Animals learn some things more easily than others. To explain this so-called prepared learning, investigators commonly appeal to the evolutionary history of stimulus-consequence relationships experienced by a population or species. We offer a simple model that formalizes this long-standing hypothesis. The key variable in our model is the statistical reliability of the association between stimulus, action, and consequence. We use experimental evolution to test this hypothesis in populations of Drosophila. We systematically manipulated the reliability of two types of experience (the pairing of the aversive chemical quinine with color or with odor). Following 40 generations of evolution, data from learning assays support our basic prediction: Changes in learning abilities track the reliability of associations during a population's selective history. In populations where, for example, quinine-color pairings were unreliable but quinine-odor pairings were reliable, we find increased sensitivity to learning the quinine-odor experience and reduced sensitivity to learning quinine-color. To the best of our knowledge this is the first experimental demonstration of the evolution of prepared learning.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4136563 | PMC |
http://dx.doi.org/10.1073/pnas.1404176111 | DOI Listing |
Nanoscale
January 2025
Medcom Advance, Carrer de Marcel·lí Domingo 2-4, Edifici N5, 43007 Tarragona, Spain.
Surface-enhanced Raman scattering (SERS) substrates are garnering increasing interest for ultrasensitive high-throughput sensing. Notably, SERS-encoded nanostructures stand out due to their potential for nearly unlimited codification with excellent optical properties. In this paper we report a simple, versatile and cost-effective method for preparing SERS-encoded clusters.
View Article and Find Full Text PDFBMC Nurs
January 2025
Nursing and Midwifery Programme, Pengiran Anak Puteri Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Bandar Seri Begawan, Jalan Tungku-Link, Gadong, BE1410, Brunei Darussalam.
Background: Existing literature has emphasized the importance of certain skills vital for student nurses as they prepare for leadership and management roles before becoming registered nurses. This review aims to provide a more comprehensive insight into the essential leadership and management skills identified in previous research. The current study seeks to explore the leadership and management skills necessary to prepare student nurses for their roles in clinical settings.
View Article and Find Full Text PDFBMC Med Educ
January 2025
Department of Nursing, Tabriz Islamic Azad University of Medical Sciences, Tabriz, Iran.
Background: An appropriate clinical environment by providing learning opportunities, plays an important role in preparing students to apply the knowledge learned at the bedside. Since the lived experiences of patients in the clinical environment are effective on the quality of student's learning, the present study was conducted with the aim of explaining the lived experiences of patients regarding bedside teaching.
Materials And Methods: The present qualitative study was conducted using a content analysis approach in 2023 at the Imam Sajjad educational and therapeutic center affiliated with Tabriz Islamic Azad University of Medical Sciences.
Environ Sci Pollut Res Int
January 2025
ICAR-Central Inland Fisheries Research Institute, Barrackpore, Kolkata, 700120, West Bengal, India.
Nitrate, a highly reactive form of inorganic nitrogen, is commonly found in aquatic environments. Understanding the dynamics of nitrate-N concentration in rivers and its interactions with other water-quality parameters is crucial for effective freshwater ecosystem management. This study uses advanced machine learning models to analyse water quality parameters and predict nitrate-N concentrations in the lower stretch of the Ganga River from the observations of six annual periods (2017 to 2022).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!