decision times. Subjects are also able to report the level of confidence in their decision. Here we show that, when making decisions between more than two alternatives, the decisionmaker can even report a second guess that is clearly informed by the sensory evidence. These second guesses show a distribution that is consistent with subjects having access to how much sensory evidence was accumulated for the unchosen options. The decisionmaker therefore has knowledge about the outcome of the decision process that goes beyond just the choice and an associated confidence.

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Neural correlates of metacognition: A critical perspective on current tasks

Cited by 15 publications

References 47 publications

Limitations of proposed signatures of Bayesian confidence

Limitations of proposed signatures of Bayesian confidence

The folded X-pattern is not necessarily a statistical signature of decision confidence

Second Guessing in Perceptual Decision-Making

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