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We studied the ability of pigs to discriminate tone cues using successive and conditional discrimination tasks. Pigs (n = 8) were trained in a successive discrimination Go/No-Go task (Experiment 1) to associate a Go-cue with a reward at the end of a runway and a No-Go-cue with the absence of reward. Latency to reach the goal-box was recorded for each cue-type. Learning of a conditional discrimination task was compared between low-birthweight (LBW, n = 5) and normal-birthweight (NBW, n = 6) pigs (Experiment 2) and between conventional farm (n = 7) and Göttingen miniature (n = 8) pigs (Experiment 3). In this active-choice task, one cue signalled a response in a right goal-box was correct and a second cue signalled a response in a left goal-box was correct. Cues were differentially rewarded. The number of sessions to learn the discrimination and number of correct choices per cue-type were recorded. In Experiment 1, four out of eight pigs showed learning on the task, that is, a higher latency to respond to the No-Go-cue, within 25 sessions. In Experiment 2, eight out of 11 pigs learned the discrimination within 46 sessions. LBW learners learned faster than NBW learners. In Experiment 3, all 15 pigs learned the task within 16 sessions. Göttingen miniature pigs learned faster than conventional farm pigs. While some methodological issues may improve the Go/No-Go design, it is suggested that an active-choice task yields clearer and more consistent results than one relying on latency alone.
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http://dx.doi.org/10.1007/s10071-013-0621-3 | DOI Listing |
J Exp Anal Behav
September 2025
Oslo Metropolitan University, Norway.
Go/no-go successive matching (GNG-matching) tasks are one of several procedures used to establish conditional discriminations. This study presents a systematic review aimed at comparing procedures and outcomes of empirical studies using GNG-matching tasks for the emergence of symmetry, transitive, and global equivalence relations in humans and non-humans. A total of 22 articles were analyzed-nine with nonhumans and thirteen with humans.
View Article and Find Full Text PDFAcad Radiol
September 2025
Department of Radiology, Başakşehir Çam and Sakura City Hospital, Istanbul, Turkey (E.E.).
Purpose: This study aimed to evaluate the performance of ChatGPT (GPT-4o) in interpreting free-text breast magnetic resonance imaging (MRI) reports by assigning BI-RADS categories and recommending appropriate clinical management steps in the absence of explicitly stated BI-RADS classifications.
Methods: In this retrospective, single-center study, a total of 352 documented full-text breast MRI reports of at least one identifiable breast lesion with descriptive imaging findings between January 2024 and June 2025 were included in the study. Incomplete reports due to technical limitations, reports describing only normal findings, and MRI examinations performed at external institutions were excluded from the study.
J Neurol Sci
September 2025
Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, United States of America.
Background: A key limitation of the IMPACT model for prognostication after severe traumatic brain injury (TBI) is the use of predictors from hospital admission only. We sought to identify if including daily blood labs (e.g.
View Article and Find Full Text PDFPLoS One
September 2025
School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Computer networks are highly vulnerable to cybersecurity intrusions. Likewise, software-defined networks (SDN), which enable 5G users to broadcast sensitive data, have become a primary target for vulnerability. To protect the network security against attacks, various security protocols, including authorization, the authentication process, and intrusion detection techniques, are essential.
View Article and Find Full Text PDFStat Methods Med Res
September 2025
Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
This study describes and compares the performance of several semi-parametric statistical modeling approaches to dynamically classify subjects into two groups, based on an irregularly and sparsely sampled curve. The motivating example of this study is the diagnosis of a complication following cardiac surgery, based on repeated measures of a single cardiac biomarker where early detection enables prompt intervention by clinicians. We first simulate data to compare the dynamic predictive performance over time for growth charts, conditional growth charts, a varying-coefficient model, a generalized functional linear model and longitudinal discriminant analysis.
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