Understanding metacognitive confidence: Insights from judgment-of-learning justifications
This study employed the delayed judgment-of-learning (JOL) paradigm to investigate the content of metacognitive judgments; after studying cue-target word-pairs, participants predicted their ability to remember targets on a future memory test (cued recognition in Experiments 1 and 2 and cued recall in Experiment 3). In Experiment 1 and the confidence JOL group of Experiment 3, participants used a commonly employed 6-point numeric confidence JOL scale (0–20–40–60–80–100%). In Experiment 2 and the binary JOL group of Experiment 3 participants first made a binary yes/no JOL prediction followed by a 3-point verbal confidence judgment (sure-maybe-guess). In all experiments, on a subset of trials, participants gave a written justification of why they gave that specific JOL response. We used natural language processing techniques (latent semantic analysis and word frequency [n-gram] analysis) to characterize the content of the written justifications and to capture what types of evidence evaluation uniquely separate one JOL response type from others. We also used a machine learning classification algorithm (support vector machine [SVM]) to quantify the extent to which any two JOL responses differed from each other. We found that: (i) participants can justify and explain their JOLs; (ii) these justifications reference cue familiarity and target accessibility and so are particularly consistent with the two-stage metacognitive model; and (iii) JOL confidence...
Edgardo Sepulveda, Andrei N. Lupas
Cory Schwartz, Keith Frogue, Joshua Misa, Ian Wheeldon
Carole Pfister, St éphane Bourque, Odile Chatagnier, Annick Chiltz, Jérôme Fromentin, Diederik Van Tuinen, Daniel Wipf, Nathalie Leborgne-Castel
Xue-Ying Tian, Cheng-Sheng Zhang
Guangshan Yao, Feng Zhang, Xinyi Nie, Xiuna Wang, Jun Yuan, Zhenhong Zhuang, Shihua Wang
Hany S. Zinad, Inas Natasya, Andreas Werner
Majdiah Othman, Arbakariya B. Ariff, Leonardo Rios-Solis, Murni Halim
ConclusionsA MEL‐A2 with novel composition and surface activities was efficiently produced from a novel MEL producer. This is the first report on production of MEL‐A2 as the major product and from soybean oil. The biosurfactant has potential application as a wetting agent and oil‐in‐water emulsifier. Significance and Impact of the StudyDiscovery of novel structures and novel strains is valuable for further commercial development and application of MELs. Sporisorium sp. aff. sorghi SAM20 can be considered as a potential candidate for commercial production of biosurfactants.This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved. The phosphodiesterase VieA regulates gene expression by modifying the intracellular cyclic diguanylate pool. This article reveals the differential regulation of VieA in Vibrio cholerae O1 biotypes. Expression of VieA is repressed by the nucleoid‐associated protein H‐NS and the LysR‐type regulator LeuO in classical biotype V. cholerae, and by H‐NS and the quorum sensing regulator HapR in the El Tor biotype.
We describe approaches for back‐calculating model parameter estimates and their standard errors from available summary statistics with various techniques, including approximate Bayesian computation. We propose to use a quadratic approximation to the log‐likelihood for each historical trial based on 2 independent terms for the log mean rate and the log of the dispersion parameter. A Bayesian hierarchical meta‐analysis model then provides the posterior predictive distribution for these parameters. Simulations show this approach with back‐calculated parameter estimates results in very similar inference as using parame...