Dynamic visual noise (DVN) selectively impairs memory for some types of stimuli (e.g., colors, textures, concrete words), but not for others (e.g., matrices, Chinese characters, simple shapes). According to the image definition hypothesis, the key difference is whether the stimulus leads to images that are ill-defined or well-defined. The former will be affected because the addition of noise quickly reduces the usefulness of the image in supplying information about the item's identity. The image definition hypothesis predicts that fonts should lead to ill-defined images and therefore should be affected by DVN, and although three previous studies appear to show this result, they lack a key control condition and report only proportion correct. Two experiments reassessed whether DVN affects memory for fonts, but, unlike the previous studies, both included a static visual noise condition and both were analyzed using signal detection measures. There was no evidence that DVN affected memory for font information, thus disconfirming a prediction of the original version of image definition hypothesis. We suggest a revised version that focuses on redintegration can explain the results.
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http://dx.doi.org/10.1027/1618-3169/a000491 | DOI Listing |
Med Phys
January 2025
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Respiratory motion during radiotherapy (RT) may reduce the therapeutic effect and increase the dose received by organs at risk. This can be addressed by real-time tracking, where respiration motion prediction is currently required to compensate for system latency in RT systems. Notably, for the prediction of future images in image-guided adaptive RT systems, the use of deep learning has been considered.
View Article and Find Full Text PDFSci Rep
January 2025
College of Computer Sciences, Anhui University, Hefei, 230039, China.
Decoding the semantic categories of complex sceneries is fundamental to numerous artificial intelligence (AI) infrastructures. This work presents an advanced selection of multi-channel perceptual visual features for recognizing scenic images with elaborate spatial structures, focusing on developing a deep hierarchical model dedicated to learning human gaze behavior. Utilizing the BING objectness measure, we efficiently localize objects or their details across varying scales within scenes.
View Article and Find Full Text PDFPhys Med Biol
January 2025
National Institute of Radiological Sciences, 4-9-1 Anagawa, Inage-ku, Chiba, 263-8555, JAPAN.
PET has become an important clinical modality but is limited to imaging positron emitters. Recently, PET imaging withZr, which has a half-life of 3 days, has attracted much attention in immuno-PET to visualize immune cells and cancer cells by targeting specific antibodies on the cell surface. However,Zr emits a single gamma ray at 909 keV four times more frequently than positrons, causing image quality degradation in conventional PET.
View Article and Find Full Text PDFPLoS One
January 2025
LIB, Université de Bourgogne, Franche-Comté, Dijon, France.
The backbone extraction process is pivotal in expediting analysis and enhancing visualization in network applications. This study systematically compares seven influential statistical hypothesis-testing backbone edge filtering methods (Disparity Filter (DF), Polya Urn Filter (PF), Marginal Likelihood Filter (MLF), Noise Corrected (NC), Enhanced Configuration Model Filter (ECM), Global Statistical Significance Filter (GloSS), and Locally Adaptive Network Sparsification Filter (LANS)) across diverse networks. A similarity analysis reveals that backbones extracted with the ECM and DF filters exhibit minimal overlap with backbones derived from their alternatives.
View Article and Find Full Text PDFAnalyst
January 2025
Questrom School of Business, Boston University, Boston, MA, 02215, USA.
Latent fingerprints (LFPs) are invisible impressions that need to be developed before being used for criminal investigation; however, existing fingerprint visualization techniques face challenges, such as complex preparation and poor contrast. To advance practical fingerprint detection, green-emissive micron-sized curcumin/kaolin composites were synthesized a facile and cost-effective one-step physical cross-linking method, which exhibited unprecedented performance in developing diversified marks, including LFPs, knuckle prints, palm prints, and footprints, with clear three-level details on various substrates. Notably, the powders successfully developed LFPs that were aged for 30 days and even up to 100 days, meeting the stringent requirements for comprehensive forensic application.
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