AbstractA change to a population's social network is a change to the substrate of cultural transmission, affecting behavioral diversity and adaptive cultural evolution. While features of network structure such as population size and density have been well studied, less is understood about the influence of social processes such as population turnover-or the repeated replacement of individuals by naive individuals. Experimental data have led to the hypothesis that naive learners can drive cultural evolution by better assessing the relative value of behaviors, although this hypothesis has been expressed only verbally. We conducted a formal exploration of this hypothesis using a generative model that concurrently simulated its two key ingredients: social transmission and reinforcement learning. We simulated competition between high- and low-reward behaviors while varying turnover magnitude and tempo. Variation in turnover influenced changes in the distributions of cultural behaviors, irrespective of initial knowledge-state conditions. We found optimal turnover regimes that amplified the production of higher reward behaviors through two key mechanisms: repertoire composition and enhanced valuation by agents that knew both behaviors. These effects depended on network and learning parameters. Our model provides formal theoretical support for, and predictions about, the hypothesis that naive learners can shape cultural change through their enhanced sampling ability. By moving from experimental data to theory, we illuminate an underdiscussed generative process that can lead to changes in cultural behavior, arising from an interaction between social dynamics and learning.
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http://dx.doi.org/10.1086/730110 | DOI Listing |
Nat Commun
December 2024
Department of Molecular Pharmacology and Physiology, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
PeerJ
December 2024
Department of Fundamental and Applied Sciences, Faculty of Science and Information Technology, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia.
Background: Alzheimer's Disease (AD) poses a major challenge as a neurodegenerative disorder, and early detection is critical for effective intervention. Magnetic resonance imaging (MRI) is a critical tool in AD research due to its availability and cost-effectiveness in clinical settings.
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BMC Med Educ
October 2024
Department of Obstetrics and Prenatal Medicine, University Hospital Bonn, Venusberg Campus 1, 53127, Bonn, Germany.
Background: Ultrasound technology is indispensable in perinatal care due to its non-invasive and painless nature, offering vital insights into foetal development and childbirth. With the academisation of midwifery in Germany, there is a growing necessity to incorporate ultrasound training into midwifery education. This paper discusses the development and implementation of an introductory obstetric ultrasound curriculum tailored for midwifery students, focusing on fundamental ultrasound techniques in obstetrics.
View Article and Find Full Text PDFSensors (Basel)
July 2024
School of Electrical and Mechanical, Beijing Institute of Technology, Beijing 100081, China.
The detection performance of radar is significantly impaired by active jamming and mutual interference from other radars. This paper proposes a radio signal modulation recognition method to accurately recognize these signals, which helps in the jamming cancellation decisions. Based on the ensemble learning stacking algorithm improved by meta-feature enhancement, the proposed method adopts random forests, K-nearest neighbors, and Gaussian naive Bayes as the base-learners, with logistic regression serving as the meta-learner.
View Article and Find Full Text PDFOrthop Traumatol Surg Res
December 2024
Department of Hand Surgery, Strasbourg University Hospitals, FMTS, 1 Avenue Molière, 67200 Strasbourg, France; ICube CNRS UMR7357, Strasbourg University, 2-4 rue Boussingault, 67000 Strasbourg, France; Gepromed, Bâtiment d'Anesthésiologie, 4 rue Kirschleger 67085 Strasbourg Cedex, France. Electronic address:
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