scholarly journals Retrospective confidence judgments across tasks: domain-general processes underlying metacognitive accuracy

2018 ◽  
Author(s):  
Audrey Mazancieux ◽  
Stephen M Fleming ◽  
Céline Souchay ◽  
Chris Moulin
Keyword(s):  
Bayesian Model ◽  
Statistical Power ◽  
Task Structure ◽  
Hierarchical Bayesian Model ◽  
First Order ◽  
Domain Specific ◽  
Confidence Judgments ◽  
Domain Generality ◽  
Forced Choice Tasks ◽  
Choice Tasks

Is metacognition a general resource shared across domains? Previous research has documented consistent biases in confidence judgments across tasks. However, the ability to discriminate between correct and incorrect answers (metacognitive sensitivity) is often held to be domain-specific, based on non-significant correlations across domains. Such null findings may be due to low statistical power and differences in task structure or performance, thereby masking a latent domain-generality in metacognition. We examined across-domain correlations in bias and sensitivity in a large sample (N=181). Participants performed four two-alternative-forced-choice tasks (episodic memory, semantic memory, executive function, and visual perception) with trial-by-trial confidence judgments. We found significant correlations between metacognitive biases across tasks. By applying a hierarchical Bayesian model to estimate cross-task covariance, we found significant correlations in metacognitive efficiency (meta-d’/d’) across tasks, even for pairs of tasks in which first-order performance was not correlated. This suggests a domain-general resource supporting metacognitive sensitivity in retrospective confidence.

scholarly journals Decision confidence as a mapping of bayesian posterior belief

2017 ◽  
Author(s):  
Luciano Paz ◽  
Alejo Salles ◽  
Mariano Sigman
Keyword(s):  
Correct Choice ◽  
Task Structure ◽  
Bayesian Decision ◽  
Decision Confidence ◽  
Perceptual Performance ◽  
Forced Choice Tasks ◽  
Decision Making Model ◽  
Parameter Values ◽  
Choice Tasks

We study the confidence response distributions for several two alternative forced choice tasks with different structure, and assess whether their behavioral responses are accurately accounted for as a mapping from bayesian inferred probability of having made a correct choice. We propose an extension to an existing bayesian decision making model that allows us to quantitatively compare the relative quality of different function mappings from bayesian belief onto responded confidence. We find that a simple linear rescaling from bayesian belief best fits the observed response distributions. Furthermore, the parameter values allow us to study how task structure affects differently the decision policy and confidence mapping, highlighting a dissociable effect between confidence and perceptual performance.

Gender Differences in the Mental Rotations Test (MRT) Are Not Due to Task Complexity

2008 ◽  
Vol 29 (3) ◽  
pp. 130-133 ◽  
Author(s):  
Corinna Titze ◽  
Martin Heil ◽  
Petra Jansen
Keyword(s):  
Gender Differences ◽  
Mental Rotation ◽  
Task Complexity ◽  
Time Constraints ◽  
Medium Effects ◽  
Performance Factors ◽  
Mental Rotations ◽  
Sufficient Power ◽  
Forced Choice Tasks ◽  
Choice Tasks

Gender differences are one of the main topics in mental rotation research. This paper focuses on the influence of the performance factor task complexity by using two versions of the Mental Rotations Test (MRT). Some 300 participants completed the test without time constraints, either in the regular version or with a complexity reducing template creating successive two-alternative forced-choice tasks. Results showed that the complexity manipulation did not affect the gender differences at all. These results were supported by a sufficient power to detect medium effects. Although performance factors seem to play a role in solving mental rotation problems, we conclude that the variation of task complexity as realized in the present study did not.

Robust resource demand estimation using hierarchical Bayesian model in a distributed service system

2020 ◽  
Author(s):  
Sumanta Mukherjee ◽  
Krishnasuri Narayanam ◽  
Nupur Aggarwal ◽  
Digbalay Bose ◽  
Amith Singhee
Keyword(s):  
Bayesian Model ◽  
Service System ◽  
Demand Estimation ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Resource Demand ◽  
Distributed Service

scholarly journals scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chantriolnt-Andreas Kapourani ◽  
Ricard Argelaguet ◽  
Guido Sanguinetti ◽  
Catalina A. Vallejos
Keyword(s):  
Single Cell ◽  
Bayesian Modeling ◽  
Bayesian Model ◽  
Regulation Of Gene Expression ◽  
Differential Methylation ◽  
Hierarchical Bayesian Model ◽  
Genomic Features ◽  
Distinct Cell ◽  
Biological Heterogeneity

AbstractHigh-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. scMET is available at https://github.com/andreaskapou/scMET.

scholarly journals Additive genetic variance and covariance between relatives in synthetic wheat crosses with variable parental ploidy levels

Genetics ◽  
2021 ◽  
Vol 217 (2) ◽  
Author(s):  
L E Puhl ◽  
J Crossa ◽  
S Munilla ◽  
P Pérez-Rodríguez ◽  
R J C Cantet
Keyword(s):  
Hexaploid Wheat ◽  
Variance Components ◽  
Bayesian Model ◽  
Synthetic Hexaploid Wheat ◽  
Data Sets ◽  
Hierarchical Bayesian Model ◽  
Additive Variance ◽  
Ploidy Levels ◽  
Breeding Values ◽  
Synthetic Hexaploid

Abstract Cultivated bread wheat (Triticum aestivum L.) is an allohexaploid species resulting from the natural hybridization and chromosome doubling of allotetraploid durum wheat (T. turgidum) and a diploid goatgrass Aegilops tauschii Coss (Ae. tauschii). Synthetic hexaploid wheat (SHW) was developed through the interspecific hybridization of Ae. tauschii and T. turgidum, and then crossed to T. aestivum to produce synthetic hexaploid wheat derivatives (SHWDs). Owing to this founding variability, one may infer that the genetic variances of native wild populations vs improved wheat may vary due to their differential origin and evolutionary history. In this study, we partitioned the additive variance of SHW and SHWD with respect to their breed origin by fitting a hierarchical Bayesian model with heterogeneous covariance structure for breeding values to estimate variance components for each breed category, and segregation variance. Two data sets were used to test the proposed hierarchical Bayesian model, one from a multi-year multi-location field trial of SHWD and the other comprising the two species of SHW. For the SHWD, the Bayesian estimates of additive variances of grain yield from each breed category were similar for T. turgidum and Ae. tauschii, but smaller for T. aestivum. Segregation variances between Ae. tauschii—T. aestivum and T. turgidum—T. aestivum populations explained a sizable proportion of the phenotypic variance. Bayesian additive variance components and the Best Linear Unbiased Predictors (BLUPs) estimated by two well-known software programs were similar for multi-breed origin and for the sum of the breeding values by origin for both data sets. Our results support the suitability of models with heterogeneous additive genetic variances to predict breeding values in wheat crosses with variable ploidy levels.

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