Confidence in forced-choice recognition: What underlies the ratings?

Zawadzka, Katarzyna; Higham, Philip A.; Hanczakowski, Maciej

doi:10.1037/xlm0000321

Cited by 14 publications

(18 citation statements)

References 44 publications

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“…Both models have the advantage of reducing costly storage and computation requirements in maintaining the full posterior probability distribution over many unchosen alternatives; in many real-life scenarios, this factor may overcome the need to minimize error in the expected estimation of motion direction, Confidence, or other similar judgments. Additionally, despite reports that memory confidence appears to reflect the balance of evidence at the single neuron level 72 , it has also been suggested that similar Decision-Congruent Evidence dependence may underlie memory confidence in a task specifically designed to compare the two computational approaches 73 , as we did here.…”

Section: Discussionmentioning

confidence: 56%

Perceptual confidence neglects decision-incongruent evidence in the brain

et al. 2017

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Section: Discussionmentioning

confidence: 56%

Perceptual confidence neglects decision-incongruent evidence in the brain

et al. 2017

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“…Mounting evidence points to the existence of metacognitive noise in perceptual decision making (Shekhar & Rahnev, 2021a). Several studies have shown that the addition of metacognitive noise substantially improves model fits (Bang et al, 2019;Maniscalco & Lau, 2016;Shekhar & Rahnev, 2021b;van den Berg et al, 2017), and many others have identified potential sources of this noise (Allen et al, 2016;Fleming et al, 2015Fleming et al, , 2015Maniscalco & Lau, 2016;Peters et al, 2017;Rahnev et al, 2015;Zawadzka et al, 2017). Nevertheless, very little work has been done on understanding the nature of this metacognitive noise with the default assumption of a Gaussian distribution dominating the literature despite not having been directly tested.…”

Section: Discussionmentioning

confidence: 99%

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

Xue¹

2021

Preprint

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It is now widely appreciated that confidence ratings are corrupted by metacognitive noise. We recently demonstrated that the level of metacognitive noise increases as the evidence for a decision becomes stronger. This effect leads to the prediction that metacognitive sensitivity and metacognitive bias are confounded such that increasing one’s confidence should result in higher estimated metacognitive sensitivity. In order to test this predicted relationship, we developed a new method to simulate a change in confidence by removing the confidence criteria separating either the highest or the lowest confidence ratings. The method enables us to manipulate a subject’s metacognitive bias after data have already been collected. We applied this manipulation to the data from three tasks from the Confidence Database (N > 400 in each) and found that simulating a bias towards higher confidence indeed led to higher estimates of metacognitive sensitivity. These results provide support for the notion that metacognitive noise increases with decision evidence, and point to an important confound between metacognitive sensitivity and metacognitive bias.

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“…Indeed, such over-reliance on decision-congruent evidence --i.e., a "confirmation bias" (Abrahamyan et al, 2016) --has also been observed in other post-decisional (non-metacognitive) perceptual judgments (Stocker and Simoncelli, 2008;Luu and Stocker, 2018), value judgments (Brehm, 1956;Festinger, 1957;Gerard and White, 1983;Steele et al, 1993;Heine and Lehman, 1997;Koster et al, 2015), and metamemory (Koriat, 2012;Zawadzka et al, 2016), suggesting it may be a domain-general strategy that serves also to reduce cognitive dissonance and improve self-consistency.…”

Section: Discussionmentioning

confidence: 71%

Tuned normalization in perceptual decision-making circuits can explain seemingly suboptimal confidence behavior

Maniscalco

Odegaard

Grimaldi

et al. 2019

Preprint

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of the model; M.A.K.P. and B.M. formally specified the model and conducted model simulations; P.G., S.H.C., and M.A.B. collected the monkey data; M.A.K.P., B.O., P.G. and S.H.C. analyzed the monkey data; M.A.K.P., B.M., and H.L. wrote the manuscript. AbstractCurrent dominant views hold that perceptual confidence reflects the probability that a decision is correct. Although these views have enjoyed some empirical support, recent behavioral results indicate that confidence and the probability of being correct can be dissociated. An alternative hypothesis suggests that confidence instead reflects the magnitude of evidence in favor of a decision while being relatively insensitive to the evidence opposing the decision. We considered how this alternative hypothesis might be biologically instantiated by developing a simple leaky competing accumulator neural network model incorporating a known property of sensory neurons: tuned normalization. The key idea of the model is that each accumulator neuron's normalization 'tuning' dictates its contribution to perceptual decisions versus confidence judgments. We demonstrate that this biologically plausible model can account for several counterintuitive findings reported in the literature, where confidence and decision accuracy were shown to dissociate --and that the differential contribution a neuron makes to decisions versus confidence judgments based on its normalization tuning is vital to capturing some of these effects. One critical prediction of the model is that systematic variability in normalization tuning exists not only in sensory cortices but also in the decision-making circuitry. We tested and validated this prediction in macaque superior colliculus (SC; a region implicated in decisionmaking). The confirmation of this novel prediction provides direct support for our model. These findings suggest that the brain has developed and implements this alternative, heuristic theory of perceptual confidence computation by capitalizing on the diversity of neural resources available. Samaha et al., 2016Samaha et al., , 2017. In other words, to compute confidence the system uses a suboptimal heuristic that overly relies on decision-congruent evidence magnitude rather than optimal computations. Indeed, a recent study reported evidence for these decision-congruent evidence confidence computations using human intracranial electrocorticography (Peters et al., 2017b).However, to date no biologically plausible mechanism has been proposed that might explain these dissociations between confidence and performance, or the decision-congruent confidence computations on which they seem to depend. We therefore developed a simple leaky competing accumulator network model (Usher and McClelland, 2001) to test a new hypothesis of how these computations might be implemented. This model extends previous work to incorporate a known property of perceptual circuitry: tuned normalization Ruff et al., 2016;Verhoef and Maunsell, 2017), meaning each neuron is characterized by the specific degree to which i...

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Confidence in forced-choice recognition: What underlies the ratings?

Cited by 14 publications

References 44 publications

Perceptual confidence neglects decision-incongruent evidence in the brain

Perceptual confidence neglects decision-incongruent evidence in the brain

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

Tuned normalization in perceptual decision-making circuits can explain seemingly suboptimal confidence behavior

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