The Confidence Database

Rahnev, Dobromir; Desender, Kobe; Lee, Alan; Adler, William T.; Aguilar-Lleyda, David; Akdoğan, Başak; Arbuzova, Polina; Atlas, Lauren Y.; Balcı, Fuat; Bang, Ji Won; Bègue, Indrit; Birney, Damian P.; Brady, Timothy F.; Calder-Travis, Joshua; Chetverikov, Andrey; Clark, Torin K.; Davranche, Karen; Denison, Rachel; Dildine, Troy C.; Double, Kit S.; Duyan, Yalçın Akın; Faivre, Nathan; Fallow, Kaitlyn; Filevich, Elisa; Gajdos, Thibault; Gallagher, Regan; Gardelle, Vincent de; Gherman, Sabina; Haddara, Nadia; Hainguerlot, Marine; Hsu, Tian‐Chuan; Hu, Xiao; Iturrate, Iñaki; Jaquiery, Matt; Kantner, Justin; Koculak, Marcin; Konishi, Mahiko; Koß, Christina; Kvam, Peter D.; Kwok, Sze Chai; Lebreton, Maël; Lempert, Karolina M.; Lo, Chien Ming; Luo, Liang; Maniscalco, Brian; Martín, António; Massoni, Sébastien; Matthews, Julian; Mazancieux, Audrey; Merfeld, Daniel M.; O’Hora, Denis; Palser, Eleanor R.; Paulewicz, Borysław; Pereira, Michael; Peters, Caroline; Philiastides, Marios G.; Pfuhl, Gerit; Prieto, Fernanda; Rausch, Manuel K.; Recht, Samuel; Reyes, Gabriel; Rouault, Marion; Sackur, Jérôme; Sadeghi, Saeedeh; Samaha, Jason; Seow, Tricia; Shekhar, Medha; Sherman, Maxine T.; Siedlecka, Marta; Skóra, Zuzanna; Song, Chen; Soto, David; Sun, Sai; Boxtel, Jeroen J.A. van; Wang, Shuo; Weidemann, Christoph T.; Weindel, Gabriel; Wierzchoń, Michał; Xu, Xinming; Ye, Qun; Yeon, Jiwon; Zou, Futing; Zylberberg, Ariel

doi:10.1038/s41562-019-0813-1

Cited by 133 publications

(215 citation statements)

References 44 publications

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“…For instance, serial dependency effects in perceptual experience are strongest based on the similarity between the current stimulus and the stimulus of the previous trial 35 . We also note that strong lag-1 dependency has also been observed in other dependent variables that are critical for confidence generation, such as the latencies of perceptual choices 36 and also the latencies of confidence judgments 37 . Hence we believe that a simple recency-bias is likely to explain why the predictions from the just the preceding trial are strongest.…”

Section: Discussionsupporting

confidence: 58%

Similar history biases for distinct prospective decisions of self-performance

Mei

Rankine

Olafsson

et al. 2020

Sci Rep

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Random forest classification results:A regularized random forest (Scikit-learn implementation). The implementation utilized the cross-entropy as an objective function with 500 decision trees. This number of decision trees was used as part of the regularization in order to avoid overfitting and also to approximate a simpler model which would enable comparison with the results from the logistic regression. Tree-based models are composed of nodes with each representing a level of depth in the model resulting from binary decision nodes where each node compares one feature value of the samples to a threshold. The maximum depth of each decision tree classifier was set to 1 so that only 1 best feature would be eventually used to make the decision. This means that during the training, with bootstrapped samples and features, the individual decision tree classifier ranks the importance of the feature by dropping one of the features and estimate a new decoding score after the dropping. The more loss in the decoding score compared to the original score, the more important the given feature was. In other words, we aimed to estimate the best feature among all features used (i.g confidence, awareness, and correctness). The majority rule was then applied to estimate the feature importance across all decision tree classifiers.Note the sum of feature importance of all features add up to 1 in the implementation of Scikit-learn: ( https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn . ensemble.RandomForestClassifier.feature_importances_ ), and so this will be the case across all trials back. Hence, the main effect of the time window in the ANOVA is not meaningful because the average feature importance at each time window would be exactly the same (i.e. 0.33) and consequential it is not reported.

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Section: Discussionsupporting

confidence: 58%

Similar history biases for distinct prospective decisions of self-performance

Mei

Rankine

Olafsson

et al. 2020

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“…It has been speculated that the monitoring of internally-generated signals differs from externally-generated ones. Surprisingly, the only non-perceptual domain studied recently is memory (Rahnev et al, 2020) but other domains that rely on internallygenerated signals, like motor control, emotions and attention, have not been extensively examined. Motor metacognition represents a very interesting case in the context of this putative internal-external distinction: Voluntary movements are internally generated but, unlike memory, they also elicit rich multi-modal sensory feedback about the executed movement (Haggard, 2005;Wolpert & Ghahramani, 2000).…”

Section: Motor Metacognition: a Special Case For Metacognitionmentioning

confidence: 99%

“…Data, code and pre-registration protocols are available at https://osf.io/kyhu7/ (Experiment 1) and https://osf.io/sy342/ (Experiment 2). The data discussed in this article were first published in "The Confidence Database", https://osf.io/s46pr/ (Rahnev et al, 2020). We have no known conflict of interest to disclose.…”

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Measuring Metacognition of Direct and Indirect Parameters of Voluntary Movement

Arbuzova

Peters

Roed

et al. 2020

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were supported by DFG, German Research Foundation (project number 222641018 -SFB/TTR 135). Data, code and pre-registration protocols are available at https://osf.io/kyhu7/ (Experiment 1) and https://osf.io/sy342/ (Experiment 2). The data discussed in this article were first published in "The Confidence Database", https://osf.io/s46pr/ (Rahnev et al., 2020). We have no known conflict of interest to disclose. AbstractWe can make exquisitely precise movements without the apparent need for conscious monitoring.But can we monitor the low-level movement parameters when prompted? And what are the mechanisms that allow us to monitor our movements? To answer these questions, we designed a semi-virtual ball throwing task. On each trial, participants first threw a virtual ball by moving their arm (with or without visual feedback, or replayed from a previous trial) and then made a twoalternative forced choice on the resulting ball trajectory. They then rated their confidence in their decision. We measured metacognitive efficiency using meta-d'/d' and compared it between different informational domains of the first-order task (motor, visuomotor or visual information alone), as well as between two different versions of the task based on different parameters of the movement: proximal (position of the arm) or distal (resulting trajectory of the ball thrown).We found that participants were able to monitor their performance based on distal motor information as well as when proximal information was available. Their metacognitive efficiency was also equally high in conditions with different sources of information available. The analysis of correlations across participants revealed an unexpected result: while metacognitive efficiency correlated between informational domains (which would indicate domain-generality of metacognition), it did not correlate across the different parameters of movement. We discuss possible sources of this discrepancy and argue that specific first-order task demands may play a crucial role in our metacognitive ability and should be considered when making inferences about domain-generality based on correlations.

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“…All data and codes for the behavioral analyses are freely available at https://osf.io/pn283 and have also been uploaded to the Confidence Database (Rahnev et al, 2020). In addition, unthresholded fMRI maps have been are uploaded in NeuroVault (Gorgolewski et al, 2015) and…”

Section: Data and Codementioning

confidence: 99%

Overlapping and unique neural circuits are activated during perceptual decision making and confidence

Yeon

Shekhar

Rahnev

2018

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Word count: 178)Perceptual decisions are naturally accompanied by a sense of confidence in the accuracy of the decision. However, it remains unclear whether perceptual decision making and confidence are supported by the same or different neural circuits. To address this question, we conducted two functional MRI (fMRI) experiments in which we dissociated the periods related to perceptual decision making and confidence by either decorrelating their respective regressors or asking for confidence ratings only in the second half of the experiment. We found that perceptual decision making and confidence were supported by large and mostly overlapping brain circuits including frontal, parietal, posterior, and cingulate regions with the results being remarkably consistent across the two experiments. Further, the confidence period recruited a number of unique regions, whereas there was no evidence for the decision period recruiting unique regions not involved in the confidence period. These results suggest that the neural circuits supporting perceptual decision making and confidence have a very high degree of overlap, which suggests largely shared computational mechanisms with unique components for the confidence but not for the perceptual judgments.

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The Confidence Database

Cited by 133 publications

References 44 publications

Similar history biases for distinct prospective decisions of self-performance

Similar history biases for distinct prospective decisions of self-performance

Measuring Metacognition of Direct and Indirect Parameters of Voluntary Movement

Overlapping and unique neural circuits are activated during perceptual decision making and confidence

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