The dissociations of confidence from accuracy in forced-choice recognition judgments

Hanczakowski, Maciej; Butowska, Ewa; Beaman, C. Philip; Jones, Dylan M.; Zawadzka, Katarzyna

doi:10.1016/j.jml.2020.104189

“…In Experiments 1 and 2, we assumed that participants rated confidence about their decision in 2AFC tasks using the difference in evidence strength between the two stimuli presented in the same trial (Wickens, 2002). However, recent studies have proposed the winner-take-all rule which argues that when rating their confidence in 2AFC tasks, people may solely rely on the evidence strength of the stimulus they have chosen in each trial, rather than on the difference in strength between the two stimuli (Hanczakowski et al, 2021; Miyoshi & Lau, 2020; Peters et al, 2017). Although the winner-take-all rule has been supported by some empirical and theoretical studies, it is based on the strength theory suggesting that people set confidence criteria directly on the axis of evidence strength, and thus is relatively difficult to explain how they adjust evidence strength criteria across levels of task difficulty to produce the three regularities observed in the current study.…”

Section: Discussionmentioning

confidence: 99%

Decision criteria in signal detection model are not based on the objective likelihood ratio.

Hu¹,

Yang²,

Luo³

2023

Journal of Experimental Psychology: General

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How people set decision criteria in signal detection model is an important research question. The likelihood ratio (LR) theory, which is one of the most influential theories about criteria setting, typically assumes that (a) decisions are based on the objective LR of the signal and noise distributions, and (b) LR criteria do not change across tasks with various difficulty levels. However, it is often questioned whether people are really able to know the exact shape of signal and noise distributions, and compute the objective LR accordingly.Here we suggest whether decision criteria are set based on objective LR can be tested in two-condition experiments with different difficulty levels across conditions. We then asked participants in three empirical experiments to perform two-condition perceptual or memory tasks, and give their answer using confidence rating scale. Results revealed that the two assumptions of LR theory contradicted with each other: if we assumed decision criteria were based on objective LR, then the estimated LR criteria differed across difficulty levels, and fanned out as task difficulty decreased. We suggest people might inaccurately estimate the LR in signal detection tasks, and several possible explanations for the distortion of LR are discussed. Public Significance StatementThis study demonstrates that the three regularities, including the mirror effect, variance effect and z-transformed receiver operating characteristic length effect, hold even when the objective likelihood ratio corresponding to the decision criteria differs between experimental conditions. This study indicates that the estimated decision criteria on the axis of objective likelihood ratio differ across difficulty levels, and fan out as task difficulty decreases. This study suggests that people might inaccurately estimate the likelihood ratio in signal detection tasks.

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“…Studies in the memory domain have used manipulations which differentially affect first-order performance and confidence, for instance, changing the magnitude of confidence judgements without changing memory performance 110 . So far, the same manipulations have not been studied across domains, and as such, metacognitive illusions within a domain do not provide direct evidence for domain generality, although it may point to spurious domain generality, in so far as identifying factors which are specific to second order judgements, which may or may or not be shared between domains.…”

Section: Dissociations Between First-order and Second-order Processesmentioning

confidence: 99%

Towards a common conceptual space for metacognition in perception and memory

Mazancieux,

Pereira,

Faivre

et al. 2023

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Evaluating, controlling and representing our cognitive states is paramount for efficient behavior. In this Review, we bridge landmark concepts of metacognitive abilities in the memory and perceptual domains to examine the different types of cognitive architectures that are at play when humans provide metacognitive judgments. Building upon this common conceptual framework, we review empirical evidence supporting or challenging domaingeneral metacognition. We also review commonalities across domains, focusing notably on the influence of decisional processes on metacognitive judgements. We emphasize the challenges of isolating metacognitive from cognitive processes and how this affects our interpretation of the domain-generality of metacognition, including in clinical conditions that are hypothesized to have metacognitive impairments. Finally, we also give an overview of the literature on what we classify as 'adecisional' metacognition: evaluations made outside the context of a decisional process. Based on current knowledge, we find no evidence for a strong form of domain-generality, but we outline how we may identify such an architecture in future research.

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“…In Experiments 1 and 2, we assumed that participants rated confidence about their decision in 2AFC tasks using the difference in evidence strength between the two stimuli presented in the same trial (Wickens, 2002). However, recent studies have proposed the winner-take-all rule which argues that when rating their confidence in 2AFC tasks, people may solely rely on the evidence strength of the stimulus they have chosen in each trial, rather than on the difference in strength between the two stimuli (Hanczakowski et al, 2021;Miyoshi & Lau, 2020;Peters et al, 2017). Although the winner-take-all rule has been supported by some empirical and theoretical studies, it is based on the strength theory suggesting that people set confidence criteria directly on the axis of evidence strength, and thus is relatively difficult to explain how they adjust evidence strength criteria across levels of task difficulty to produce the three regularities observed in the current study.…”

Section: Limitation Of the Current Studymentioning

confidence: 99%

Decision criteria in signal detection model are not based on the objective likelihood ratio

Hu¹,

Yang²,

Luo³

2023

Preprint

0

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How people set decision criteria in signal detection model is an important research question. The likelihood ratio (LR) theory, which is one of the most influential theories about criteria setting, typically assumes that (a) decisions are based on the objective LR of the signal and noise distributions, and (b) LR criteria do not change across tasks with various difficulty levels. However, it is often questioned whether people are really able to know the exact shape of signal and noise distributions, and compute the objective LR accordingly. Here we suggest whether decision criteria are set based on objective LR can be tested in two-condition experiments with different difficulty levels across conditions. We then asked participants in three empirical experiments to perform two-condition perceptual or memory tasks, and give their answer using confidence rating scale. Results revealed that the two assumptions of LR theory contradicted with each other: if we assumed decision criteria were based on objective LR, then the estimated LR criteria differed across difficulty levels, and fanned out as task difficulty decreased. We suggest people might inaccurately estimate the LR in signal detection tasks, and several possible explanations for the distortion of LR are discussed.

show abstract

The dissociations of confidence from accuracy in forced-choice recognition judgments

Cited by 9 publications

References 52 publications

Decision criteria in signal detection model are not based on the objective likelihood ratio.

Decision criteria in signal detection model are not based on the objective likelihood ratio.

Towards a common conceptual space for metacognition in perception and memory

Decision criteria in signal detection model are not based on the objective likelihood ratio

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