Questions of how we know our own and other minds, and whether metacognition and mindreading rely on the same processes, are longstanding in psychology and philosophy. In Experiment 1, children/adolescents with autism (who tend to show attenuated mindreading) showed significantly lower accuracy on an explicit metacognition task than neurotypical children/adolescents, but not on an allegedly metacognitive implicit one. In Experiment 2, neurotypical adults completed these tasks in a single-task condition or a dual-task condition that required concurrent completion of a secondary task that tapped mindreading. Metacognitive accuracy was significantly diminished by the dual-mindreading-task on the explicit task but not the implicit task. In Experiment 3, we included additional dual-tasks to rule out the possibility that secondary task (regardless of whether it required mindreading) would diminish metacognitive accuracy. Finally, in both Experiments 1 and 2, metacognitive accuracy on the explicit task, but not the implicit task, was associated significantly with performance on a measure of mindreading ability. These results suggest that explicit metacognitive tasks (used frequently to measure metacognition in humans) share metarepresentational processing resources with mindreading, whereas implicit tasks (which are claimed by some comparative psychologists to measure metacognition in nonhuman animals) do not. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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http://dx.doi.org/10.1037/xge0000878 | DOI Listing |
Nat Commun
January 2025
Institute of NeuroScience, Université catholique de Louvain, Brussels, Belgium.
Large Language Models have demonstrated expert-level accuracy on medical board examinations, suggesting potential for clinical decision support systems. However, their metacognitive abilities, crucial for medical decision-making, remain largely unexplored. To address this gap, we developed MetaMedQA, a benchmark incorporating confidence scores and metacognitive tasks into multiple-choice medical questions.
View Article and Find Full Text PDFBehav Sci (Basel)
November 2024
Santa Fe Institute, Santa Fe, NM 87501, USA.
Dealing with uncertainty is a pivotal skill for adaptive decision-making across various real-life contexts. Cognitive models suggest that individuals continuously update their knowledge based on past choices and outcomes. Traditionally, uncertainty has been linked to negative states such as fear and anxiety.
View Article and Find Full Text PDFFront Psychol
December 2024
Department of Science and Technology, Hebei Agricultural University, Huanghua, China.
This study aims to explore various key factors influencing the academic performance of college students, including metacognitive awareness, learning motivation, participation in learning, environmental factors, time management, and mental health. By employing the chi-square test to identify features closely related to academic performance, this paper discussed the main influencing factors and utilized machine learning models (such as LOG, SVC, RFC, XGBoost) for prediction. Experimental results indicate that the XGBoost model performs the best in terms of recall and accuracy, providing a robust prediction for academic performance.
View Article and Find Full Text PDFBehav Res Methods
January 2025
Institute of Developmental Psychology, Faculty of Psychology, Beijing Normal University, No.19 Xinjiekouwai Street, Haidian District, Beijing, 100875, China.
Over the past few decades, Swahili-English and Lithuanian-English word pair databases have been extensively utilized in research on learning and memory. However, these normative databases are specifically designed for generating study stimuli in learning and memory research involving native (or fluent) English speakers. Consequently, they are not suitable for investigations that encompass populations whose first language is not English, such as Chinese individuals.
View Article and Find Full Text PDFCognition
December 2024
Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, UK; School of Psychology, University of Birmingham, Birmingham B15 2TT, UK.
The ability to represent and infer accurately others' mental states, known as Theory of Mind (ToM), has been theorised to be associated with metacognitive ability. Here, we considered the role of metacognition in mental state inference through the lens of a recent theoretical approach to explaining ToM, the 'Mind-space' framework. The Mind-space framework posits that trait inference, representation of the qualities of the mind giving rise to the mental state, is important in forming accurate mental state inferences.
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