The well-established advantage of low-frequency words over high-frequency words in recognition memory has been found to occur in remembering and not knowing. Two experiments employed remember and know judgements, and divided attention to investigate the possibility of an effect of word frequency on know responses given appropriate study conditions. With undivided attention at study, the usual low-frequency advantage in the accuracy of remember responses, but no effect on know responses, was obtained. Under a demanding divided attention task at encoding, a high-frequency advantage in the accuracy of know responses was obtained. The results are discussed in relation to theories of knowing, particularly those incorporating perceptual and conceptual fluency.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1080/09658210544000051 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A novel nomogram based on the patient's clinical data and CT signs to predict poor outcomes in AIS patients.
PeerJ
January 2025
Department of Medical Imaging, Guangzhou Hospital of Integrated Traditional and Western Medicine, Guangzhou, China.
Background: The 2019 American Heart Association/American Stroke Association (AHA/ASA) guidelines strongly advise using non-contrast CT (NCCT) of the head as a mandatory test for all patients with suspected acute ischemic stroke (AIS) due to CT's advantages of affordability and speed of imaging. Therefore, our objective was to combine patient clinical data with head CT signs to create a nomogram to predict poor outcomes in AIS patients.
Methods: A retrospective analysis was conducted on 161 patients with acute ischemic stroke who underwent mechanical thrombectomy at the Guangzhou Hospital of Integrated Traditional and Western Medicine from January 2019 to June 2023.
On the Bayesian generalized extreme value mixture autoregressive model with adjusted SNR in non-standard actuarial data.
MethodsX
June 2025
Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya 60111 Indonesia.
This research introduces the Generalized Extreme Value Mixture Autoregressive (GEVMAR) model as an innovative approach for examining non-standard actuarial datasets within general insurance. Information concerning claim reserves often reveals notable volatility and multimodal distributions, attributes that standard models, including previous method such as the Gaussian Mixture Autoregressive (GMAR) model and other autoregressive methodologies, find problematic to manage effectively. The GEVMAR model integrates the Generalized Extreme Value (GEV) distribution alongside Bayesian estimation techniques, augmented by a modified Signal-to-Noise Ratio (SNR) metric to improve predictive accuracy.
View Article and Find Full Text PDFEvaluation of myocardial perfusion imaging techniques and artificial intelligence (AI) tools in coronary artery disease (CAD) diagnosis through multi-criteria decision-making method.
Cardiovasc Diagn Ther
December 2024
Operational Research Center in Healthcare, Near East University, Nicosia, Turkey.
Background: Cardiovascular diseases (CVDs) continue to be the world's greatest cause of death. To evaluate heart function and diagnose coronary artery disease (CAD), myocardial perfusion imaging (MPI) has become essential. Artificial intelligence (AI) methods have been incorporated into diagnostic methods such as MPI to improve patient outcomes in recent years.
View Article and Find Full Text PDFAutoGP: An Intelligent Breeding Platform for Enhancing Maize Genomic Selection.
Plant Commun
January 2025
National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Hubei, China. Electronic address:
In the face of climate change and the growing global population, there is an urgent need to accelerate the development of high-yielding crop varieties. To this end, vast amounts of genotype-to-phenotype data have been collected, and many machine learning (ML) models have been developed to predict phenotype from a given genotype. However, the requirement for high densities of single-nucleotide polymorphisms (SNPs) and the labor-intensive collection of phenotypic data are hampering the use of these models to advance breeding.
View Article and Find Full Text PDFSmart crop disease monitoring system in IoT using optimization enabled deep residual network.
Sci Rep
January 2025
Department of Economics, Kardan University, Kabul, Afghanistan.
The Internet of Things (IoT) has recently attracted substantial interest because of its diverse applications. In the agriculture sector, automated methods for detecting plant diseases offer numerous advantages over traditional methods. In the current study, a new model is developed to categorize plant diseases within an IoT network.
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!