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Relevant bibliographies by topics / Metacontrol

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Academic literature on the topic 'Metacontrol'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Metacontrol"

1

Qi, Hang, and Chunlei Liu. "Metacontrol Regulates Creative Thinking: An EEG Complexity Analysis Based on Multiscale Entropy." Brain Sciences 14, no. 11 (2024): 1094. http://dx.doi.org/10.3390/brainsci14111094.

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Previous studies have shown that creative thinking is associated with metacontrol, but its neural basis is unknown. The present study explored the neural basis of both by assessing EEG complexity through multiscale entropy. Subjects were engaged in a metacontrol task and an Alternative Uses Task, grouped according to task performance, and the EEG was analysed by multiscale entropy. The results showed that EEG complexity was significantly higher in the high-metacontrol and high-creativity groups than in the low-metacontrol and low-creativity groups, respectively, at high time scales. The metaco

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Mekern, Vera N., Zsuzsika Sjoerds, and Bernhard Hommel. "How metacontrol biases and adaptivity impact performance in cognitive search tasks." Cognition 182 (January 1, 2019): 251–59. https://doi.org/10.1016/j.cognition.2018.10.001.

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Cognitive control requires a balance between persistence and flexibility. We studied inter- and intraindividual differences in the metacontrol bias towards persistence or flexibility in cognitive search tasks from various cognitive domains that require continuous switching between persistence and flexibility. For each task, clustering and switching scores were derived to assess persistence and flexibility, respectively, as well as a total performance score to reflect general performance. We compared two, not mutually exclusive accounts according to which the balance between clustering and

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Zuo, Zhaoyu, Lizhuang Yang, and Hai Li. "Structural knowledge error, rather than reward insensitivity, explains the reduced metacontrol in aging." JUSTC 53, no. 12 (2023): 1202. http://dx.doi.org/10.52396/justc-2023-0132.

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Humans flexibly adjust their reliance on model-free (habitual) and model-based (goal-directed) strategies according to cost‒benefit trade-offs, the ability of which is known as metacontrol. Recent studies have suggested that older adults show reduced flexibility in metacontrol. However, whether the metacontrol deficit in aging is due to cognitive or motivational factors remains ambiguous. The present study investigated this issue using pupillometry recording and a sequential decision-making task with varied task structures and reward stakes. Our results revealed that older adults performed les

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Hommel, Bernhard, and Lorenza S. Colzato. "Meditation and Metacontrol." Journal of Cognitive Enhancement 1, no. 2 (2017): 115–21. http://dx.doi.org/10.1007/s41465-017-0017-4.

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Hommel, Bernhard, and Lorenza S. Colzato. "Meditation and Metacontrol." Journal of Cognitive Enhancement 1, no. 2 (2017): 115–21. https://doi.org/10.1007/s41465-017-0017-4.

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In addition to longer-term engagement in meditation, the past years have seen an increasing interest in the impact of single bouts of meditation on cognition. In this hypothesis and theory article, we adopt the distinction between focused-attention meditation (FAM) and open-monitoring meditation (OMM) and argue that these different types of meditation have different, to some degree, opposite impact on cognitive processes. We discuss evidence suggesting that single bouts of FAM and OMM are sufficient to bias cognitive control styles towards more versus less top-down control, respectively. We co

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Liu, Chunlei, Yuhong Lin, Chaoqun Ye, Jiaqin Yang, and Wenguang He. "Alpha ERS-ERD Pattern during Divergent and Convergent Thinking Depends on Individual Differences on Metacontrol." Journal of Intelligence 11, no. 4 (2023): 74. http://dx.doi.org/10.3390/jintelligence11040074.

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The role of metacontrol in creativity is theoretically assumed, but experimental evidence is still lacking. In this study, we investigated how metacontrol affects creativity from the perspective of individual differences. Sixty participants completed the metacontrol task, which was used to divide participants into a high-metacontrol group (HMC) versus a low (LMC) group. Then, these participants performed the alternate uses task (AUT; divergent thinking) and the remote associates test (RAT; convergent thinking), while their EEG results were recorded continuously. Regarding their behavior, the H

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Mariusdottir, Thorey, Vadim Bulitko, and Matthew Brown. "Maximizing Flow as a Metacontrol in Angband." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 11, no. 1 (2021): 149–55. http://dx.doi.org/10.1609/aiide.v11i1.12801.

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Flow is a psychological state that is reported to improve people’s performance. Flow can emerge when the person’s skills and the challenges of their activity match. This paper applies this concept to artificial intelligence agents. We equip a decision-making agent with a metacontrol policy that guides the agent to activities where the agent’s skills match the activity difficulty. Consequently, we expect the agent’s performance to improve. We implement and evaluate this approach in the role-playing game of Angband.

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Wang, Xi, Xianzhen Zhou, Christian Beste, and Bernhard Hommel. "Neurophysiological cross-task similarities between metacontrol states." Journal of Vision 24, no. 10 (2024): 382. http://dx.doi.org/10.1167/jov.24.10.382.

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Sanz, Ricardo, M. Guadalupe Sánchez-Escribano, and Carlos Herrera. "A model of emotion as patterned metacontrol." Biologically Inspired Cognitive Architectures 4 (April 2013): 79–97. http://dx.doi.org/10.1016/j.bica.2013.02.001.

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Kool, Wouter, Samuel J. Gershman, and Fiery A. Cushman. "Planning Complexity Registers as a Cost in Metacontrol." Journal of Cognitive Neuroscience 30, no. 10 (2018): 1391–404. http://dx.doi.org/10.1162/jocn_a_01263.

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Decision-making algorithms face a basic tradeoff between accuracy and effort (i.e., computational demands). It is widely agreed that humans can choose between multiple decision-making processes that embody different solutions to this tradeoff: Some are computationally cheap but inaccurate, whereas others are computationally expensive but accurate. Recent progress in understanding this tradeoff has been catalyzed by formalizing it in terms of model-free (i.e., habitual) versus model-based (i.e., planning) approaches to reinforcement learning. Intuitively, if two tasks offer the same rewards for

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Book chapters on the topic "Metacontrol"

1

Hernández, Carlos, José L. Fernández, Guadalupe Sánchez-Escribano, Julita Bermejo-Alonso, and Ricardo Sanz. "Model-Based Metacontrol for Self-adaptation." In Intelligent Robotics and Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22879-2_58.

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Päßler, Juliane, Esther Aguado, Gustavo Rezende Silva, Silvia Lizeth Tapia Tarifa, Carlos Hernández Corbato, and Einar Broch Johnsen. "A Formal Model of Metacontrol in Maude." In Leveraging Applications of Formal Methods, Verification and Validation. Verification Principles. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19849-6_32.

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Paredes-Frigolett, Harold José. "Metacontrol of a Traffic Simulator Using Situation Semantics." In Computer Science 2. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4757-9805-0_30.

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"Goal-Directed Metacontrol for Integrated Procedure Learning." In Metareasoning. The MIT Press, 2011. http://dx.doi.org/10.7551/mitpress/8069.003.0009.

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Kim, Jihie, Karen Myers, Melinda Gervasio, and Yolanda Gil. "Goal-Directed Metacontrol for Integrated Procedure Learning." In Metareasoning. The MIT Press, 2011. http://dx.doi.org/10.7551/mitpress/9780262014809.003.0006.

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