Previous studies suggest that social learning in bumblebees can occur through second-order conditioning, with conspecifics functioning as first-order reinforcers. However, the behavioural mechanisms underlying bumblebees' acquisition of socially learned associations remain largely unexplored. Investigating these mechanisms requires detailed quantification and analysis of the observation process. Here we designed a new 2D paradigm suitable for simple top-down high-speed video recording and analysed bumblebees' observational learning process using a deep-learning-based pose-estimation framework. Two groups of bumblebees observed live conspecifics foraging from either blue or yellow flowers during a single foraging bout, and were subsequently tested for their socially learned colour preferences. Both groups successfully learned the colour indicated by the demonstrators and spent more time facing rewarding flowers-whether occupied by demonstrators or not-compared to non-rewarding flowers. While both groups showed a negative correlation between time spent facing non-rewarding flowers and learning outcomes, the observer bees in the blue group benefited from time spent facing occupied rewarding flowers, whereas the yellow group showed that time facing unoccupied rewarding flowers by the observer bees positively correlated with their learning outcomes. These results suggest that socially influenced colour preferences are shaped by the interplay of different types of observations rather than merely by observing a conspecific at a single colour. Together, these findings provide direct evidence of the dynamical viewing process of observer bees during social observation, opening up new opportunities for exploring the details of more complex social learning in bumblebees and other insects.
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http://dx.doi.org/10.1007/s10071-024-01918-x | DOI Listing |
Sci Rep
December 2024
Department of Biology, University of South Dakota, 414 East Clark Street, Vermillion, SD, 57069-2390, USA.
Psychological distress, including anxiety or mood disorders, emanates from the onset of chronic/unpredictable stressful events. Symptoms in the form of maladaptive behaviors are learned and difficult to treat. While the origin of stress-induced disorders seems to be where learning and stress intersect, this relationship and molecular pathways involved remain largely unresolved.
View Article and Find Full Text PDFNat Commun
December 2024
Department of Psychology, Cornell University, Ithaca, NY, USA.
Subjective feelings are thought to arise from conceptual and bodily states. We examine whether the valence of feelings may also be decoded directly from objective ecological statistics of the visual environment. We train a visual valence (VV) machine learning model of low-level image statistics on nearly 8000 emotionally charged photographs.
View Article and Find Full Text PDFAutism Res
December 2024
Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder and its underlying neuroanatomical mechanisms still remain unclear. The scaled subprofile model of principal component analysis (SSM-PCA) is a data-driven multivariate technique for capturing stable disease-related spatial covariance pattern. Here, SSM-PCA is innovatively applied to obtain robust ASD-related gray matter volume pattern associated with clinical symptoms.
View Article and Find Full Text PDFFront Public Health
December 2024
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
Objective: To characterize the public conversations around long COVID, as expressed through X (formerly Twitter) posts from May 2020 to April 2023.
Methods: Using X as the data source, we extracted tweets containing #long-covid, #long_covid, or "long covid," posted from May 2020 to April 2023. We then conducted an unsupervised deep learning analysis using Bidirectional Encoder Representations from Transformers (BERT).
Front Robot AI
December 2024
Robot Learning Laboratory, Instituto de Ciências Matemáticas e de Computação (ICMC), University of São Paulo (USP), SãoCarlos, Brazil.
Research on social assistive robots in education faces many challenges that extend beyond technical issues. On one hand, hardware and software limitations, such as algorithm accuracy in real-world applications, render this approach difficult for daily use. On the other hand, there are human factors that need addressing as well, such as student motivations and expectations toward the robot, teachers' time management and lack of knowledge to deal with such technologies, and effective communication between experimenters and stakeholders.
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