The study of how we develop art knowledge can provide valuable insights into the underlying cognitive systems that support expertise and knowledge transfer to new contexts. An important and largely unanswered question is whether art knowledge training impacts subsequent judgements of artworks and executive functions. Across three pre-registered experiments ( > 630 total), which used a training intervention and Bayesian regression modelling, we explore whether art knowledge training impacts subsequent judgements of artworks and executive functions. Experiments 1 and 2 revealed an effect of art training on aesthetic judgements for trained but not untrained artworks. These training effects were generalized to unseen artworks produced by the same artist (Experiment 1) or another artist with a similar style (Experiment 2), but not to different art styles. Experiment 2 also showed that with larger training 'doses' (>16 minutes), the generalization effects are stronger. Experiment 3 showed invariance of the attentional network to art training versus non-art training, suggesting similar sensitivity of executive functions to different types of training. This work shines new light on the cognitive systems that support learning and generalization of learning to new contexts. Likewise, from an applied perspective, it emphasizes that learning and generalization can occur rapidly with a relatively short (approx. 16 minutes) training video.
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http://dx.doi.org/10.1098/rsos.240175 | DOI Listing |
Ann Allergy Asthma Immunol
March 2025
University Hospital of Reims, Immunology Laboratory, Biology and Pathology Department, Reims, France; University of Reims Champagne-Ardenne, INSERM UMR 1250, Reims, France. Electronic address:
Tryptase is currently the most specific mast cell biomarker available in clinical laboratories. Tryptase levels in peripheral blood contribute to the diagnostic, prognostic and therapeutic evaluation of three clinical categories: (1) immediate hypersensitivity reactions including the life-threatening systemic form known as anaphylaxis, (2) clonal mast cell diseases and other myeloid malignancies, including as a biomarker for efficacy of chemotherapeutic agents targeting mast cell survival, and (3) hereditary α-tryptasemia (HαT), a genetic trait found in 4 - 8% of general population associated to increased risk of severe immediate hypersensitivity reactions. Rapidly evolving pathophysiology knowledge and management guidelines impact tryptase use in clinical practice, explaining the need for frequent updates.
View Article and Find Full Text PDFComput Med Imaging Graph
March 2025
School of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, 200230, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China. Electronic address:
Magnetic Resonance Imaging (MRI) has become a pivotal tool in diagnosing brain diseases, with a wide array of computer-aided artificial intelligence methods being proposed to enhance diagnostic accuracy. However, early studies were often limited by small-scale datasets and a narrow range of disease types, which posed challenges in model generalization. This study presents UniBrain, a hierarchical knowledge-enhanced pre-training framework designed for universal brain MRI diagnosis.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
March 2025
Recent advancements in food image recognition have underscored its importance in dietary monitoring, which promotes a healthy lifestyle and aids in the prevention of diseases such as diabetes and obesity. While mainstream food recognition methods excel in scenarios with large-scale annotated datasets, they falter in few-shot regimes where data is limited. This paper addresses this challenge by introducing a variational generative method, the Multivariate Knowledge-guided Variational AutoEncoder (MK-VAE), for few-shot food recognition.
View Article and Find Full Text PDFBrief Bioinform
March 2025
School of Artificial Intelligence, Jilin University, 3003 Qianjin Street, Changchun 130012, Jilin Province, China.
Identifying genes causally linked to cancer from a multi-omics perspective is essential for understanding the mechanisms of cancer and improving therapeutic strategies. Traditional statistical and machine-learning methods that rely on generalized correlation approaches to identify cancer genes often produce redundant, biased predictions with limited interpretability, largely due to overlooking confounding factors, selection biases, and the nonlinear activation function in neural networks. In this study, we introduce a novel framework for identifying cancer genes across multiple omics domains, named ICGI (Integrative Causal Gene Identification), which leverages a large language model (LLM) prompted with causality contextual cues and prompts, in conjunction with data-driven causal feature selection.
View Article and Find Full Text PDFHCA Healthc J Med
February 2025
Riverside Community Hospital, Riverside, California.
Description In the intersection of medicine and art lies a profound synergy that nurtures both professional and personal well-being. This piece embodies this connection through a detailed and abstract representation of the human body and its complexities. At the center of the piece, the gross anatomical depiction of the head and neck serves as a nod to the foundational knowledge in medicine.
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