In delay-specific remembering, accuracy in delayed matching-to-sample tasks is enhanced after single delays or retention intervals relative to performance at other delays. In the differential-outcomes effect (DOE), accuracy is enhanced at all delays when the outcomes of correct choices are quantitatively or qualitatively different, compared to when outcomes are the same. In the present experiments, we aimed to demonstrate a delay-specific DOE by arranging differential outcomes for correct responses at some delays and same outcomes at other delays. In each of two experiments, four pigeons worked in delayed matching-to-sample tasks with delays of 0.5, 5, and 15 s, or 0 s, 3 s, and 12 s mixed within session. Correct choices produced different reward durations (differential outcomes) at one or two delays, or the same reward durations (same outcomes) at the other delays, on a within-session basis. There was evidence of improved accuracy at delays at which differential outcomes were arranged, compared to accuracy at delays at which same outcomes were arranged, that is, a delay-specific DOE. The more usual DOE was confirmed in a third experiment with same outcomes at all delays in one condition and differential outcomes at all delays in another. We discuss implications of a delay-specific DOE for theories of the DOE which attribute the effect to enhanced stimulus control by expectancies of reward outcomes generated at the time of sample presentation.
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http://dx.doi.org/10.3758/s13420-015-0174-1 | DOI Listing |
Front Oncol
January 2025
Department of Medical and Health Sciences, Collegium Medicum, WSB University, Dabrowa Górnicza, Poland.
Background: Breast cancer remains a leading cause of mortality among women, driven by the molecular complexity of its various subtypes. This study aimed to investigate the differential expression of genes and miRNAs involved in the PI3K/AKT/mTOR signaling pathway, a critical regulator of cancer progression.
Methods: We analyzed tumor tissues from five breast cancer subtypes-luminal A, luminal B HER2-negative, luminal B HER2-positive, HER2-positive, and triple-negative breast cancer (TNBC)-and compared them with non-cancerous tissues.
BMJ Neurol Open
January 2025
Siriraj Neuroimmunology Center, Division of Neurology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Salaya, Thailand.
Objective: This study aimed to elucidate the clinical manifestations, laboratory findings and outcomes of patients with intravascular large B cell lymphoma (IVLBCL) with neurological involvement and to differentiate IVLBCL with and without neurological involvement.
Methods: A cohort study was conducted at Siriraj Hospital, Mahidol University, Thailand, between January 2005 and September 2024. Clinical data, laboratory values and central nervous system imaging results were analysed.
Introduction: Glaucoma is a leading cause of blindness, often progressing asymptomatically until significant vision loss occurs. Early detection is crucial for preventing irreversible damage. The pupillary light reflex (PLR) has proven useful in glaucoma diagnosis, and mobile technologies like the AI-based smartphone pupillometer (AI Pupillometer) offer a promising solution for accessible screening.
View Article and Find Full Text PDFIndian Dermatol Online J
December 2024
Financial Research and Executive Insights, Everest Group, Gurugram, Haryana, India.
Background: Artificial intelligence (AI) is revolutionizing healthcare by enabling systems to perform tasks traditionally requiring human intelligence. In healthcare, AI encompasses various subfields, including machine learning, deep learning, natural language processing, and expert systems. In the specific domain of onychology, AI presents a promising avenue for diagnosing nail disorders, analyzing intricate patterns, and improving diagnostic accuracy.
View Article and Find Full Text PDFFront Pharmacol
January 2025
Department of General Surgery, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China.
Background: Pancreatic cancer remains one of the deadliest malignancies, largely due to its late diagnosis and lack of effective therapeutic targets.
Materials And Methods: Using traditional machine learning methods, including random-effects meta-analysis and forward-search optimization, we developed a robust signature validated across 14 publicly available datasets, achieving a summary AUC of 0.99 in training datasets and 0.
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