Visually guided equivalence learning is a special type of associative learning, which can be evaluated using the Rutgers Acquired Equivalence Test (RAET) among other tests. RAET applies complex stimuli (faces and colored fish) between which the test subjects build associations. The complexity of these stimuli offers the test subject several clues that might ease association learning. To reduce the number of such clues, we developed an equivalence learning test (Polygon), which is structured as RAET but uses simple grayscale geometric shapes instead of faces and colored fish. In this study, we compared the psychophysical performances of the same healthy volunteers in both RAET and Polygon test. Equivalence learning, which is a basal ganglia-associated form of learning, appears to be strongly influenced by the complexity of the visual stimuli. The simple geometric shapes were associated with poor performance as compared to faces and fish. However, the difference in stimulus complexity did not affect performance in the retrieval and transfer parts of the test phase, which are assumed to be mediated by the hippocampi.
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http://dx.doi.org/10.1016/j.neuroscience.2022.01.022 | DOI Listing |
Front Artif Intell
January 2025
Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA, United States.
Background: Large language models (LLMs) have demonstrated impressive performance on medical licensing and diagnosis-related exams. However, comparative evaluations to optimize LLM performance and ability in the domain of comprehensive medication management (CMM) are lacking. The purpose of this evaluation was to test various LLMs performance optimization strategies and performance on critical care pharmacotherapy questions used in the assessment of Doctor of Pharmacy students.
View Article and Find Full Text PDFHeliyon
January 2025
Department of Cataract, Shanxi Eye Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, 030001, China.
Purpose: Using a fully automated multitask deep learning method, which enabled simultaneous segmentation and quantification of all major anterior segment structures with swept-source optical coherence tomography (SS-OCT), we aimed to investigate the three-dimensional (3D) alterations in iris morphology before and after implantable collamer lens (ICL) surgery.
Methods: All enrolled patients underwent anterior segment SS-OCT (ANTERION) within one week before and after ICL surgery. A multitask network automatically performed iris SS-OCT image segmentation and quantitative measurements of 3D iris morphology (iris thickness and volume of the inner 1-mm annular area and the outer 1-2-mm annular area, iris curvature [I-Curve], and iris smooth index [SI]).
Pediatr Res
January 2025
Laboratorio de Bacteriología Experimental. Instituto Nacional de Pediatría, Mexico City, México.
Background: Congenital hypothyroidism's sequelae include visuomotor and intellectual developmental deficits. Visual-motor perception is a cognitive function related to academic performance. Intellect is the ability to learn and use acquired knowledge to solve and achieve goals.
View Article and Find Full Text PDFSci Rep
January 2025
UNESCO Centre of Water Law, Policy & Science, University of Dundee, Dundee, UK.
Understanding snow and ice melt dynamics is vital for flood risk assessment and effective water resource management in populated river basins sourced in inaccessible high-mountains. This study provides an AI-enabled hybrid approach integrating glacio-hydrological model outputs (GSM-SOCONT), with different machine learning and deep learning techniques framed as alternative 'computational scenarios, leveraging both physical processes and data-driven insights for enhanced predictive capabilities. The standalone deep learning model (CNN-LSTM), relying solely on meteorological data, outperformed its counterpart machine learning and glacio-hydrological model equivalents.
View Article and Find Full Text PDFComput Biol Med
January 2025
Department of Mechanical Engineering, Stanford University, United States.
We present a built-in physics neural network architecture, known as inelastic Constitutive Artificial Neural Network (iCANN), to discover the inelastic phenomenon of tensional homeostasis. In this course, identifying the optimal model and material parameters to accurately capture the macroscopic behavior of inelastic materials can only be accomplished with significant expertise, is often time-consuming, and prone to error, regardless of the specific inelastic phenomenon. To address this challenge, built-in physics machine learning algorithms offer significant potential.
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