The equal-variance Gaussian signal detection theory (SDT) decision model for the dual-pair (4IAX) change-detection paradigm has been described in earlier publications. In this research article, we consider the equal-variance Gaussian SDT model for the related 4IAX AB-versus-BA identification paradigm. The likelihood ratios, optimal decision rules, receiver-operating characteristics (ROCs), and relationships between d' and proportion correct (PC) are analyzed for two special cases: that of statistically independent observations, which typically applies in constant-stimuli experiments, and that of highly correlated observations, which typically applies in experiments where stimuli are roved widely across trials or pairs. A surprising outcome of this analysis is that, although these two situations lead to different optimal decision rules, the predicted ROCs and PC responses for these two cases are not substantially different and are either identical to or similar to those observed in the basic yes-no paradigm. Supplemental materials for this study can be downloaded from app.psychonomic-journals.org/content/supplemental.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2811854 | PMC |
| http://dx.doi.org/10.3758/APP.71.6.1426 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Systematic Review of Hybrid Vision Transformer Architectures for Radiological Image Analysis.
J Imaging Inform Med
January 2025
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.
View Article and Find Full Text PDFIntelligent shipping: integrating autonomous maneuvering and maritime knowledge in the Singapore-Rotterdam Corridor.
Commun Eng
January 2025
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, Singapore.
Designing safe and reliable routes is the core of intelligent shipping. However, existing methods for industrial use are inadequate, primarily due to the lack of considering company preferences and ship maneuvering characteristics. To address these challenges, here we introduce a methodological framework that integrates maritime knowledge and autonomous maneuvering model.
View Article and Find Full Text PDFDeep learning based decision-making and outcome prediction for adolescent idiopathic scoliosis patients with posterior surgery.
Sci Rep
January 2025
Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, 200433, China.
With the emergence of numerous classifications, surgical treatment for adolescent idiopathic scoliosis (AIS) can be guided more effectively. However, surgical decision-making and optimal strategies still lack standardization and personalized customization. Our study aims to devise proper deep learning (DL) models that incorporate key factors influencing surgical outcomes on the coronal plane in AIS patients to facilitate surgical decision-making and predict surgical results for AIS patients.
View Article and Find Full Text PDFUrban pandemic governance personal protective equipment allocation strategies: a system dynamics simulation.
Sci Rep
January 2025
Institute for Disaster Management and Reconstruction, Sichuan University, No. 122, Section 1, Huanghe Middle Road, Chengdu, 610211, China.
In the early days of the urban pandemic, many cities had personal protective equipment (PPE) shortages, which adversely affected urban pandemic governance. Using the COVID-19 strategies employed in Wuhan as the pivotal case study, this study sought to determine effective strategies to optimize city PPE distribution. System dynamics modeling was employed to explore the influence of PPE allocation strategies on pandemic control measures.
View Article and Find Full Text PDFAn endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer.
Sci Rep
January 2025
Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
To retrospectively develop and validate an interpretable deep learning model and nomogram utilizing endoscopic ultrasound (EUS) images to predict pancreatic neuroendocrine tumors (PNETs). Following confirmation via pathological examination, a retrospective analysis was performed on a cohort of 266 patients, comprising 115 individuals diagnosed with PNETs and 151 with pancreatic cancer. These patients were randomly assigned to the training or test group in a 7:3 ratio.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!