Attention is biased in favor of stimuli that signal either threat or reward; this experience-dependent attentional bias develops via associative learning and persists into extinction. Physically salient yet task-irrelevant stimuli are also prioritized by the attention system, but the attentional priority of a physically salient distractor can be suppressed when it appears in a location in which it has been frequently encountered in the past. Similar effects of statistical learning on distractor suppression have been observed for distractors appearing in a predictable color. A pair of recent studies demonstrate that statistically learned distractor suppression and valence-based attentional biases combine additively, suggesting independent influences of learning on attentional priority. One limitation of these prior studies, however, is that the effects of statistical learning were defined with respect to spatial attention and the effects of associative learning with respect to feature-based attention. A strong version of the independence account would predict additive influences on attention even when both sources of priority are represented within a single domain of attentional control, which we tested in the present study. The attentional priority of a distractor was elevated when its color was previously associated with electric shock and reduced when its shape was frequently encountered as a distractor in a prior training phase, with these two influences on priority combining additively. Our findings provide strong evidence for the idea that statistical learning and valance-based associative learning exert independent influences on the control of attention, which has implications for contemporary theories of selection history.
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http://dx.doi.org/10.3758/s13414-022-02622-z | DOI Listing |
BMC Med Educ
January 2025
The First Clinical Medicine School of Guangdong Pharmaceutical University, Guangdong, People's Republic of China.
Objective: This study examines a novel teaching model that integrates the development and use of a Medical Cloud Dictionary with project-based learning (PBL). We investigate whether this integrated approach improves teaching effectiveness, enhances student learning outcomes, and reduces teaching pressure compared to traditional PBL.
Methods: One hundred student volunteers were randomly assigned to an experimental group (n = 50) and a control group (n = 50).
BMC Oral Health
January 2025
Department of Stomatology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde, Foshan), Foshan, 528308, Guangdong, China.
Background: A comprehensive analysis of the occlusal plane (OP) inclination in predicting anteroposterior mandibular position (APMP) changes is still lacking. This study aimed to analyse the relationships between inclinations of different OPs and APMP metrics and explore the feasibility of OP inclination in predicting changes in APMP.
Methods: Overall, 115 three-dimensional (3D) models were reconstructed using deep learning-based cone-beam computed tomography (CBCT) segmentation, and their accuracy in supporting cusps was compared with that of intraoral scanning models.
Nat Genet
January 2025
Calico Life Sciences LLC, South San Francisco, CA, USA.
Sequence-based machine-learning models trained on genomics data improve genetic variant interpretation by providing functional predictions describing their impact on the cis-regulatory code. However, current tools do not predict RNA-seq expression profiles because of modeling challenges. Here, we introduce Borzoi, a model that learns to predict cell-type-specific and tissue-specific RNA-seq coverage from DNA sequence.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Statistics, Faculty of Science, Fasa University, Fasa, 74616-86131, Iran.
Air pollution is a significant challenge in metropolitan areas, where increasing amounts of air pollutants threaten public health and environmental safety. The present study aims to forecast the concentrations of various air pollutants, including CO, O, NO, SO, PM, and PM, from 2013 to 2023 in the Tehran megacity, Iran, via deep learning (DL) models and evaluate their effectiveness over conventional machine learning (ML) methods. Key driving variables, including temperature, relative humidity, dew point, wind speed, and air pressure, were considered.
View Article and Find Full Text PDFEur Radiol
January 2025
Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Background: Chronic liver disease (CLD) is a substantial cause of morbidity and mortality worldwide. Liver stiffness, as measured by MR elastography (MRE), is well-accepted as a surrogate marker of liver fibrosis.
Purpose: To develop and validate deep learning (DL) models for predicting MRE-derived liver stiffness using routine clinical non-contrast abdominal T1-weighted (T1w) and T2-weighted (T2w) data from multiple institutions/system manufacturers in pediatric and adult patients.
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